HealthLeap

healthleap

The healthcare industry has been grappling with a severe shortage of personnel, a growing challenge that has been exacerbated by an aging population and rising demand for healthcare services. According to an (ACHE), workforce challenges ranked number one on the list of hospital CEO top concerns in 2022. annual survey conducted by the American College of Healthcare Executives Shortages in the healthcare workforce became a large problem for clinical operations, hospitals, and physician practices. Healthcare organizations across the United States are facing workforce shortages with nurses, physicians, and many other clinical roles reported. HealthLeaders To combat this shortage, technology leaders such as Philips are increasingly turning to AI to improve efficiency, accuracy, and productivity in healthcare operations. global report shows that market leaders in healthcare are trying to address staffing shortages with AI. Philips Future Health Index 2023 According to the study, major investments are focused on improving critical decision support and operational efficiency, which will also help address staffing shortages. First of all, marketers are concerned about staffing shortages and are looking to properly address the problem with AI and machine learning, which will help them do more with less. The second thing the authors of the report saw was that, coming out of the pandemic, there is still a large demand for online care. 82% of healthcare leaders say their organizations are already providing intensive care or resuscitation supported by online work today (41%) or plan to in the next three years (41%). Online care plays a vital role in attracting and retaining talent, with 44% of young healthcare professionals indicating that new models of care delivery are a top priority when choosing where to work. One in three (34%) healthcare leaders are building partnerships outside of their system to be able to provide the best care possible. In this post, I will explore how Philips is using AI to address healthcare personnel shortages and improve patient outcomes. AI-powered tools are changing the game in the healthcare industry by analyzing vast amounts of medical data to help healthcare professionals make more accurate diagnoses. Philips has developed an innovative AI-powered diagnostic imaging tool called , which helps radiologists detect diseases earlier and with higher accuracy, reducing the burden on healthcare personnel while increasing the efficiency and accuracy of the diagnostic process. IntelliSpace In the face of healthcare personnel shortages, Philips is also utilizing AI in telemedicine. It has developed a telehealth platform called which uses AI to provide remote monitoring and consultation for patients with chronic conditions. eCareCoordinator, This technology enables doctors to provide care remotely, reducing the need for in-person consultations and reducing the burden on healthcare personnel. AI-powered virtual assistants are helping to improve the efficiency of healthcare operations, freeing up healthcare personnel to focus on more complex tasks. Philips has developed a virtual assistant for patients with chronic diseases called , which can perform tasks such as scheduling appointments, answering patient questions, and updating medical records. eCareCompanion This technology reduces the burden on healthcare personnel and allows them to focus on more complex tasks, such as diagnosing and treating patients. Philips is also using AI-powered predictive analytics to identify patients who are at high risk of developing health problems. By analyzing medical data, AI algorithms can identify patterns that indicate a patient is at risk of developing a specific condition. Predictive analytics engine enables hospitals to manage their patient populations by proactively identifying at-risk patients most likely to need emergency transport in the next 30 days, helping prevent avoidable hospitalizations. CareSage Robotic surgery has become more sophisticated in recent years, and with the help of AI, it is becoming even more advanced. Philips has developed a platform called , which uses AI to assist doctors during surgeries. Azurion This technology helps doctors perform complex surgeries with greater accuracy and precision, reducing the risk of complications. It also enables remote surgeries, making it possible for doctors to provide surgical services in areas with a shortage of qualified personnel. There is no doubt  that AI will become the backbone of hospital operations in the future, allowing healthcare providers to see more patients and provide better care. We can expect to see even more innovative solutions to address the shortage of healthcare personnel in the nearest future. Disclaimer: AI was used to write portions of this article. Specifically, it was used to write a number of key paragraphs, namely paragraphs 10-12.

AI-Powered Healthcare: Bridging the Personnel Gap

June 26, 2024— DeltaPrime, a decentralized borrowing and investing platform, is thrilled to announce their Token Generation Event (TGE) on Uniswap and Trader Joe. The TGE marks another innovation in crypto with the launch of the $PRIME and $sPRIME tokens on July 1st, 2024. Born in the bear market and operating on the Arbitrum and Avalanche networks, DeltaPrime’s inventive tokenomics design focuses on stable growth of its liquidity pools, as well as enhanced community rewards and governance capabilities. Community-minded and transparent, DeltaPrime has developed a novel protocol with outstanding security measures for trustless, undercollateralized borrowing of crypto assets to be used for trading, yield farming, and liquidity providing on other whitelisted protocols. Protocol revenue in Q1 this year exceeded $1.5M, and the community Seed round of ColonyLab was notably 58% oversubscribed, demonstrating strong investor confidence in DeltaPrime’s vision and execution. Additionally, a significant LTIPP grant of 750,000 ARB, the same size as those received by well-established platforms like Lido and Aave, highlights DeltaPrime’s emerging stature and reliability in the DeFi space. But unlike Aave, DeltaPrime propels the DeFi industry from the overcollateralized past to the future of undercollateralized crypto loans. By launching $PRIME and $sPRIME, DeltaPrime preserves its sustainable business model while also rewarding and steadily transitioning governance to its biggest supporter, its community. Revolutionizing DeFi: Advanced Trading and Governance $PRIME and $sPRIME empower users by providing enhanced trade and LP capabilities utilizing idle capital and robust governance structures, setting new standards for participation and profitability in the decentralized finance (DeFi) landscape. $PRIME, the primary tradable token of the DeltaPrime ecosystem, is integral to the platform’s liquidity framework. As a versatile ERC20 token, $PRIME can be traded on open markets, utilized in liquidity pools on decentralized exchanges (DEXs), and transferred between entities, facilitating seamless market interactions. $PRIME Means Community. Unique to $PRIME is its role in creating $sPRIME through liquidity pairings, such as PRIME-AVAX or PRIME-ETH, embedding it deeply within DeltaPrime's operational and governance mechanisms. Contribution to the LP is simplified through the DeltaPrime app, making $sPRIME acquisition a breeze. $sPRIME marks a significant innovation in the realm of DeFi governance and revenue sharing. This LP-token serves as a utility token within the DeltaPrime ecosystem and also as a vehicle for governance, enabling holders to actively participate in decision-making processes. $sPRIME holders that support the platform’s growth enjoy the dual benefits of influencing the platform’s direction and contributing to- and sharing in its financial success, which notably amassed $1.65 million in Q1 of 2024. Of the protocol’s total revenue, 33% goes to active $sPRIME holders, 33% to the treasury for further development, and 33% to the protocol’s insurance funds. $PRIME emissions are dynamically calibrated based on current market conditions to ensure economic stability and sustainable growth. This adaptive supply strategy helps stabilize $PRIME’s value during market fluctuations, with mechanisms in place to increase supply during high demand and initiate buybacks when demand wanes. $PRIME is not a stablecoin however, and therefore is susceptible to price variations based on its supply and demand. $sPRIME stands out by being the first concentrated LP-token used as a platform’s main currency, next to generating governance power. The token becomes ‘active’ by keeping the underlying liquidity centered around the current price, increasing its value to those that support the platform’s growth. DeltaPrime Leads with Community-Driven Governance At the heart of DeltaPrime’s approach is a commitment to transparency and a governance model that places decision-making directly in the hands of its users. $sPRIME holders that contribute to the platform’s growth do not only receive a staggering 33% of the protocol's revenue, but they also gain voting rights that shape the platform's policies and direction. A user receives governance points by holding a 10:1 ratio of $sPRIME to protocol usage, either as a borrower or lender on the platform. This democratic process ensures that every decision reflects the collective will of those who use the platform, fostering a truly decentralized governance structure made up of active DeltaPrime users. Prioritizing Security DeltaPrime prioritizes the security of user funds above all. This commitment to security, coupled with DeltaPrime's strategic expansion, has solidified its position as a reliable and enduring presence in the DeFi landscape. To mitigate inherent risks, DeltaPrime has established a comprehensive suite of safety features designed to protect user assets and maintain the security of the platform: borrowed funds can be used only on whitelisted protocols and tokens, users can’t run away without repaying the loan, and there is insolvency protection through liquidation bots that oversee loan health.Gearing up for its 7th successful audit, DeltaPrime from market manipulation and exploits, and, in the unlikely event that multiple on-chain checks miss an issue, an insurance fund covers any affected depositors. DeltaPrime remains fully committed to providing a secure and reliable platform for all users today and in the future. About DeltaPrime DeltaPrime  is your prime broker on a mission to unlock the full potential of DeFi. They do this by providing secure undercollateralized loans, with a focus on maximum capital efficiency. Being built on the foundations of Avalanche and Arbitrum, their Prime Account holders know they get fast and reliable transactions for their most important investments. Built on its inventive Diamond Beacon Proxy pattern, DeltaPrime’s escrow smart contracts are infinitely scalable and allow the platform to sustain the quickest turnaround of integrations in the DeFi space. Learn more: X | Discord | Medium | GitHub This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. June 26, 2024— DeltaPrime, a decentralized borrowing and investing platform, is thrilled to announce their Token Generation Event (TGE) on Uniswap and Trader Joe. The TGE marks another innovation in crypto with the launch of the $PRIME and $sPRIME tokens on July 1st, 2024. Born in the bear market and operating on the Arbitrum and Avalanche networks, DeltaPrime’s inventive tokenomics design focuses on stable growth of its liquidity pools, as well as enhanced community rewards and governance capabilities. Community-minded and transparent, DeltaPrime has developed a novel protocol with outstanding security measures for trustless, undercollateralized borrowing of crypto assets to be used for trading, yield farming, and liquidity providing on other whitelisted protocols. DeltaPrime DeltaPrime Protocol revenue in Q1 this year exceeded $1.5M, and the community Seed round of ColonyLab was notably 58% oversubscribed, demonstrating strong investor confidence in DeltaPrime’s vision and execution. Additionally, a significant LTIPP grant of 750,000 ARB, the same size as those received by well-established platforms like Lido and Aave, highlights DeltaPrime’s emerging stature and reliability in the DeFi space. But unlike Aave, DeltaPrime propels the DeFi industry from the overcollateralized past to the future of undercollateralized crypto loans. By launching $PRIME and $sPRIME, DeltaPrime preserves its sustainable business model while also rewarding and steadily transitioning governance to its biggest supporter, its community. Revolutionizing DeFi: Advanced Trading and Governance $PRIME and $sPRIME empower users by providing enhanced trade and LP capabilities utilizing idle capital and robust governance structures, setting new standards for participation and profitability in the decentralized finance (DeFi) landscape. $PRIME, the primary tradable token of the DeltaPrime ecosystem, is integral to the platform’s liquidity framework. As a versatile ERC20 token, $PRIME can be traded on open markets, utilized in liquidity pools on decentralized exchanges (DEXs), and transferred between entities, facilitating seamless market interactions. $PRIME Means Community. Unique to $PRIME is its role in creating $sPRIME through liquidity pairings, such as PRIME-AVAX or PRIME-ETH, embedding it deeply within DeltaPrime's operational and governance mechanisms. Contribution to the LP is simplified through the DeltaPrime app, making $sPRIME acquisition a breeze. $sPRIME marks a significant innovation in the realm of DeFi governance and revenue sharing. This LP-token serves as a utility token within the DeltaPrime ecosystem and also as a vehicle for governance, enabling holders to actively participate in decision-making processes. $sPRIME holders that support the platform’s growth enjoy the dual benefits of influencing the platform’s direction and contributing to- and sharing in its financial success, which notably amassed $1.65 million in Q1 of 2024. Of the protocol’s total revenue, 33% goes to active $sPRIME holders, 33% to the treasury for further development, and 33% to the protocol’s insurance funds. $PRIME emissions are dynamically calibrated based on current market conditions to ensure economic stability and sustainable growth. This adaptive supply strategy helps stabilize $PRIME’s value during market fluctuations, with mechanisms in place to increase supply during high demand and initiate buybacks when demand wanes. $PRIME is not a stablecoin however, and therefore is susceptible to price variations based on its supply and demand. $sPRIME stands out by being the first concentrated LP-token used as a platform’s main currency, next to generating governance power. The token becomes ‘active’ by keeping the underlying liquidity centered around the current price, increasing its value to those that support the platform’s growth. DeltaPrime Leads with Community-Driven Governance DeltaPrime Leads with Community-Driven Governance At the heart of DeltaPrime’s approach is a commitment to transparency and a governance model that places decision-making directly in the hands of its users. $sPRIME holders that contribute to the platform’s growth do not only receive a staggering 33% of the protocol's revenue, but they also gain voting rights that shape the platform's policies and direction. A user receives governance points by holding a 10:1 ratio of $sPRIME to protocol usage, either as a borrower or lender on the platform. This democratic process ensures that every decision reflects the collective will of those who use the platform, fostering a truly decentralized governance structure made up of active DeltaPrime users. Prioritizing Security Prioritizing Security DeltaPrime prioritizes the security of user funds above all. This commitment to security, coupled with DeltaPrime's strategic expansion, has solidified its position as a reliable and enduring presence in the DeFi landscape. To mitigate inherent risks, DeltaPrime has established a comprehensive suite of safety features designed to protect user assets and maintain the security of the platform: borrowed funds can be used only on whitelisted protocols and tokens, users can’t run away without repaying the loan, and there is insolvency protection through liquidation bots that oversee loan health. Gearing up for its 7th successful audit, DeltaPrime from market manipulation and exploits, and, in the unlikely event that multiple on-chain checks miss an issue, an insurance fund covers any affected depositors. DeltaPrime remains fully committed to providing a secure and reliable platform for all users today and in the future. About DeltaPrime About DeltaPrime DeltaPrime is your prime broker on a mission to unlock the full potential of DeFi. They do this by providing secure undercollateralized loans, with a focus on maximum capital efficiency. Being built on the foundations of Avalanche and Arbitrum, their Prime Account holders know they get fast and reliable transactions for their most important investments. DeltaPrime DeltaPrime Built on its inventive Diamond Beacon Proxy pattern, DeltaPrime’s escrow smart contracts are infinitely scalable and allow the platform to sustain the quickest turnaround of integrations in the DeFi space. Learn more: X | Discord | Medium | GitHub Learn more: X X Discord Discord Medium Medium GitHu b GitHu This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

