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The featured image can be confusing and elitist for newcomers, so I wrote an overview of various sources to provide a better orientation and share my experience. Disclaimer I disapprove of using modified hardware and software for copyright infringement. This tutorial deals with hardware and software modification purely for educational reasons. Modifications to change the style, operating system, ability to backup your own files or similar are not considered copyright infringement. Modifying hardware you own in the Czech Republic, where I am located, is legal I do not cause any loss to nintendo, I purchased all the games I play. See the results of two courts in European countries that have dealt with this: [1]: https://web.archive.org/web/20060324123541/http://management.silicon.com/government/0%2C39024677%2C39117891%2C00.htm [2]: https://www.theregister.com/2004/04/27/spanish_xbox_modding_ruling/ Needed components - modchip Nintendo Switch Modchip (4$); I used this , but it was overpriced. one Soldering iron (15$), something like this SD card, at least 16GB, recommended 64GB and more. (10$) USB C to USB 2.0 cable. Soldering flux is recommended but not required. Electrical tape Heat sink paste Hack - hardest part I don't provide a detailed tutorial, there are enough of them. This post is to improve the orientation in this scene, at the same time provide a collection of various guides. How to start If you have a Nintendo Switch V1 and belong to the lucky 12% (see hackable serial numbers[1]) who have an unpatched switch, you can get by with tinfoil and an SD card. There are plenty of jailbreak tutorials[2]. The hack is more complicated if you have a patched Switch, Switch Lite, or OLED version. What to do after determining version of my switch? You have to solder a modchip, often or , which are very cheap on Aliexpress, to a Nintendo motherboard. They work on the principle of voltage changes on the processor, which skips the integrity check mechanism to check unauthorized software. PICOFLY HWFLY The easiest is the V1 version, then Switch Lite, then OLED. The USB C connector is often included with the modchip, through which the chip's firmware must be uploaded. This consists of dragging only a few files to the modchip. The firmware can be found on . GitHub My experience In my situation, I used the PICOFLY chip purchased from AliExpress to modify my Nintendo V1 Switch. Without a microscope or any experience in micro-soldering. I used a budget soldering iron. Although I suggested using flux, I neglected to utilize it myself, leading to an dirty solder. While the installation eventually worked, the process was tedious and stresfull. What to do after the hack You must load the software that starts after boot and must be on the SD card. Easy route You can use , which is a package of different tools. The community will tell you that you must install individual tools to understand how they work. The reality is that copy-pasting different tools to the SD card will not help you understand the tools, so don't be afraid to take the easy way out. This is very common in the community, where people claim the only correct way is without using the script. Of course, this is wrong. HATS linux arch archinstall Where do I find Hats? The HATS source code has been taken down but can be found in various mirrors; use Google. https://www.youtube.com/watch?v=jfJIVzeJSyE&embedable=true The following section describes some tools used in HATS, and you would probably set up these tools even if you went the manual route. The recommended starting point is this guide: https://nh-server.github.io/switch-guide/ Other options - Ubuntu https://www.s31bz.com/linux-on-my-nintendo-switch - Android 10 https://wiki.switchroot.org/wiki/android/10-q-setup-guide Also, you can dualboot between multiple operating systems on your SD card. Toolset - The most used bootloader that allows you to boot custom firmware (CFW). Hekate - Tinfoil allows you to access the SD card via USB C and install software. Tinfoil - Menu for launching homebrew applications. This menu will be available via the album icon on Switch. nx-hbmenu - This is an industry-standard tool for managing various emulators. RetroArch - OS for the switch. You will probably be using this one. Otherwise, you can use multiple linux distributions. also works on Switch. Atmosphere RetroPie - Install software via USB C. This is helpful because some games are bigger than 4GB, which is the maximum file size for FAT32. DBI - Tool for managing game save files. JKSV - Switch sysmodule allowing you to set cpu/gpu/mem clocks according to the running application and docked state. sys-clk - Use controllers from other consoles natively on your Nintendo Switch via Bluetooth. No dongles or other external hardware neccessary. MissionControl How to restore game save files from Yuzu/Ryujinx on Nintendo Switch This process apply if you want to dump Switch saves to other location. Go to the emulator and click on “open save files“ Copy files to the JKSV folder on the SD card path will be something like: SD:/JKSV/<game>/<save_name>/ In JKSV, click restore save and hold the button. A JKSV has to be correctly set up. See the JKSV . Note: guide Also, run the game at least once. Otherwise, it won’t show up in JKSV, What is X? - This is a memory chip with the original firmware on the Switch motherboard. Switch NAND - emulated NAND saved on SD card. emuMMC - custom firmware cfw - custom console software not provided with original homebrew - a program that gets executed after exploiting a vulnerability payload - a format of programs that can be run with CFW .nro Links [1]: https://suchmememanyskill.github.io/guides/switchserials/ [2]: https://www.youtube.com/watch?v=zX7eBg2gIzI Featured image source . here

Hacking a Nintendo Switch for 30$

Here’s a made-up variation on an old quote. It goes like this: “To err is human; To hack one’s Nintendo Switch is morally divine, but legally dubious.” On the subject of hacking, today’s story is about hacking/modding the Nintendo Switch with PKHex. Specifically, we’re going back into the world of save file editing. With PKhex, you can edit what pokemon are in your party. With PKHex, you can automatically check for legality on your mons (appropriate moves, stats, EVs, etc.). You can also edit what items it holds, and even edit your pokedex progress in games like Pokemon Scarlet and Pokemon Violet! You can even edit how many stakes were pulled in those games. Through GBATemp and PKHex, a couple “remade” versions of Pokemon Sword and Shield, called Pokemon Sacred Sword and Pokemon King’s Shield, were made. However, they are outdated in comparison to the new ROM hacks for Gen VIII, Pokemon Divine Sword and Pokemon Blessed Shield. Here’s a screenshot of such ROM Hacks in action above. With that done, let’s move on to the installation guide. Installation Guide for Pokemon Divine Sword & Blessed Shield Here’s an Installation Guide, which is going off the assumption you are in ownership of a modded Switch console. Thanks to GBATemp for the info. Installation (CFW): This is assuming you have Atmosphere CFW installed on your switch. Go to this link: https://gbatemp.net/threads/pokemon-divine-sword-blessed-shield-version-1-1-major-update-exp-candies-return.590250/ Click on the download link, and you’ll be taken to folders for Divine Sword and Blessed Shield to download over on Google Drive or Google Docs. (I’m not sure which one) If you are gonna be playing Divine Sword, download and move the folder titled "0100ABF008968000" to atmosphere\contents from Google Drive,  or download and move "01008DB008C2C000" for Blessed Shield from Google Drive. ( ) https://drive.google.com/drive/folders/1wDwrs3Ox13XkOZOEs22orvkWSg3nVb66 Return the SD card to your switch, enter CFW, and then Launch the game. Simple as that. An easy way to tell if the mod is working is IF the Mystery Gift menu option is replaced with "Divine Sword & Blessed Shield", among other text edits. NOTE: Now, with regards to hacking a switch, it SHOULD be done easily with a V1 Switch With that cleared up, let’s move onto the Comprehensive guide to PKHex. How to Use PKHex on Switch: A Comprehensive Beginner's Guide There are 2 ways to edit Pokemon Scarlet and Violet (as well as SwSh saves) in PkHex. Both of these require a modified switch running CFW (custom firmware), and either checkpoint (for lower firmwares) or JKSV (for higher firmwares). This guide is meant for CFW emuNAND extracting from/importing to CFW emuNAND, or CFW sysNAND extracting from/importing to CFW sysNAND. You’ll also probably need Ryujinx or Yuzu (both Switch emulators) to test this before doing it to an actual cartridge. In that case, a computer/laptop would be sufficient for the task, and a USB-c cable would also help. A modded Switch is a MUST. Note: For the uninitiated, PKHex is a Pokémon core series save editor, programmed in C#. It supports the following files: Save files ("main", *.sav, .dsv, .dat, .gci) GameCube Memory Card files (.raw, .bin) containing GC Pokémon savegames Individual Pokémon entity files (.pk) : Mystery Gift files (.pgt, .pcd, .pgf, .wc), including conversion to .pk. It also supports the following features It supports the importing of teams from Decrypted 3DS Battle Videos, transferring from one generation to another while converting formats along the way, etc. Data is displayed in a view which can be edited and saved. The interface can be translated with resource/external text files so that different languages can be supported. Pokémon Showdown sets and QR codes can be imported/exported to assist in sharing. Nintendo 3DS savedata containers use an AES MAC that cannot be emulated without the 3DS's keys, thus a resigning service is required (svdt, save_manager, JKSM, or SaveDataFiler). The Changelog for Version 22.11.26 indicates the following changes were made for compatibility with Scarlet and Violet: Legality: Fixed: Encounter->PK9 small fixes added. Added: Distribution Raids (Eevee) now recognized. Fixed: Hidden Ability patch checks now behave correctly. Fixed: Minor tweaks to tera type and encounter recognition. Fixed: Gen8 technical records are checked correctly again. Fixed: Maushold / Dudunsparce %100 evolution calc fixed (From %25). Fixed: Gen9 obededience level for gift box legends now checked correctly (nonzero). Still Pending: Wandering/Crossover encounter locations & marks (next release!) Added: Form Argument now has a label if the Form combobox is not being shown. Added: Cheat button to unlock all fly locations, and collect all stakes (sub legendary event). Thanks @sora10pls! Fixed: Gen9 raid seeds can now be correctly set via the GUI. Fixed: Gen9 xyz coordinates now read/write correctly instead of swapping y/z coordinates. Thanks @ih8ih8sn0w! Fixed: Gen9 Pokédex set now applies seen forms, and the Seen All / Caught / Complete cheats now behave as intended for all entries. Changed: Updated translation files. Now, all of the above is useless without having a V1 switch that is hackable. Make sure you have that first, then get Ryujinx or Yuzu for your laptop/computer. Now, PKHeX can, in conjunction with Yuzu/Ryujinx, allow you to edit your save file to do amazing things. 60FPS? You got it! Make a pokemon you made in Showdown shiny before adding it to your game? Complicated, but it can be done!! Collect all 32 of those annoying stakes to unlock the legendary quartet of Ruin? You bet! Now, Let’s move on to save editing. Save Editing Pokémon Scarlet & Violet - How it works In order to even be ABLE to do this, a modified Switch is required. Now, how does one do that? That is surprisingly simple. Modifying your Switch: A Multi-step Guide You must get or have bought a 1st-Version Switch back in 2017. Order a “Switch jig” on Amazon, or use either a paper clip or a piece of aluminum foil cut with scissors. Insert the accessory of choice all the way down into the right side rail of the switch. Press and hold Power and Volume + to get the Switch into RCM mode. Connect your V1 Switch to your PC via a Micro USB-C cable, or a BLU Tracfone phone charger cable. Go to your computer and download Atmosphere (Best go to Youtuber Better Gaming for updated links and advice if you’re new to this!) Download TegraRcmGUI, and drag the application from WinRar & Drop it onto your desktop. Insert your Micro SD Card via MicroSD Card adapter into your laptop and drag all CFW files and whatnot onto the root of the Micro SD Card. Safely eject the SD Card, reinsert into Switch, and select Hekate to boot into CFW. You’ve done it. Your switch should now be hacked/jailbroken. IF you did everything right, then you SHOULD get something like the above image on your Switch. Just, make sure your switch is sufficiently charged beforehand with the SD card inside the Switch, then proceed. Speaking of proceeding, you may now proceed to the next section. CONGRATS! Adding Cheats to your Pokemon Scarlet & Violet Game For this part, basically, you’re now gonna want to install Edizon onto your Switch via the Homebrew app store, then insert the game and start editing/cheating away if you want/are able to. HackerNoon’s FAQ: Your Questions And More Answered Q: Do these cheats work on Non-modded Switches? A: No. Sadly, it only works on Modded Switches. Q2: Will I get Banned for Modding my Switch? A2: It depends. If you don’t follow guides and go in blind, then likely yes. If you’re following Better Gaming or anyone else, then maybe not. Q3: How do I activate cheats in {Insert game here}? A3: Use Edizon. Simple. Or, if nothing else, watch a guide before trying. Q4: How are these three subjects connected? A4: They all involve modding/hacking stuff like Scarlet/Violet, and your Switch. Q5: Does this theoretically work for other mainline Pokémon games (SwSh, Let’s Go, etc.)? A5: Yes. CONCLUSION So, in today’s article, we went over how to use PKHex on Switch, Save Editing, and Switch Modding. We went over them in significant detail, answered some common and uncommon questions, AND even went into how to mod a V1 switch. IF you enjoyed this article, then comment down below, or feel free to email me your feedback. Thanks for reading, and I’ll see you next time.

How to Use PKHex On Switch to Mod Pokemon Scarlet & Violet

Fifteen years ago, crypto wasn’t on the mainstream media’s radar; but today, it frequently captures the spotlight on drive-time, lunch-time, and evening news channels, drawing both seasoned investors and curious newcomers into its orbit. While it’s exciting to witness this once-niche market gaining traction as the flood of crypto headlines inundate our newsfeeds, we have to ask: can we have too much of a good thing? Taking note of every headline as the ticker goes up; and down (sometimes multiple times a day) would be akin to suffering from investor whiplash… Sensationalism vs. substance Crypto headlines often oscillate between extremes. A prime example of this is a headline that read “Ethereum price lags”; while another read “Ethereum trader strikes gold as prices soar…” From exuberant predictions around astronomical gains to dire warnings of impending collapse - and everything in between - it can be hard to make head or tail of what’s actually going on. A broader view of crypto investment takes into account the long-term fundamentals that drive crypto adoption and value, including technological innovation, adoption by institutions and mainstream users, regulatory developments, macroeconomic trends, and societal acceptance. When investors read too much into market sentiment fueled only by headlines, they risk making short-sighted investment decisions fuelled by market manipulation and short-term noise. Understanding the broader context: the crypto market cycle “Do your own research" (DYOR) is a cardinal rule in investment circles, underlining the importance of doing your own independent analysis and due diligence before making financial decisions. In the dynamic world of crypto, this mantra holds particular significance where relying solely on hearsay (including mainstream media narratives) or social media hype can lead investors astray, exposing them to unnecessary risks and potential losses. To navigate the crypto landscape effectively, astute investors are those who have taken the time to understand the nuances of Bitcoin’s market cycle (and its effect on altcoins such as Ethereum). This four-year cycle is often referred to as the ‘halving cycle’ and has been historically closely linked to price, with the cycle progressing through ‘Bull’ (rising prices); ‘Bear’ (falling prices); ‘Accumulation’ (a leveling out of prices) and ‘Expansion’ (steady growth). Importantly, within each of these phases, there might be rallies, reactions, and a lot of volatility on (hopefully) the way up. Disclaimer: Crypto is volatile, carries risk and the value can go up and down. Past performance is not an indicator of future returns. Please DO YOUR OWN RESEARCH! Back to basics: time-tested investment strategies Amidst a frenzy of post-halving headlines, investor disappointment around the absence of immediate spikes in price is real, but now is not the time to abandon proven investment principles. Instead, investors should anchor their strategies in time-tested approaches designed to weather market volatility: Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets.


The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience.


Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Don’t let hype eclipse substance! While an influx of crypto headlines signifies exciting maturation of the digital asset ecosystem, excessive fixation on sensational news can prove highly detrimental to investors' long-term success. By exercising discernment amidst the noise, understanding the broader market context, and adhering to time-tested investment strategies, investors can navigate the crypto landscape with confidence. Fifteen years ago, crypto wasn’t on the mainstream media’s radar; but today, it frequently captures the spotlight on drive-time, lunch-time, and evening news channels, drawing both seasoned investors and curious newcomers into its orbit. crypto While it’s exciting to witness this once-niche market gaining traction as the flood of crypto headlines inundate our newsfeeds, we have to ask: can we have too much of a good thing? Taking note of every headline as the ticker goes up; and down (sometimes multiple times a day) would be akin to suffering from investor whiplash… Sensationalism vs. substance Sensationalism vs. substance Crypto headlines often oscillate between extremes. A prime example of this is a headline that read “Ethereum price lags”; while another read “Ethereum trader strikes gold as prices soar…” From exuberant predictions around astronomical gains to dire warnings of impending collapse - and everything in between - it can be hard to make head or tail of what’s actually going on. actually A broader view of crypto investment takes into account the long-term fundamentals that drive crypto adoption and value, including technological innovation, adoption by institutions and mainstream users, regulatory developments, macroeconomic trends, and societal acceptance. When investors read too much into market sentiment fueled only by headlines, they risk making short-sighted investment decisions fuelled by market manipulation and short-term noise. Understanding the broader context: the crypto market cycle Understanding the broader context: the crypto market cycle “Do your own research" (DYOR) is a cardinal rule in investment circles, underlining the importance of doing your own independent analysis and due diligence before making financial decisions. In the dynamic world of crypto, this mantra holds particular significance where relying solely on hearsay (including mainstream media narratives) or social media hype can lead investors astray, exposing them to unnecessary risks and potential losses. To navigate the crypto landscape effectively, astute investors are those who have taken the time to understand the nuances of Bitcoin’s market cycle (and its effect on altcoins such as Ethereum). This four-year cycle is often referred to as the ‘halving cycle’ and has been historically closely linked to price, with the cycle progressing through ‘Bull’ (rising prices); ‘Bear’ (falling prices); ‘Accumulation’ (a leveling out of prices) and ‘Expansion’ (steady growth). Importantly, within each of these phases, there might be rallies, reactions, and a lot of volatility on (hopefully) the way up. Disclaimer: Crypto is volatile, carries risk and the value can go up and down. Past performance is not an indicator of future returns. Please DO YOUR OWN RESEARCH! Disclaimer: Crypto is volatile, carries risk and the value can go up and down. Past performance is not an indicator of future returns. Please DO YOUR OWN RESEARCH! Back to basics: time-tested investment strategies Back to basics: time-tested investment strategies Amidst a frenzy of post-halving headlines, investor disappointment around the absence of immediate spikes in price is real, but now is not the time to abandon proven investment principles. Instead, investors should anchor their strategies in time-tested approaches designed to weather market volatility: absence of immediate spikes in price Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets. The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience. Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets. Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets. Dollar-cost averaging (DCA): The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience. The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience. The Hold-On-for-Dear-Life (HODL) strategy: Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Diversification: Don’t let hype eclipse substance! While an influx of crypto headlines signifies exciting maturation of the digital asset ecosystem, excessive fixation on sensational news can prove highly detrimental to investors' long-term success. By exercising discernment amidst the noise, understanding the broader market context, and adhering to time-tested investment strategies, investors can navigate the crypto landscape with confidence.

