5 Health Inc., doing business as Bot M.D., develops health care software. The Company offers a platform which provides patient health care information such as drug interactions, medical calculators, guidelines, and protocols, and diseases information. Bot M.D. serves clients in the State of California.

5 Health

5health

According to Rock Health, the digital health funding in the first quarter of 2018 reached a whopping $1.6 billion. This is the highest first quarter on record as it surpassed the much-hyped $1.4 billion funding in venture capital during Q1 2016. AI is being applied in 5 different areas of healthcare to improve its overall operations. It is helping in administration, care provision, clinical decision-making, big data analysis, and remote patient monitoring. We consider the top 5 startups which are relying on AI to provide the best healthcare services to their patients: Ucare.AI Founded by Neal Liu, an MIT graduate, the company started receiving funding in 2016. Its AI algorithms and the neural network is used to predict the healthcare patterns in patients. The aim is to prevent hospitalization so that the costs and the time can be saved. The sole purpose of this app is to provide the medical practitioners with an opportunity to focus on only such patients who are at a higher level of risk. Bay Labs, Inc This is a San Francisco based startup which has been able to use AI in cardiovascular imaging. This helps in the diagnosis and treatment of heart disease. The data which is produced through cardiovascular imaging can be hard to understand for some. Nonetheless, AI technology uses such algorithms and videos from the huge library that help the medical personnel in proper interpretation and cure. Talkspace Mental health is a growing issue and needs to be addressed properly before we lose a number of precious lives around us. People like Michael Phelps have also suffered from the worst form of so, consider this epidemic to be as real as possible. Lately, Phelps partnered with Talkspace to increase awareness on mental health. The company was founded by Oren and Roni Frank in 2012 and has already treated around half a million patients. It helps you connect with licensed therapists through their app or website. depression Vitagene Until now, the company has raised $6.6 million and is a renowned name in genetic sequencing. This app will run your DNA model through its AI platform comparing it with other samples in their database. The results help the app in recommending a healthy diet, an exercise routine, and a supplement plan. Not only will you be able to get the DNA health test but it will also present you with the ancestry report. VaGenie Childbirth crazily affects a woman’s body and weakens the pelvic floor. As per the research, 2 out of 3 women suffer from a weak pelvic floor and this causes various problems in them including back pain, incontinence, and prolapse. VaGenie provides a combination device and app to help women in strengthening their pelvic floor through Kegel training. It teaches the women how to perform the exercises correctly while making them fun for them. These startups are disrupting the health industry and using AI to improve the care which is provided to the patients. There are plenty of more startups working in the field and in near future, AI is going to bring more ease to this industry.