DeltaPrime Unveils $PRIME Token

Move over, Dr. House. There's a new medical genius in town, and it doesn't even have a stethoscope. Spike, a Silicon Valley startup that focuses on data technology and artificial intelligence (AI), just landed $3.5 million in seed funding. Its mission? To give healthcare AI, which is set to reach $148 billion by 2029, the brainpower it needs. The oversubscribed investment was co-led by Practica Capital and TheVentureCity, and a who's who of tech investors joined the party, including CEAS Investments, Plug & Play Ventures, Geek Ventures, and Axel Springer Porsche (APX). Spike is ready to supercharge product development, expand into new markets, and cement its status as a medical director in the AI health technology field. So, what's Spike's secret remedy? Its B2B solution allows developers to add AI superpowers to their healthcare apps. Spike aims to cut development time and costs to a fraction of what they used to be and connect to the data collected by hundreds of medical, wearable, and IoT devices. Think of Spike's API as a health data buffet, offering insights from over 300 sources – from your trusty Fitbit to high-tech medical devices. It's the fuel that powers Spike's AI algorithms to deliver personalized healthcare solutions smarter than a med school grad (well, almost). "Advancements in retrieval-augmented generation AI mean that LLMs are no longer taking a 'best guess,' but are now able to seek out specialized context, producing outputs that are much more accurate and considerably less prone to hallucinations," Povilas Gudzius, CEO and co-founder of Spike, said. "By incorporating this technology on top of Spike's high-quality health data API, we're enabling our clients to easily leverage these tools and improve the health of millions of end users." Spike vs. the Goliaths, with a RAG, not a Stone Sure, Google, Amazon, and Microsoft have their AI health data solutions. But let's be honest: They're like old-school doctors who still use pagers. Spike is the young, energetic upstart shaking things up. Google Cloud Healthcare API provides access to healthcare data and offers AI and machine learning tools for building healthcare applications. Amazon HealthLake is a HIPAA-eligible service that helps healthcare organizations store, transform, query, and analyze health data. And Microsoft Azure API for FHIR enables the exchange of healthcare data using the FHIR standard and offers AI tools for building healthcare applications. So while the megaliths in the room are busy managing massive cloud infrastructures, Spike is laser-focused on healthcare. They're not just aggregating data; they're curating it, ensuring it's top-notch and tailor-made for the unique needs of the medical world. And Spike uses retrieval-augmented generation (RAG), a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. RAG helps AI models avoid embarrassing "hallucination" moments by cross-checking their answers with real-world facts. Think of it as a reality check for AI, ensuring its answers are as accurate as a doctor's diagnosis. This makes AI more reliable, less prone to making things up, and ultimately, much more useful in the healthcare world. "We are utilizing health expertise networks and curated in specialized context databases in RAG AI infrastructure," Gudzius said. "RAG AI allows us to incorporate these problem-specific sources effectively and makes the LLM responses more grounded. The design of the RAG AI infrastructure and domain-expert curated medical content allows us to further innovate in developing the 'Trustworthy Health AI.'" All of this technology doesn't mean Spike can scrimp on security and privacy. Like the Big 3, it has to make strong commitments to regulatory protection, especially with searches for AI in healthcare being orders of magnitude higher than searches for AI safety. "Spike takes data security and privacy very seriously, so our infrastructure products follow strict GDRP, HIPAA, and CCPA regulatory guidelines," Gudzius said. "From the technical perspective, we are also adopting specific techniques to prioritize data privacy. For example, we have built our data access layers that provide limited control to the LLM agent while handling data acquisition logic and complying with regulatory requirements. As the quality and volume of individuals' health data grows, there's a huge need to develop and innovate regulatory-compliant techniques for private health data-to-LLM interactions like that." The Future of Healthcare: Spike's Prescription This $3.5 million isn't just pocket change; it's the catalyst for Spike's grand vision. They're not just building AI tools but trustworthy AI tools that are as reliable as your favorite family doctor (but hopefully with a better bedside manner). They're expanding their reach to include everything from electronic health records to genomics data. "We believe in a future where health data volume will grow exponentially driven by the improvements in sensor hardware," Gudzius said. "These sensors include optical, electrical, and electromechanical sensors from wearables, IoT devices, and other sources like EHRs and lab tests. We are well positioned to capture this potential because our team has extensive experience working with data acquisition and a scientific background in Health AI. Our edge is ensuring we provide the capabilities of these emerging non-diagnostic technologies to our clients in a safe and regulatory compliant way." Spike imagines a future where AI isn't just a buzzword; it's an integral part of healthcare, improving patient care and making those dreaded doctor's visits a little less dreadful. A version of this article was originally published on Dataconomy and is republished with permission. Move over, Dr. House. There's a new medical genius in town, and it doesn't even have a stethoscope. Spike , a Silicon Valley startup that focuses on data technology and artificial intelligence (AI), just landed $3.5 million in seed funding. Its mission? To give healthcare AI, which is set to reach $148 billion by 2029 , the brainpower it needs. Spike set to reach $148 billion by 2029 The oversubscribed investment was co-led by Practica Capital and TheVentureCity, and a who's who of tech investors joined the party, including CEAS Investments, Plug & Play Ventures, Geek Ventures, and Axel Springer Porsche (APX). Spike is ready to supercharge product development, expand into new markets, and cement its status as a medical director in the AI health technology field. So, what's Spike's secret remedy? Its B2B solution allows developers to add AI superpowers to their healthcare apps. Spike aims to cut development time and costs to a fraction of what they used to be and connect to the data collected by hundreds of medical, wearable, and IoT devices. Think of Spike's API as a health data buffet, offering insights from over 300 sources – from your trusty Fitbit to high-tech medical devices. It's the fuel that powers Spike's AI algorithms to deliver personalized healthcare solutions smarter than a med school grad (well, almost). "Advancements in retrieval-augmented generation AI mean that LLMs are no longer taking a 'best guess,' but are now able to seek out specialized context, producing outputs that are much more accurate and considerably less prone to hallucinations," Povilas Gudzius, CEO and co-founder of Spike, said. "By incorporating this technology on top of Spike's high-quality health data API, we're enabling our clients to easily leverage these tools and improve the health of millions of end users." Spike vs. the Goliaths, with a RAG, not a Stone Spike vs. the Goliaths, with a RAG, not a Stone Sure, Google, Amazon, and Microsoft have their AI health data solutions. But let's be honest: They're like old-school doctors who still use pagers. Spike is the young, energetic upstart shaking things up. Google Cloud Healthcare API provides access to healthcare data and offers AI and machine learning tools for building healthcare applications. Amazon HealthLake is a HIPAA-eligible service that helps healthcare organizations store, transform, query, and analyze health data. And Microsoft Azure API for FHIR enables the exchange of healthcare data using the FHIR standard and offers AI tools for building healthcare applications. So while the megaliths in the room are busy managing massive cloud infrastructures, Spike is laser-focused on healthcare. They're not just aggregating data; they're curating it, ensuring it's top-notch and tailor-made for the unique needs of the medical world. And Spike uses retrieval-augmented generation (RAG), a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. RAG helps AI models avoid embarrassing "hallucination" moments by cross-checking their answers with real-world facts. Think of it as a reality check for AI, ensuring its answers are as accurate as a doctor's diagnosis. This makes AI more reliable, less prone to making things up, and ultimately, much more useful in the healthcare world. "We are utilizing health expertise networks and curated in specialized context databases in RAG AI infrastructure," Gudzius said. "RAG AI allows us to incorporate these problem-specific sources effectively and makes the LLM responses more grounded. The design of the RAG AI infrastructure and domain-expert curated medical content allows us to further innovate in developing the 'Trustworthy Health AI.'" All of this technology doesn't mean Spike can scrimp on security and privacy. Like the Big 3, it has to make strong commitments to regulatory protection, especially with searches for AI in healthcare being orders of magnitude higher than searches for AI safety . searches for AI in healthcare being orders of magnitude higher than searches for AI safety "Spike takes data security and privacy very seriously, so our infrastructure products follow strict GDRP, HIPAA, and CCPA regulatory guidelines," Gudzius said. "From the technical perspective, we are also adopting specific techniques to prioritize data privacy. For example, we have built our data access layers that provide limited control to the LLM agent while handling data acquisition logic and complying with regulatory requirements. As the quality and volume of individuals' health data grows, there's a huge need to develop and innovate regulatory-compliant techniques for private health data-to-LLM interactions like that." The Future of Healthcare: Spike's Prescription The Future of Healthcare: Spike's Prescription This $3.5 million isn't just pocket change; it's the catalyst for Spike's grand vision. They're not just building AI tools but trustworthy AI tools that are as reliable as your favorite family doctor (but hopefully with a better bedside manner). They're expanding their reach to include everything from electronic health records to genomics data. "We believe in a future where health data volume will grow exponentially driven by the improvements in sensor hardware," Gudzius said. "These sensors include optical, electrical, and electromechanical sensors from wearables, IoT devices, and other sources like EHRs and lab tests. We are well positioned to capture this potential because our team has extensive experience working with data acquisition and a scientific background in Health AI. Our edge is ensuring we provide the capabilities of these emerging non-diagnostic technologies to our clients in a safe and regulatory compliant way." Spike imagines a future where AI isn't just a buzzword; it's an integral part of healthcare, improving patient care and making those dreaded doctor's visits a little less dreadful. A version of this article was originally published on Dataconomy and is republished with permission. A version of this article was originally published on Dataconomy and is republished with permission. published on Dataconomy