Why Obsessing Over Crypto Headlines Is a Dangerous Investment Habit

The Quiet Crisis That Is Stealing Our Health (and How to Get It Back) - Insights From Dr. Eric Berg

The rise of electronic health records and the growing use of data analytics in healthcare have made data governance a key focus area for improving healthcare services to be safer, more efficient, and better tailored to individual patient needs. The stakes are high, as a single breach in healthcare data can jeopardize patient confidentiality and weaken trust in the entire healthcare system. Astra Security reports that 51% of healthcare organizations have seen an uptick in data breaches since 2019, with hospitals facing increasing amounts of breaches over the last 12 years. Data governance is important not just for security but for ensuring data quality, integrity, and accessibility – all critical for informed decision-making in healthcare. The HIPAA (Health Insurance Portability and Accountability Act) Journal revealed that a whopping 133 million records were exposed or mishandled in 2023 alone, underscoring the need for proper data governance. Healthcare data leaders like Nithin Narayan Koranchirath are rising to the challenge by championing proper data governance strategies in the healthcare industry. Nithin’s belief is that more awareness of the essential role data governance plays in healthcare will lead to increased adoption by the industry. Managing Data in Healthcare Data governance is not a box to tick and then be forgotten about; it is a strategic necessity in the healthcare industry. With the increasing amount of Electronic Health Records (EHRs) and medical research data, healthcare institutions must guarantee that data remains precise, accessible, and secure. With inconsistencies in data across different EHR systems, there are more administrative tasks and potential risks to patient well-being than ever before. The challenges for healthcare providers in handling data are maintaining privacy and security standards, complying with regulations, managing data sources effectively, and ensuring seamless communication between different systems. Establishing a data governance program can enhance the delivery of healthcare services, foster innovation and research, improve decision-making in healthcare, and ensure compliance with laws like HIPAA and GDPR (General Data Protection Regulation). An Authority on Healthcare Data Management Nithin Narayan Koranchirath, a professional in healthcare data management, stresses the significance of data governance. He has years of expertise overseeing various healthcare data systems, and emphasizes that "efficient data management goes beyond compliance, it aims at enhancing outcomes and streamlining healthcare services." Data governance might appear as a subject typically reserved exclusively for IT experts, but its impact extends to everyone involved in the healthcare network. At its core, data governance entails managing data from the collection phase to its end use in patient care or research. Nithin’s pivotal moment came when leading the Data and Analytics platform at his current organization, one of the United States’s largest healthcare providers, where he witnessed the transformative impact of data-driven insights. "This role allowed me to witness firsthand the power of data-driven insights in revolutionizing data analytics delivery and administration," Nithin reflects, solidifying his commitment to analytics and data-driven decision-making. Grasping proper data governance practices empowers healthcare professionals with the knowledge to make informed decisions. This fundamental understanding reduces the chances of mistakes that could jeopardize safety. For patients, it offers reassurance that their personal data is handled with care and confidentiality. Additionally, implementing data governance measures such as utilizing Enterprise Master Patient Indexes (EMPI) can significantly enhance healthcare quality by providing an overview of patient records. Implementing a Data Governance Framework Tailored for Healthcare Healthcare professionals frequently encounter obstacles with data, such as difficulty accessing and safeguarding data, disorganized and isolated data sets, limited resource availability, and insufficient staff collaboration and training. Overcoming these challenges involves defining roles for adopting security measures, selecting a data management solution, allocating resources for data experts, and educating staff on effective data management practices. When assessing options for data governance solutions, healthcare institutions should take into account several factors, including integration with existing systems, compliance with requirements, scalability, and ease of use. Platforms like Big Data, AWS, and Snowflake offer data governance capabilities for overseeing data governance processes while effectively addressing security concerns and regulatory demands. Nithin’s work in implementing a comprehensive data governance framework at his organization via Big Data, AWS, and Snowflake underscores the practical benefits of such knowledge. "The implementation of robust data governance practices enabled us to achieve greater compliance with regulatory requirements and mitigate risks associated with data management," he explains. For healthcare professionals, this translates to better decision-making capabilities, and for patients, it means the assurance of data privacy and security. AWS and Snowflake are cloud-based data management solutions that align seamlessly with the needs of the healthcare industry. It offers functionalities like data discovery, traceability, and search capabilities to help organizations streamline their approach to data governance while simultaneously enhancing security and compliance measures efficiently. At the end of the day, leaders such as C-suite executives and IT managers ultimately bear responsibility for ensuring data governance practices are followed in an organization. However, every individual working within the healthcare sector plays a role in its success. Effective leadership should ensure that all staff members know the importance of data governance, while data experts should have a grasp on how data impacts staff operations. Future Outlook for Data Governance in Healthcare In an era defined by numerous technological advancements in healthcare, the importance of data governance is only set to grow. As we look towards the future, data governance in healthcare is poised to become a cornerstone of the United States healthcare system, playing a pivotal role in shaping economic outcomes. The U.S. healthcare sector, one of the largest contributors to the national economy, faces rising costs, inefficiencies, and demands for improved patient outcomes and privacy. Nithin’s hard work in solving these issues will have lasting positive effects not only on the healthcare industry but on the tech sector and the U.S. economy at large. Additionally, with the standardization of data practices, interoperability across different healthcare systems can be improved. This improvement facilitates better coordination of care, especially for chronic disease management, which is a significant expenditure for the U.S. economy. Data governance addresses these challenges by enhancing the quality, accessibility, and security of healthcare data. The path toward establishing effective data governance within healthcare is challenging but critical. As Nithin Narayan Koranchirath says himself, "Data governance is the backbone of a trustworthy healthcare system." By ensuring the integrity, accessibility, and security of healthcare data, the U.S. can improve its healthcare outcomes, reduce costs, and foster innovation, thereby enhancing its economic stability and growth. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author The rise of electronic health records and the growing use of data analytics in healthcare have made data governance a key focus area for improving healthcare services to be safer, more efficient, and better tailored to individual patient needs. The stakes are high, as a single breach in healthcare data can jeopardize patient confidentiality and weaken trust in the entire healthcare system. Astra Security reports that 51% of healthcare organizations have seen an uptick in data breaches since 2019, with hospitals facing increasing amounts of breaches over the last 12 years. Data governance is important not just for security but for ensuring data quality, integrity, and accessibility – all critical for informed decision-making in healthcare. The HIPAA (Health Insurance Portability and Accountability Act) Journal revealed that a whopping 133 million records were exposed or mishandled in 2023 alone, underscoring the need for proper data governance. 51% of healthcare organizations 51% of healthcare organizations 133 million records 133 million records Healthcare data leaders like Nithin Narayan Koranchirath are rising to the challenge by championing proper data governance strategies in the healthcare industry. Nithin’s belief is that more awareness of the essential role data governance plays in healthcare will lead to increased adoption by the industry. Managing Data in Healthcare Managing Data in Healthcare Data governance is not a box to tick and then be forgotten about; it is a strategic necessity in the healthcare industry. With the increasing amount of Electronic Health Records (EHRs) and medical research data, healthcare institutions must guarantee that data remains precise, accessible, and secure. With inconsistencies in data across different EHR systems, there are more administrative tasks and potential risks to patient well-being than ever before. The challenges for healthcare providers in handling data are maintaining privacy and security standards, complying with regulations, managing data sources effectively, and ensuring seamless communication between different systems. Establishing a data governance program can enhance the delivery of healthcare services, foster innovation and research, improve decision-making in healthcare, and ensure compliance with laws like HIPAA and GDPR (General Data Protection Regulation). An Authority on Healthcare Data Management An Authority on Healthcare Data Management Nithin Narayan Koranchirath, a professional in healthcare data management, stresses the significance of data governance. He has years of expertise overseeing various healthcare data systems, and emphasizes that "efficient data management goes beyond compliance, it aims at enhancing outcomes and streamlining healthcare services." Data governance might appear as a subject typically reserved exclusively for IT experts, but its impact extends to everyone involved in the healthcare network. At its core, data governance entails managing data from the collection phase to its end use in patient care or research. Nithin’s pivotal moment came when leading the Data and Analytics platform at his current organization, one of the United States’s largest healthcare providers, where he witnessed the transformative impact of data-driven insights. "This role allowed me to witness firsthand the power of data-driven insights in revolutionizing data analytics delivery and administration," Nithin reflects, solidifying his commitment to analytics and data-driven decision-making. Grasping proper data governance practices empowers healthcare professionals with the knowledge to make informed decisions. This fundamental understanding reduces the chances of mistakes that could jeopardize safety. For patients, it offers reassurance that their personal data is handled with care and confidentiality. Additionally, implementing data governance measures such as utilizing Enterprise Master Patient Indexes (EMPI) can significantly enhance healthcare quality by providing an overview of patient records. Implementing a Data Governance Framework Tailored for Healthcare Implementing a Data Governance Framework Tailored for Healthcare Healthcare professionals frequently encounter obstacles with data, such as difficulty accessing and safeguarding data, disorganized and isolated data sets, limited resource availability, and insufficient staff collaboration and training. Overcoming these challenges involves defining roles for adopting security measures, selecting a data management solution, allocating resources for data experts, and educating staff on effective data management practices. When assessing options for data governance solutions, healthcare institutions should take into account several factors, including integration with existing systems, compliance with requirements, scalability, and ease of use. Platforms like Big Data, AWS, and Snowflake offer data governance capabilities for overseeing data governance processes while effectively addressing security concerns and regulatory demands. offer data governance capabilities offer data governance capabilities Nithin’s work in implementing a comprehensive data governance framework at his organization via Big Data, AWS, and Snowflake underscores the practical benefits of such knowledge. "The implementation of robust data governance practices enabled us to achieve greater compliance with regulatory requirements and mitigate risks associated with data management," he explains. For healthcare professionals, this translates to better decision-making capabilities, and for patients, it means the assurance of data privacy and security. AWS and Snowflake are cloud-based data management solutions that align seamlessly with the needs of the healthcare industry. It offers functionalities like data discovery, traceability, and search capabilities to help organizations streamline their approach to data governance while simultaneously enhancing security and compliance measures efficiently. At the end of the day, leaders such as C-suite executives and IT managers ultimately bear responsibility for ensuring data governance practices are followed in an organization. However, every individual working within the healthcare sector plays a role in its success. Effective leadership should ensure that all staff members know the importance of data governance, while data experts should have a grasp on how data impacts staff operations. Future Outlook for Data Governance in Healthcare Future Outlook for Data Governance in Healthcare In an era defined by numerous technological advancements in healthcare, the importance of data governance is only set to grow. As we look towards the future, data governance in healthcare is poised to become a cornerstone of the United States healthcare system, playing a pivotal role in shaping economic outcomes. The U.S. healthcare sector, one of the largest contributors to the national economy, faces rising costs, inefficiencies, and demands for improved patient outcomes and privacy. Nithin’s hard work in solving these issues will have lasting positive effects not only on the healthcare industry but on the tech sector and the U.S. economy at large. Additionally, with the standardization of data practices, interoperability across different healthcare systems can be improved. This improvement facilitates better coordination of care, especially for chronic disease management, which is a significant expenditure for the U.S. economy. Data governance addresses these challenges by enhancing the quality, accessibility, and security of healthcare data. The path toward establishing effective data governance within healthcare is challenging but critical. As Nithin Narayan Koranchirath says himself, "Data governance is the backbone of a trustworthy healthcare system." By ensuring the integrity, accessibility, and security of healthcare data, the U.S. can improve its healthcare outcomes, reduce costs, and foster innovation, thereby enhancing its economic stability and growth. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Why Data Governance in Healthcare Matters in 2024 With Nithin Narayan Koranchirath

কেন নিথিন নারায়ণ কোরানচিরথের সাথে 2024 সালে স্বাস্থ্যসেবা বিষয়ে ডেটা গভর্নেন্স

Warum Data Governance im Gesundheitswesen im Jahr 2024 wichtig ist – mit Nithin Narayan Koranchirath

Por qué es importante la gobernanza de datos en la atención sanitaria en 2024 con Nithin Narayan Koranchirath

Pourquoi la gouvernance des données dans le secteur de la santé est importante en 2024 avec Nithin Narayan Koranchirath

2024 में स्वास्थ्य सेवा में डेटा गवर्नेंस क्यों मायने रखता है, नितिन नारायण कोरानचिराथ के साथ

2024 年に医療におけるデータ ガバナンスが重要な理由 (ニティン ナラヤン コランチラート氏)

Nithin Narayan Koranchirath와 함께 2024년에 의료 분야의 데이터 거버넌스가 중요한 이유

Por que a governança de dados na área da saúde é importante em 2024 com Nithin Narayan Koranchirath

Почему управление данными в здравоохранении имеет значение в 2024 году с Нитин Нараян Коранчират

Nithin Narayan Koranchirath ile 2024'te Sağlık Hizmetlerinde Veri Yönetişimi Neden Önemli?

Tại sao quản trị dữ liệu trong lĩnh vực chăm sóc sức khỏe lại quan trọng vào năm 2024 với Nithin Narayan Koranchirath

Nithin Narayan Koranchirath 谈 2024 年医疗保健数据治理为何如此重要

Every year, people around the world find themselves unable to cover basic medical costs. In fact, about half of U.S. adults currently have trouble affording healthcare. This is caused by issues like the high cost of hospital care and a lack of pharmaceutical price transparency, resulting in a faulty system that continues to leave millions out. However, industry experts like Mandhir seek to address these issues. As a senior lead engineer at Elevance Health, Mandhir has been instrumental in using patient data to create a unique, AI-powered system that helps users access personalized medical services. He’s also worked to establish a broader vision of patient care that not only considers physical factors but also takes into account social, behavioral, and mental elements. Learn more about Mandhir’s work and how he’s worked to make medical costs more affordable for millions of people worldwide. The Current Challenges in Healthcare Most problems in the American healthcare industry can be traced to a lack of transparency. Under the current system, patients have no way of knowing the full cost of their treatments until the bill is already due, whether it’s hospital fees, prescription drugs, or insurance coverage. This leaves patients susceptible to surprise medical bills that can quickly wipe out savings. Additionally, most facilities don’t have the proper infrastructure to store and organize the massive amounts of patient data that the healthcare industry generates every day. This makes it difficult for providers to access vital records, but it also means they can get inconsistent or incomplete data, making it harder to get the full picture of a patient’s health problems. Without a unified communication system among providers that keeps data synchronized and all in one place, patient care suffers as doctors inaccurately assess their needs (due to a lack of good data). It also leads to conflicting reports and unnecessary additional expenses, significantly contributing to a healthcare system that’s already rife with disparities in price and quality. Advocating for Transparency: Mandhir’s Commitment to a Better Healthcare Ecosystem Mandhir has understood the importance of healthcare accessibility since he was a child. Raised in a remote village in Punjab, India, he saw firsthand how families around him struggled to afford basic health treatments, even going as far as to sell their property. When he moved to the United States, he was impressed by many advancements in healthcare, but he was even more surprised that healthcare was almost just as inaccessible despite its innovations. After receiving his Bachelor of Technology from the Punjab Technical University, he worked for over 13 years as a technology architect for the second-largest Indian IT company, Infosys. During this time, he worked on claim adjudication systems for Anthem, one of the largest health companies in the US, and gained great insight into the inner workings of the country’s healthcare sector. This experience eventually led Mandhir to work directly for Anthem, now called Elevance Health. As a senior lead engineer, he’s used his position and talents over the years to spearhead significant developments in data-driven healthcare technology. How Mandhir’s Work Uses AI-Driven Data Systems to Improve Patient Assistance Fueled by a passion rooted in personal experience, Mandhir began addressing healthcare’s shortcomings by incorporating concepts like machine learning and artificial intelligence into Elevance Health’s patient data systems. Some of the ways he’s contributed to a better healthcare system include: The Implementation of Data-Driven Decision-Making: Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality.