5 Health Startups Using AI to Improve Daily Wellness

Let's learn about via these 41 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Social Impact /Learn Repo 1. Technology Is Killing Creativity Technology is killing creativity in the sense that it is making it more challenging to be creative. 2. Why Is Bitcoin the Best Secret Society Ever? The Rise of Illuminati 2.0 Why is Bitcoin the best Secret Society ever? The rise of Illuminati 2.0 and a Greater Idea behind it. 3. 5 Trends That Could Mark Social Media’s Evolution in the Next 5 Years Social media trends to watch 4. Social-as-a-Service Concept “There are only two ways to make money in business: one is to bundle; the other is unbundle.” — Jim Barksdale, former CEO and President of Netscape. 5. The Great Polarisation When you look at the trends of the last few decades, it’s difficult to miss the signs, overt and covert, of there being an increasing polarization in the business world. Even in the last decade, firms have been seeing extremely high profit rates primarily driven by the lack of competition. But this hides the underlying fragmentation of a previously solid market, which is likely to be the beginning of the next phase of exploration vs exploitation. To take a few examples: 6. Mental Health Basics: Diagnosis, Treatment, Tech Tools Last year I found I had ADD. 7. Learn How to Crack the Code of Emotional Intelligence The definition of Emotional Intelligence states that it is: ”the ability to perceive, understand and influence our own and others’ emotions, across a range of contexts, to guide our current thinking and actions, to help us to achieve our goals”. 8. Science is About Truth; Engineering is About Compromise The Fuller: A Unit of Humanitarian Invention Impact 9. Understanding "Impact Marketing" and Its Importance It's time for all the tech companies adding disability customers in their roadmap. 10. The Social Dilemma on Netflix is the Wake Up Call We've Been Waiting For I watched “The Social Dilemma”, the documentary by the Center for Humane Technology and featuring Tristan Harris, yesterday. It taught me zero new things. I learned nothing that I had not already come across during my research for my latest book Life Beyond the Touch Screen. Or that I had not even incorporated in the book. 11. Walking the Talk: How Gen Z and Social Media Are Affecting Real Social Change Generation Z, or Gen Z, coming on the heels of the Millennials, are more racially and ethnically diverse than any generation before. They are truly the first digital natives, with the smart phone around them since birth. As the saying goes, “Today, 2-year-olds can unlock an iPhone, open and close their favorite apps all by themselves. When I was that age, I was eating dirt.” In fact, this generation cannot imagine life without the iPhone and the Internet. 12. Tezos Commons Boston Chapter Pitch Competition Authentically Innovates at Babson College 13. Social Impact Using Your Heart and Mind in Tech My generation has seen a world much less forgiving. We grew up understanding the fundamentals of climate change, and saw rising sea levels and record setting temperatures all over the world. 14. Tinder for Hackathon Teams For those of you who are Tinder users or just watchful to the tech news, you should know that the App is offering the option to match with anyone in the entire world, helping people to have virtual dates during this time of pandemic crisis. 15. Digital Education: Opportunities and Challenges Coronavirus Exposed The number of students not attending schools or universities due to the outbreak of the coronavirus COVID-19 is growing rapidly. This is why governments around the world have decided to convert education to electronic form. While the Covid-19 pandemic significantly pushed the digitalization of the learning process, it also exposed major stumbling blocks in this way. 16. How can AI turn Social Media into a Smart Fundraising tool for Nonprofits? Hopeful was nominated as one of the best startups in Toronto in Startups of the Year hosted by HackerNoon. 17. What is the Engage-to-Earn Social Media Model? Taki is an “engage-to-earn” social network that combines user currency with social media elements to reward and encourage activities. 18. Social Entrepreneurship Can Help You Level Up Your Success - Here's How 19. Social Entrepreneurship Qualities You Should Harness Social entrepreneurship is an excellent way to profit and change the world at the same time, offering a business model that will be pivotal for many years ahead 20. App Gạo is a New & Digitized Charity Experience In the times of COVID-19, how do we help others and ensure that we give them the help that they need? Here's how some Vietnamese students managed to do it. 21. TikTok Ban: The Biggest Trends to Arrive from 2020's Controversial Social Network TikTok’s rise in popularity has taken the world by storm. Launched in 2017, the social network only reached world markets in the summer of 2018. Two years on and the video-based platform has amassed over two billion downloads globally. 22. Telco Professional & Charity Food Delivery Rider: A Chat About Trying To Raise 100 K In 60 Days I remote chat with Anthony Houlahan, a telco professional who has been working in Singapore for the last 18 years. He initially started on his mission on the 22nd of April to use his time after hours and on weekends to do food delivery, both to stave off isolation, and to raise funds for a good cause. He ends his donation campaign and delivery activity on 21st June. 23. The Tech Industry is a Balancing Act Between Two Value Systems: Cash and Humanity Tech is a balancing act between two value systems (cash and humanity) and it can never not be. Here's why: [24. Cryptocurrency and Philanthropy: The present! ✨👏❤️✨ 5 Reasons Why Nonprofits Love Cryptocurrency](https://hackernoon.com/cryptocurrency-and-philanthropy-the-present-5-reasons-why-nonprofits-love-cryptocurrency-do78j3110) I first of all would like to thank you for giving me trust. We have an opportunity to impact the world together. Im are glad to share an amazing article with some additions (my own). By Alex Wilson and Pat Duffy, co-founders of The Giving Block 25. We're Expanding Our Refurbished Computers Program to Meet New Demands As COVID-19 moves across the country, nearly all businesses and educational institutions have shifted to remote work and distance learning. As a result, the demand for resources to support that shift have taken a sharp upturn — particularly, the demand for laptop computers. In response, TechSoup is working with our partners to expand our inventory of laptops by adding more Condition B products to our catalog. 26. The Benefits of Taking a CPR Course Online Obtaining your cardiopulmonary resuscitation (CPR) certification online is now a very real and viable option for all people. Regardless of the reason why you’re obtaining your certification, you can either take it online, in-person, or a combination of the two, often called a “blended” course option. 27. Boris Johnson Surrenders to Marcus Rashford's Amazing Twitter Sprints Manchester striker breaks down conservative government resistance to feeding the hungry with Twitter onslaught. 28. A History Of The Fat Liberation Movement Via The Lens of Social Media Fat Liberation is a social justice movement focused on making social policy and practice more inclusive and equitable for fat people. 29. Mozilla Spring MVP Lab TLDR: Apply to be a part of the Mozilla Spring MVP Lab at mozilla.org/builders 30. How to Launch a Social Project in 6 Steps Being a socially responsible business is no longer a trend, it is a must. 31. How to Fight Global Climate Change with Web3 Maxity.io, the world’s largest charity NFT marketplace, is launching its mobile Dapp called Maxity App and a pioneering ‘Plant and Reward’ model, MAX Forest... 32. AI for Social Good: Addressing the Top 5 Challenges To realize the potential of Ai, we must address the top 5 challenges facing it: data bias, lack of access, security, scalability and impact evaluation. 33. Food Tech Stories: Food On Wheels Can Solve Last Mile Problem Urbanization is a common process in many regions of the world, including a huge proportion of people in less developed countries. Rapid urbanization can cause many problems, including poverty and food insecurity, which can make it harder for citizens to eat a healthy diet. No matter how hard policymakers are trying to promote healthy and sustainable diets, it’s still an acute problem in modern cities. 34. How Diets and Dying Related to  Demographics When we seek to understand consequences of our actions and the hidden complexity that underlies them we can make better decisions. One set of decisions that everyone participates in throughout life is choice of diet. The impact of what we consume on a daily basis is often only felt many years later — and can be an accident of birth. 35. The Precariat: A New Social Phenomenon The precariat is a class of socially unsettled people who do not have full guaranteed employment. 36. Becoming Furlonteer: Using Technology to Connect Talented Workers And Good Causes Furlonteer – the new non-profit project connecting furloughed employees with the people who need it most. 37. Food Tech Stories: How Italy Fighting with Food Waste Based on this story: Italy passes law to send unsold food to charities instead of dumpsters 38. Users to Communities: Leveraging Social Feature SDKs to Boost User Engagement In the quest to ensure user engagement, app developers should know how to develop connections beyond the transactional aspects of their user relationships. 39. How I Got an International “Custom MBA” Degree for $36,337 How I gained more for less than a fifth of the price of a top MBA 40. An Introduction to Regenerative Finance (ReFi) ReFi, or Regenerative Finance, is a new and innovative financial solution that aims to address many of the issues that traditional banking presents. I 41. Exploring Mental Health and UX Design with Jennifer Wong Jennifer Wong is the Founder of a UX design boot camp called Product Design Fam and the CEO of Empathie, an app dedicated to empowering racial minorities. Thank you for checking out the 41 most read stories about Social Impact on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