Spike Aims to Give Healthcare AI a Brain Boost and Outsmart the Tech Giants

On December 27th, 2022—just a couple of days after Christmas and less than a month post-ChatGPT’s grand reveal to the world—a team of Google and DeepMind researchers published on a new large language model they named and its in a medical and clinical setting. their findings MedPaLM “encouraging results” And while the globe is still firmly grasped in a ChatGPT frenzy, its virtually anonymous “cousin” MedPaLM could potentially herald a not-too-distant revolution in how we administer care. So What Exactly Is MedPaLM? To understand what MedPaLM is, let’s first break down what each letter in its abbreviated name means (a bit of an acrostic poem, if you will): Med: Medical MedPaLM is based on Google’s 540 billion parameters strong and was specially adapted and optimized to be tested on , a newly formulated benchmark for testing large language models (LLMs) for medical and clinical applications combining six existing open question-answering datasets spanning professional medical exams, research, and consumer queries (NedQA, MedMCQA, PubMedQA, LiveQA, MedicationQA, and MMLU) as well as a new dataset named HealthSearchQA focused on general medical knowledge searched for by consumers. PaLM model MultiMedQA Pa: Pathways Pa stands for , a new AI architecture launched by in October 2021, designed to handle a multitude of tasks simultaneously and learn new tasks at hyper-speed. Breaking away from the conventional method of training machine learning models to perform a single task well, Pathways ushered in a new era of AI models capable of performing multiple tasks at once while also drawing upon and combining their existing skills to learn new tasks faster and more effectively. Pathways Google - Jeff Dean, Google Senior Fellow and SVP, Google Research. “We want [this] model to have different capabilities that can be called upon as needed and stitched together to perform new, more complex tasks – a bit closer to the way the mammalian brain generalizes across tasks.” ‍ Google's Pathways AI architecture ‍ LM: Language Model LM is short for (in this instance, a Large Language Model), a computational model designed to understand and generate human language. Or, in simpler terms, a statistical model that predicts the probability of the next word in a given sequence of words. Language Model Given a sentence such as "The cat sat on the ____," a language model can predict that the most likely word to follow is "mat," "rug," or "chair," based on the probabilities of these words appearing in the context of the sentence. Large language models can be trained on vast amounts of text corpora using machine learning algorithms to learn the patterns and structure of language. Once trained, these models can be utilized for a range of natural language processing tasks, including text generation, translation, sentiment analysis, and more. How Was MedPaLM Developed? MedPaLM is the product of an extensive iterative experiment conducted by a team of Google and researchers to assess the efficiency and accuracy of large language models for medical and clinical applications. DeepMind ‍ What does that mean? Imagine you’re trying to perfectly tune a violin. To hit the perfect note, you need to continuously increase and decrease string tension and turn the knobs ever so gently to reach the desired outcome. In many ways, this is precisely what MedPaLM’s creators did to achieve their objective. So rather than thinking about it as an entirely new language model, think about it as Google’s PaLM model deliberately and meticulously calibrated for maximum precision in a medical setting. Ok, so how was it “tuned”? To tune or, more precisely, train MedPaLM, its researchers used prompts. Prompts for large language models are pieces of text used to guide or direct the generation of a new text by the model. They are intended to help the model produce output that is relevant to a particular task or topic and can take various forms, such as fill-in-the-blank statements, questions, or context-setting phrases. So, for example: "Complete the following sentence: The capital of France is ____." "What is the best way to prepare a grilled cheese sandwich?" "Write a paragraph describing the causes of the American Civil War." "In this passage from a novel, a character is introduced: 'She was tall and thin, with piercing blue eyes and a serious expression on her face.' What is the character's name and what is she doing in this scene?" Why do I need a healthcare-purposed language model to tell me the best way to prepare a grilled cheese sandwich? You don’t. This is exactly why MedPaLM’s developers employed a technique called Instruction Prompt Tuning (IPT).  Instruction Prompt Tuning is a method of fine-tuning a large language model for a specific task by providing examples of desired input and output pairs in the form of prompts. So rather than construct an LLM with general answers to general topics, MedPaLM’s team used guidelines and exemplars from a panel of qualified clinicians for each consumer medical question-answering datasets to prompt tune Flan-PaLM (MedPaLM’s previous iteration) and form a hyper-healthcare-focused LLM: MedPaLM. ‍ ‍ So How’s MedPaLM Performing? Well, that really depends on what you want it to do. In terms of providing answers to consumer medical questions, 94.4% of MedPaLM’s answers were judged as directly addressing the user’s question intent—only 1.5% less than a human clinician. 80.3% of its answers were found to be helpful, 11% less than a human clinician but 30% better than its predecessor Flan-PaLM. Impressive? You bet. ‍ ‍ But in other fields tested on, MedPaLM leaves much to be desired. In 16.9% of its answers, MedPaLM retrieved incorrect information as opposed to 3.6% for human clinicians. Incorrect reasoning was seen in 10.1% of MedPaLM’s answers and in just 2.1% of clinician answers. Incorrect comprehension occurred in 18.7% of cases for MedPaLM and in 2.2% for clinicians. What Does This Mean for Healthcare? Trying to definitively predict how MedPaLM and other LLMs will be harnessed for clinical and medical purposes is a bit of a fool's game at this stage. As impressive as recent advancements in this field are, a single error could have disastrous results within the life-or-death stakes of medical care. One incorrect answer to a patient's query (i.e., what drug should be taken to address a specific condition) could lead to hospitalization and even death. This issue is further exacerbated by the fact that large language models are essentially enormous black boxes and lack the explainability or the ability to pinpoint exactly why they gave a certain input. Solving an error starts with knowing how to find the problem, and as spectacularly capable as MedPaLM may be, that's just one (out of many) things it can't do. So, What Can LLMs be Used for in Healthcare? Approximately 7,287 articles ( ) are published monthly in medical journals. To theoretically keep up with the literature, a physician would need to dedicate 627.5 hours per month, or about 29 hours per weekday, or 3.6 full-time equivalents of physician effort to the task. And though this isn't realistic in any scenario, LLMs are incredibly effective in summarizing, analyzing, and presenting key points from long-form text, drastically cutting down the time needed to stay up to date with the latest research. Summarizing and analyzing research papers: with an average of 5.16 minutes reading time Large language models can be used to improve the quality of clinical documentation by identifying missing or incomplete information in medical records. Improving clinical documentation: By generating appropriate codes for procedures and treatments, LLMs could help reduce errors and ensure clinicians are adequately compensated for their work. Coding and billing: Using LLMs, clinicians can identify trends and patterns in patient health and develop targeted interventions to improve population health outcomes. Population health management: By analyzing large amounts of data and identifying patterns and trends that may not be easily traceable with traditional statistical methods, LLMs can be employed to facilitate clinical research at increased velocity and scale. Clinical research: LLMs can be leveraged to sift through and analyze patient feedback and sentiment through thousands of surveys and social media interactions to identify critical areas for improvement in patient satisfaction, access, and experience. Patient satisfaction analysis: What About Appointment Scheduling? LLMs are only as good as the data they’ve been trained on. ChatGPT, for example—albeit its objectively impressive knowledge retrieval capabilities—was only trained on parameters dating to 2021. Any event that took place at any time past its cut-off date of September 2021 is entirely out of its scope. So even though Boris Johnson resigned as Prime Minister of the United Kingdom in September 2022 and has since been succeeded by two prime ministers (Liz Truss and currently Rishi Sunak), ChatGPT is (self-admittedly) completely unaware. ‍ ‍ But what if I train an LLM on a list of all of my health system’s physicians? Wouldn't that work? It would. But only until the moment even the most minute change takes place. So, for instance, if just one of your physicians moves to a different clinic, adds another specialty to their care offerings, updates their working hours, or even resigns, your appointment-scheduling LLM will provide your patients with wrong and misleading information. If even a single piece of data is no longer relevant, the entire model becomes corrupted and, therefore, obsolete. The same can be said for any use case requiring real-time data to perform