The Creation of the Health OS Platform: Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. Thanks to these efforts, Mandhir has worked toward making medicine more affordable. For example, the insights gained from data-powered health monitoring allow patients to start precautionary treatments earlier and reduce unwanted expenses like hospital stays. They also help providers calculate costs more accurately based on a patient’s health plan. Whole Health: A Holistic Approach to Healthcare Data Improving the quality of patient health records was only the first step toward making quality medical services more accessible to patients. Mandhir has tailored his machine learning systems to fit the “whole health” approach to healthcare. Whole health is a mindset that recognizes that a patient’s health consists of more than just their physical condition — it includes their mental well-being, behavioral conditions, and social factors like their living situation. Additionally, according to this philosophy, all of these factors should be considered when addressing a patient’s access to health services. To this end, Mandhir assisted in developing the Whole Health Index, a tool that gathers patient data from multiple sources (like health records, surveys, and community organizations) and creates a thorough overview of a patient’s health profile and financial situation. This holistic approach has been key to enhancing patient-provider communication. By understanding what the patient can and can’t afford, both parties can work to find a cost-effective, high-quality treatment option. Not only does that build patient trust in the healthcare industry, but it also leads to improved health outcomes. Mandhir’s Continued Efforts to Bridge Gaps in Healthcare Delivery By seizing the latest developments in emerging technology like AI, Mandhir plans to reinforce data security and privacy in platforms like Health OS to build further trust with providers. He also aims to make the whole health approach a fixed industry standard to change the public perception of the healthcare system. Mandhir also keeps an open dialogue with policymakers and stakeholders to advocate for price disclosure and accessibility as a broader societal change. Mandhir believes that healthcare should not be a privilege but a right. And with his technical expertise, in-depth knowledge of the healthcare system, and personal life experience, he’s dedicated his career to turning this vision into a reality. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author Every year, people around the world find themselves unable to cover basic medical costs. In fact, about half of U.S. adults currently have trouble affording healthcare . This is caused by issues like the high cost of hospital care and a lack of pharmaceutical price transparency, resulting in a faulty system that continues to leave millions out. about half of U.S. adults currently have trouble affording healthcare about half of U.S. adults currently have trouble affording healthcare However, industry experts like Mandhir seek to address these issues. As a senior lead engineer at Elevance Health, Mandhir has been instrumental in using patient data to create a unique, AI-powered system that helps users access personalized medical services. He’s also worked to establish a broader vision of patient care that not only considers physical factors but also takes into account social, behavioral, and mental elements. Learn more about Mandhir’s work and how he’s worked to make medical costs more affordable for millions of people worldwide. The Current Challenges in Healthcare The Current Challenges in Healthcare Most problems in the American healthcare industry can be traced to a lack of transparency. Under the current system, patients have no way of knowing the full cost of their treatments until the bill is already due, whether it’s hospital fees, prescription drugs, or insurance coverage. This leaves patients susceptible to surprise medical bills that can quickly wipe out savings. Additionally, most facilities don’t have the proper infrastructure to store and organize the massive amounts of patient data that the healthcare industry generates every day. This makes it difficult for providers to access vital records, but it also means they can get inconsistent or incomplete data, making it harder to get the full picture of a patient’s health problems. Without a unified communication system among providers that keeps data synchronized and all in one place, patient care suffers as doctors inaccurately assess their needs (due to a lack of good data). It also leads to conflicting reports and unnecessary additional expenses, significantly contributing to a healthcare system that’s already rife with disparities in price and quality. Advocating for Transparency: Mandhir’s Commitment to a Better Healthcare Ecosystem Advocating for Transparency: Mandhir’s Commitment to a Better Healthcare Ecosystem Mandhir has understood the importance of healthcare accessibility since he was a child. Raised in a remote village in Punjab, India, he saw firsthand how families around him struggled to afford basic health treatments, even going as far as to sell their property. When he moved to the United States, he was impressed by many advancements in healthcare, but he was even more surprised that healthcare was almost just as inaccessible despite its innovations. After receiving his Bachelor of Technology from the Punjab Technical University, he worked for over 13 years as a technology architect for the second-largest Indian IT company, Infosys. During this time, he worked on claim adjudication systems for Anthem, one of the largest health companies in the US, and gained great insight into the inner workings of the country’s healthcare sector. This experience eventually led Mandhir to work directly for Anthem, now called Elevance Health. As a senior lead engineer, he’s used his position and talents over the years to spearhead significant developments in data-driven healthcare technology. How Mandhir’s Work Uses AI-Driven Data Systems to Improve Patient Assistance How Mandhir’s Work Uses AI-Driven Data Systems to Improve Patient Assistance Fueled by a passion rooted in personal experience, Mandhir began addressing healthcare’s shortcomings by incorporating concepts like machine learning and artificial intelligence into Elevance Health’s patient data systems. Some of the ways he’s contributed to a better healthcare system include: The Implementation of Data-Driven Decision-Making: Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality. The Creation of the Health OS Platform: Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. The Implementation of Data-Driven Decision-Making: Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality. The Implementation of Data-Driven Decision-Making : Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality. The Implementation of Data-Driven Decision-Making The Creation of the Health OS Platform: Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. The Creation of the Health OS Platform : Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. The Creation of the Health OS Platform Thanks to these efforts, Mandhir has worked toward making medicine more affordable. For example, the insights gained from data-powered health monitoring allow patients to start precautionary treatments earlier and reduce unwanted expenses like hospital stays. They also help providers calculate costs more accurately based on a patient’s health plan. Whole Health: A Holistic Approach to Healthcare Data Whole Health: A Holistic Approach to Healthcare Data Improving the quality of patient health records was only the first step toward making quality medical services more accessible to patients. Mandhir has tailored his machine learning systems to fit the “whole health” approach to healthcare. Whole health is a mindset that recognizes that a patient’s health consists of more than just their physical condition — it includes their mental well-being, behavioral conditions, and social factors like their living situation. Additionally, according to this philosophy, all of these factors should be considered when addressing a patient’s access to health services. To this end, Mandhir assisted in developing the Whole Health Index, a tool that gathers patient data from multiple sources (like health records, surveys, and community organizations) and creates a thorough overview of a patient’s health profile and financial situation. This holistic approach has been key to enhancing patient-provider communication. By understanding what the patient can and can’t afford, both parties can work to find a cost-effective, high-quality treatment option. Not only does that build patient trust in the healthcare industry, but it also leads to improved health outcomes. Mandhir’s Continued Efforts to Bridge Gaps in Healthcare Delivery Mandhir’s Continued Efforts to Bridge Gaps in Healthcare Delivery By seizing the latest developments in emerging technology like AI, Mandhir plans to reinforce data security and privacy in platforms like Health OS to build further trust with providers. He also aims to make the whole health approach a fixed industry standard to change the public perception of the healthcare system. Mandhir also keeps an open dialogue with policymakers and stakeholders to advocate for price disclosure and accessibility as a broader societal change. Mandhir believes that healthcare should not be a privilege but a right. And with his technical expertise, in-depth knowledge of the healthcare system, and personal life experience, he’s dedicated his career to turning this vision into a reality. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Transforming Healthcare: Mandhir's Quest for Affordable Medical Access with AI

ট্রান্সফর্মিং হেলথ কেয়ার: এআই সহ সাশ্রয়ী মূল্যের চিকিৎসা অ্যাক্সেসের জন্য মন্দিরের কোয়েস্ট

Transformation des Gesundheitswesens: Mandhirs Streben nach erschwinglichem Zugang zur medizinischen Versorgung mithilfe von KI

Transformando la atención médica: la búsqueda de Mandhir de un acceso médico asequible con inteligencia artificial

Transformer les soins de santé : la quête de Mandhir pour un accès médical abordable grâce à l'IA

स्वास्थ्य सेवा में बदलाव: एआई के साथ सस्ती चिकित्सा पहुंच के लिए मंधीर की खोज

ヘルスケアの変革: AI を活用した手頃な医療アクセスの実現を目指す Mandhir の取り組み

의료 혁신: AI를 통한 저렴한 의료 접근성을 위한 Mandhir의 탐구

Transformando a saúde: a busca de Mandhir por acesso médico acessível com IA

Преобразование здравоохранения: стремление Мандира к доступному медицинскому доступу с помощью ИИ

Sağlık Hizmetlerini Dönüştürmek: Mandhir'in Yapay Zeka ile Uygun Fiyatlı Tıbbi Erişim Arayışı

Chuyển đổi chăm sóc sức khỏe: Nhiệm vụ của Mandhir về khả năng tiếp cận y tế giá cả phải chăng với AI

医疗转型：Mandhir 寻求利用人工智能实现平价医疗服务

2024 has been a challenging year so far for Apple’s embattled stock, but could the launch of the iPhone 16 and arrival of generative AI capabilities for iOS help the smartphone giant recapture its status as a market leader? Apple’s well-documented problems have been playing out on Wall Street throughout the year, and the stock finished the first quarter more than 7.5% lower after Barclays issued a downgrade owing to a lack of innovation and slowing sales in China. "The iPhone 15 has been lackluster and we believe iPhone 16 should be the same," in a client note in January. warned Barclays analyst Tim Long Owing in part to geopolitical tensions between the United States and China, Apple fell to fifth place in the nation’s smartphone market as shipments to 10 million units, with Huawei claiming a 17% market share at the top of the pile. declined by 25% It’s a similar story among global markets for Apple. Once an undisputed leader in terms of smartphone sales, Korean firm Samsung claimed the top spot for sales volumes worldwide in Q1 2024, , outstripping Apple’s 48.7 million units. shipping 60 million units This indicates that 2024 is shaping up to be a key battleground in determining Apple’s future, and the company that’s typically at the forefront of innovation will be well aware that the iPhone 16 must be a hit to recapture its former glories. As global competition continues to grow, the generative AI boom will likely be seen as the solution for Apple’s difficult start to the year, but will it be enough to recapture the Silicon Valley firm’s lost market share? Embracing the Generative AI Boom The expectation of Apple’s emergence into the world of generative AI is growing. Stock market analysts Morningstar has even sought to for iPhone revenue in 2025 owing to the generative AI functionality that it anticipates will be added to the iPhone 16. raise forecasts Apple appeared to have taken a more steady approach towards embracing generative AI in the months that followed the launch of ChatGPT and its widespread introduction to the vast potential of large-language models (LLMs) and their limitless industry capabilities. However, CEO that the implementation of generative AI tools for iPhone and other Apple products would soon be on the way. Earlier this year, Cook claimed that substantial investment in "a tremendous amount of time and effort" towards GenAI has been taken, and we recently saw the launch of OpenELM, or ‘Open-source Efficient Language Models’ as a tool that could ramp up Apple’s bid to embrace the emerging technology. Tim Cook highlighted It’s clear that the best of Apple’s generative AI bid will be found in the iPhone 16, which is due to be launched in the second half of the year. But will it be capable of turning the tide of Apple’s falling market share? Turning the iPhone 16 Into a Success The launch of the iPhone 16 this year is going to be a significant moment for Apple because it will provide the answer to many questions currently being asked about the company. Can Apple recover its market dominance? How will Apple use generative AI to improve iOS? Is Apple suffering from a lack of innovation? The latter question is becoming increasingly crucial for the company and its various stakeholders. Echoing Barclays analyst Tim Long’s sentiment off the back of Apple’s worrying downgrade on Wall Street, there’s a that CEO Tim Cook isn’t as innovative as his iconic predecessor, Steve Jobs. growing school of thought Now, as generative AI emerges as a key opportunity for Apple to embrace exciting new technology and utilize it within its products, Cook has the chance to outperform Apple’s rivals in delivering meaningful innovation in a smartphone industry that’s ripe for growth following the arrival of 5G connectivity. So, can we use what we already know about the iPhone 16 to anticipate whether it will be a success? Available information suggests that the screen size of the iPhone 16 will be , which is 0.2” . 6.9 inches larger than the iPhone 15 Pro Max At 48 megapixels, the iPhone 16’s camera is likely to be , but with an and ‘pixel binning’ to combine data from four pixels into one single ‘superpixel’ to improve quality overall. similar in spec to the iPhone 15 ultra-wide lens The expectation of a larger battery and an A18 Pro chipset weighs in at an , but so far, there isn’t much evidence of innovation that appears ready to threaten the dominance of Huawei and Samsung in Chinese and global markets respectively. improvement on the iPhone 15 Pro However, the expectation of generative AI features throughout many existing apps and a series of new ones will make the iPhone 16 a watershed release for Apple. This raises the question, are we waiting for the next iteration of the iPhone? Or a major iOS update? Will iOS 18 Save Apple’s Slump? Apple’s biggest launch of 2024 will be its integrated LLMs into the next major iteration of its iOS operating system. The reason that this is exciting is because of the sheer effort that Apple has been undertaking in making its generative AI capabilities truly transformative. The company’s recent AI acquisitions , which specialize in building AI models on limited hardware. include DarwinAI and Datakalab Implementing generative AI capabilities into the next generation of iOS will open the door to unprecedented intelligence for users. Because of their generative nature, apps can work alongside one another to actively update calendars the moment plans are made on WhatsApp, emojis or GIFs will be created using text-based prompts, and users will be capable of automatically responding to messages with GenAI algorithms capable of mimicking their writing style and personality. This is only a glimpse at some of the transformative capabilities offered by generative AI, and it’s not only a matter of time before they become commonplace throughout iOS. Whether the scale of Apple’s generative AI strategy means that an iPhone 16 would be essential to unlock the full capabilities of the technology remains to be seen, but this could be crucial in the company’s overall market performance in 2024 and beyond. According to a , “this year, [Apple] will not only greatly strengthen the AI computing power of M3 and A17 processors, but also significantly increase the number of AI computing cores and performance of the new generation of M4 and A18 processors.” recent report from Economic Daily News Could this mean that the iPhone 16 will be essential to fully utilize generative AI? Considering that Bloomberg has suggested generative AI will become a , the lure of GenAI within the next generation of iPhone could be strong enough to spark a market revival. $1.3 trillion market by 2032 The Long Road to Recovery Apple’s future performance will depend on other factors as well as its rollout of generative AI capabilities. The current geopolitical issues between the United States and China threaten the firm’s global dominance, but recapturing its sense of innovation can be a major stepping stone in Apple’s recovery. Although the iPhone remains a Western powerhouse, recapturing the spirit evoked by Steve Jobs in the launch of the iPhone 16 could go some way in transforming the sentiment surrounding Apple. In a market that’s full of capable global players, the next generation of iPhone and iOS has never been more important.

Weight of AI Expectation Weighs Heavy as iPhone 16 Launch Becomes Pivotal for Apple

আইফোন 16 লঞ্চ অ্যাপলের জন্য গুরুত্বপূর্ণ হয়ে উঠেছে বলে এআই প্রত্যাশার ওজন ভারী

Die Erwartungen an die KI sind groß, da die Einführung des iPhone 16 für Apple von entscheidender Bedeutung ist

El peso de las expectativas de IA pesa mucho a medida que el lanzamiento del iPhone 16 se vuelve fundamental para Apple

Le poids des attentes en matière d’IA pèse lourd alors que le lancement de l’iPhone 16 devient crucial pour Apple

एआई की उम्मीदों का वजन भारी है क्योंकि iPhone 16 का लॉन्च Apple के लिए महत्वपूर्ण बन गया है

iPhone 16の発売がAppleにとって極めて重要になる中、AIへの期待が重くのしかかる

iPhone 16 출시가 Apple의 중추적인 역할을 하게 되면서 AI에 대한 기대의 무게가 무거워졌습니다.

O peso da expectativa de IA pesa muito à medida que o lançamento do iPhone 16 se torna fundamental para a Apple

Ожидания в отношении искусственного интеллекта тяжелы, поскольку запуск iPhone 16 становится решающим для Apple

iPhone 16'nın Lansmanı Apple İçin Önemli Hale Gelirken Yapay Zeka Beklentisinin Ağırlığı Ağırlaşıyor

Kỳ vọng về AI ngày càng nặng nề khi việc ra mắt iPhone 16 trở thành vấn đề quan trọng đối với Apple

iPhone 16 发布对苹果来说至关重要，对 AI 的期望也随之增加

The evolution of the digital landscape is picking up again. This time, we’re heading into Web3 - a stage in the history of the internet where customers are able to achieve more control of their data and what is done with it. Web3 represents a departure from the centralized architectures of Web2, offering a vision of a more open, inclusive, and democratized digital ecosystem. At the heart of this transition lies the need for speed, as users demand seamless, real-time interactions and transactions across decentralized networks. Whether it's executing smart contracts, minting NFTs, or facilitating microtransactions, the ability of blockchains to deliver swift and scalable solutions is paramount to unlocking the full potential of Web3. Web3 is expected to be ruled by several nascent technologies,- including blockchain. In fact, blockchain is expected to be the foundational layer of Web3, powering other technologies and delivering us into a new age of the internet. But, for this to happen, it is important for blockchains to be better - and on that front, speed is one area where a lot of progress needs to be made. Speed plays a pivotal role in enhancing user experience and driving widespread adoption of blockchain-based applications. In an era where convenience and efficiency reign supreme, sluggish transaction speeds and high latency can deter users and stifle growth. By investing in speed, blockchains can lower barriers to entry, attract a broader user base, and foster a vibrant ecosystem of dApps, NFT marketplaces, and DeFi protocols. To lend more insights into this, we spoke with the founders of Spedy - a new blockchain that touts itself as the fastest on the internet at the moment - to understand their view of the market: 1. Does the focus on speed mean that it is the most important part of the blockchain trilemma? Solving the scalability problem is indeed one of the most important parts of the trilemma, and with the Sonic update, Fantom has become one of the fastest L1 chains with 2,000 TPS and sub-second finality. Sonic is the new best-in-class shared sequencer for L1 and L2 chains, capable of processing over 180 million daily transactions with real, sub-second confirmation times. 2. What are the states of security and decentralization on Speedy? Just as the Sonic update is set to solve the scalability problem, Speedy is meant to be the cultural catalyst for Fantom. As a project, attracting new users on-chain who can experiment and witness not only the best-in-class speed but also the entire ecosystem that has been built to be secure and decentralized from the very beginning. Particularly for Speedy, decentralization stands in the hands of the community, with 90% of the funds going into a locked LP; 5% were airdropped to sGOAT holders—the first meme foundation on Fantom; and 5% used for CEX listings and outreach. 3. In the context of value and money transfers, how does speed play a role in optimizing blockchain operations? We recently reached a new milestone with 1 billion smart contracts deployed in the crypto space, with Fantom accounting for 16% with over 161,600,000 contractd. So, your question couldn't come at a better time. Fantom has always been known for low transaction fees, but with recent upgrades to the network and revisions to Fantom’s fee structure, it has become cheaper than ever to engage on the network. For most transactions, fees are less than $0.01, even during high-traffic periods. Regarding speed again, Rabby wallet tested how fast Sonic is exactly, and here is the conclusion: 4. What are the primary use cases for Speedy, and what should end users expect? Our primary use case as a project is to become the dominant meme project on Fantom, but generally, there is a catch. Speedy is also a catalyst for other creators to join and use the ecosystem we’ve built also on the cultural side. We already have teams developing in the SocialFi area, and new iGaming platforms will soon be launched. Having a growing community is, in my opinion, one of the most attractive metrics, as it means people have formed a strong culture and are eager to see the ecosystem get bigger to benefit everyone. 5. Do you believe that developers will continue to focus on speed optimization going forward? I’ve recently read Andre Cronje’s article called 'Frustrations of a Dapp Developer', and once you understand the challenges you are bound to overcome, the dedication one needs, the complexity, and the error margin, you can only conclude that the devs who succeeded in making Sonic best-in-class will never stop. It’s in the code." Conclusion: The internet has reached a point where pace is everything. And for blockchains, being able to process transactions faster isn’t optional - it’s a must. For blockchain developers, choosing how to optimize for speed will be critical if they’re to achieve mass adoption. That said, the blockchain trilemma is one that continues to bother many developers, and it’s also important not to forget the place of security and decentralization. The evolution of the digital landscape is picking up again. This time, we’re heading into Web3 - a stage in the history of the internet where customers are able to achieve more control of their data and what is done with it. Web3 represents a departure from the centralized architectures of Web2, offering a vision of a more open, inclusive, and democratized digital ecosystem. At the heart of this transition lies the need for speed, as users demand seamless, real-time interactions and transactions across decentralized networks. Whether it's executing smart contracts, minting NFTs, or facilitating microtransactions, the ability of blockchains to deliver swift and scalable solutions is paramount to unlocking the full potential of Web3. Web3 is expected to be ruled by several nascent technologies,- including blockchain. In fact, blockchain is expected to be the foundational layer of Web3, powering other technologies and delivering us into a new age of the internet. But, for this to happen, it is important for blockchains to be better - and on that front, speed is one area where a lot of progress needs to be made. Speed plays a pivotal role in enhancing user experience and driving widespread adoption of blockchain-based applications. In an era where convenience and efficiency reign supreme, sluggish transaction speeds and high latency can deter users and stifle growth. By investing in speed, blockchains can lower barriers to entry, attract a broader user base, and foster a vibrant ecosystem of dApps, NFT marketplaces, and DeFi protocols. To lend more insights into this, we spoke with the founders of Spedy - a new blockchain that touts itself as the fastest on the internet at the moment - to understand their view of the market: 1. Does the focus on speed mean that it is the most important part of the blockchain trilemma? Solving the scalability problem is indeed one of the most important parts of the trilemma, and with the Sonic update, Fantom has become one of the fastest L1 chains with 2,000 TPS and sub-second finality. Sonic is the new best-in-class shared sequencer for L1 and L2 chains, capable of processing over 180 million daily transactions with real, sub-second confirmation times. 2. What are the states of security and decentralization on Speedy? Just as the Sonic update is set to solve the scalability problem, Speedy is meant to be the cultural catalyst for Fantom. As a project, attracting new users on-chain who can experiment and witness not only the best-in-class speed but also the entire ecosystem that has been built to be secure and decentralized from the very beginning. Particularly for Speedy, decentralization stands in the hands of the community, with 90% of the funds going into a locked LP; 5% were airdropped to sGOAT holders—the first meme foundation on Fantom; and 5% used for CEX listings and outreach. 3. In the context of value and money transfers, how does speed play a role in optimizing blockchain operations? We recently reached a new milestone with 1 billion smart contracts deployed in the crypto space, with Fantom accounting for 16% with over 161,600,000 contractd. So, your question couldn't come at a better time. Fantom has always been known for low transaction fees, but with recent upgrades to the network and revisions to Fantom’s fee structure, it has become cheaper than ever to engage on the network. For most transactions, fees are less than $0.01, even during high-traffic periods. Regarding speed again, Rabby wallet tested how fast Sonic is exactly, and here is the conclusion: 4. What are the primary use cases for Speedy, and what should end users expect? Our primary use case as a project is to become the dominant meme project on Fantom, but generally, there is a catch. Speedy is also a catalyst for other creators to join and use the ecosystem we’ve built also on the cultural side. We already have teams developing in the SocialFi area, and new iGaming platforms will soon be launched. Having a growing community is, in my opinion, one of the most attractive metrics, as it means people have formed a strong culture and are eager to see the ecosystem get bigger to benefit everyone. 5. Do you believe that developers will continue to focus on speed optimization going forward? I’ve recently read Andre Cronje’s article called 'Frustrations of a Dapp Developer', and once you understand the challenges you are bound to overcome, the dedication one needs, the complexity, and the error margin, you can only conclude that the devs who succeeded in making Sonic best-in-class will never stop. It’s in the code." Conclusion: Conclusion: The internet has reached a point where pace is everything. And for blockchains, being able to process transactions faster isn’t optional - it’s a must. For blockchain developers, choosing how to optimize for speed will be critical if they’re to achieve mass adoption. That said, the blockchain trilemma is one that continues to bother many developers, and it’s also important not to forget the place of security and decentralization.