41 Stories To Learn About Social Impact

Tips to Achieve Data Interoperability in Healthcare

From the Brink of Suicide to Bliss: How Hormones Destroyed and Saved My Life

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

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

Authors:
(1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions;
(2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine;
(3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions;
(4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions;
(5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Table of Links Introduction Methods Results and Discussions Conclusion & References Introduction The integration and exchange of health data across diverse platforms and systems remain challenging due to the absence of standardized formats and a shared semantic understanding. This challenge becomes more significant when critical health information is embedded in unstructured data rather than well-organized structured formats. Standardizing unstructured health data, such as clinical notes, into FHIR resources can alleviate ambiguity across different health providers and, therefore, improve interoperability. However, it is by no means an easy task. Previous studies 1,2 have attempted to transform clinical notes into FHIR resources using a combination of natural language processing and machine learning tools through multi-step processes involving clinical named entity recognition, terminology coding, mathematical calculations, structural formatting, and human calibrations. However, these approaches require additional human effort to consolidate the results from multiple tools and have achieved only moderate performances, with F1 scores ranging from 0.7 to 0.9 in different elements. To this end, we intend to harness Large Language Models (LLMs) to directly generate FHIR-formatted resources from free-text input. The utilization of LLMs is expected to simplify the previously multi-step processes, enhance the efficiency and accuracy of automatic FHIR resource generation, and ultimately improve health data interoperability. Methods Data Annotation To the best of our knowledge, there is no largely publicly available dataset in the FHIR standard that is generated from contextual data. Therefore, we have chosen to annotate a dataset containing both free-text input and structured output in FHIR formats. The free-text input was derived from the discharge summaries of the MIMICIII datase. 3 Thanks to the 2018 n2c2 medication extraction challenge 4 , which essentially involves named entity recognition tasks, elements in medication statements have been identified. Our annotations built upon these n2c2 annotations and standardized the free text into multiple clinical terminology coding systems, such as NDC, RxNorm, and SNOMED. We organized the contexts and codes into FHIR medicationStatement resources. The converted FHIR resources underwent validation by the official FHIR validator (https://validator.fhir.org/) to ensure compliance with FHIR standards, including structure, datatype, code sets, display names, and more. These validated results were considered the gold standard transformation results and could be used to test against the LLMs. No ethical concerns exist regarding data usage, as both the MIMIC and n2c2 datasets are publicly available to authorized users. Large Language Model We used OpenAI's GPT-4 model as the LLM for FHIR format transformation. We used five separate prompts to instruct the LLM to transform input free text into medication (including medicationCode, strength, and form), route, schedule, dosage, and, reason, respectively. All prompts adhered to a template with the following strucuture: task instructions, expected output FHIR templates in .JSON format, 4-5 conversion examples, a comprehensive list of codes from which the model can make selections, and then the input text. As there was no finetuning or domain-specific adaptation in our experiments, we initially had the LLM generate a small subset (N=100). Then, we manually reviewed the discrepancies between the LLM-generated FHIR output and our human annotations. Common mistakes were identified and used to refine the prompts. It's important to note that we did not have the access to the whole lists of NDC, RxNorm, and SNOMED Medication codes for drug names, as well as SNOMED Finding codes for reasons. Additionally, even if we had such comprehensive lists, they would have exceeded the token limits for LLMs. Thus, we did not task LLMs with coding these entities; instead, we instructed them to identify the contexts mentioned in the input text. For other elements, e.g. drug routes and forms, numbering in the hundreds, we allowed LLMs to directly code them. When evaluating the LLM-generated output, our primary criterion was the exact match rate, which necessitates precise alignment with human annotations in all aspects, including codes, structures, and more. Additionally, we reported precision, recall, and F1 scores for specific element occurrences. We accessed the GPT-4 APIs through the Azure OpenAI service, aligning with responsible use guidelines for MIMIC data. The specific model we used was 'gpt-4-32k' in its '2023-05-15' version. Each text input was individually transformed into a MedicationStatement resource. To optimize efficiency, we made multiple asynchronous API calls. Results and Discussions The results of annotation and FHIR generation are presented in Table 1. In summary, we annotated 3,671 medication resources, covering over 625 distinct medications and associated with 354 reasons. The Large Language Model (LLM) achieved an impressive accuracy rate of over 90% and an F1 score exceeding 0.96 across all elements. In prior studies, F1 scores reached 0.750 in timing.repeat, 0.878 in timing.route, and 0.899 in timing dosage. 1 The LLM improved these F1 scores by at least 8%. It's worth noting that the previous studies used a smaller private dataset, did not employ the strictest evaluation metrics like exact match rate, skipped terminology coding, and required extensive training. On further investigation, we were also impressed by the high accuracy in terminology coding (which essentially involves a classification task with more than 100 classes), mathematical conversion (e.g., inferring a duration of 10 days when the input mentions 'TID, dispense 30 tablets'), format conformity (with less than a 0.3% chance that the results cannot be interpreted in .JSON format), and cardinality (the LLM can handle both 1:N and 1:1 relationships). The accuracy of the output is highly dependent on the instruction prompts used. Based on our extensive trials and errors, we have the following recommendations: i) provide diverse conversion examples that encompass a wide range of heterogeneous edge cases; ii) use strong language, such as “MUST”, to ensure that the output adheres to the expected formats and rults; iii) continuously update and refine the prompts by reviewing results from a small subset, which can help identify common mistakes and enhance overall accuracy; iv) be cautious about out-of-vocabulary codings. LLMs may attempt to cater users by inventing codes that do not exist when they cannot find a close match. Conclusion In this study, we provided the foundations of leveraging LLMs to enhance health data interoperability by transforming free-text input into the FHIR resources. Future studies will aim to build upon these successes by extending the generation to additional FHIR resources and comparing the performance of various LLM models. Reference 1. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, Jiang G. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Summits on Translational Science Proceedings. 2018;2018:74. 2. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA open. 2019 Dec;2(4):570-9. 3. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9. 4. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020 Jan;27(1):3-12. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions; (2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine; (3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions; (4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions; (5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Authors: Authors: (1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions; (2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine; (3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions; (4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions; (5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine. Table of Links Introduction Introduction Methods Methods Results and Discussions Results and Discussions Conclusion & References Conclusion & References Introduction The integration and exchange of health data across diverse platforms and systems remain challenging due to the absence of standardized formats and a shared semantic understanding. This challenge becomes more significant when critical health information is embedded in unstructured data rather than well-organized structured formats. Standardizing unstructured health data, such as clinical notes, into FHIR resources can alleviate ambiguity across different health providers and, therefore, improve interoperability. However, it is by no means an easy task. Previous studies 1,2 have attempted to transform clinical notes into FHIR resources using a combination of natural language processing and machine learning tools through multi-step processes involving clinical named entity recognition, terminology coding, mathematical calculations, structural formatting, and human calibrations. However, these approaches require additional human effort to consolidate the results from multiple tools and have achieved only moderate performances, with F1 scores ranging from 0.7 to 0.9 in different elements. To this end, we intend to harness Large Language Models (LLMs) to directly generate FHIR-formatted resources from free-text input. The utilization of LLMs is expected to simplify the previously multi-step processes, enhance the efficiency and accuracy of automatic FHIR resource generation, and ultimately improve health data interoperability. Methods Data Annotation To the best of our knowledge, there is no largely publicly available dataset in the FHIR standard that is generated from contextual data. Therefore, we have chosen to annotate a dataset containing both free-text input and structured output in FHIR formats. The free-text input was derived from the discharge summaries of the MIMICIII datase. 3 Thanks to the 2018 n2c2 medication extraction challenge 4 , which essentially involves named entity recognition tasks, elements in medication statements have been identified. Our annotations built upon these n2c2 annotations and standardized the free text into multiple clinical terminology coding systems, such as NDC, RxNorm, and SNOMED. We organized the contexts and codes into FHIR medicationStatement resources. The converted FHIR resources underwent validation by the official FHIR validator (https://validator.fhir.org/) to ensure compliance with FHIR standards, including structure, datatype, code sets, display names, and more. These validated results were considered the gold standard transformation results and could be used to test against the LLMs. No ethical concerns exist regarding data usage, as both the MIMIC and n2c2 datasets are publicly available to authorized users. Data Annotation Large Language Model We used OpenAI's GPT-4 model as the LLM for FHIR format transformation. We used five separate prompts to instruct the LLM to transform input free text into medication (including medicationCode, strength, and form), route, schedule, dosage, and, reason, respectively. All prompts adhered to a template with the following strucuture: task instructions, expected output FHIR templates in .JSON format, 4-5 conversion examples, a comprehensive list of codes from which the model can make selections, and then the input text. As there was no finetuning or domain-specific adaptation in our experiments, we initially had the LLM generate a small subset (N=100). Then, we manually reviewed the discrepancies between the LLM-generated FHIR output and our human annotations. Common mistakes were identified and used to refine the prompts. It's important to note that we did not have the access to the whole lists of NDC, RxNorm, and SNOMED Medication codes for drug names, as well as SNOMED Finding codes for reasons. Additionally, even if we had such comprehensive lists, they would have exceeded the token limits for LLMs. Thus, we did not task LLMs with coding these entities; instead, we instructed them to identify the contexts mentioned in the input text. For other elements, e.g. drug routes and forms, numbering in the hundreds, we allowed LLMs to directly code them. When evaluating the LLM-generated output, our primary criterion was the exact match rate, which necessitates precise alignment with human annotations in all aspects, including codes, structures, and more. Additionally, we reported precision, recall, and F1 scores for specific element occurrences. We accessed the GPT-4 APIs through the Azure OpenAI service, aligning with responsible use guidelines for MIMIC data. The specific model we used was 'gpt-4-32k' in its '2023-05-15' version. Each text input was individually transformed into a MedicationStatement resource. To optimize efficiency, we made multiple asynchronous API calls. Large Language Model Results and Discussions The results of annotation and FHIR generation are presented in Table 1. In summary, we annotated 3,671 medication resources, covering over 625 distinct medications and associated with 354 reasons. The Large Language Model (LLM) achieved an impressive accuracy rate of over 90% and an F1 score exceeding 0.96 across all elements. In prior studies, F1 scores reached 0.750 in timing.repeat, 0.878 in timing.route, and 0.899 in timing dosage. 1 The LLM improved these F1 scores by at least 8%. It's worth noting that the previous studies used a smaller private dataset, did not employ the strictest evaluation metrics like exact match rate, skipped terminology coding, and required extensive training. On further investigation, we were also impressed by the high accuracy in terminology coding (which essentially involves a classification task with more than 100 classes), mathematical conversion (e.g., inferring a duration of 10 days when the input mentions 'TID, dispense 30 tablets'), format conformity (with less than a 0.3% chance that the results cannot be interpreted in .JSON format), and cardinality (the LLM can handle both 1:N and 1:1 relationships). The accuracy of the output is highly dependent on the instruction prompts used. Based on our extensive trials and errors, we have the following recommendations: i) provide diverse conversion examples that encompass a wide range of heterogeneous edge cases; ii) use strong language, such as “MUST”, to ensure that the output adheres to the expected formats and rults; iii) continuously update and refine the prompts by reviewing results from a small subset, which can help identify common mistakes and enhance overall accuracy; iv) be cautious about out-of-vocabulary codings. LLMs may attempt to cater users by inventing codes that do not exist when they cannot find a close match. Conclusion In this study, we provided the foundations of leveraging LLMs to enhance health data interoperability by transforming free-text input into the FHIR resources. Future studies will aim to build upon these successes by extending the generation to additional FHIR resources and comparing the performance of various LLM models. Reference 1. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, Jiang G. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Summits on Translational Science Proceedings. 2018;2018:74. 2. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA open. 2019 Dec;2(4):570-9. 3. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9. 4. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020 Jan;27(1):3-12. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license.