accurately. So, for example, site search, prescription refills, and up-to-date FAQs. ‍ ‍ Oh, so LLMs can really only help me with administrative tasks? On their own, yes, which is nothing to sneeze at and will save healthcare practitioners innumerable hours otherwise spent tackling bureaucratic office work. But for any use cases that demand real-time, up-to-date data, LLMs can only serve as an ingredient of a much more adaptive and flexible conversational platform, not the entire dish. , Hyro’s CEO lays out the ingredients needed to mix LLMs with enterprise-level conversational AI platforms to create truly elastic patient interactions that are driven by continuously updated organizational knowledge (proprietary data) and dynamic business logic (ever-changing internal and external processes). In this in-depth analysis Israel Krush Big Hero 6 When asked in a recent what he thought of ChatGPT and other LLMs, Andy Chu, SVP of Product and Technology at Providence Digital Innovation Group, said the industry is still a a 2014 Disney film depicting a futuristic healthcare robot—But added he envisions this technology used to answer patient-related administrative, "decision-tree", or general health-education questions. Becker's Hospital Review interview "long way off from the days of Big Hero 6,” https://youtu.be/jbKSvOfqxF0?embedable=true Chu's thoughts echo a broader sentiment shared by most digital health leaders that LLMs such as MedPaLM should be greeted with equal parts of excitement and caution. While there's no question these extraordinary "machines" can automate and accelerate a vast spectrum of primarily administrative applications, there are still glaring red flags when it comes to medical and clinical use. Furthermore, LLMs' absence of data adaptability renders them ineffective for any goal which calls for current and evolving internal organizational knowledge. Chu put it best by saying: "It's important to remember that healthcare is very personal, and generative AI technologies are as good as the data accessed. Data sources within healthcare are rightfully secure and protected, so we must be careful not to overhype the promise — especially when it comes to areas such as symptom detections, triage, and clinical treatment suggestions." Also published here.

An Intro to MedPaLM: ChatGPT's Healthcare-Focused "Cousin" 

Technology is making history more often than the healthcare industry. But if you consider that healthcare is saving human lives, they are equal. The greatest solution for humankind is when they work together to provide a better quality of life, making doctors’ lives easier or routine paperwork less boring. That sounds far too good, so let’s get to the practical side of it. Healthcare applications are becoming more popular with . That’s only mobile applications! To build the mHealth app, we need to choose the right tech stack. The following questions come up: 350 000 mHealth apps available in major app stores How do we set up medical features with backend technologies? How do top healthcare companies use AI in their work? What are successful tech stack examples in the industry? How to set up healthcare cybersecurity? We are simple software developers, so we are interested in technologies for health tech. It’s an interesting observation that health stacks are not entirely different from other industries' tech stacks. There are for sure more requirements for healthcare applications but the same languages, frameworks, and cloud technologies are used to build them. Let’s try to answer the questions we asked above and give tech stack examples. What Is Health Stack? Health stack is the backend and frontend technologies that healthcare companies use to manage their operations. So, except for clinical tasks, healthcare apps can do everything else. Non-clinical tasks include electronic health or medical records (EHR) systems, patient portals, telemedicine platforms, practice management software, billing and claims processing systems, analytics tools, etc. Health stack solutions have to help healthcare organizations improve their processes and improve patient care. But to choose the right tech stack for any app, we need to consider the following factors: Features and functionality of the product (in our case, medical features such as patient and doctor authorization, medical records database, etc.) Project requirements (user quantity, device support, performance time, etc.) The credibility of technologies’ open-source community Simple scalability Security considerations (in medical apps, also HIPAA compliance) Flexibility and the possibility to go beyond standard ways Size and type of your project. Every healthcare solution is unique, although the requirements for most apps will be similar. The reason is that a lot of technologies are added to the development process. Modern Healthcare Tech Stack Components It’s not a complete set of components a health app would need but those that are commonly used by digital health companies and healthcare providers. Java, Python, C#, C++, JavaScript, PHP, and HTML5. Programming Languages: The choice of specific language would depend on the functionality you need. If the app would store and analyze large amounts of data or somehow deal with artificial intelligence, it’s wise to choose Python + Java as backend technologies and Javascript as frontend technologies. Windows, macOS, Linux, and mobile Apple iOS or Google Android. Operating Systems: Here we just need to choose a trustable provider with great support. In the development process, we choose the operating system that has security features and top performance. MySQL, Oracle, PostgreSQL, Memcached, and MongoDB. Databases: To choose the right database or mix a few, seek advice from a development team. They would want to know what type of data you have, the amounts, and how often it’ll be requested. Based on that, they’ll advise the capacity of the database and the provider. Apache server and NGINX. Servers: These two are the most popular ones, but what’s important for us is that they’re HIPAA compliant. Other : tools that often come in handy in the healthcare industry GitHub, Jenkins, Selenium, Rollbar, or Bitbucket DevOps tools: such as Slack, and G Suite - for communication and management Business tools such as Google Analytics, Stripe, or Elasticsearch - for search, analytics, and payments Utilities such as Amazon S3, Amazon EC2, or DigitalOcean Cloud storage and hosting providers Now that we have the general picture of the technology stack that might be used in healthcare apps, here are practical use cases. Tech Stack Examples To be more precise, let’s see the technology stack of some apps developed at Incora. The 1st example is the service we provided to the doctor’s appointment app. used frontend technologies such as JavaScript, TypeScript, React, Antdesign, Java for the backend, and database PostgreSQL. HealthApp, with such a tech stack, allows you to make an appointment online as well as get a consultation from a doctor, nurse, or psychologist online. HealthApp The second example is different as it involved backend technologies. For an where we set the backend and created structure and functionality, we used runtime environment Node.js, also Nest Js for backend, React.js for frontend, AWS services (AWS Lambda), and database PostgreSQL. injury management system With the help of these technologies, Incora developed the management system that provides scheduling of appointments, management of “play status” of players, statistics and reports of all injured players, and emails for different events. Last but not least, the development team set up a backend for It allows the viewing of data about insulin injections, sugar levels, etc., for patients that use specific devices that do injections. patient administration service. The service was designed for doctors to manage patients and adjust treatment. The health stack included Node.js, Express.js, MySQL, React, Redux, Saga, and AWS. Medical Standards in Different Regions This paragraph is to remind you that most industries have . No matter what app you want to develop, make sure the data there (especially patients’ data) is treated the right way. standards and policies when it comes to data protection One of the most common healthcare cybersecurity issues is personal data leakage. Here are : 4 main regulations you need to meet Health Insurance Portability and Accountability Act (HIPAA) (USA). Any mobile health app that needs to comply with HIPAA. Only personal health apps are an exception. deals with protected health information (PHI) Personal Information Protection and Electronic Documents Act (PIPEDA) (Canada). The act sets rules to regulate any personal data usage and sharing with third parties. General Data Protection Regulation (GDPR) (EU). This regulation says ; any information that might identify a person, a name, email, an IP address, and so on. how businesses can and cannot use personal data Data Protection Act (DPA) (UK). It’s similar to GDPR in terms of rules on using and storing personal data. The technologies that we mentioned in the article are well-known and meet the above regulations. However, be sure to check it as the consequences are unpleasant, to say the least. Sum Up So, let’s rewind. We've defined what a health stack is, and what factors to consider when choosing one. Also, we made a list of commonly used technology stacks and tools in the healthcare industry. Furthermore, the tech stack examples were described to have a full picture of app functionality. And finally, we were reminded about healthcare app development standards that are a must nowadays. To save the world, save yourself from bureaucracy, and write us to handle both tech stack and standards.