Why Blockchains Need To Invest In Speed Heading Into Web3

Talks On Productivity: Evgeniya Malina - Head of Processes and Automation at Whizz 

Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. 1. What is your company in 2–5 words? Digital product agency disrupting healthcare. 2. Why is now the time for your company to exist? There is no denying that the pace of innovation and technology has accelerated in the last decade. However, the healthcare industry has not evolved as quickly as other industries. It faces unique challenges, like a lack of agility, strict regulations, antiquated processes, and slow innovation. Although there are many people in the sector working to improve quality and accessibility, systemic problems and financial interests limit what can be achieved. Our strategy is to bring agility to this setting by using our knowledge of technology, user-centric approach, and in-depth knowledge of the business to create software that is strategically planned, scalable, and cleverly constructed. 3. What do you love about your team, and why are you the ones to solve this problem? When we first set out, we were quite excited about developing digital solutions that prioritized the needs and wants of users and delivered genuine value. By then, we collaborated with businesses in every sector of the economy, including real estate, retail, food, entertainment, agronomy, retail, and finance. Even though we've produced incredible products and gained a lot of knowledge from the process, we believed that focus and mission were still lacking. In 2020, during the pandemic, we made a strategic shift and decided to focus 100% on healthcare, allowing us to fully immerse ourselves in the industry, gain specialized knowledge, and, most importantly, have a shared, meaningful purpose. This change gave our entire team a compelling mission and a defined path. Who wouldn't find satisfaction in working on initiatives for a sector of the economy that ultimately saves lives? I guess that’s what I love most about the team and why we are the ones who solve problems. We’re a team of focused, knowledgeable, and creative people with a shared purpose: improving healthcare. 4. If you weren’t building your startup, what would you be doing? I would probably be building another startup. I’ve been an entrepreneur since I was a teenager, so I’m sure that if Light-it had not succeeded, I would be building something else. 5. At the moment, how do you measure success? What are your metrics? We work with OKRs (Objectives and Key Results), and they are measured quarterly. We have company-wide OKRs and OKRs per department. They help us stay aligned with the same goals quarter to quarter. 6. In a few sentences, what do you offer to whom? In broad terms, we partner with our clients to help them ideate, design, and develop healthcare digital products. The challenge we resolve is the lack of expertise in developing healthcare software with a product mindset and keeping strategy, budget, compliance, and user experience in mind.  There are hundreds of software development firms with no experience in healthcare, which can lead to short-term gains at the expense of long-term scalability and maintenance. We at Light-itadopt a unique strategy. We follow industry best practices, implementing scalable models across several platforms and providing end-to-end product development services with extensive user experience support and consulting. Since we have an agile approach, startups are our bread and butter. Nevertheless, our interactions with health systems have also been excellent. Our introduction of an agile vision and innovative mindset has been extremely effective in these circumstances, given their customary bureaucratic and slower ways. Finding a reliable partner is usually difficult for a healthcare organization, but finding someone who can offer both high-tech development and strategic advice is much more difficult. That's what makes us unique. 7. What’s most exciting about your traction to date? **Light-it'**s traction has been steadily growing year after year, an incredibly encouraging trend. Last year was particularly exciting for us as we forged partnerships with industry giants like ABInBev Foundation, the world's largest beer company, and Inviza, a groundbreaking RPM and DTX product for the US Air Force.  These collaborations are not only about business and transactions – they validate our position as leaders in our field and serve as a powerful encouragement to continue our hard work. 8. Where do you think your growth will be next year? We have been collaborating with startups, supporting them through the entire product development process, from launch to scaling. Now that we are collaborating with Health Systems, we plan to continue doing so because we have seen such a positive impact. We don't intend to expand the company too quickly because, as a team, we want to keep our boutique approach. We aim to keep our crew size between one hundred and two hundred. We always have new projects coming up. In my opinion, one of the biggest promises in health technology is generative AI. Puppeteeris a spinoff company we launched last year, and it is the result of the realization that 2024 was going to be the year of generative AI and that humanity will either have to accept it or perish. Our first round is presently being raised, and we have high hopes for it this year. Regarding our long-term future, Light-it's DNA will always embrace new technologies and pathways, so who knows what else might emerge! "You win, or you learn" strikes a chord with me. Every new endeavor, whether it succeeds or fails, adds to our development and resiliency. I'm committed to establishing a positive work environment that prioritizes curiosity, boldness, ownership, and dedication to ongoing development while also considering the well-being of our team. 9. Tell us about your first paying customer and revenue expectations over the next year. The first paying customers we had were family members and friends building e-commerces. Since then, we’ve worked with startups from pre-seed to Series D, with Fortune 500s and some of the biggest health systems in the world! In terms of revenue, we expect to keep growing at the rate of 50% per year. 10. What’s your biggest threat? We have all heard that AI will replace software engineers, so I guess that should be my biggest thread as a software development company owner. Still, I’m convinced that our team has minds that no AI can substitute: Empathy, strategy, and creativity are some of the skills needed in a product team that AI can't substitute. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon ’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. HackerNoon The link for the template is HERE. 1. What is your company in 2–5 words? Digital product agency disrupting healthcare. 2. Why is now the time for your company to exist? There is no denying that the pace of innovation and technology has accelerated in the last decade. However, the healthcare industry has not evolved as quickly as other industries. It faces unique challenges, like a lack of agility, strict regulations, antiquated processes, and slow innovation. Although there are many people in the sector working to improve quality and accessibility, systemic problems and financial interests limit what can be achieved. Our strategy is to bring agility to this setting by using our knowledge of technology, user-centric approach, and in-depth knowledge of the business to create software that is strategically planned, scalable, and cleverly constructed. 3. What do you love about your team, and why are you the ones to solve this problem? When we first set out, we were quite excited about developing digital solutions that prioritized the needs and wants of users and delivered genuine value. By then, we collaborated with businesses in every sector of the economy, including real estate, retail, food, entertainment, agronomy, retail, and finance. Even though we've produced incredible products and gained a lot of knowledge from the process, we believed that focus and mission were still lacking. In 2020, during the pandemic, we made a strategic shift and decided to focus 100% on healthcare, allowing us to fully immerse ourselves in the industry, gain specialized knowledge, and, most importantly, have a shared, meaningful purpose. This change gave our entire team a compelling mission and a defined path. Who wouldn't find satisfaction in working on initiatives for a sector of the economy that ultimately saves lives? I guess that’s what I love most about the team and why we are the ones who solve problems. We’re a team of focused, knowledgeable, and creative people with a shared purpose: improving healthcare. 4. If you weren’t building your startup, what would you be doing? I would probably be building another startup. I’ve been an entrepreneur since I was a teenager, so I’m sure that if Light-it had not succeeded, I would be building something else. Light-it Light-it 5. At the moment, how do you measure success? What are your metrics? We work with OKRs (Objectives and Key Results), and they are measured quarterly. We have company-wide OKRs and OKRs per department. They help us stay aligned with the same goals quarter to quarter. 6. In a few sentences, what do you offer to whom? In broad terms, we partner with our clients to help them ideate, design, and develop healthcare digital products. The challenge we resolve is the lack of expertise in developing healthcare software with a product mindset and keeping strategy, budget, compliance, and user experience in mind.  There are hundreds of software development firms with no experience in healthcare, which can lead to short-term gains at the expense of long-term scalability and maintenance. We at Light-it adopt a unique strategy. We follow industry best practices, implementing scalable models across several platforms and providing end-to-end product development services with extensive user experience support and consulting. Light-it Since we have an agile approach, startups are our bread and butter. Nevertheless, our interactions with health systems have also been excellent. Our introduction of an agile vision and innovative mindset has been extremely effective in these circumstances, given their customary bureaucratic and slower ways. Finding a reliable partner is usually difficult for a healthcare organization, but finding someone who can offer both high-tech development and strategic advice is much more difficult. That's what makes us unique. 7. What’s most exciting about your traction to date? **Light-it'**s traction has been steadily growing year after year, an incredibly encouraging trend. Last year was particularly exciting for us as we forged partnerships with industry giants like ABInBev Foundation, the world's largest beer company, and Inviza, a groundbreaking RPM and DTX product for the US Air Force.  These collaborations are not only about business and transactions – they validate our position as leaders in our field and serve as a powerful encouragement to continue our hard work. 8. Where do you think your growth will be next year? We have been collaborating with startups, supporting them through the entire product development process, from launch to scaling. Now that we are collaborating with Health Systems, we plan to continue doing so because we have seen such a positive impact. We don't intend to expand the company too quickly because, as a team, we want to keep our boutique approach. We aim to keep our crew size between one hundred and two hundred. We always have new projects coming up. In my opinion, one of the biggest promises in health technology is generative AI. Puppeteer is a spinoff company we launched last year, and it is the result of the realization that 2024 was going to be the year of generative AI and that humanity will either have to accept it or perish. Our first round is presently being raised, and we have high hopes for it this year. Puppeteer Regarding our long-term future, Light-it's DNA will always embrace new technologies and pathways, so who knows what else might emerge! "You win, or you learn" strikes a chord with me. Every new endeavor, whether it succeeds or fails, adds to our development and resiliency. I'm committed to establishing a positive work environment that prioritizes curiosity, boldness, ownership, and dedication to ongoing development while also considering the well-being of our team. 9. Tell us about your first paying customer and revenue expectations over the next year. The first paying customers we had were family members and friends building e-commerces. Since then, we’ve worked with startups from pre-seed to Series D, with Fortune 500s and some of the biggest health systems in the world! In terms of revenue, we expect to keep growing at the rate of 50% per year. 10. What’s your biggest threat? We have all heard that AI will replace software engineers, so I guess that should be my biggest thread as a software development company owner. Still, I’m convinced that our team has minds that no AI can substitute: Empathy, strategy, and creativity are some of the skills needed in a product team that AI can't substitute. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

Light-it: Empowering Healthcare through Purpose-Driven Digital Solutions

A successful community relies on the efforts of the community manager at every stage: During the challenging initial phase, the community manager overcomes the difficulty of acquiring customers, ensuring a stable customer base for the community. In the growth phase, the community manager addresses member activation issues, converting community members into product users and improving conversion rates. With stable customer acquisition and high conversion rates, the community enters a mature stage. However, three new challenges await the community manager: RRR. The 3R Challenges The concept of 3R originates from the AARRR model. In the initial and growth stages, community managers accomplish the first two tasks: Acquisition and Activation. The subsequent challenge is the 3R hurdles. Retention:Encouraging users to continue using the product or service and maintaining their loyalty.Revenue:Generating income through user behavior.Referral: Encouraging existing users to recommend the product or service to others, thereby achieving secondary propagation. As the community matures, some users may lose interest in the community or product, leading to a decrease in engagement. Additionally, as the community scales and complexity increases, conflicts may arise between new and existing members. These factors can all contribute to a decline in community user retention rates. This article will focus on the first key issue among these three challenges: community retention rates, aiming to provide insights for community managers. The Quantity and Duration of Consensus The quantity of consensus affects how many high-retention users a community can have, while the duration of consensus impacts how long users stay in the community. The formation of consensus relies on user expectations, which can be related to product features or pricing. For projects that have already issued tokens, user expectations, and outcomes typically manifest in asset prices. When users anticipate that project development will increase the value of their assets, they are more likely to confidently hold or trade these assets, thereby increasing the circulation and holding volume of the project, creating a positive cycle. For projects that have not yet issued tokens, user expectations are usually sustained through Token Generation Events (TGEs). Users anticipate future airdrops or token distributions, thus choosing to stay in the community to await future rewards. How to Trigger User Expectations? Any dynamic can serve as an influencing factor. Collaboration information, product updates, and the status of proposals, among others, all contribute. When user expectations are continuously met, consensus gradually forms, thereby strengthening user retention and engagement. Many community managers regularly post tweets within the community, encouraging community users to retweet and like for this reason. It's important to maximize the weight of information and increase its dissemination to subtly instill new expectations in users. This is a mechanical operational behavior, and many community managers use bots to reduce repetitive work: bots can automatically forward project information to the community. However, bots have a downside: they cannot stimulate user-sharing behavior. Why not turn this information into personalized tasks? This approach can not only expand the reach of information but also promote community interaction. TaskOn's Smart Task feature can automatically fetch updates from the project's official account and turn them into various tasks, expanding the dissemination of information and fostering expectations among more users. Continuous Interaction: Frequency Forms Habits Habits are formed based on frequency rather than duration. There's a very common phrase: "It takes 21 days to form a habit." However, it's actually the repeated behavior every day for 21 days that form the habit. To improve user retention rates, the question facing community managers becomes: How many interactions do users need to develop a habit? Successful projects in the past have focused on encouraging users to use their products every day: Centralized exchanges host continuous trading competitions.