Enhancing Health Data Interoperability with Large Language Models: A FHIR Study

বৃহৎ ভাষার মডেলগুলির সাথে স্বাস্থ্য ডেটা আন্তঃক্রিয়াশীলতা বৃদ্ধি করা: একটি এফএইচআইআর স্টাডি

Verbesserung der Interoperabilität von Gesundheitsdaten mit großen Sprachmodellen: Eine FHIR-Studie

Mejora de la interoperabilidad de los datos sanitarios con modelos de lenguaje grandes: un estudio del FHIR

Améliorer l'interopérabilité des données de santé avec de grands modèles linguistiques : une étude FHIR

बड़े भाषा मॉडल के साथ स्वास्थ्य डेटा इंटरऑपरेबिलिटी को बढ़ाना: एक FHIR अध्ययन

大規模言語モデルによる医療データの相互運用性の強化: FHIR 調査

대규모 언어 모델을 통한 건강 데이터 상호 운용성 향상: FHIR 연구

Melhorando a interoperabilidade de dados de saúde com grandes modelos de linguagem: um estudo FHIR

Повышение совместимости медицинских данных с помощью больших языковых моделей: исследование FHIR

Büyük Dil Modelleriyle Sağlık Verilerinin Birlikte Çalışabilirliğini Artırma: Bir FHIR Çalışması

Nâng cao khả năng tương tác dữ liệu sức khỏe với các mô hình ngôn ngữ lớn: Nghiên cứu của FHIR