What Tech Stack do Healthcare Companies Use?

Python for Data Science: How to Scrape Website Data via the Internet's Top 300 APIs

In the context of the coronavirus pandemic, governments are taking measures that, together with a general panic, weaken industry and financial markets. As in other areas, the largest conferences in the crypto industry are cancelled and projects are frozen. Bitcoin as the first and foremost cryptocurrency has also suffered from economic shocks, but what will happen next? How the virus affects the population? Detected at the end of December 2019 in Wuhan in central China, the virus spread throughout the planet. The has officially announced a pandemic, a global epidemic. The risks of such a development of events were the first to be seen not only by specialists but also by technology. The AI made the first warning about coronavirus on December 31, 6 days earlier than the US Centers for Disease Control and Prevention. World Health Organization (WHO) BlueDot According to , on March 26, more than 474,900 cases and 21,353 deaths associated with the COVID-19 virus were recorded. Until the end of February, China remained the epicentre of coronavirus with more than 80,000 cases, but now the virus has spread to all continents, spanning Europe, the USA, Africa, Australia .. HealthMap now updates its map of the distribution of coronavirus in real-time using artificial intelligence. WorldMeters Countries are taking emergency and preventive measures. Schools and universities are closed, cultural and sports events are cancelled, the question of cancelling the Olympics, scheduled for July and August 2020 in Japan, is being discussed. Following Israel, the European Union decided to close the borders and let in only citizens and residents. The combination of these measures and the panic on the part of investors are some of the causes of global economic shocks. What are the global implications? 13 years ago, in his book The Black Swan, Nassim Nicholas Taleb predicted “a very strange virus spreading all over the planet”, the active progress of which was associated with the number of people travelling. The outdated design of the aircraft, which allows the spread of infections in the cabin, combined with the airport infrastructure unprepared for the epidemic, allowed travel to accelerate the spread of the dangerous virus. Data analysts from Russia concluded that coronavirus is more dangerous than seasonal flu, but only for people at the epicentre of the virus. But even if actual mortality rates are not so serious compared to other diseases, it is expected that panic will only increase. Government measures to combat the spread of the virus include closing borders, limiting working hours and the so-called “social distance”, which implies a decrease in the social activity of each individual and groups of people. Each business related to tourism and travel, events, retail, training or transportation is expected to incur significant losses. When restrictive measures are applied in regions where small and medium-sized enterprises are heavily dependent on tourism, as, for example, in Italy, the plight of the economy of such regions is awaiting.  Of course, when some doors close, others open, and enterprises can flourish in the digital world, as the video game and video streaming industry in China has demonstrated this winter. According to Sensor Tower research company, the total number of game downloads in the Apple Store in China this year increased by 27.5% compared to last year, and the revenue of these applications rose by 12.1%. Nevertheless, global economic shocks rarely remain without consequences for the financial infrastructure, on which even fast-growing enterprises are highly dependent. And the financial industry is already showing signs of a deep crisis. According to the New York Times on March 8, Wall Street experienced a greater shock than after the 2008 financial crisis, when important indicators like the S & P500 and Dow Jones plummeted. On March 20, US exchanges closed at a three-year low. During one trading session, the Dow Jones and S&P500 lost 4% each, and at the end of the week, the S&P500 fell 15%. This is a major failure since October 2008. How coronavirus affected blockchain industry? Major cryptocurrency events are being cancelled or postponed due to the spread of the virus and new restrictive measures. And this is true not only for the most affected countries. For example, London Blockchain Week, although it managed to take place before the ban on crowded events was introduced, ran into low attendance. Some conferences, such as WeWork Blockchain Labs, switched to online formats, which means that partnerships that could be concluded during the event did not take place. The cancellation and rescheduling of international conferences and forums mean for the blockchain and crypto industry that there will be less collaboration and transactions. Mining was also negatively affected by one of the key infrastructure areas of the crypto world. Two-thirds of mining capacity is still concentrated in China, the epicentre of the virus outbreak. While the hash rate of bitcoins, apparently, does not depend on the crisis and continues to grow, in the worst-case scenario, mining farms are still expected to close and, as a result, the hash rate will decrease. On the other hand, this month the epidemic in China is slowing down, which gives some hope to miners who were previously paralyzed by the lack of internal logistics. If the situation improves, ASIC miner manufacturers will again be able to deliver their products to customers. In general, the introduction of a blockchain is possible is not of primary importance in the face of devastating consequences for public health and economic downturn. One can expect a reduction in blockchain transactions, especially in the sectors most affected by the crisis, such as tourism and entertainment. When the whole world is paused, this is reflected in large innovative projects, for example, the project of the Chinese government to launch the digital renminbi. What are the main indicators of the cryptocurrency market now? On March 10 of last year, Bitcoin was trading for less than $ 4,000, at the beginning of March of this year, it overcame the $ 9,000 mark, but could not hold its position and returned to $ 6,000. The cryptocurrency market has long been accustomed to such volatility. But in early March, news of coronavirus outbreaks was also accompanied by a collapse in the oil market. Futures on Brent Crude Oil fell to a record $ 25.98 per barrel. Moreover, the sharp decline that began on March 5 accelerated in a matter of days, forcing experts to forecast $ 20 per barrel, calling it the largest collapse in 30 years. Although it is expected that short-term volatility will continue, the medium-term outlook for Bitcoin looks good. Halving is approaching, hashing speed is growing steadily, reaching record heights. Positive actions of regulators are also observed on the horizon: France and Germany are on the way to recognizing bitcoin as a financial asset, South Korea fully legalizes cryptocurrencies, and even the long-standing position of rejection of cryptocurrencies in India begins to change. Some experts argue that in the long run, the current economic crisis may reveal the true value of cryptocurrencies. Where does blockchain come into play? Technological solutions are critical during any major epidemic event. Flash COVID-19 is no exception. DeepMind, a division of Google’s artificial intelligence, has openly published coronavirus structural forecasts created by artificial intelligence. Insilico Medicine, a Hong Kong-based artificial insemination startup, has also opened its database of artificial intelligence-based drug research components to other companies around the world to accelerate the development of an anti-coronavirus drug. In February, Chinese health authorities proposed improving the existing system using AI and robotics, with the support of the business to combat the outbreak of the virus. As a result, robots have been launched in China to disinfect and deliver drugs to combat the outbreak of coronavirus, and engineers continue to develop robots that can help doctors working on an advanced epidemiological front.

The CoronaVirus' Impact On World Economies: The Good and The Bad

has rapidly gone from something we only see in sci-fi movies to a technology we interact with every day. From making product recommendations to finishing your sentences, AI is everywhere. Artificial Intelligence (AI) But will AI replace doctors in a hospital? If you’re freaking out about being cut open by a robot doctor, the good news is that we’re not quite there (just) yet. But we’re not that far off either, so (like it or not) you better get ready for it. Is Dr. A.I. for Real? In some cases, an AI diagnosis is better than doctors. In fact, computers have been and cancer screening for years. So it makes sense to start leveraging smart algorithms in the diagnostic process. outperforming doctors in disease diagnoses If we take , for example, it helps get the diagnostic ball rolling right in the comfort of your home. HealthTap’s, Dr. A.I. Developed to work with Amazon’s Alexa, Dr. A.I. is programmed to have empathetic conversations about your overall health, symptoms, and provide highly personalized medical explanations. It will also recommend an appropriate (human) doctor to set up a consultation. Obviously, it’s not a viable substitute for a trained medical professional, but it can be instrumental in expediting the diagnostic process. This is because the app is designed to analyze data from a wide variety of sources, some that your own doctor may not even know about. While Dr. A.I. is far from perfect, its deep learning capabilities are making the technology better every day. So the false positives that potential patients might get today can be a distant memory in a few years. There are also that are designed to assist people. Like Dr. A.I., these AI-powered chatbots can support diagnostic initiatives, book appointments with specialists (in the area), and ensure patient medication adherence: other smart chatbots Babylon Health Ellie Florence MD Safedrugbot Will Doctors Become Obsolete? The short answer is “not exactly.” As technology gets better and healthcare institutions make better use of the , you can expect to see more doctors using AI regularly to accelerate diagnosis and enhance the level of care. Internet of Things (IoT) AI will change the face of traditional medicine and improve outcomes dramatically. If we take , for example, it optimizes the doctor’s diagnostic capabilities and even helps . Google’s DeepMind predict conditions two days before any symptoms present themselves In fact, Google’s smart algorithm (which was developed using 42,000 patient scans), outperformed six radiologists to determine if patients had lung cancer. It detected 5% more cancers than its human counterparts and reduced false positives by 11%. While that might seem like a small percentage, this is fantastic news for a field that’s plagued by false positives. For example, according to a study in , 2,100 patients had a false-positive rate of 97.5%. JAMA Internal Medicine In another study conducted at Stanford University, smart algorithms were developed to diagnose skin cancer by using a database with almost 130,000 images of skin diseases. In the diagnostic tests that followed, . the results were virtually identical to that of 21 dermatologists This is revolutionary for the field as it creates almost superhuman medical practitioners. When you merge human experience, knowledge from deep data, and mechanical precision, you can deliver enhanced patient experiences. So does this mean that we’ll see AI replacing surgeons in a couple of years? No! While AI won’t replace medical professionals, these accurate predictions could also lead to preventative medicine. So it’s never going to be a case of AI vs. doctors. Instead, smart algorithms will improve their chances of making an accurate diagnosis. For example, doctors and researchers at the Mayo Clinic in Rochester, Minnesota, on the ability of AI to quickly detect atrial fibrillation. This life-threatening heart condition is notorious for being difficult to diagnose. While the symptoms were too intermittent or imperceptible for humans, AI was able to identify it. recently shared their findings on the Lancet In similar studies conducted in China, on average, . the algorithm performed better than junior doctors When will robots replace doctors? Probably not in our lifetime. There are many reasons why robots can't replace doctors,and the same goes for smart algorithms. However, the main reason it won’t happen is that it can’t be trusted completely. AI works with data to make a diagnosis, and data can always come with some limitations. For example, . So when algorithms are , it can be a challenge to make an accurate diagnosis when the patient is an ethnic minority. AI is often trained on ethnically homogenous datasets trained and validated on Caucasian populations with the typical exclusion of minorities While doctors themselves might have their about these new technologies, AI in healthcare is here to stay. apprehensions and complaints AI will help and treatments, ensure medication adherence, make doctors more efficient, and deliver enhanced patient experiences. accelerate the development of new medicines Let's take this conversations over to Twitter !