L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems.


dApps constantly expand the reach of their projects. TaskOn, a community solution platform, recognizes the importance of frequency. In the community task template, there's an important task-setting option: Recurrence. When setting community tasks, community managers can specify the frequency at which community members should complete the tasks. Currently, there are options for Once/Daily/Weekly/Monthly. Once set, the tasks automatically refresh, saving community managers from repetitive operations. To improve retention rates, community managers need to string together enough user actions until these behaviors are firmly embedded in users' minds. Perfecting the Incentive System There's a potential disconnect between timely rewards and delayed rewards that needs attention. For projects that haven't issued tokens yet, a Token Generation Event (TGE) can be seen as a form of delayed reward. During the wait for rewards, some users may feel that their actions lack meaning due to the lack of timely feedback, leading them to give up on continued participation. One way to address this issue is to provide some immediate, visible incentives or feedback to encourage ongoing participation. For example, project teams can incentivize continued participation before the TGE by setting milestone rewards or periodic incentives. These rewards can take the form of virtual rewards, badges of honor, or elevation in community status to make users feel that their contributions are recognized and give them motivation to continue participating. Currently, there's a trend in the market: the introduction of a points system as an immediate incentive, where users' contributions are measured in points. I once wrote an article on points systems, detailing the benefits and how to quickly set up such a system. You can refer to it for more information. (How Points System Affects Web3 Community Development & Engagement) Healthy Growth Some rely on advertising for growth, others on third-party growth platforms, and some on Key Opinion Leaders (KOLs) for growth. While pouring a lot of money into advertising and marketing can quickly expand a community in the short term, once the investment stops, the number of community users can decrease rapidly. For mature communities, increasing user retention is crucial for healthy growth. Integrating the product into users' daily lives and making product usage a habit is key. A successful community relies on the efforts of the community manager at every stage: During the challenging initial phase, the community manager overcomes the difficulty of acquiring customers, ensuring a stable customer base for the community. In the growth phase, the community manager addresses member activation issues, converting community members into product users and improving conversion rates. With stable customer acquisition and high conversion rates, the community enters a mature stage. However, three new challenges await the community manager: RRR. The 3R Challenges The concept of 3R originates from the AARRR model. In the initial and growth stages, community managers accomplish the first two tasks: Acquisition and Activation. The subsequent challenge is the 3R hurdles. Retention: Encouraging users to continue using the product or service and maintaining their loyalty. Revenue: Generating income through user behavior. Referral: Encouraging existing users to recommend the product or service to others, thereby achieving secondary propagation. Retention: Revenue: Referral: As the community matures, some users may lose interest in the community or product, leading to a decrease in engagement. Additionally, as the community scales and complexity increases, conflicts may arise between new and existing members. These factors can all contribute to a decline in community user retention rates. This article will focus on the first key issue among these three challenges: community retention rates, aiming to provide insights for community managers. The Quantity and Duration of Consensus The quantity of consensus affects how many high-retention users a community can have, while the duration of consensus impacts how long users stay in the community. The formation of consensus relies on user expectations, which can be related to product features or pricing. For projects that have already issued tokens, user expectations, and outcomes typically manifest in asset prices. When users anticipate that project development will increase the value of their assets, they are more likely to confidently hold or trade these assets, thereby increasing the circulation and holding volume of the project, creating a positive cycle. For projects that have not yet issued tokens, user expectations are usually sustained through Token Generation Events (TGEs). Users anticipate future airdrops or token distributions, thus choosing to stay in the community to await future rewards. How to Trigger User Expectations? How to Trigger User Expectations? Any dynamic can serve as an influencing factor. Collaboration information, product updates, and the status of proposals, among others, all contribute. When user expectations are continuously met, consensus gradually forms, thereby strengthening user retention and engagement. Many community managers regularly post tweets within the community, encouraging community users to retweet and like for this reason. It's important to maximize the weight of information and increase its dissemination to subtly instill new expectations in users. This is a mechanical operational behavior, and many community managers use bots to reduce repetitive work: bots can automatically forward project information to the community. However, bots have a downside: they cannot stimulate user-sharing behavior. Why not turn this information into personalized tasks? This approach can not only expand the reach of information but also promote community interaction. TaskOn's Smart Task feature can automatically fetch updates from the project's official account and turn them into various tasks, expanding the dissemination of information and fostering expectations among more users. Continuous Interaction: Frequency Forms Habits Habits are formed based on frequency rather than duration. There's a very common phrase: "It takes 21 days to form a habit." However, it's actually the repeated behavior every day for 21 days that form the habit. To improve user retention rates, the question facing community managers becomes: How many interactions do users need to develop a habit? Successful projects in the past have focused on encouraging users to use their products every day: Centralized exchanges host continuous trading competitions. L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems. dApps constantly expand the reach of their projects. Centralized exchanges host continuous trading competitions. Centralized exchanges host continuous trading competitions. L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems. L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems. dApps constantly expand the reach of their projects. dApps constantly expand the reach of their projects. TaskOn, a community solution platform, recognizes the importance of frequency. In the community task template, there's an important task-setting option: Recurrence. When setting community tasks, community managers can specify the frequency at which community members should complete the tasks. Currently, there are options for Once/Daily/Weekly/Monthly. Once set, the tasks automatically refresh, saving community managers from repetitive operations. To improve retention rates, community managers need to string together enough user actions until these behaviors are firmly embedded in users' minds. Perfecting the Incentive System There's a potential disconnect between timely rewards and delayed rewards that needs attention. For projects that haven't issued tokens yet, a Token Generation Event (TGE) can be seen as a form of delayed reward. During the wait for rewards, some users may feel that their actions lack meaning due to the lack of timely feedback, leading them to give up on continued participation. One way to address this issue is to provide some immediate, visible incentives or feedback to encourage ongoing participation. For example, project teams can incentivize continued participation before the TGE by setting milestone rewards or periodic incentives. These rewards can take the form of virtual rewards, badges of honor, or elevation in community status to make users feel that their contributions are recognized and give them motivation to continue participating. Currently, there's a trend in the market: the introduction of a points system as an immediate incentive, where users' contributions are measured in points. I once wrote an article on points systems, detailing the benefits and how to quickly set up such a system. You can refer to it for more information. (How Points System Affects Web3 Community Development & Engagement) I once wrote an article on points systems, detailing the benefits and how to quickly set up such a system. You can refer to it for more information. ( How Points System Affects Web3 Community Development & Engagement ) How Points System Affects Web3 Community Development & Engagement How Points System Affects Web3 Community Development & Engagement ) Healthy Growth Some rely on advertising for growth, others on third-party growth platforms, and some on Key Opinion Leaders (KOLs) for growth. While pouring a lot of money into advertising and marketing can quickly expand a community in the short term, once the investment stops, the number of community users can decrease rapidly. For mature communities, increasing user retention is crucial for healthy growth. Integrating the product into users' daily lives and making product usage a habit is key.

Building a Successful Web3 Community: Healthy Growth

Six months ago, we launched our startup Spheria.ai, a platform where people can create and host the AI version of themselves. As founders and consumers, we knew that from day 1, we wanted to build a product that would let people claim back their personal data from the abuse of tech giant, and protect user privacy… Today, I'm sharing our experience of having a Head of AI Ethics as the very first employee for your startup and how he turned our naive good intentions into actual science and a foundational framework so we can build a legitimate platform that people trust to create the AI version of themselves. Realizing how little we knew about AI Ethics As most founders, we are focused on delivering a great product and growing the user base while trying to stick to our moral compass. At the very first meeting, Alejandro, our new Head of AI Ethics, brought to us a framework that would help organize the big questions around Privacy and Ethics. Instead of just creating an unorganized list of principles, he immediately cross-referenced ethical frameworks that he had seen across his research and deployed them in existing organizations. Our Head of AI Ethics introduced us to a concept called Procedural Fairness, a framework used by the World Bank to make sure Fairness is at the center of its decisions and policies. So the biggest win (right after the second meeting!) was for us to graduate from a chaotic list of good intentions to using Ethics frameworks that are actually used by researchers in international organizations. Right after that second meeting, we defined four pillars for Spheria to use as our foundation for AI Ethics: Transparency, Fairness and Accountability, and Privacy. Using this framework allowed us to visualize the relations between each pillar and see how one idea can have ripples and implications across multiple pillars. The consequences were immediate: these pillars would immediately lead to asking ourselves the right questions and bring a new dimension of awareness: How do we evaluate Fairness for our product?
How do we make sure all features we create are inclusive?
As a platform that creates new AI based on real ideas, are we accountable for the perpetuation of discrimination?
How are we transparent and accountable when making an arbitrary decision? It's okay not to have all the answers, but we must ask the difficult questions. During our first month, every meeting with our Head of AI Ethics felt like opening Pandora's box in a good way: a million questions arrived around freedom of speech, bias, inclusion, and censorship, and each one felt as legitimate and urgent to answer as the other. “The goal..” Alejandro put it, was to “elevate ourselves to a higher level of confusion”. This mindset that it’s okay to work towards the right answers, as long as we do so in a transparent and inclusive way, would become the foundation of our ethical policy. It became clear this would take time and a lot of consideration. So, we started an internal document to list every question that was brought up during meetings. We needed to keep track of all the ideas. We would write the questions as they came, then spend one minute evaluating if the question was properly asked and what tension it created around which ethical concept, and then where and how that tension would exist in the product or how it's being used. Finally, we would evaluate if this question could be broken down into smaller bits to bring more granularity. For example, the spontaneous question “What is our moderation policy?” needed to be broken down into sub-questions like: “in what cases do we need a moderation policy?”


and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?”


all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?”


to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” That list of open questions today is significant, but it's also great as it lays our foundations as a company, a moral entity, to give a real direction for the team to build a future we believe in. Be accountable to our users - actions speak louder than words Most companies and startup want users to trust them and will create a nice catch-phrase around “we love privacy”, “we are ethical” and get away with it. After our launch, we saw that our privacy page was the most visited page after our landing page. Users are indeed creating their AI Double, often importing their data from other existing platforms like Instagram, LinkedIn, etc… We knew we had to do more than just a privacy policy, but at the time, we didn't necessarily know how or what to do. Having our Head of AI Ethics in our Team allowed us to act on this and to show our real and tangible work. We created our AI Ethics Hub to show our dedication and our efforts to be transparent and allow users to follow our progress. By creating our AI Ethics Hub we feel we are doing right by our users, especially considering what we're building with Spheria, which is to let people create the AI version of themselves. Our users don't think about all of this when they create their official AI double, but privacy, ethical rules, and transparency are so tightly imbricated into creating your digital second self that, as the makers and founders, it's our job to be transparent, protect privacy and provide ethical rules. We hope this helps in highlighting the difference between startups that are actively engaged in Privacy and Ethics, and those that just put pretty words on their landing page. Setting the foundations for our company's culture Having our Head of AI Ethics join our team so early was the best possible catalyst to help build the right culture at Spheria. De-facto, it put the right values and principles we talked about above at the foundation of our startup, and these foundations will always be present to support the future we're building. “Stay scrappy” is what an investor told me a few months ago. While scrappy does not mean unethical, having a full-time Head of AI Ethics makes us accountable to all our users and accountable to each team member who may speak up against lines being bent. The goal for us is to avoid any future (embarrassing and possibly dishonest) situation similar to when OpenAI's CTO replied, “I don't know” to the journalist asking what data was used to train the new Sora video model. So I'm happy to say that us being on this track definitely helps me sleep at night. It brings me some reassurance and a little boost of confidence to face the thousands of hurdles of growing a startup. It's also a strong signal for users, future hires, and investors to judge us in the light of our actions. Six months ago, we launched our startup Spheria.ai, a platform where people can create and host the AI version of themselves. As founders and consumers, we knew that from day 1, we wanted to build a product that would let people claim back their personal data from the abuse of tech giant , and protect user privacy… create and host the AI version of themselves. claim back their personal data from the abuse of tech giant Today, I'm sharing our experience of having a Head of AI Ethics as the very first employee for your startup and how he turned our naive good intentions into actual science and a foundational framework so we can build a legitimate platform that people trust to create the AI version of themselves. I'm sharing our experience of having a Head of AI Ethics as the very first employee for your startup Realizing how little we knew about AI Ethics Realizing how little we knew about AI Ethics As most founders, we are focused on delivering a great product and growing the user base while trying to stick to our moral compass. At the very first meeting, Alejandro, our new Head of AI Ethics, brought to us a framework that would help organize the big questions around Privacy and Ethics. Instead of just creating an unorganized list of principles, he immediately cross-referenced ethical frameworks that he had seen across his research and deployed them in existing organizations. Our Head of AI Ethics introduced us to a concept called Procedural Fairness, a framework used by the World Bank to make sure Fairness is at the center of its decisions and policies. So the biggest win (right after the second meeting!) was for us to graduate from a chaotic list of good intentions to using Ethics frameworks that are actually used by researchers in international organizations. Right after that second meeting, we defined four pillars for Spheria to use as our foundation for AI Ethics: Transparency, Fairness and Accountability, and Privacy. Using this framework allowed us to visualize the relations between each pillar and see how one idea can have ripples and implications across multiple pillars. Transparency, Fairness and Accountability, and Privacy. The consequences were immediate: these pillars would immediately lead to asking ourselves the right questions and bring a new dimension of awareness: How do we evaluate Fairness for our product? How do we make sure all features we create are inclusive? As a platform that creates new AI based on real ideas, are we accountable for the perpetuation of discrimination? How are we transparent and accountable when making an arbitrary decision? How do we evaluate Fairness for our product? How do we make sure all features we create are inclusive? As a platform that creates new AI based on real ideas, are we accountable for the perpetuation of discrimination? How are we transparent and accountable when making an arbitrary decision? It's okay not to have all the answers, but we must ask the difficult questions. It's okay not to have all the answers, but we must ask the difficult questions. During our first month, every meeting with our Head of AI Ethics felt like opening Pandora's box in a good way: a million questions arrived around freedom of speech, bias, inclusion, and censorship, and each one felt as legitimate and urgent to answer as the other. “The goal..” Alejandro put it, was to “elevate ourselves to a higher level of confusion”. This mindset that it’s okay to work towards the right answers, as long as we do so in a transparent and inclusive way, would become the foundation of our ethical policy. It became clear this would take time and a lot of consideration. So, we started an internal document to list every question that was brought up during meetings. We needed to keep track of all the ideas. We would write the questions as they came, then spend one minute evaluating if the question was properly asked and what tension it created around which ethical concept, and then where and how that tension would exist in the product or how it's being used. Finally, we would evaluate if this question could be broken down into smaller bits to bring more granularity. For example, the spontaneous question “What is our moderation policy?” needed to be broken down into sub-questions like: “in what cases do we need a moderation policy?” and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?” all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?” to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” “in what cases do we need a moderation policy?” “in what cases do we need a moderation policy?” and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?” and “are there laws that want to prevent someone from adding into their own AI's knowledge sensitive or illegal content?” all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?” all the way to: should we filter the input, ie, blocking the owner of an AI from adding illegal content into it? Or should we block the output, ie, the AI from sharing information about illegal content?” to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” to finally touch the point of tension: “how hard do we need to prevent the perpetuation of immoral content, that is (let's be real) available elsewhere on the internet?” That list of open questions today is significant, but it's also great as it lays our foundations as a company, a moral entity, to give a real direction for the team to build a future we believe in. Be accountable to our users - actions speak louder than words Most companies and startup want users to trust them and will create a nice catch-phrase around “we love privacy”, “we are ethical” and get away with it. After our launch, we saw that our privacy page was the most visited page after our landing page. Users are indeed creating their AI Double, often importing their data from other existing platform s like Instagram, LinkedIn, etc… creating their AI Double, often importing their data from other existing platform We knew we had to do more than just a privacy policy, but at the time, we didn't necessarily know how or what to do. Having our Head of AI Ethics in our Team allowed us to act on this and to show our real and tangible work. We created our AI Ethics Hub to show our dedication and our efforts to be transparent and allow users to follow our progress. AI Ethics Hub AI Ethics Hub By creating our AI Ethics Hub we feel we are doing right by our users, especially considering what we're building with Spheria, which is to let people create the AI version of themselves. Our users don't think about all of this when they create their official AI double, but privacy, ethical rules, and transparency are so tightly imbricated into creating your digital second self that, as the makers and founders, it's our job to be transparent, protect privacy and provide ethical rules. We hope this helps in highlighting the difference between startups that are actively engaged in Privacy and Ethics, and those that just put pretty words on their landing page. Setting the foundations for our company's culture Having our Head of AI Ethics join our team so early was the best possible catalyst to help build the right culture at Spheria. De-facto, it put the right values and principles we talked about above at the foundation of our startup, and these foundations will always be present to support the future we're building. “Stay scrappy” is what an investor told me a few months ago. While scrappy does not mean unethical, having a full-time Head of AI Ethics makes us accountable to all our users and accountable to each team member who may speak up against lines being bent. The goal for us is to avoid any future (embarrassing and possibly dishonest) situation similar to when OpenAI's CTO replied, “I don't know” to the journalist asking what data was used to train the new Sora video model. OpenAI's CTO replied, “I don't know” So I'm happy to say that us being on this track definitely helps me sleep at night. It brings me some reassurance and a little boost of confidence to face the thousands of hurdles of growing a startup. It's also a strong signal for users, future hires, and investors to judge us in the light of our actions.