利用大型语言模型增强健康数据互操作性：FHIR 研究

For the last couple of years, instability has been the most stable thing in the world. This has been affecting almost every aspect of our well-being — mental health in particular. Talks about burnout, anxiety, panic, and the ways to deal with them have become more frequent in our professional and personal lives. Keeping our heads clear and our mental health minty fresh (as opposed to being on fire) is one of the keys to going through these times intact. Here are the steps that I take to remain focused and keep my mental health… well, healthy. Setting goals I am a firm believer that there should be a clear goal for every action. For a long time, I have been writing down and tracking almost every task I have. And I have passed this approach to the entire SupportYourApp team. For years, we have been using Kanban-style systems and boards like Asana and Jira. They allow us to track every little detail and progress on any major project and minute task. It is also a good tool to assess the productivity of every team member and adjust their schedule. This allows them to have enough time to be productive and successful at everything they do without burning or rusting out. I am also a big advocate of creating timelines and roadmaps. Our team uses them to assess the amount of steps and time required to complete complex tasks. We know exactly who we need to cooperate and collaborate with to achieve a desired result in a timely manner and what to do in case of roadblocks. Talking, talking, talking The culture of all-embracing communication came to SupportYourApp together with the COVID-19 pandemic. Of course, we had meetings before that, but they were nowhere nearly as numerous as they are now. The fact that SupportYourApp started looking for new teammates all over the world only served as a catalyst. We needed to constantly be connected with new members of our team, especially during the first several months when they were only getting started. We utilized the same approach after Russia launched its unjustified attack against our country. When our teammates were scattered all over, we constantly remained in touch. We wanted to make informed decisions and make sure everyone stayed in the loop. In the most uncertain of times, open and frequent communication was the key to staying stable. Today, my team and I discuss every step SupportYourApp needs to take to keep on growing and remain successful. We talk about every idea and brainstorm whenever we have the opportunity. We do everything we can to support each other, which helps us remain calm even in the most uncertain situations. Making sport a part of life Sport and physical activities have been my companions for a long time. Playing squash, running, wakeboarding (whenever I can), going to the gym, and even walking are all part of my regular schedule. Sport grounds me and gives me time and opportunity to be with my thoughts, come up with new ideas, and relax after a long day. It is a long-proven fact that sport plays a crucial part in relieving stress. It helps with the release of endorphins and helps the mind switch from our daily routines to something completely different. Regular exercise is also a good way to improve the mood. And I am not even going to say anything about the feeling of accomplishment people have after a workout. It’s just fabulous. For me, physical activity is a near-perfect way to put my mind at ease when it is going a hundred miles an hour. As ironic as it may seem, a treadmill that is going fast seems to be the best way to feel as if the world stops spinning and gives you time to unwind. Maintaining work-life balance This may seem like a cliché that has been talked about one too many times. But this doesn’t take away from the fact that maintaining a work-life balance is one of the most important things to support one’s mental well-being. It took me some time to realize — staying connected and available 24/7 is not the recipe for success. It only promotes burnout and can make professionals irritated and annoyed with their jobs. This can happen even to those who love what they do. Right now, my favorite way to maintain my work-life balance is to put my phone somewhere away from me, unplug from all my devices, and relax with a book or in the gym (something I have already covered). This gives me time to come up with new ideas and dedicate my free time to myself, which helps me maintain my mental health. For the last couple of years, instability has been the most stable thing in the world. This has been affecting almost every aspect of our well-being — mental health in particular. Talks about burnout, anxiety, panic, and the ways to deal with them have become more frequent in our professional and personal lives. Keeping our heads clear and our mental health minty fresh (as opposed to being on fire) is one of the keys to going through these times intact. Here are the steps that I take to remain focused and keep my mental health… well, healthy. Setting goals I am a firm believer that there should be a clear goal for every action. For a long time, I have been writing down and tracking almost every task I have. And I have passed this approach to the entire SupportYourApp team. For years, we have been using Kanban-style systems and boards like Asana and Jira . They allow us to track every little detail and progress on any major project and minute task. It is also a good tool to assess the productivity of every team member and adjust their schedule. This allows them to have enough time to be productive and successful at everything they do without burning or rusting out . Asana Asana Jira Jira rusting out rusting out I am also a big advocate of creating timelines and roadmaps. Our team uses them to assess the amount of steps and time required to complete complex tasks. We know exactly who we need to cooperate and collaborate with to achieve a desired result in a timely manner and what to do in case of roadblocks. Talking, talking, talking The culture of all-embracing communication came to SupportYourApp together with the COVID-19 pandemic. Of course, we had meetings before that, but they were nowhere nearly as numerous as they are now. The fact that SupportYourApp started looking for new teammates all over the world only served as a catalyst. We needed to constantly be connected with new members of our team, especially during the first several months when they were only getting started. We utilized the same approach after Russia launched its unjustified attack against our country. When our teammates were scattered all over, we constantly remained in touch. We wanted to make informed decisions and make sure everyone stayed in the loop. In the most uncertain of times, open and frequent communication was the key to staying stable. Today, my team and I discuss every step SupportYourApp needs to take to keep on growing and remain successful. We talk about every idea and brainstorm whenever we have the opportunity. We do everything we can to support each other, which helps us remain calm even in the most uncertain situations. Making sport a part of life Sport and physical activities have been my companions for a long time. Playing squash, running, wakeboarding (whenever I can), going to the gym, and even walking are all part of my regular schedule. Sport grounds me and gives me time and opportunity to be with my thoughts, come up with new ideas, and relax after a long day. It is a long-proven fact that sport plays a crucial part in relieving stress . It helps with the release of endorphins and helps the mind switch from our daily routines to something completely different. Regular exercise is also a good way to improve the mood. And I am not even going to say anything about the feeling of accomplishment people have after a workout. It’s just fabulous. relieving stress relieving stress For me, physical activity is a near-perfect way to put my mind at ease when it is going a hundred miles an hour. As ironic as it may seem, a treadmill that is going fast seems to be the best way to feel as if the world stops spinning and gives you time to unwind. Maintaining work-life balance This may seem like a cliché that has been talked about one too many times. But this doesn’t take away from the fact that maintaining a work-life balance is one of the most important things to support one’s mental well-being. It took me some time to realize — staying connected and available 24/7 is not the recipe for success. It only promotes burnout and can make professionals irritated and annoyed with their jobs. This can happen even to those who love what they do. Right now, my favorite way to maintain my work-life balance is to put my phone somewhere away from me, unplug from all my devices, and relax with a book or in the gym (something I have already covered). This gives me time to come up with new ideas and dedicate my free time to myself, which helps me maintain my mental health.