Dr. Alexa Is Ready to See You: Are You Ready for AI Doctors?

Mental health is increasingly becoming a topical issue that has traditionally been glossed over. We have began to understand the impact mental health has on productivity, general wellbeing, relationships and physical health and placed greater focus on mental wellbeing. Even employers have began placing greater emphasis on providing work environments and conditions that keep their employees as happy and healthy as possible, with progressive companies providing perks such as monthly massages, catered meals, free and subsidised gym and unlimited vacation. In light of this and as an individual interested in developing my programming and data science skills I was also partially motivated by a data visualisation and analysis project on a Coursera Statistics specialisation I was studying. I decided to play around with data from the Centers for Disease Control and Prevention ( ), extracting the 2013 (BRFSS). This data is collected through monthly telephonic interviews and is one of the largest datasets focused on understanding trends in preventative health practices and risk behaviours that link to chronic diseases injuries and preventable infectious diseases affecting adults in the US. The goal of my analysis is to understand the relationship between income level and one's reported mental health and the impact how much you earn per year, how physically active you are and the amount of fruits and vegetables you eat has on your mental health. CDC Behavioral Risk Factor Surveillance System data Extracting data and understanding structure install.packages("ggplot2")install.packages("mice")install.packages("effects")library(ggplot2)library(MASS)library(mice)library(data.table)library(dplyr)library(effects)load("~/Documents/R Stats Assignments/brfss2013.RData") After uploading the necessaries libraries and loading my data, I wanted to look at the general shape and structure of the dataset to understand the number of dimensions, the types of data in each column and the clean up I would need to run to get my data in a consistent format to facilitate my analysis. > dim(brfss2013)[1] 491775    330 #check structure of columnsstr(brfss2013$columname)#check for NA valuessum(is.na(brfss2013$columname)) After reviewing the provided , I had a good idea of the columns I would need to interact with and decided to focus specifically on these columns. This included columns with data pertaining to minutes of physical activity per week, fruits eaten per week, vegetables consumed per week, BMI, annual income ranges and of-course mental health measured by the number of days a person reported to not have been in good mental health in a given period. Since NA values can obfuscate the accuracy of an analysis and potentially introduce a non-response bias, I looked for NA values in columns these ranged from 4.8% to 34% of the data. I assumed that these were non ignorable missing values and could not simply be dropped from my analysis. For numerical values in cases where 34% of the data was missing, I made the assumption that simply filling in with the standard mean or median would skew my analysis and reduce its accuracy. I opted instead for an easy to implement and relatively accurate imputation method to replace my NA values. codebook/index Multiple imputation by bayesian linear regression One of the quickest ways to impute values accurately is through a linear regression algorithm that attempts to predict potential values based on a pre-defined list of of predictors. With the frequentist approach (ordinary linear regression) we assume a linear relationship between the independent variable, additionally making an assumption that there are enough values to make an accurate prediction. However, for this imputation I opted for the Bayesian model, with this approach we assume both the response and predictors are random (factoring in uncertainty in our imputations),try to look for the probability of a particular event, update the probability based on prior data and make predictions based on these probabilities. The ability to incorporate prior information to update probabilities makes the bayesian model more flexible and possibly more accurate in making predictions, particularly for smaller samples. Multiple imputation lets me run through the imputation model n times providing different possible imputations based on the prediction model used. Typically this is a three stage process, that involves the actual imputation, substituting each NA with a possible prediction and then the analysis and pooling of all results. brfss2 <- brfss2013[,c('X_bmi5','X_drnkmo4','X_frutsum','X_vegesum','fc60_','pa1min_')]imp_model <- mice(brfss2,method="norm",m=5)#fill na functionfillna_fun <- function(data,columns){df <- setNames(data.frame(rowMeans(squeeze(imp_model$imp[[columns]], bounds = c(0,max(brfss2013[[columns]]))))),"col2")brf <- setNames(data.frame(data[[columns]]),"col2")brf$col1 <- rownames(brf)df$col1 <- rownames(df)setDT(brf)[df,col2 :=i.col2,on=.(col1)]brf$col2}brfss2013$pa1min_ <- fillna_fun(brfss2,"pa1min_")brfss2013$X_bmi5 <- fillna_fun(brfss2,"X_bmi5")brfss2013$X_drnkmo4 <- fillna_fun(brfss2,"X_drnkmo4")brfss2013$X_frutsum <- fillna_fun(brfss2,"X_frutsum")brfss2013$X_vegesum <- fillna_fun(brfss2,"X_vegesum") The Multivariate Imputation by Chained Equations (mice) package allows for easy imputation using the bayesian linear regression. I opted to narrow down the imputation to apply to columns with numerical values that would be required for my analysis and ran the imputations the default number of times -5. I noticed that bayesian linear regression returned negative numbers for some of the imputations which was impossible given the data these columns where measuring. For example it is impossible for a person to spend -300 minutes a week working out. To address this I used the to add a constraint to limit the predictions to be within a plausible custom-defined range. squeeze function Contrary to the , I opted to get the mean of all 5 imputations and use that to fill my NA values. I completely understand that this is not how machine learning should be used, that I missed the essential step of pooling my combined imputations and that some would argue that I would be better off picking one imputation than the average. Despite this, I adopted this option for ease of application and to hopefully receive further feedback from readers about this option. mice documentation I finished off the function by coercing the vectors in my function to a table using the and joining the vectors by index since the mean imputations vector only had values for indexes corresponding to NA values in my original dataframe. setDT function Mapping An important step of my analysis involved mapping the numerical values for some columns into groups and ranges. For example, instead of seeing BMI numbers I wanted to see which group/range a particular BMI belonged to (e.g 25 = normal). brfss2013$income2 <- as.character(brfss2013$income2)brfss2013$X_bmi5 <- brfss2013$X_bmi5/100brfss2013$healtheat <- (brfss2013$X_frutsum+brfss2013$X_vegesum)/100labels <- c('Excellent','Good','Ok','Bad','Very Bad')breaks <- c(0,5,10,15,25,10000)bmiLabs <- c('10','20','30','40','50','60','>60')bmiBreaks <-c(0,10,20,30,40,50,60,10000)activLabs <-c('0-200','200-500','500-1000','1000-2000','2000-4000','4000-10000','>10000')activBreaks <-c(0,200,500,1000,2000,4000,10000,100000)brfss2013 <- brfss2013 %>%mutate(mentalHealth = cut(menthlth,breaks=breaks,labels=labels,include.lowest=TRUE)) %>%mutate(bmiLev = cut(X_bmi5, breaks=bmiBreaks,labels=bmiLabs,include.lowest = TRUE)) %>%mutate(physLev = cut(pa1min_, breaks=activBreaks,labels=activLabs,include.lowest = TRUE)) %>%mutate(incomeLev = case_when(grepl("15|20",income2)~"0-$20k",grepl("25|35",income2)~"25-$35k",grepl("50",income2)~"35-$50k",income2 %in% "Less than $75,000" ~ "50-$75k",grepl("more",income2)~">$75k")) Since cut is designed to take a numerical vector and split it into bins according to set of custom breakpoints, I decided to use the , defining my breaks and labels to map the data according to my labels definitions. For the income level column, I converted the values to characters to enable me to use . This is a pattern matching function that I use to create income ranges by looking for keywords that match the custom words I used to identify each row's income range. cut function grepl It is important to note is that I made a few assumptions regarding mental health. I assumed that a person who only reports having mental health issues 0–5 times in a given period would be classified as being in excellent mental health, 5–10 in good health, 10–15 ok, 15–25 bad and any thing exceeding that very bad. This was based on observing the data and finding the ideal break points to make such classifications. The relationship between mental health and annual income I identified the stacked bar-plot as the most effective and appealing way to visualise the relationship between mental health and range of annual income range. #replace NA with missingbrfss2013$mentalHealth <- forcats::fct_explicit_na(brfss2013$mentalHealth, na_level = "Missing")#convert income back to factorbrfss2013$incomeLev <- as.factor(brfss2013$incomeLev)brfss2013 <- subset(brfss2013, !is.na(incomeLev))brfss2013 %>%add_count(incomeLev) %>%rename(count_inc = n) %>%count(incomeLev, mentalHealth, count_inc) %>%rename(count_mentalHealth = n) %>%mutate(percent= count_mentalHealth / count_inc) %>%mutate(incomeLev = factor(incomeLev,levels=c('0-$20k','25-$35k','35-$50k','50-$75k','>$75k')))%>%ggplot(aes(x= incomeLev,y= count_mentalHealth,group= mentalHealth)) +xlab('Annual Income')+ylab('Number of People')+geom_bar(aes(fill=mentalHealth),stat="identity",na.rm=TRUE)+ Using the scales package does the percent formatting for me geom_text(aes(label = scales::percent(percent)),position = position_stack(vjust = 0.5))+theme_minimal() Using the , I used the %>% operator to write multiple operations that would tally income levels and mental health by group, find the relative percentage of reported mental health numbers by income level, group the barplots according to my customisation and visualise the stacked barplot and adding percentage labels on each plot. dplyr package Mental Health Distribution by Annual Income Range Using the assumptions I have created about what constitutes good mental health, the data appears to confirm what one would expect, the more money you make the more likely you are to be in a better mental state. What do I do with NA values in income level? From the mental health distribution by annual income range visualisation it is evident that there are a lot of NA values in the income level column. While I could simply drop the NA values, assume they will not alter the accuracy of my analysis and move on, I believe that a lot of people may not have been willing to talk about their income level possibly. Meaning a significant portion of NAs came from people who were not comfortable sharing their annual income over the phone. It could be the case that people who are not comfortable sharing information about their earnings are on the lower income range (0–20k) or very high earners (over 70k), leaving out these rows which are over 15% of income data, could easily introduce a non response bias. I would like to impute the potential values of these NAs through machine learning. This time I opted to impute my ordered data through a model. This method uses the proportional odds logistic regression model, the mechanics of which are discussed in a bit more detail in following paragraph. polytomous regression #filling na values of income level columnbrfss <- brfss2013[,c('incomeLev','healtheat','X_age_g','employ1','renthom1','sex','physLev')]ordered_brfss <-mice(brfss, m=1, method='polr', maxit=1)fillna_inc <- function(data,columns){df <- setNames(data.frame(ordered_brfss$imp[[columns]]),"col2")brf <- setNames(data.frame(data[[columns]]),"col2")brf$col1 <- rownames(brf)df$col1 <- rownames(df)setDT(brf)[df,col2 :=i.col2,on=.(col1)]brf$col2}brfss2013$incomeLev_ <- fillna_inc(brfss2013,"incomeLev") For the purposes of this imputation I focus on a few columns that I believed would act as the biggest predictors of potential income. I focused specifically on whether the individual reported renting or owning a home, whether s/he was employed, the individual's gender, age, level of physical activity and the amount of fruits and vegetables the respondent reported. I opted to run a single imputation, purely for ease of application. Visualising mental distribution by income level We all love data viz! I wanted to see how my mental health distribution by income level would look after this imputation and built another visualisation. Mental Health Distribution by Annual Income Range Proportional Odds Ratio The act of mapping out mental health into groups such as good, ok, excellent, transformed this column to a factor with 5 levels. These factored are ordered according to levels. Given the structure of this column I opted to use the proportional odds logistic regression. For the purposes of my analysis, I need to choose a model that would help me understand the probability of an individual’s mental health falling under a certain category given the independent variables I have chosen and understanding what impact these variables have on probability. While linear regression may, to a certain extent, work for binary classification problems (e.g if the response variable is whether an email is spam or not) it falls apart when you have multiple categories for your response variable. Ordinal logistic regression transforms that linear model to cater for ordered response categories using the logit function, ensuring that the probabilities returned fall within the 0 to 1 range. I could have instead opted for the more flexible , however this relies on the assumption that the categories in the response variable cannot be ordered in any meaningful way- an assumption that I believe does not apply to my mental health response variable. multinomial logistic regression model My model relies on calculating the proportional odds ratio. Put in simple terms the proportional odds ratio is a tool used in ordinal logistic regression to help make assumptions about relationships between independent variables and ordered response factors/categories by predicting the conditional probability of belonging the response variable falling under a particular category given the values in the independent variables being observed. Probability Odds Ratio with multiple explanatory variables J in this instance represents the categories/factors we are trying to predict for. The formula stated above takes in n number of factors with n dependent on the number of factors I have in my dependent variable. The numbers refer to the independent variables I am using to build the model. No Math I use this formula to understand the independent variables that have the greatest relationship with our outcome(mental health)- and try to understand the extent the independent variables have an effect on mental health. brfss2_model = polr(mentalHealth ~ incomeLev+bmiLev+X_drnkmo4+healtheat+physLev,data=brfss2013,Hess=TRUE) #Hessian used to get standard errors I used the to fit a proportional odds logistics regression to the response variable(mental health) and predictors variables. In this case I decided to include BMI, total sum of fruits and vegetables consumed in a month, physical activity level, range of annual income and alcoholic beverages drank over a monthly period. I included the observed information matrix by adding the TRUE argument to the Hessian in order to get the standard errors and try and assess the applicability of this model to the data. polr() function (ctable<-coef(summary(brfss2_model)))#calculating p_value by comparing the t-value against the stnd norm distr similar to a z-testp<-pnorm(abs(ctable[, "t value"]),lower.tail = FALSE)*2#combining p-value(ctable<-cbind(ctable,"p value"=p)) I proceeded to find the regression coefficients and calculate the p_value to determine the significance of these results. The regression coefficients ranges from 0.001 for alcoholic beverages per month to a little over 2.55 for a group in BMI level and the p-values indicated statistical significant. From my understanding, the closer the regression coefficient is to zero the more it indicates that the distribution of predictors is exactly the same for each level of our response variable. For coefficients approaching zero this loosely means that the effect of adding more units to an independent variable on the response variable is close to zero. Visualising the effects of the independent variables To clearly show the effects of the independent variables on mental health, I decided to use the . Since the polr function returns estimated regression coefficients, I used . effects package this package to help me interpret my polr results plot(Effect(focal.predictors = c("incomeLev","bmiLev"),brfss2_model)) After a trying a few combinations I opted to show the annual income and BMI effect plot. I used these two variables as the focal predictors, holding the other predictors with typical values (fixed and averaged). Income Level * BMI effect on Mental Health According to the result we see that the probability of being in excellent mental health decreases as your BMI increases regardless of your income range. Interestingly, the decrease seems to taper off as you approach the $75 000 per year income mark. We can see a similar effect in the very bad mental health state to a smaller extent, the wealthier you are, the less of an effect an increase in BMI may have on your mental health. If you are going to gain weight you may be in a better off mental state if you do so while increasing your income. One of the most important lessons I am learning, aside from how little I know, is that data analysis and data science is an iterative process. You try out different variables and models, clean up methods and visualisations based on changing assumptions and test what impact this has on your result and its accuracy. Feel free to reach out or send any feedback on Twitter @Emmoemm

Learning to understand possible effects variables such as income and physical activity have on…