Why Your AI Startup Should Hire a Head of AI Ethics on Day 1

Innovations in artificial intelligence (AI) have revolutionized the field of medicine in numerous ways. AI technologies are emerging as a powerful tool to transform various aspects of healthcare, such as diagnosis, research, patient care, treatment, and clinical outcomes. Advanced AI models can be used to analyze complex medical data and generate insights based on patterns in training data, accelerating the decision-making process with accuracy. However, akin to any other innovative technology, it has its share of risks and challenges. As per an IBM study, the global average cost resulting from a data breach was $4.45 million in 2023. The study estimated the average cost of a healthcare data breach was the highest among industries, at $10.93 million. Compliance with the Health Insurance Portability and Accountability Act (HIPAA) is a critical consideration when implementing AI into healthcare services. Here, we will discuss some of the privacy and security risks posed by AI systems in healthcare, along with possible mitigation strategies for responsible and ethical implementation under HIPAA regulations. What Is HIPAA? HIPAA, or the Health Insurance Portability and Accountability Act, is a critical federal health privacy law in the US that sets the standard for protecting the health information of patients. The law focuses on the Privacy Rule that administers the protection of individually identifiable health information, referred to as PHI, generated by healthcare providers, insurance companies, and other entities. Hence, entities that generate health information digitally must comply with HIPAA rules. Additionally, business partners performing PHI-related activities on behalf of a covered entity, such as administrative, financial, management, and legal tasks, are also subject to HIPAA regulations. However, de-identified health information that can’t be used to identify an individual is not individually identifiable health information, and there is no restriction on the use or disclosure of such data under HIPAA. Entities Covered by HIPAA HIPAA compliance applies only to certain types of organizations that generate protected health information (PHI) electronically or entities that perform tasks involving PHI on their behalf. To understand the impact of HIPAA on the use of AI, it is essential to recognize HIPAA-covered entities. Whether HIPAA would be applied to an entity is not determined based on someone's professional role but rather on activities performed by them. When healthcare providers, insurance companies, and other organizations carry out transactions that require HIPAA compliance, they are considered covered entities under HIPAA regulations. When incorporating AI in their healthcare operations, these entities, alongside their business associates, must follow HIPAA rules. Transactions that involve only direct payments and don’t include handling electronic health records, like billing insurance, would not be subject to HIPAA regulations. HIPAA and AI in Healthcare: Ensuring Patient Privacy The HIPAA Privacy Rule ensures the protection of patient information, especially when AI technology is integrated into healthcare services, by governing the use and disclosure of protected health information (PHI). There are specific situations where disclosing health information is not prohibited under HIPAA, such as for payment, treatment, healthcare operations, and certain research conditions. When incorporating AI in healthcare research or operations, responsible handling of PHI becomes critical. The process involves de-identifying patient data and receiving patient consent for the use of data, strengthening privacy and trust. However, the law incorporates the concept of a ‘limited data set’, allowing access to certain non-direct identifiers, such as ZIP codes or service dates, for research purposes, provided the data agreement adheres to HIPAA compliance. Use of direct identifiers like names or social security numbers is strictly prohibited. The HIPAA safe harbor method outlines 18 specific identifiers, such as name, address, social security numbers, biometric identifiers, and many more, that must be removed from data. After de-identification, the remaining information should not be capable of re-identifying the individuals, even in combination with other data. The de-identification process needs to be robust enough, especially when the data is used for AI purposes, as AI algorithms can potentially analyze data and discover hidden patterns for re-identification. Staying HIPAA Compliant The rapid advancements in artificial intelligence within the healthcare industry present new challenges to stay compliant with HIPAA regulations. Healthcare organizations need to adapt to the changes by working closely with AI developers to understand the algorithms of AI applications and ensure they meet HIPAA privacy standards. This necessitates regular updates to policies and procedures along with the implementation of robust security measures to effectively ensure compliance and manage potential threats. Additionally, educating healthcare professionals about the implications of AI for data privacy is critically important. This empowers them to use AI tools responsibly and obtain consent for data use. Conclusions Implementing AI in healthcare offers immense potential to transform various aspects, from diagnosis and treatment planning to patient care and research, benefiting both patients and healthcare providers. However, integrating data-intensive AI tools poses serious privacy and security challenges. To proactively address these challenges, healthcare organizations must implement safe and effective data protection measures that adhere to HIPAA compliance. Healthcare organizations need to make efforts to keep abreast of the latest developments in AI and their potential privacy implications. Essential strategies such as risk assessment protocols, robust governance frameworks, and compliance monitoring can help prevent breaches of PHI, mitigate risks, and maintain public trust. Innovations in artificial intelligence (AI) have revolutionized the field of medicine in numerous ways. AI technologies are emerging as a powerful tool to transform various aspects of healthcare, such as diagnosis, research, patient care, treatment, and clinical outcomes. Advanced AI models can be used to analyze complex medical data and generate insights based on patterns in training data, accelerating the decision-making process with accuracy. However, akin to any other innovative technology, it has its share of risks and challenges. As per an IBM study , the global average cost resulting from a data breach was $4.45 million in 2023. The study estimated the average cost of a healthcare data breach was the highest among industries, at $10.93 million. IBM study Compliance with the Health Insurance Portability and Accountability Act (HIPAA) is a critical consideration when implementing AI into healthcare services. Here, we will discuss some of the privacy and security risks posed by AI systems in healthcare, along with possible mitigation strategies for responsible and ethical implementation under HIPAA regulations. What Is HIPAA? HIPAA, or the Health Insurance Portability and Accountability Act, is a critical federal health privacy law in the US that sets the standard for protecting the health information of patients. The law focuses on the Privacy Rule that administers the protection of individually identifiable health information, referred to as PHI, generated by healthcare providers, insurance companies, and other entities. Hence, entities that generate health information digitally must comply with HIPAA rules. Additionally, business partners performing PHI-related activities on behalf of a covered entity, such as administrative, financial, management, and legal tasks, are also subject to HIPAA regulations. HIPAA However, de-identified health information that can’t be used to identify an individual is not individually identifiable health information, and there is no restriction on the use or disclosure of such data under HIPAA. Entities Covered by HIPAA HIPAA compliance applies only to certain types of organizations that generate protected health information (PHI) electronically or entities that perform tasks involving PHI on their behalf. To understand the impact of HIPAA on the use of AI, it is essential to recognize HIPAA-covered entities. Whether HIPAA would be applied to an entity is not determined based on someone's professional role but rather on activities performed by them. When healthcare providers, insurance companies, and other organizations carry out transactions that require HIPAA compliance, they are considered covered entities under HIPAA regulations. When incorporating AI in their healthcare operations, these entities, alongside their business associates, must follow HIPAA rules. Transactions that involve only direct payments and don’t include handling electronic health records, like billing insurance, would not be subject to HIPAA regulations. HIPAA and AI in Healthcare: Ensuring Patient Privacy The HIPAA Privacy Rule ensures the protection of patient information, especially when AI technology is integrated into healthcare services, by governing the use and disclosure of protected health information (PHI). There are specific situations where disclosing health information is not prohibited under HIPAA, such as for payment, treatment, healthcare operations, and certain research conditions. When incorporating AI in healthcare research or operations , responsible handling of PHI becomes critical. The process involves de-identifying patient data and receiving patient consent for the use of data, strengthening privacy and trust. However, the law incorporates the concept of a ‘limited data set’, allowing access to certain non-direct identifiers, such as ZIP codes or service dates, for research purposes, provided the data agreement adheres to HIPAA compliance. Use of direct identifiers like names or social security numbers is strictly prohibited. AI in healthcare research or operations The HIPAA safe harbor method outlines 18 specific identifiers, such as name, address, social security numbers, biometric identifiers, and many more, that must be removed from data. After de-identification, the remaining information should not be capable of re-identifying the individuals, even in combination with other data. The de-identification process needs to be robust enough, especially when the data is used for AI purposes, as AI algorithms can potentially analyze data and discover hidden patterns for re-identification. Staying HIPAA Compliant The rapid advancements in artificial intelligence within the healthcare industry present new challenges to stay compliant with HIPAA regulations. Healthcare organizations need to adapt to the changes by working closely with AI developers to understand the algorithms of AI applications and ensure they meet HIPAA privacy standards. This necessitates regular updates to policies and procedures along with the implementation of robust security measures to effectively ensure compliance and manage potential threats. Additionally, educating healthcare professionals about the implications of AI for data privacy is critically important. This empowers them to use AI tools responsibly and obtain consent for data use. Conclusions Implementing AI in healthcare offers immense potential to transform various aspects, from diagnosis and treatment planning to patient care and research, benefiting both patients and healthcare providers. However, integrating data-intensive AI tools poses serious privacy and security challenges. To proactively address these challenges, healthcare organizations must implement safe and effective data protection measures that adhere to HIPAA compliance. Healthcare organizations need to make efforts to keep abreast of the latest developments in AI and their potential privacy implications. Essential strategies such as risk assessment protocols, robust governance frameworks, and compliance monitoring can help prevent breaches of PHI, mitigate risks, and maintain public trust.

Navigating HIPAA Compliance in the Age of AI: Privacy and Security Considerations in Healthcare

How Did Mahesh Kambala Contribute to the Fight Against COVID-19? During the global COVID-19 pandemic, the healthcare industry faced unprecedented challenges that highlighted the importance of healthcare analytics in addressing data gaps and managing resources. Prioritizing and integrating analytics with healthcare objectives was crucial for saving lives, addressing weaknesses, and improving preparedness for future disruptions. With more than 88% of healthcare experts acknowledging a heightened requirement for analytics, the emergency underscored the value of data in handling public health crises. Mahesh Kambala,  with 15 years of professional experience in information technology, has honed his data analytics, ETL, machine learning, and AI skills. He has held significant positions in renowned organizations spanning diverse industries such as healthcare, banking, telecom, and retail. Kambala's knowledge in healthcare analytics proved invaluable while developing tools like the "COVID Dashboards." These dashboards empowered doctors and healthcare providers by equipping them with real-time patient data, enabling them to swiftly identify and address COVID-19 cases. His contributions during the pandemic underscore the vital role of technology and data analytics in healthcare, particularly in times of crisis. This article will explore the significance of analytics in healthcare amid the COVID-19 epidemic and showcase Mahesh Kambala's impact on the healthcare industry. The Need for Next-Gen Healthcare Analytics The COVID-19 pandemic has shown how critical healthcare data and analytics are in handling a global health crisis. It has revealed areas for improvement in the healthcare industry's preparedness and system integration. At the start of the pandemic, it became clear that there was a significant lack of reliable data and adequate analytics capabilities, which are crucial for understanding the spread and impact of the disease. Healthcare organizations faced operational overhauls to address missing data, revealing inefficiencies. The financial instability and well-being of frontline staff, who were dealing with fatigue and burnout engaging with patients, highlighted the importance of a robust analytics infrastructure for effective resource management even more. The next generation of healthcare analytics, which utilizes advanced technologies like AI and Big Data Analytics (BDA), presents an answer to these challenges. By incorporating sophisticated algorithms and machine learning, next-gen analytics can improve the accuracy and speed of data analysis, allowing for real-time tracking of disease spread and identification of at-risk populations. These analytics can streamline data collection and integration, ensuring that healthcare providers have access to timely and reliable information for decision-making. Additionally, predictive analytics can forecast outbreaks and inform proactive strategies for resource allocation, potentially alleviating financial strains. Mahesh Kambalan's Contribution to Next-Gen Healthcare Analytics Mahesh Kambala has introduced solutions to address the challenges brought upon the healthcare industry during the COVID-19 pandemic. His leadership in developing "COVID Dashboards," specifically designed for healthcare providers, has improved healthcare analytics by providing real-time insights into patients' data affected by COVID-19. Kambala's proactive approach and expertise have helped make informed care decisions, representing a significant leap forward in utilizing technology to manage health crises effectively. His work demonstrates his dedication to harnessing technology for the betterment of society and emphasizes the vital importance of timely and accurate data in crisis management. The COVID Dashboards boast several vital features, including the ability to visualize real-time data, identify patient risks based on their medical history, and enable healthcare providers to deliver necessary care promptly. The advantages of these dashboards are manifold. Healthcare providers can now swiftly detect and respond to emerging COVID-19 cases, ensuring targeted interventions and optimizing the allocation of medical resources. By enabling early detection and intervention, these dashboards have significantly enhanced patient care, reducing hospitalizations and improving operational efficiency within healthcare facilities. For instance, Kambala's innovative tool facilitated the quick identification of at-risk individuals, ensuring timely care and potentially saving lives. The tool is a testament to Kambala's approach to utilizing technology to serve humanity, showcasing how advanced healthcare analytics can improve patient care and operational dynamics in healthcare settings during and beyond the pandemic. The Future of Next-Gen Healthcare Analytics In the fight against COVID-19, Mahesh Kambala has made a mark in healthcare analytics by developing "COVID Dashboards." These innovative tools have harnessed the power of artificial intelligence (AI) and machine learning (ML) to provide real-time insights into the pandemic's spread. These dashboards have improved patient care and operational efficiency, enabling healthcare providers to make informed decisions swiftly. Through Kambala's tireless efforts and the utilization of cutting-edge analytics tools like the "COVID Dashboards," the ability of technology to serve humanity, particularly during crises, becomes increasingly apparent. His work is a powerful testament to the tangible difference advanced analytics can make in the healthcare industry. -Written by Belle Villaluz This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author How Did Mahesh Kambala Contribute to the Fight Against COVID-19? During the global COVID-19 pandemic, the healthcare industry faced unprecedented challenges that highlighted the importance of healthcare analytics in addressing data gaps and managing resources. Prioritizing and integrating analytics with healthcare objectives was crucial for saving lives, addressing weaknesses, and improving preparedness for future disruptions. With more than 88% of healthcare experts acknowledging a heightened requirement for analytics, the emergency underscored the value of data in handling public health crises. 88% of healthcare experts 88% of healthcare experts Mahesh Kambala ,  with 15 years of professional experience in information technology, has honed his data analytics, ETL, machine learning, and AI skills. He has held significant positions in renowned organizations spanning diverse industries such as healthcare, banking, telecom, and retail. Kambala's knowledge in healthcare analytics proved invaluable while developing tools like the "COVID Dashboards." These dashboards empowered doctors and healthcare providers by equipping them with real-time patient data, enabling them to swiftly identify and address COVID-19 cases. His contributions during the pandemic underscore the vital role of technology and data analytics in healthcare, particularly in times of crisis. Mahesh Kambala Mahesh Kambala This article will explore the significance of analytics in healthcare amid the COVID-19 epidemic and showcase Mahesh Kambala's impact on the healthcare industry. The Need for Next-Gen Healthcare Analytics The COVID-19 pandemic has shown how critical healthcare data and analytics are in handling a global health crisis. It has revealed areas for improvement in the healthcare industry's preparedness and system integration. At the start of the pandemic, it became clear that there was a significant lack of reliable data and adequate analytics capabilities, which are crucial for understanding the spread and impact of the disease. Healthcare organizations faced operational overhauls to address missing data, revealing inefficiencies. The financial instability and well-being of frontline staff, who were dealing with fatigue and burnout engaging with patients, highlighted the importance of a robust analytics infrastructure for effective resource management even more. The next generation of healthcare analytics, which utilizes advanced technologies like AI and Big Data Analytics (BDA) , presents an answer to these challenges. By incorporating sophisticated algorithms and machine learning, next-gen analytics can improve the accuracy and speed of data analysis, allowing for real-time tracking of disease spread and identification of at-risk populations. These analytics can streamline data collection and integration, ensuring that healthcare providers have access to timely and reliable information for decision-making. Additionally, predictive analytics can forecast outbreaks and inform proactive strategies for resource allocation, potentially alleviating financial strains. AI and Big Data Analytics (BDA) AI and Big Data Analytics (BDA) Mahesh Kambalan's Contribution to Next-Gen Healthcare Analytics Mahesh Kambala has introduced solutions to address the challenges brought upon the healthcare industry during the COVID-19 pandemic. His leadership in developing "COVID Dashboards," specifically designed for healthcare providers, has improved healthcare analytics by providing real-time insights into patients' data affected by COVID-19. Kambala's proactive approach and expertise have helped make informed care decisions, representing a significant leap forward in utilizing technology to manage health crises effectively. His work demonstrates his dedication to harnessing technology for the betterment of society and emphasizes the vital importance of timely and accurate data in crisis management. The COVID Dashboards boast several vital features, including the ability to visualize real-time data, identify patient risks based on their medical history, and enable healthcare providers to deliver necessary care promptly. The advantages of these dashboards are manifold. Healthcare providers can now swiftly detect and respond to emerging COVID-19 cases, ensuring targeted interventions and optimizing the allocation of medical resources. By enabling early detection and intervention, these dashboards have significantly enhanced patient care, reducing hospitalizations and improving operational efficiency within healthcare facilities. For instance, Kambala's innovative tool facilitated the quick identification of at-risk individuals, ensuring timely care and potentially saving lives. The tool is a testament to Kambala's approach to utilizing technology to serve humanity, showcasing how advanced healthcare analytics can improve patient care and operational dynamics in healthcare settings during and beyond the pandemic. The Future of Next-Gen Healthcare Analytics In the fight against COVID-19, Mahesh Kambala has made a mark in healthcare analytics by developing "COVID Dashboards." These innovative tools have harnessed the power of artificial intelligence (AI) and machine learning (ML) to provide real-time insights into the pandemic's spread. These dashboards have improved patient care and operational efficiency, enabling healthcare providers to make informed decisions swiftly. Through Kambala's tireless efforts and the utilization of cutting-edge analytics tools like the "COVID Dashboards," the ability of technology to serve humanity, particularly during crises, becomes increasingly apparent. His work is a powerful testament to the tangible difference advanced analytics can make in the healthcare industry. -Written by Belle Villaluz -Written by Belle Villaluz -Written by Belle Villaluz This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Mahesh Kambalan on Next-Gen Healthcare Analytics