Mental Health (Just Like The World) Is On Fire: Here's How It Can Be Put Out

The Growing Influence of Mobile Apps in Healthcare, Education, and Remote Work

In an era defined by rapid technological advancements, Healix emerges as a beacon of innovation, all set to reshape the landscape of healthcare. With its commitment to leveraging technology for the betterment of humanity, Healix introduces a groundbreaking initiative - the Healix presale, offering investors an opportunity to be part of a transformative journey towards accessible, patient-centric healthcare solutions. Healix: Pioneering Healthcare Innovation Healix stands at the forefront of healthcare innovation, harnessing the power of cutting-edge technologies to address the evolving needs of patients and healthcare providers. From telemedicine platforms to AI-driven diagnostics, Healix's solutions empower individuals to take control of their health and wellness like never before. Introducing HLX Tokens: The Currency of Healthcare Transformation At the heart of Healix's ecosystem lies the HLX token, a digital currency designed to facilitate seamless transactions within the Healix platform. Built on blockchain technology, HLX tokens offer security, transparency, and efficiency, enabling frictionless interactions between patients, providers, and stakeholders across the healthcare continuum. Unlocking Opportunities with the Healix Presale Today marks a pivotal moment in healthcare as Healix launches its presale, inviting investors to join forces in revolutionizing the industry. By participating in the presale, investors not only gain access to HLX tokens at an exclusive rate but also contribute to the advancement of innovative healthcare solutions that have the potential to impact millions of lives globally. The Presale Is Live: Seize the Opportunity The is now live, offering early adopters a chance to become key players in the future of healthcare. Whether you're an investor seeking financial returns or a believer in the power of technology to drive positive change, the Healix presale presents a unique opportunity to be part of a transformative journey toward a healthier, more equitable world. Healix presale As Healix embarks on its presale journey, the vision of accessible, patient-centric healthcare draws closer to reality. With HLX tokens serving as the currency of transformation, investors have the chance to not only profit but also make a meaningful difference in the lives of individuals around the globe. Join Healix today and be a catalyst for change in healthcare innovation. Visit Healix: | | | website Telegram X Instagram This story was distributed as a release by BTCWire under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/

Healix Presale: Revolutionizing Healthcare with HLX Tokens

Healix Presale: HLX টোকেন সহ স্বাস্থ্যসেবায় বিপ্লবীকরণ

Healix-Vorverkauf: Revolutionierung des Gesundheitswesens mit HLX-Tokens

Preventa de Healix: revolucionando la atención médica con tokens HLX

Prévente Healix : révolutionner les soins de santé avec les jetons HLX

हीलिक्स प्रीसेल: एचएलएक्स टोकन के साथ हेल्थकेयर में क्रांति लाना

Healix プレセール: HLX トークンで医療に革命を起こす

Healix 사전 판매: HLX 토큰을 통한 의료 혁신

Pré-venda Healix: revolucionando a saúde com tokens HLX

Предпродажа Healix: революция в здравоохранении с помощью токенов HLX

Healix Ön Satış: HLX Tokenlarıyla Sağlık Hizmetlerinde Devrim Yaratıyor

Bán trước Healix: Cách mạng hóa hoạt động chăm sóc sức khỏe với mã thông báo HLX

Healix 预售：利用 HLX 代币彻底改变医疗保健

Objectives To learn more clearly what emotions we are feeling related to AI To help us deal with emotions related to AI in a better way To review implicit bias in ourselves and also in AI To review ethical principles as a possible model to rethink human involvement with AI. Why should we care? Not addressing human mental health concerns can decrease our capacity for resilience Having strong emotions about AI might negatively affect decisions made about AI’s use Humans are complex and have complex and nuanced emotions. Researcher Paul Ekman studied human emotions and determined that humans have 6 basic emotions. They are like the primary colors of art. The 6 emotions are fear, anger, joy, sadness, disgust and surprise. If you are feeling any anxiety about AI, that’s an emotion that could be related to fear. Anxiety is fear of the future. Our capacity for sensing and reacting to the environment with our emotions comes from our limbic system. Parts of the limbic system are made up of structures that form a complex network for controlling emotion. This part of the brain coordinates to regulate responses to stress, emotion, trauma, and fear. This might be nicknamed lizard brain. In 2002, a psychologist named Daniel Kahneman won the Nobel Prize in economics for determining a model for how human beings think.  He discovered that humans have 2 types of thinking patterns which he named System 1 thinking and System 2 thinking. System 1 thinking is automatic, fast and error prone System 2 thinking is deliberate, slow and reliable When we are thinking in a fear motivated way we will trigger our system 1 thinking and also trigger our capacity to make errors in judgment.  These errors in judgment will be based on our implicit bias. Implicit biases are our prejudices, predetermined beliefs, childhood ingrained values. They are things we have been taught from a very early age which were taught to us to “keep us safe.” They may be part of our core beliefs about ourselves. These implicit biases might be triggered in our system 1 thinking and cause us to act in ways that are negative, prejudicial, and fearful. We may do this without thinking, on automatic impulse. The danger in thinking fearfully about AI is that we may have thinking fallacies: Use system 1 thinking and make quick and fear based thinking about AI and in the proceess not making good decisions about is use in society Use system 1 thinking when teaching AI new skills - inserting into AI prejudices that can’t be easily removed Not realizing we have implicit bias in the first place. Not realize that we are installing into everything we do implicit biases, especially when we are acting in a fear based way Not realizing that though we might be able to determine our individual implicit biases through testing and learning and acting differently, if we teach AI implicit biases we may not be able to remove the AI implicit bias.  AI does not necessarily have consciousness to understand that it is being biased. We might exacerbate the trauma we have just felt collectively as a global community because of COVID pandemic by being afraid of AI.  The anxiety we feel will cause us to not make good decisions about the use of this new technology. In order to reduce our fallacies in thinking, we might consider changing our thinking to system 2 thinking about AI. We could be deliberate and more conscious and more alert in our management and implementation of AI. System 2 thinking will bring us back to basics.  Though there are many possible models for system 2 thinking, it might be helpful to use ethical principles. Here we revisit ethical principles to more deliberately examine AI. System 2 thinking can be studied by realizing the fear we have about AI can be managed through deliberate, conscious, mindful thought and action in a system 2 way. What are ethics anyway?  Moral principles that govern a person's behavior or how to conduct an activity. There are 7 basic ethical principles and 7 principles of medical ethics described here we can consider as an example framework. These are the 7 basic ethical principles presented here: Integrity - consistency of actions and values, methods and principles So that what you believe and what you do are in harmony Respect - acknowledging the inherent value of all individuals and treat all with dignity Honoring others beliefs even if they are different from your own Responsibility - is our awareness that our actions have consequences that we must accept no matter if positive or negative. Included in this is the idea of accountability which means we should answer to our actions and especially to those affected by our actions. Being a good steward is an example of this. Fairness - ethical decisions must be unbiased, treat everyone equally regardless of status, position, or personal attributes Compassion - empathy and understanding towards others is important even if you disagree with them. We need to consider others' feelings when making decisions.  All stakeholders should have equal say. Remembering always: there is a human element in every decision we make Courage - Courage means doing what is right despite the idea being risky or unpopular.  It means standing up for what you believe in Wisdom - means making decisions based on being informed on the issues and allowing oneself to be guided by all the information or facts, but also taking into account stakeholders.  “Balancing head and heart in decisions.” These are the 7 medical ethics principles described here. These medical principles are considered to help human mental health which has been affected by the emergence of AI. Non-maleficence - This is the idea that medical providers should do no harm. Beneficence - This is the ideas that medical providers should go beyond doing no harm to actively help others’ welfare. Health maximization - This principle allow for an environment that maximizes health and opportunities for health of the general public. Efficiency - This idea encourages the use of scarce medical resources efficiently. Respect for autonomy - This is the patients’ right to determine what will happen to them. The ability to make their own medical choices. Justice - Justice includes equity for all people so that all people can have health care at the same quality. Proportionality - This principle involves weighing the individual needs versus the needs of the greater good These are some ideas we could be focusing on when talking about AI. We could use system 2 thinking to make these decisions about AI with more clarity and deliberation. Though there could be other models that would encourage systematic thinking and reduce fear, one idea could be using an ethical guidance model. References: Peter Schröder-Bäck, et al. Teaching seven principles for public health ethics: towards a curriculum for a short course on ethics in public health programmes. Schröder-Bäck et al. BMC Medical Ethics 2014, 15:73 http://www.biomedcentral.com/1472-6939/15/73 https://axies.digital/ethical-decision-making/ Picture of Limbic system/brain by Shutterstock.com Family image was created by DALL-E/ChatGPT