The potential is growing tremendously for Transforming Healthcare at Home Elderly and ailing patients might not have the resources and energy to visit doctors every now and then, which consequently leads them to choose the option of healthcare at home. Due to this, healthcare companies are always competing with each other in the race to adapt to the increasingly digitized world. Specifically talking about the US, the healthcare organizations are well aware of the issues prevailing such as the need for increased care for home treatments of the aging population, mostly due to mobility and budget issues. The only savior that can aid in such a situation is technology, given that it is implemented correctly. While working with technology, it is vital for home healthcare organizations to choose the right option to achieve the desired results, such as increasing efficiency while providing patients with the utmost care and affordable solutions. What Constitutes the Spectrum of Home Healthcare? Other than providing healthcare services for the elderly, healthcare at home is also useful for patients who need constant medical care and treatment once they get back to their homes from the medical facility or hospital. Home healthcare makes it possible for people to receive a variety of medical services in their homes. Healthcare at home can be beneficial for people dealing with different conditions. Whether the patient is an individual recovering from an illness after a hospital stay or someone who is disabled, old or suffering from a chronic disease, healthcare at home can be the right option in such situations. Healthcare at home allows people to get any kind of treatment available; be it therapeutic or nursing or even routine medical assistance. , this has also resulted in the reduction of hospital admission rates.  Healthcare at home comprises of the following factors: According to a survey by Canadian Home Care Association, with the aid of healthcare services at home, there has been a decline of around 72% in emergency department visits The primary focus of healthcare at home is for people to get medical support at home rather than spending time in a medical facility. Healthcare at home allows people to get the freedom of staying at their residential facility rather than opting for long-term nursing care at hospitals. With home assistance, medical professionals can assist patients who are incapable of conducting daily activities with help for tasks like food preparation, basic housekeeping, and emergency measures. This can be done until the patient can do all such tasks by themselves. • Home Assistance Home assistance also allows medical professionals to take care of medical and psychological assessments while allowing individuals to learn about . pain management, disease education, and physical therapy, etc Home care agencies keep their focus on providing medical expertise in the form of healthcare professionals at a patient’s home. In such cases, medical professionals or doctors are sent to a patient’s house to treat their injuries or illnesses. Once the health condition of the patient is restored, the need for medical professionals end and their services are discontinued thereafter. • Home Care Agencies Other than expert doctors, there are certain cases where other medical professionals are involved in home healthcare programs such as . The demand for such professionals is expected to significantly increase in the next decade as people of all age groups are growing more conscious of healthy lifestyles. • Home Health Care Workers nurses, physiotherapist, nutritionist, occupational therapist, etc Benefits of Healthcare at Home Healthcare at home can be beneficial for many reasons including but not limited to the following ones. Even when the patient is not in a condition to visit the hospital, home healthcare professionals can be of great help. They are specially trained and qualified to provide treatment while assessing the safety as well as risks at the patient’s home itself. They can also offer simple suggestions and corrections for the patient’s well-being, such as the medicines to keep in handy for emergencies or recommendations for immediate ambulance assistance etc. Numbers don’t lie, and looking at the above info-graphic gives a clearer picture as to what the future holds for Home Healthcare Services are supported in-home care too. These activities can include any basic necessity such as bathing, medicine reminders as well as grooming, etc. Such facilities allow elderly patients to get regular help along with their personal care requirements while maintaining a high quality of life for them. Activities of daily living at home is one of the significant benefits of healthcare services at home. Skilled medical professionals can carry out specific critical medical procedures at home with the help of supervised nurses who are certified and hold knowledge about technically advanced medical equipment. Healthcare at home allows your loved ones to get the personalized care that they need at their doorstep. Expert nursing care is a part of home healthcare. Patients who suffer from certain diseases and are discharged from the hospitals after treatments, often grow very weak as they lack nutrition. Their problems can worsen with ageing, illness, and injury. Healthcare at home may include dietary and nutritional counselling along with home-cooked meals that can prevent your loved ones from lack of nutrition. Dietary and nutrition support can turn to be confusing. Home healthcare experts can help your loved ones to consume the right medicines at the right time to control their medical conditions and prevent the intake of wrong drugs. Multiple medical prescriptions When home care is provided to patients with chronic health issues such as diabetes or pneumonia, clinical trials have turned out to be better with fewer complications according to researchers. Healthcare at home can result in better health outcomes. Moreover, home healthcare allows patients to get treated in a better manner as it provides one-on-one support for individuals. Such personalized and skilled care strengthens the bond between healthcare professionals and their clients. What’s Driving this Change in Home Healthcare Services? Companies focusing on providing home healthcare services rely on automation heavily as it allows to ensure that the technology used in the process remains invisible and will enable them to deliver the best care for the users. Due to this factor, more and more home healthcare providing companies are discovering the potential value of automation and optimization as they need to stay competitive in the field. is one of the examples of automated processes that result in increased efficiency. Leading technologies like can help healthcare businesses in providing better organized patient-centric services. Automated appointment scheduling Artificial Intelligence Source: isalushealthcare.com For instance, medicines and equipment required by the patients can be determined in real-time with technology. Along with this AI can allow the feature of duration along with in case of emergencies. It can also improve customer service through to avoid no-show appointment prevention. predictive caregiver scheduling and appointment predictive routing for travel predictive cancellation The whole purpose of automation in home healthcare is to streamline the overall processes such as scheduling and providing services as and when needed, along with all the necessary equipment, thus, improving the patient care at an accelerating pace. This way, all the hassle of fixing random appointments can be eliminated while increasing the productivity of the overall system. The power of the latest ground-breaking technologies like can change a lot in the healthcare sector as by resolving current issues and helping patients to get the best experience when it comes to home healthcare. Healthcare organizations are getting aware of the benefits that technology can provide them such as finishing basic tasks and saving time. Blockchain and AI 5 Technologies Reforming Home-Healthcare According to Kalorama information, out of which a good part of the demand was accounted for home healthcare facilities and services. Eldercare providers have picked up interest in the home healthcare treatments rather than shifting people to another treatment facility. 1. Sensors sales for remote patient monitoring was estimated to around $29.7 Billion in the year 2014 As per the trend, sensor technology could be placed in home premises, around doors, windows as well as the patient’s appliances and equipment. These sensors can provide alerts to caregivers in case of any abnormal activity. They can also track and send notifications if the patient misses their meals. This can be particularly important for patients dealing with diseases such as Alzheimer’s. Such patients can remain at home, and their activities can be monitored via mobile healthcare apps as well as sensors. In cases of emergency, such as when senior citizens are away from home for a long time, GPS tracking can help their families, healthcare workers or law enforcement bodies to locate them quickly. Technologies with features such as mobile alert services also allow these devices to follow a hybrid approach that enables people to find the elderly even in places where GPS might not be available such as garage parking and indoors. It can also help by automated calling for assistance in case users fall. 2. GPS Moreover, in need for emergency health services, there can be a difference of life and death concerning calling the closest emergency squad and rapid dispatching. GPS tracking becomes and much-needed option in emergency business service. Regular health monitoring is not an option but a necessity when it comes to elderly patients. Many devices are available these days which can be connected to smartphones to help reduce the costs as well as the time that is consumed when it comes to doctor visits in endangered health situations. 3. Remote Monitoring Tools Technology has enabled different options in the form of fitness bands that can keep a regular track of sleep, diet, number of steps, etc. Along with these, there are many other devices which can be used for the purpose of tracking and measuring glucose and blood pressure levels along with heart rate monitoring and other measurements. Such tools allow the patient’s family members as well as nurses and doctors to keep a detailed track of their daily healthcare needs and eliminate the need of calling a doctor time and again. Home healthcare relies highly on the data available from multiple resources and its analysis to gain vital insights for the right kind of treatment. Some home health services analyze data in massive amounts and use their outcomes to take preventive measures even before a medical situation can turn into a problem. 4. Big Data Big-data enabled sensor can detect the regular activity levels such as sleep patterns etc. and inform the caretaking authorities to take immediate action concerning any abnormality. Telehealth systems are a boon to the healthcare industry as they provide people with the ability to use phones and other digital systems to connect patients with doctors and specialists. Moreover, the promising advances in these systems can help the disabled or elderly citizens from traveling to-and-fro to healthcare facilities. Devices such as smartphones and iPads with a simple internet connection can make it easier for the people to implement telehealth and make results easier to understand. They allow doctors to monitor patients from different places and share the outcomes in real time. 5. Telehealth 3 Applications Driving Healthcare Innovation Constantly evolving mobile technology has led us to implement new and improved ideas to reality. Healthcare mobile apps are a big revolution in the industry as they have made our work more accessible and improved the rate of efficiency.  Following is a list of some of the healthcare applications that are rapidly transforming the healthcare industry for good. Doctor-on-demand is a mobile application that allows the patient to get a consultation with their doctors through video calling. 1. Doctor-On-Demand: The application uses the concept of telemedicine to provide the patients with an on-demand doctor in situations of urgent care. Source: dribbble.com/babuma Certified physicians offer their services for urgent care_,_ chronic conditions, preventive medicines, medical advice, etc for both medical care and mental health. The app could also be helpful in non-emergency situations like flu and cold, respiratory issues, allergies, back pain, and UTI. 2. HealthTap: A live screen capture from HealthTapSource: dribbble.com/joshshiau The application allows a patient to fix a with a doctor or to get some answers to their questions. It provides a convenient way to access healthcare from real doctors for , through consultations via video, voice or text. The app also has a knowledge library of around 700,000 topics and articles for people to read and learn about healthcare on their own. virtual medical appointment 24*7 It uses an AI-powered symptom checker for one-to-one recommendations and guidance . Microsoft HealthVault is a digital service as well as a mobile app that offers a patient the freedom to . The patients can also share these records with digital professionals securely in the time of need. The system is exceptionally suitable for patients with chronic diseases as they can keep a proper track of their routine health metrics. 3. Microsoft HealthVault: keep their medical records updated and safe HealthVault stores and maintains health and fitness information and addresses both individuals and healthcare professionals. It also allows patients to . set healthcare goals, track progress, and discover health trends Concluding View Healthcare at home sure has a lot of potential but it can only be leveraged if the technologies are utilized in an effective manner. Crucial data such as patient history should be managed properly and researched for insights and understanding without compromising its security. People need to believe in technologies in order to adapt and use them in daily lives. Trending names like Blockchain and Artificial intelligence can bring a revolution in the healthcare industry with emerging use-cases and a proper understanding on how they can be used at their best for patients, doctors as well as the overall healthcare ecosystem. — — — — — — — — — — — — — — — — — — — — — — — — — — Read My Other Related Articles 1. Blockchain in Healthcare: Opportunities, Challenges, and Applications , Also on Hackernoon 2. Healthcare Innovation in 2018 , Also on Hackernoon 3. Coordinated Care — The Rise of Communication Tools & Mobile in Healthcare 4. “E-Prescriptions” — Making Patient Care Effective & Efficient 5. AI in 2019 — What Tech Companies are Betting Big On? 6. Top 10 Healthcare Companies in the US I am Mayank, Cofounder of EngineerBabu . Feel free to reach out to me on LinkedIn | mayank@engineerbabu.com (About EngineerBabu — Medium ). In addition Help startups, enterprises, and owners to grow their business by building high class IT solutions. 95% of projects developed by them were completed on-time. , Win most innovative Premier Design Award, selected in Y-Combinator 2016 & 2017. EngineerBabu 30+ funded by VCs

Stories, News & Mentions about HEALTHLEAP

Healthleap’s Stories on HackerNoon