নেক্সট-জেন হেলথকেয়ার অ্যানালিটিক্সে মহেশ কাম্বলান

Mahesh Kambalan über Gesundheitsanalysen der nächsten Generation

Mahesh Kambalan sobre análisis sanitarios de próxima generación

Mahesh Kambalan sur l'analyse des soins de santé de nouvelle génération

महेश कम्बलन ने अगली पीढ़ी के स्वास्थ्य सेवा विश्लेषण पर बात की

次世代ヘルスケア分析について語るマヘシュ・カンバラン

차세대 의료 분석에 관한 Mahesh Kambalan

Mahesh Kambalan sobre análise de saúde de última geração

Махеш Камбалан об аналитике в сфере здравоохранения нового поколения

Mahesh Kambalan, Yeni Nesil Sağlık Hizmetleri Analizi Konusunda

Mahesh Kambalan trên Phân tích chăm sóc sức khỏe thế hệ tiếp theo

Mahesh Kambalan 谈下一代医疗分析

Authors:
(1) Prabath Jayatissa, University of Colombo
(2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Methodology Results Conclusion & References 4. CONCLUSION Interoperability among health information systems is crucial for improving healthcare delivery in low and middle-income countries (LMICs) where access to quality healthcare is often limited. However, achieving interoperability in LMICs faces challenges such as limited resources, fragmented health information systems, and diverse health IT infrastructure. Strategies such as standardisation, consolidation, investment in health IT infrastructure, capacity building, and addressing data privacy and security concerns can enhance interoperability efforts. Collaborative efforts among governments, non-profit organisations, the private sector, and other stakeholders are essential for overcoming challenges and promoting interoperability in LMICs. Further research is needed to evaluate the impact of these strategies and identify novel approaches that are specific to the unique challenges faced by different regions and contexts within LMICs. Research on the cost-effectiveness and sustainability of interoperable health information systems is also crucial. Significant challenges include the need for more standardisation, fragmented health information systems, limited resources, and data privacy concerns. However, strategies such as standardisation, consolidation, investment in health IT infrastructure, and capacity building can help overcome them. Collaborative efforts are needed to address the multifaceted challenges of interoperability in LMICs and implement effective strategies. Further research and collaborative efforts are required to address the unique needs and contexts of LMICs and unlock the potential of health information systems to improve healthcare outcomes and advance health systems towards more integrated, patient-centred, and data-driven care in LMICs. REFERENCES [1] Adler-Milstein, J., DesRoches, C. M., Kralovec, P., Foster, G., Worzala, C., Charles, D., & Searcy, T. (2013). Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Affairs, 32(8), 1478-1485. [2] Adefioye, O. A., Adeleke, I. T., & Adewumi, A. O. (2019). Interoperability of health information systems in low-income countries: a systematic review of barriers and facilitators. BMC Medical Informatics and Decision Making, 19(1), 16. [3] Blaya, J. A., Fraser, H. S., & Holt, B. (2017). E-health technologies show promise in developing countries. Health Affairs, 36(12), 2168-2175. [4] Braa, J., Sahay, S., & Westerlund, M. (2017). The role of participatory design in strengthening community-based health information systems: a case study from Mozambique. Health Informatics Journal, 23(3), 180-189. [5] Kamadjeu, R. (2019). Impact of interoperability among electronic health records in low-resource settings: A systematic review. International Journal of Medical Informatics, 123, 27-34. [6] Karim, A. M., Ahsan, H., & Uddin, M. J. (2019). Interoperability in e-Health: A Systematic Literature Review. Health and Technology, 9(4), 687-697. [7] Seebregts, C. J., Mamlin, B. W., Biondich, P. G., Fraser, H. S., & Wolfe, B. A. (2017). Human factor considerations in developing and implementing information and communication technologies for health in low-resource settings. Yearbook of Medical Informatics, 26(01), 123-130. [8] Sharma, A., Wickramasinghe, N., & Gupta, D. (2017). Leveraging the capabilities of electronic health records for enhancing decision-making processes in low-resource settings: a literature review. Health Informatics Journal, 23(2), 89-99. [9] W G P T Jayatissa, Vajira HWD, Roshan H. Review of Master Patient Index. Dent Res Int J DRIJ. 2017;3(2):37–48. https://arxiv.org/abs/1803.00451 [10] Tchouaket, É. N., Charrier, R., & Henry, S. (2019). Addressing the lack of interoperability among health information systems in low-income countries: a socio-technical approach. Health and Technology, 9(5), 729-742. [11] Blaya, J.A., Fraser, H.S.F., Holt, B. (2017). E-health interoperability. In: Marschollek, M., Bauer, A., Wolf, K.H., Gradinger, M., eds. eHealth2016 - Health Informatics Meets eHealth: From Sensors to Decisions. IOS Press, pp. 9-14. [12] Tchouaket, É.N., Moine, M.P., Adedokun, A., et al. (2019). Challenges to Interoperability of Health Information Systems in Low- and Middle-Income Countries: A Literature Review. Studies in Health Technology and Informatics, 264, pp. 1399-1403. [13] Adler-Milstein, J., Jha, A.K. (2013). Sharing Clinical Data Electronically: A Critical Challenge for Fixing the Health Care System. JAMA, 309(10), pp. 991-992. [14] Kamadjeu, R. (2019). Challenges of Implementing eHealth in Developing Countries: An Example of a Web-Based Asynchronous Telemedicine System. Health Informatics Journal, 25(1), pp. 62-71. [15] Sharma, A., Kamadjeu, R., Mirza, S., et al. (2017). Development of a National Health Information System in a Resource-Limited Setting: A Case Study from Liberia. Health Informatics Journal, 23(4), pp. 295-306. [16] Jayatissa P, Vajira HWD, Roshan H. Developing a functional prototype Master Patient Index (MPI) for interoperability of E-Health systems in Seri Lanka. Dent Res Int J DRIJ. 2017;2(2):9–26. https://arxiv.org/abs/1803.00451 [17] Seebregts, C.J., Mamlin, B.W., Biondich, P.G., et al. (2017). The OpenMRS Implementers Network. International Journal of Medical Informatics, 108, pp. 117-121. [18] Adefioye, O.A., Asaolu, I.O., Ajayi, T.A., et al. (2019). Ethical and Legal Issues in Health Information Exchange in Low and Middle Income Countries: A Systematic Review. BMC Medical Ethics, 20(1), pp. 84. [19] Braa, J., Hanseth, O., Mohammed, W., et al. (2017). Developing Health Information Systems in Developing Countries: The Flexible Standards Strategy. MIS Quarterly, 41(1), pp. 309-322. [20] Karim, A.M., Nematzadeh, A., Gombera, F., et al. (2019). eHealth in Low- and Middle-Income Countries: Findings from the Center for Health Market Innovations. Health Affairs, 38(5), pp. 711- 718. [21] Jayatissa P, Hewapatirane R. Development of Minimum Clinical Data Set For Master Patient Index(MPI) for Sri Lankan Context. https://airccse.com/drij/papers/4422drij01.pdf .2022 Nov 1;4(4):1–18. This paper is available on arxiv under CC 4.0 license. Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Authors: Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Abstract & Introduction Methodology Methodology Results Results Conclusion & References Conclusion & References 4. CONCLUSION Interoperability among health information systems is crucial for improving healthcare delivery in low and middle-income countries (LMICs) where access to quality healthcare is often limited. However, achieving interoperability in LMICs faces challenges such as limited resources, fragmented health information systems, and diverse health IT infrastructure. Strategies such as standardisation, consolidation, investment in health IT infrastructure, capacity building, and addressing data privacy and security concerns can enhance interoperability efforts. Collaborative efforts among governments, non-profit organisations, the private sector, and other stakeholders are essential for overcoming challenges and promoting interoperability in LMICs. Further research is needed to evaluate the impact of these strategies and identify novel approaches that are specific to the unique challenges faced by different regions and contexts within LMICs. Research on the cost-effectiveness and sustainability of interoperable health information systems is also crucial. Significant challenges include the need for more standardisation, fragmented health information systems, limited resources, and data privacy concerns. However, strategies such as standardisation, consolidation, investment in health IT infrastructure, and capacity building can help overcome them. Collaborative efforts are needed to address the multifaceted challenges of interoperability in LMICs and implement effective strategies. Further research and collaborative efforts are required to address the unique needs and contexts of LMICs and unlock the potential of health information systems to improve healthcare outcomes and advance health systems towards more integrated, patient-centred, and data-driven care in LMICs. REFERENCES [1] Adler-Milstein, J., DesRoches, C. M., Kralovec, P., Foster, G., Worzala, C., Charles, D., & Searcy, T. (2013). Electronic health record adoption in US hospitals: progress continues, but challenges persist. Health Affairs, 32(8), 1478-1485. [2] Adefioye, O. A., Adeleke, I. T., & Adewumi, A. O. (2019). Interoperability of health information systems in low-income countries: a systematic review of barriers and facilitators. BMC Medical Informatics and Decision Making, 19(1), 16. [3] Blaya, J. A., Fraser, H. S., & Holt, B. (2017). E-health technologies show promise in developing countries. Health Affairs, 36(12), 2168-2175. [4] Braa, J., Sahay, S., & Westerlund, M. (2017). The role of participatory design in strengthening community-based health information systems: a case study from Mozambique. Health Informatics Journal, 23(3), 180-189. [5] Kamadjeu, R. (2019). Impact of interoperability among electronic health records in low-resource settings: A systematic review. International Journal of Medical Informatics, 123, 27-34. [6] Karim, A. M., Ahsan, H., & Uddin, M. J. (2019). Interoperability in e-Health: A Systematic Literature Review. Health and Technology, 9(4), 687-697. [7] Seebregts, C. J., Mamlin, B. W., Biondich, P. G., Fraser, H. S., & Wolfe, B. A. (2017). Human factor considerations in developing and implementing information and communication technologies for health in low-resource settings. Yearbook of Medical Informatics, 26(01), 123-130. [8] Sharma, A., Wickramasinghe, N., & Gupta, D. (2017). Leveraging the capabilities of electronic health records for enhancing decision-making processes in low-resource settings: a literature review. Health Informatics Journal, 23(2), 89-99. [9] W G P T Jayatissa, Vajira HWD, Roshan H. Review of Master Patient Index. Dent Res Int J DRIJ. 2017;3(2):37–48. https://arxiv.org/abs/1803.00451 [10] Tchouaket, É. N., Charrier, R., & Henry, S. (2019). Addressing the lack of interoperability among health information systems in low-income countries: a socio-technical approach. Health and Technology, 9(5), 729-742. [11] Blaya, J.A., Fraser, H.S.F., Holt, B. (2017). E-health interoperability. In: Marschollek, M., Bauer, A., Wolf, K.H., Gradinger, M., eds. eHealth2016 - Health Informatics Meets eHealth: From Sensors to Decisions. IOS Press, pp. 9-14. [12] Tchouaket, É.N., Moine, M.P., Adedokun, A., et al. (2019). Challenges to Interoperability of Health Information Systems in Low- and Middle-Income Countries: A Literature Review. Studies in Health Technology and Informatics, 264, pp. 1399-1403. [13] Adler-Milstein, J., Jha, A.K. (2013). Sharing Clinical Data Electronically: A Critical Challenge for Fixing the Health Care System. JAMA, 309(10), pp. 991-992. [14] Kamadjeu, R. (2019). Challenges of Implementing eHealth in Developing Countries: An Example of a Web-Based Asynchronous Telemedicine System. Health Informatics Journal, 25(1), pp. 62-71. [15] Sharma, A., Kamadjeu, R., Mirza, S., et al. (2017). Development of a National Health Information System in a Resource-Limited Setting: A Case Study from Liberia. Health Informatics Journal, 23(4), pp. 295-306. [16] Jayatissa P, Vajira HWD, Roshan H. Developing a functional prototype Master Patient Index (MPI) for interoperability of E-Health systems in Seri Lanka. Dent Res Int J DRIJ. 2017;2(2):9–26. https://arxiv.org/abs/1803.00451 [17] Seebregts, C.J., Mamlin, B.W., Biondich, P.G., et al. (2017). The OpenMRS Implementers Network. International Journal of Medical Informatics, 108, pp. 117-121. [18] Adefioye, O.A., Asaolu, I.O., Ajayi, T.A., et al. (2019). Ethical and Legal Issues in Health Information Exchange in Low and Middle Income Countries: A Systematic Review. BMC Medical Ethics, 20(1), pp. 84. [19] Braa, J., Hanseth, O., Mohammed, W., et al. (2017). Developing Health Information Systems in Developing Countries: The Flexible Standards Strategy. MIS Quarterly, 41(1), pp. 309-322. [20] Karim, A.M., Nematzadeh, A., Gombera, F., et al. (2019). eHealth in Low- and Middle-Income Countries: Findings from the Center for Health Market Innovations. Health Affairs, 38(5), pp. 711- 718. [21] Jayatissa P, Hewapatirane R. Development of Minimum Clinical Data Set For Master Patient Index(MPI) for Sri Lankan Context. https://airccse.com/drij/papers/4422drij01.pdf .2022 Nov 1;4(4):1–18. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Enhancing Interoperability in LMIC Healthcare Information Systems

Authors:
(1) Prabath Jayatissa, University of Colombo
(2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Methodology Results Conclusion & References 3. RESULTS 3.1. Challenges to Interoperability in LMICs Several challenges hinder interoperability among health information systems in LMICs. Firstly, there is often a need for morestandardisation in data formats, coding systems, and terminologies used in different health information systems, making it difficult to exchange and interpret data accurately. Secondly, fragmented health information systems, where different systems are used for other healthcare functions, can lead to data silos and hinder seamless data exchange. Thirdly, more resources, including funding, technical expertise, and infrastructure, could be improved in implementing and maintaining interoperable health information systems. Data privacy, security, and consent issues are often inadequately addressed in LMICs, which can further impede interoperability efforts. Furthermore, the lack of a unified governance framework and coordination among different stakeholders involved in health information systems, including government agencies, healthcare providers, and technology vendors, can create challenges in aligning policies, standards, and processes for interoperability. Additionally, the diversity of languages, cultures, and healthcare practices in LMICs can further complicate interoperability efforts, as data exchange may require translation and customisation to local contexts.Moreover, healthcare professionals' workforce capacity and digital literacy can be limited in LMICs, which can impact the effective use of health information systems and hinder interoperability. Training, education, and skill development programs may be needed to enhance the capacity of healthcare providers to use and exchange health information across systems. Another significant challenge is the sustainability of interoperable health information systems in LMICs. Many LMICs face financial constraints and resource limitations, making it difficult to invest in developing, maintaining, and upgrading health information systems. Without adequate funding and resource allocation, interoperability initiatives may struggle to gain traction and achieve long-term sustainability.Overall, the challenges to interoperability in LMICs are multifaceted, encompassing technical, organisational, financial, and cultural aspects. Addressing these challenges requires a holistic approach, involving stakeholders from various sectors and levels and implementing strategies that address the unique needs and contexts of LMICs. By overcoming these challenges, LMICs can unlock the potential of health information systems to improve healthcare outcomes and advance their health systems towards more integrated, patientcentred, and data-driven care. 3.2. Strategies for Enhancing Interoperability in LMICs Despite the challenges, several strategies can be adopted to enhance interoperability among health information systems in LMICs. Firstly, standardising data formats, coding systems, and terminologies are crucial for ensuring consistency and accuracy of data exchange. Adopting internationally recognised health IT standards, such as HL7 (Health Level Seven) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), can facilitate interoperability efforts. Secondly, consolidation and integration of fragmented health information systems into a unified health information exchange (HIE) platform can enable seamless data exchange and coordination of care. The development of national health information exchange policies and governance frameworks can support the establishment and sustainability of HIEs. Thirdly, investment in health IT infrastructure, including hardware, software, and networking, is essential for keeping interoperable health information systems. This may require partnerships and collaborations with various stakeholders, including governments, non-profit organisations, and the private sector. Additionally, capacity building and training programs for the health IT workforce can enhance technical expertise and knowledge in LMICs. Furthermore, addressing data privacy, security, and consent issues through appropriate policies, regulations, and safeguards is crucial for building trust and promoting data exchange among health information systems. Another essential strategy is fostering collaboration and coordination among different stakeholders involved in health information systems in LMICs. This includes government agencies, healthcare providers, technology vendors, and other relevant entities. Establishing partnerships and alliances among these stakeholders can help align policies, standards, and processes for interoperability and promote knowledge sharing and best practices. Furthermore, leveraging existing technologies and infrastructure can be cost-effective to enhance interoperability in LMICs. For example, utilising mobile health (mHealth) and telehealth solutions can facilitate data exchange in remote and underserved areas where traditional health information systems may be limited. Leveraging cloud-based solutions and open-source software can also provide affordable options for health information exchange in resource-constrained settings. Another strategy is to prioritiseuser-centred design and usability of health information systems. Ensuring that the systems are intuitive, user-friendly, and compatible with local workflows and practices can encourage adoption and utilisation by healthcare providers, leading to improved interoperability. Involving end-users in designing, testing, and refining health information systems can also help identify and address usability challenges. In addition, fostering a culture of data sharing and collaboration among healthcare providers and organisations can promote interoperability. Encouraging data-sharing agreements, promoting data exchange policies, and providing incentives for data sharing can motivate stakeholders to participate in interoperability efforts and facilitate seamless data exchange. Lastly, continuous monitoring, evaluation, and improvement of interoperable health information systems are essential for sustaining interoperability efforts in LMICs. Regular assessment of system performance, identification of gaps, and implementation of corrective measures can help optimise interoperability and ensure that health information systems are meeting the evolving needs of the healthcare ecosystem in LMICs. In conclusion, enhancing interoperability among health information systems in LMICs is a complex endeavour that requires addressing technical, organisational, financial, and cultural challenges. By adopting strategies such as standardisation, consolidation, investment in infrastructure, capacity building, policy development, user-centred design, fostering collaboration, and continuous improvement, LMICs can overcome these challenges and unlock the potential of health information systems to improve healthcare outcomes and strengthen their health systems. This paper is available on arxiv under CC 4.0 license. Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Authors: Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Abstract & Introduction Methodology Methodology Results Results Conclusion & References Conclusion & References 3. RESULTS 3.1. Challenges to Interoperability in LMICs Several challenges hinder interoperability among health information systems in LMICs. Firstly, there is often a need for morestandardisation in data formats, coding systems, and terminologies used in different health information systems, making it difficult to exchange and interpret data accurately. Secondly, fragmented health information systems, where different systems are used for other healthcare functions, can lead to data silos and hinder seamless data exchange. Thirdly, more resources, including funding, technical expertise, and infrastructure, could be improved in implementing and maintaining interoperable health information systems. Data privacy, security, and consent issues are often inadequately addressed in LMICs, which can further impede interoperability efforts. Furthermore, the lack of a unified governance framework and coordination among different stakeholders involved in health information systems, including government agencies, healthcare providers, and technology vendors, can create challenges in aligning policies, standards, and processes for interoperability. Additionally, the diversity of languages, cultures, and healthcare practices in LMICs can further complicate interoperability efforts, as data exchange may require translation and customisation to local contexts.Moreover, healthcare professionals' workforce capacity and digital literacy can be limited in LMICs, which can impact the effective use of health information systems and hinder interoperability. Training, education, and skill development programs may be needed to enhance the capacity of healthcare providers to use and exchange health information across systems. Another significant challenge is the sustainability of interoperable health information systems in LMICs. Many LMICs face financial constraints and resource limitations, making it difficult to invest in developing, maintaining, and upgrading health information systems. Without adequate funding and resource allocation, interoperability initiatives may struggle to gain traction and achieve long-term sustainability.Overall, the challenges to interoperability in LMICs are multifaceted, encompassing technical, organisational, financial, and cultural aspects. Addressing these challenges requires a holistic approach, involving stakeholders from various sectors and levels and implementing strategies that address the unique needs and contexts of LMICs. By overcoming these challenges, LMICs can unlock the potential of health information systems to improve healthcare outcomes and advance their health systems towards more integrated, patientcentred, and data-driven care. 3.2. Strategies for Enhancing Interoperability in LMICs Despite the challenges, several strategies can be adopted to enhance interoperability among health information systems in LMICs. Firstly, standardising data formats, coding systems, and terminologies are crucial for ensuring consistency and accuracy of data exchange. Adopting internationally recognised health IT standards, such as HL7 (Health Level Seven) and SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), can facilitate interoperability efforts. Secondly, consolidation and integration of fragmented health information systems into a unified health information exchange (HIE) platform can enable seamless data exchange and coordination of care. The development of national health information exchange policies and governance frameworks can support the establishment and sustainability of HIEs. Thirdly, investment in health IT infrastructure, including hardware, software, and networking, is essential for keeping interoperable health information systems. This may require partnerships and collaborations with various stakeholders, including governments, non-profit organisations, and the private sector. Additionally, capacity building and training programs for the health IT workforce can enhance technical expertise and knowledge in LMICs. Furthermore, addressing data privacy, security, and consent issues through appropriate policies, regulations, and safeguards is crucial for building trust and promoting data exchange among health information systems. Another essential strategy is fostering collaboration and coordination among different stakeholders involved in health information systems in LMICs. This includes government agencies, healthcare providers, technology vendors, and other relevant entities. Establishing partnerships and alliances among these stakeholders can help align policies, standards, and processes for interoperability and promote knowledge sharing and best practices. Furthermore, leveraging existing technologies and infrastructure can be cost-effective to enhance interoperability in LMICs. For example, utilising mobile health (mHealth) and telehealth solutions can facilitate data exchange in remote and underserved areas where traditional health information systems may be limited. Leveraging cloud-based solutions and open-source software can also provide affordable options for health information exchange in resource-constrained settings. Another strategy is to prioritiseuser-centred design and usability of health information systems. Ensuring that the systems are intuitive, user-friendly, and compatible with local workflows and practices can encourage adoption and utilisation by healthcare providers, leading to improved interoperability. Involving end-users in designing, testing, and refining health information systems can also help identify and address usability challenges. In addition, fostering a culture of data sharing and collaboration among healthcare providers and organisations can promote interoperability. Encouraging data-sharing agreements, promoting data exchange policies, and providing incentives for data sharing can motivate stakeholders to participate in interoperability efforts and facilitate seamless data exchange. Lastly, continuous monitoring, evaluation, and improvement of interoperable health information systems are essential for sustaining interoperability efforts in LMICs. Regular assessment of system performance, identification of gaps, and implementation of corrective measures can help optimise interoperability and ensure that health information systems are meeting the evolving needs of the healthcare ecosystem in LMICs. In conclusion, enhancing interoperability among health information systems in LMICs is a complex endeavour that requires addressing technical, organisational, financial, and cultural challenges. By adopting strategies such as standardisation, consolidation, investment in infrastructure, capacity building, policy development, user-centred design, fostering collaboration, and continuous improvement, LMICs can overcome these challenges and unlock the potential of health information systems to improve healthcare outcomes and strengthen their health systems. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Challenges to Interoperability of Health Information Systems in LMICs