Human Mental Health and Artificial Intelligence

What would you do if insurance claims disappeared? Not just for days, but for weeks? More than are asking this question after the cyberattack on Change Healthcare. 25% of American physicians Change Healthcare is America’s largest healthcare payer system, handling more than 15 billion healthcare transactions per year. After the ransomware attack on February 21, 2024, thousands of providers woke up to failing systems — including the inability to submit insurance claims. As more and more systems become operational, thousands of healthcare organizations are finding their footing. And yet, the ramifications of an attack this size may impact the industry for years on end. The Real Impact of the Change Healthcare Attack The Change Healthcare cyberattack has continued to make waves for physicians and patients. Estimates calculate for America’s affected healthcare providers. Thousands of patients have also been caught in the crossfire, with many unable to purchase much-needed medication. a loss of $100 million per day Physicians have accumulated a backlog of claims worth millions of dollars overall. Even nonprofit health plans have been asked to front advance payments on the long road to recovery. Fortunately, there is a tentative end in sight: Change Healthcare has announced electronic payment functionality . Connectivity testing with its claims network software should come a few days later on March 18. as soon as mid-March It’s clear the healthcare industry has been flipped upside down, and the unfortunate reality is, it’s likely to happen again. This isn’t just a one-off incident — it may be our new normal in the age of cyberattacks. Where We Go From Here The Change Healthcare cyberattack highlights an important reality for the healthcare industry. No payouts provider is immune from ransomware, and with , avoiding risk is all-but impossible. a growing number of cyberattacks But the silver lining to this situation is that we can learn from the past to inform the future. What do healthcare providers need to consider in the new age of cyberattacks? This is a Systemic Problem There is no single solution to outages and downtime. Everyone is at risk for cyberattacks, especially sensitive industries such as healthcare. The smartest way to avoid the heaviest hits is to decentralize risk as much as possible. Traditional healthcare systems rely on consolidated clearinghouses, which in the case of Change Healthcare, could expose businesses to huge financial risks. The best course of action is decentralization, which de-risks your business by a greater degree. Decentralization = De-Risking Variety is an essential consideration for insurance clearing houses. This is especially true of healthcare organizations relying on a single payer. is an efficient solution to traditional methods of claims submission. Payer Gateway connection redundancy Implementing this now could save many headaches in the future, especially when pitted against situations similar to this. Redundancy Matters More Than Ever In the new era of cyberattacks and ransomware, redundancy is key for long-lasting protection. Flexible systems and adaptable software ensure a seamless transition between payers and organizations. Working with newer, more modernized systems may hold the key for navigating future threats. For now, decentralization will be key for de-risking, both for healthcare businesses and the patients they serve.

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