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Our Take: [In November 2023 we wrote about the challenges of mHealth in addressing public health, please see mHealth: Challenges Remain to Enable Providers to Address Public Health this week we look at the potential for mHealth to improve public health access.] Mobile Health (mHealth) apps have emerged as a transformative force in the healthcare industry, significantly impacting public health in various ways. These applications leverage the ubiquity of smartphones and the power of digital technology to improve healthcare access, patient engagement, and health outcomes. Although these apps have the ability to improve access and convenience while reducing barriers to care, recognizing and addressing, where possible, the barriers to broadband access are essential steps in maximizing their benefits. If done correctly, the ongoing integration of mHealth into healthcare systems holds great promise for the future of public health. Key Takeaways: Approximately 71% of app users are estimated to disengage within 90 days of a new activity (Journal of Medical Internet Research) As of July 2023 there were over 54,000 mHealth apps on the Apple App Store and over 65,000 mHealth apps on the Google Play Store (InApp.com) A recent survey of 500 elderly respondents in South Korea found that seniors defined as “frail” were more likely to use such apps to get healthcare information and seek medical guidance than those defined as “healthy” (Journal of Korean Medical Science) There were approximately 2.7M residential fixed wireless connections in 2021 (latest available data), an increase of over 70% from the prior year, off an admittedly low base (FCC) The Problem: While Mobile Health (mHealth) apps have made significant strides in improving public health, they also come with several challenges and problems that need to be addressed. First and foremost is the problem of unequal access, more commonly called the digital divide. Despite smartphones being nearly universal, everyone with a smartphone uses it to access the internet and many lack access to high-speed internet connections, resulting in the aforementioned digital divide that limits the reach and impact of mHealth apps. This is particularly true for the elderly as well as people residing in poor and rural areas. As noted in the article Commercial mHealth Apps and Unjust Value Trade-offs: A Public Health Perspective ”   developers of mHealth apps often ignore differences in the socio-economic position of their users resulting in power asymmetries within healthcare.” Though this may not be as large of a concern when app developers are targeting higher income populations like those with commercial insurance, it is essential to address these constituencies for those dealing with public health as vulnerable populations, such as low-income individuals or the elderly, may be left behind. In addition there is the problem of low user engagement. Many users download mHealth apps but stop using them after a short time, leading to limited long-term health benefits. For example, a 2022 article in the Journal of Medical Internet Research noted that “approximately 71% of app users are estimated to disengage within 90 days of a new activity”. According to the study, a number of factors including lack of support, technical difficulties and usefulness of the app contributed to the low retention. Many mHealth apps offer a one-size-fits-all approach, failing to adapt to individual user needs, preferences, and goals. Without personalization, users may not find the app relevant to their specific health concerns, which can lead to disengagement over time. Some problems may be attributed to varied App Quality. The mHealth app marketplace is flooded with apps of varying quality. As noted in the article Mobile Health Apps and Health Management Behaviors: Cost-Benefit Modeling Analysis “It is evident that situational effects create some kind of general perception of risk because they inhibit the effective impact of mobile health apps on lifestyle behaviors, such as weight loss or physical activity.” Some apps may provide inaccurate information or unreliable health advice, potentially putting users' health at risk. For example, reminders and other behavioral “nudges” from weight loss apps may provoke a feeling of inadequacy and guilt inadvertently triggering inappropriate responses in people who have or may have had eating disorders. One article noted about a participant, who "starts punishing herself for not exercising by eating less. Although [she had] not been diagnosed with an eating disorder or anorexia she is certainly at risk since the use of fitness apps correlates with increases in distorted eating and exercising behavior.” The Backdrop: Although mHealth apps have been around for a number of years, their usefulness and value has only come to be a reality in the last several years in the context of several overarching societal and technological trends that have impacted the healthcare ecosystem. While the emergence of mHealth was predicated on the proliferation of smartphones, mHealth really did not begin to realize its potential until the COVID pandemic. One article noted “the COVID-19 pandemic accelerated the adoption of telemedicine and remote care solutions, with mHealth apps playing a critical role