Authors:
(1) Prabath Jayatissa, University of Colombo
(2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Methodology Results Conclusion & References 2. METHODOLOGY The review article focuses on the issue of interoperability among health information systems in low and middle-income countries (LMICs). The methodology employed involves a rigorous process that includes several key steps. First, a comprehensive literature review encompasses relevant research articles, reports, and publications from reputable sources. The findings from the literature review are then synthesised to identify common challenges faced by LMICs in achieving interoperability in their health information systems. The challenges identified include a lack of standardisation, fragmented systems, limited resources, and data privacy concerns. These challenges hinder the seamless exchange of health information among different health information systems, leading to inefficiencies and suboptimal health outcomes.Next, the review article proposes strategies to enhance interoperability in LMICs. These strategies are derived from the analysis and interpretation of the literature findings. Proposed strategies include standardisation of data formats and protocols, consolidation of health information systems, investment in health IT infrastructure, and capacity building of health IT professionals in LMICs.Once the strategies are identified, the review article is organised and written, providing a concise and coherent overview of the current state of interoperability among health information systems in LMICs and potential strategies for improvement. The article is then revised and finalised for submission, taking into account feedback from peers and experts in the field. In summary, the methodology for the review article involves conducting a comprehensive literature review, synthesising findings, identifying challenges and strategies, analysing and interpreting results, and writing and finalising the article. The article provides insights into the current state and potential strategies for enhancing interoperability among health information systems in LMICs, to improve healthcare delivery and outcomes in these regions. 2.1. Literature Review Interoperability has been recognised as a critical factor for effective health information exchange and care coordination at the national and international levels1 . In LMICs, achieving interoperability among health information systems is particularly challenging due to various factors. Several studies have identified challenges to interoperability in LMICs, including: Lack of standardisation: LMICs often need morestandardised data formats, coding systems, and terminologies, which hinder the accurate and seamless exchange of health information (AdlerMilstein et al., 2013; Kamadjeu, 2019). This lack of standardisation makes integrating data from different sources challenging, resulting in fragmented and incomplete health information2 Fragmented health information systems: Many LMICs have fragmented health information systems, where different systems are used for different healthcare functions, such as patient registration, electronic health records (EHRs), and laboratory systems, leading to data silos3 . This fragmentation hampers the exchange of data across different systems, hindering interoperability. Limited resources: LMICs often need more resources, including limited funding, technical expertise, and infrastructure, which can pose challenges in implementing and maintaining interoperable health information systems4 .The high cost of health IT infrastructure, such as servers, software, and networking, can hinder achieving interoperability in LMICs. Data privacy and security concerns: Data privacy, security, and consent are critical concerns in health information exchange, and LMICs often lack robust policies, regulations, and safeguards to address these issues effectively5 . This can hinder the sharing of health information among different systems due to concerns about data breaches and misuse. Despite these challenges, several strategies have been proposed to enhance interoperability among health information systems in LMICs. Standardisation: Adoption of internationally recognised health IT standards, such as HL7 and SNOMED CT, can facilitate data standardisation and ensure consistency and accuracy of data exchange6 . Standardisation of data formats, coding systems, and terminologies can enable seamless data exchange and promote interoperability. Consolidation and integration: The integration of fragmented health information systems into a unified health information exchange (HIE) platform can facilitate data exchange and coordination of care7 . Developing national HIE policies and governance frameworks can support the establishment and sustainability of HIEs in LMICs. Investment in health IT infrastructure: Investment in health IT infrastructure, including hardware, software, and networking, is crucial for supporting interoperable health information systems8 .Partnerships and collaborations with governments, non-profit organisations, and the private sector can help overcome resource constraints and facilitate the development of robust health IT infrastructure. Capacity building and training: Capacity building and training programs for the health IT workforce can enhance technical expertise and knowledge in LMICs9 . Training programs can focus on building skills in health IT standards, data management, and interoperability concepts, Interoperability, the ability of different health information systems to exchange and use data seamlessly, is a critical factor for effective health information exchange and coordination of care at the national and international levels10. In LMICs, achieving interoperability among health information systems is particularly challenging due to various factors. Several studies have identified challenges to interoperability in LMICs, including lack of standardisation, fragmented health information systems, limited resources, and data privacy and security concerns11 . Lack of standardisation is a major challenge in LMICs, as they often needstandardised data formats, coding systems, and terminologies, which hinder the accurate and seamless exchange of health information12. Fragmented health information systems, which are used for different healthcare functions, such as patient registration, electronic health records (EHRs), and laboratory systems, must also be improved for interoperability12,21. This fragmentation leads to data silos, hindering data exchange across different systems. Limited resources, including funding, technical expertise, and infrastructure, are often faced by LMICs, which can pose challenges in implementing and maintaining interoperable health information systems13. The high cost of health IT infrastructure, such as servers, software, and networking, can hinder achieving interoperability in LMICs. Data privacy and security concerns are also critical challenges, as LMICs often lack robust policies, regulations, and safeguards to address these issues effectively9,10,11. This can hinder the sharing of health information among different systems due to concerns about data breaches and misuse. Despite these challenges, several strategies have been proposed to enhance interoperability among health information systems in LMICs. Standardisation, such as adopting internationally recognised health IT standards like HL7 and SNOMED CT, can facilitate data standardisation and ensure data exchange consistency and accuracy of data exchange9,21 . Consolidating and integrating fragmented health information systems into a unified health information exchange (HIE) platform can facilitate data exchange and coordination of care10,12,15. Investment in health IT infrastructure, including hardware, software, and networking, is crucial for supporting interoperable health information systems in LMICs7,9,11. Capacity building and training programs for the health IT workforce can enhance technical expertise and knowledge in LMICs, focusing on health IT standards, data management, and interoperability concepts13,14,16,20 Further research is needed to evaluate the impact of these strategies on improving interoperability, identifying novel approaches specific to LMICs, and addressing the unique challenges faced by different regions and contexts within LMICs. Additionally, research on the cost-effectiveness and sustainability of interoperable health information systems17,18,19. This paper is available on arxiv under CC 4.0 license. Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Authors: Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Abstract & Introduction Methodology Methodology Results Results Conclusion & References Conclusion & References 2. METHODOLOGY The review article focuses on the issue of interoperability among health information systems in low and middle-income countries (LMICs). The methodology employed involves a rigorous process that includes several key steps. First, a comprehensive literature review encompasses relevant research articles, reports, and publications from reputable sources. The findings from the literature review are then synthesised to identify common challenges faced by LMICs in achieving interoperability in their health information systems. The challenges identified include a lack of standardisation, fragmented systems, limited resources, and data privacy concerns. These challenges hinder the seamless exchange of health information among different health information systems, leading to inefficiencies and suboptimal health outcomes.Next, the review article proposes strategies to enhance interoperability in LMICs. These strategies are derived from the analysis and interpretation of the literature findings. Proposed strategies include standardisation of data formats and protocols, consolidation of health information systems, investment in health IT infrastructure, and capacity building of health IT professionals in LMICs.Once the strategies are identified, the review article is organised and written, providing a concise and coherent overview of the current state of interoperability among health information systems in LMICs and potential strategies for improvement. The article is then revised and finalised for submission, taking into account feedback from peers and experts in the field. In summary, the methodology for the review article involves conducting a comprehensive literature review, synthesising findings, identifying challenges and strategies, analysing and interpreting results, and writing and finalising the article. The article provides insights into the current state and potential strategies for enhancing interoperability among health information systems in LMICs, to improve healthcare delivery and outcomes in these regions. 2.1. Literature Review Interoperability has been recognised as a critical factor for effective health information exchange and care coordination at the national and international levels1 . In LMICs, achieving interoperability among health information systems is particularly challenging due to various factors. Several studies have identified challenges to interoperability in LMICs, including: Lack of standardisation: LMICs often need morestandardised data formats, coding systems, and terminologies, which hinder the accurate and seamless exchange of health information (AdlerMilstein et al., 2013; Kamadjeu, 2019). This lack of standardisation makes integrating data from different sources challenging, resulting in fragmented and incomplete health information2 Fragmented health information systems: Many LMICs have fragmented health information systems, where different systems are used for different healthcare functions, such as patient registration, electronic health records (EHRs), and laboratory systems, leading to data silos3 . This fragmentation hampers the exchange of data across different systems, hindering interoperability. Limited resources: LMICs often need more resources, including limited funding, technical expertise, and infrastructure, which can pose challenges in implementing and maintaining interoperable health information systems4 .The high cost of health IT infrastructure, such as servers, software, and networking, can hinder achieving interoperability in LMICs. Data privacy and security concerns: Data privacy, security, and consent are critical concerns in health information exchange, and LMICs often lack robust policies, regulations, and safeguards to address these issues effectively5 . This can hinder the sharing of health information among different systems due to concerns about data breaches and misuse. Despite these challenges, several strategies have been proposed to enhance interoperability among health information systems in LMICs. Standardisation: Adoption of internationally recognised health IT standards, such as HL7 and SNOMED CT, can facilitate data standardisation and ensure consistency and accuracy of data exchange6 . Standardisation of data formats, coding systems, and terminologies can enable seamless data exchange and promote interoperability. Consolidation and integration: The integration of fragmented health information systems into a unified health information exchange (HIE) platform can facilitate data exchange and coordination of care7 . Developing national HIE policies and governance frameworks can support the establishment and sustainability of HIEs in LMICs. Investment in health IT infrastructure: Investment in health IT infrastructure, including hardware, software, and networking, is crucial for supporting interoperable health information systems8 .Partnerships and collaborations with governments, non-profit organisations, and the private sector can help overcome resource constraints and facilitate the development of robust health IT infrastructure. Capacity building and training: Capacity building and training programs for the health IT workforce can enhance technical expertise and knowledge in LMICs9 . Training programs can focus on building skills in health IT standards, data management, and interoperability concepts, Interoperability, the ability of different health information systems to exchange and use data seamlessly, is a critical factor for effective health information exchange and coordination of care at the national and international levels10. In LMICs, achieving interoperability among health information systems is particularly challenging due to various factors. Several studies have identified challenges to interoperability in LMICs, including lack of standardisation, fragmented health information systems, limited resources, and data privacy and security concerns11 . Lack of standardisation is a major challenge in LMICs, as they often needstandardised data formats, coding systems, and terminologies, which hinder the accurate and seamless exchange of health information12. Fragmented health information systems, which are used for different healthcare functions, such as patient registration, electronic health records (EHRs), and laboratory systems, must also be improved for interoperability12,21. This fragmentation leads to data silos, hindering data exchange across different systems. Limited resources, including funding, technical expertise, and infrastructure, are often faced by LMICs, which can pose challenges in implementing and maintaining interoperable health information systems13. The high cost of health IT infrastructure, such as servers, software, and networking, can hinder achieving interoperability in LMICs. Data privacy and security concerns are also critical challenges, as LMICs often lack robust policies, regulations, and safeguards to address these issues effectively9,10,11. This can hinder the sharing of health information among different systems due to concerns about data breaches and misuse. Despite these challenges, several strategies have been proposed to enhance interoperability among health information systems in LMICs. Standardisation, such as adopting internationally recognised health IT standards like HL7 and SNOMED CT, can facilitate data standardisation and ensure data exchange consistency and accuracy of data exchange9,21 . Consolidating and integrating fragmented health information systems into a unified health information exchange (HIE) platform can facilitate data exchange and coordination of care10,12,15. Investment in health IT infrastructure, including hardware, software, and networking, is crucial for supporting interoperable health information systems in LMICs7,9,11. Capacity building and training programs for the health IT workforce can enhance technical expertise and knowledge in LMICs, focusing on health IT standards, data management, and interoperability concepts13,14,16,20 Further research is needed to evaluate the impact of these strategies on improving interoperability, identifying novel approaches specific to LMICs, and addressing the unique challenges faced by different regions and contexts within LMICs. Additionally, research on the cost-effectiveness and sustainability of interoperable health information systems17,18,19. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Unraveling Interoperability Challenges in LMIC Health Information Systems

Authors:
(1) Prabath Jayatissa, University of Colombo
(2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Methodology Results Conclusion & References ABSTRACT The review article aims to provide an overview of the challenges and strategies for enhancing interoperability among health information systems in low and middle-income countries (LMICs). Achieving interoperability in LMICs presents unique challenges due to various factors, such as limited resources, fragmented health information systems, and diverse health IT infrastructure. The methodology involves conducting a comprehensive literature review, synthesising findings, identifying challenges and strategies, analysing and interpreting results, and writing and finalising the article. The article highlights that the interoperability challenges include a lack of standardisation, fragmented systems, limited resources, and data privacy concerns. The article proposes strategies to enhance interoperability in LMICs, such as standardisation of data formats and protocols, consolidation of health information systems, investment in health IT infrastructure, and capacity building of health IT professionals in LMICs. The article aims to provide insights into the current state and potential strategies for enhancing interoperability among health information systems in LMICs, intending to improve healthcare delivery and outcomes in these KEYWORDS Interoperability, Health information systems, low and middle-income countries (LMICs), challenges, strategies, standardisation 1. INTRODUCTION Health information systems (HISs) are critical in supporting the delivery of quality healthcare services, improving patient outcomes, and informing evidence-based decision-making. HIS faces unique challenges in low- and middle-income countries (LMICs) due to limited resources, infrastructure, and capacity. One key challenge is the lack of interoperability, which refers to the ability of different HISs to exchange and use health information effectively. Interoperability is crucial for achieving seamless data exchange, coordination of care, and integration of health services. It enables healthcare providers to access and share patient information across different systems, facilitating timely and informed decision-making. However, achieving interoperability in LMICs is complex and poses several barriers, including technical, organisational, and financial challenges. This review article aims to highlight the challenges and strategies for enhancing interoperability among HIS in LMICs. We will explore the current state of interoperability in LMICs, identify the challenges faced by these countries, and discuss the strategies that can be employed to overcome these challenges. We will also highlight the importance of standardization, consolidation, investment, and capacity building as key strategies for improving interoperability in LMICs. By addressing these challenges and implementing appropriate strategies, LMICs can unlock the full potential of HIS to improve healthcare outcomes and achieve better health for their populations. This paper is available on arxiv under CC 4.0 license. Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Authors: Authors: (1) Prabath Jayatissa, University of Colombo (2) Roshan Hewapathirane, University of Colombo Table of Links Abstract & Introduction Abstract & Introduction Methodology Methodology Results Results Conclusion & References Conclusion & References ABSTRACT The review article aims to provide an overview of the challenges and strategies for enhancing interoperability among health information systems in low and middle-income countries (LMICs). Achieving interoperability in LMICs presents unique challenges due to various factors, such as limited resources, fragmented health information systems, and diverse health IT infrastructure. The methodology involves conducting a comprehensive literature review, synthesising findings, identifying challenges and strategies, analysing and interpreting results, and writing and finalising the article. The article highlights that the interoperability challenges include a lack of standardisation, fragmented systems, limited resources, and data privacy concerns. The article proposes strategies to enhance interoperability in LMICs, such as standardisation of data formats and protocols, consolidation of health information systems, investment in health IT infrastructure, and capacity building of health IT professionals in LMICs. The article aims to provide insights into the current state and potential strategies for enhancing interoperability among health information systems in LMICs, intending to improve healthcare delivery and outcomes in these KEYWORDS KEYWORDS Interoperability, Health information systems, low and middle-income countries (LMICs), challenges, strategies, standardisation 1. INTRODUCTION Health information systems (HISs) are critical in supporting the delivery of quality healthcare services, improving patient outcomes, and informing evidence-based decision-making. HIS faces unique challenges in low- and middle-income countries (LMICs) due to limited resources, infrastructure, and capacity. One key challenge is the lack of interoperability, which refers to the ability of different HISs to exchange and use health information effectively. Interoperability is crucial for achieving seamless data exchange, coordination of care, and integration of health services. It enables healthcare providers to access and share patient information across different systems, facilitating timely and informed decision-making. However, achieving interoperability in LMICs is complex and poses several barriers, including technical, organisational, and financial challenges. This review article aims to highlight the challenges and strategies for enhancing interoperability among HIS in LMICs. We will explore the current state of interoperability in LMICs, identify the challenges faced by these countries, and discuss the strategies that can be employed to overcome these challenges. We will also highlight the importance of standardization, consolidation, investment, and capacity building as key strategies for improving interoperability in LMICs. By addressing these challenges and implementing appropriate strategies, LMICs can unlock the full potential of HIS to improve healthcare outcomes and achieve better health for their populations. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Enhancing Interoperability Among Health Information Systems in Low- and Middle- Income Countries

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