in facilitating virtual consultations, monitoring, and remote diagnostics.” Another study highlighted that “mHealth [was] used for various aims, such as fast screening, early detection, contact tracing of infected people, appointment booking, remote monitoring of patients, clinical patient care, patient monitoring, and treatment in response to the COVID-19 outbreak.” Based on these experiences, public health officials have become knowledgeable on how to leverage mHealth to “allow patients to easily obtain health information and receive medical care, thus reducing the frequency of patient visits to the hospital and minimizing population mobility in areas of high risk. Mobile health apps effectively promote information exchange, storage, and delivery, and they improve the ability of patients to monitor and respond to diseases.” As a result, mHealth can help improve public health by creating readily accessible tools for healthcare management and information. MHealth also helps address the issue of the rising costs of care. Escalating healthcare costs combined with the need for more efficient and cost-effective healthcare solutions has helped drive the development and use of mHealth apps. As illustrated in, Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective,  "the emergence of mHealth apps [have] changed the supply mode of health services and brought about benefits for both healthcare providers and recipients. On the one hand, doctors use mHealth apps to process patient information and monitor patient health. On the other hand, individuals use mHealth apps to obtain health information for immediate diagnosis." These apps aim to reduce the burden on traditional healthcare systems by enabling remote care and self-management of health conditions. It improves telemedicine and Remote Care. The COVID-19 pandemic necessitated social distancing and reduced in-person healthcare visits, leading to a surge in demand for remote care solutions. Telemedicine, which had been steadily growing, saw unprecedented adoption as healthcare providers sought safe and efficient ways to connect with patients. In the article The Impact of Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective it says, "Therefore, many countries have begun to use mHealth apps on a large scale to provide consultation, monitoring, and care services for patients. Mobile health apps allow for the exchange of two-way data between patients and healthcare personnel to realize remote medical consultation, psychological consultation, health education, and obtain medical protection. It meets users’ utilitarian medical needs. Satisfaction with utilitarian needs can positively affect user intentions." The COVID-19 pandemic accelerated the adoption of telemedicine and remote care solutions, with mHealth apps playing a critical role in facilitating virtual consultations, monitoring, and remote diagnostics. Historically one of the issues in installing broadband in low income and rural communities has been return on investment for telecommunications companies given the high infrastructure costs. However, the advent of fixed wireless and 5-G technology may make broadband deployments in these communities more feasible. For example, according to the Federal Communications Commission’s “2022 Communications Marketplace Report”, there were approximately 2.7M residential fixed wireless connections in 2021 (latest available data), an increase of over 70% from the prior year, off an admittedly low base. While this accounts for only 2.4% of connections in the U.S. given the deployment cost of fixed wireless we would expect subscriber gains to continue. In addition, when combined with the speed advantages of 5G (or fifth generation wireless technology) we would expect to improve the attractiveness of fixed wireless networks over time as well (5G generally requires shorter distances between connections). Moreover, “with its promise of lightning-fast data exchange and minimal latency, 5G has the potential to revolutionize medical practices, enhance patient care, and drive innovation in the field. 5G also has significant implications for addressing rural health. “Patients in remote or underserved areas benefit significantly from 5G-enabled telemedicine. They can virtually connect with medical experts regardless of geographical constraints. Implications: As noted in the aforementioned Journal of Medical Information article, “using apps for remote assessment allows participants to make fewer site visits, substantially reducing the burden of travel and the time needed to participate in laboratory studies. With lowered barriers, it becomes easier for participants to conduct repeated testing and share real-time data based on their daily life experiences, …which may enhance both the effectiveness of the app in its goals (eg, in disease management) and adherence in research studies” As a result, the use of remote care enabled by mHealth reduces healthcare costs associated with physical infrastructure and travel, making healthcare more cost-effective for both patients and providers. In addition, mHealth can improve the public health management of chronic conditions as “chronic diseases, but not health crises, often manifest in the form of health management routine. In this case, the use of mobile health apps helps to address the health concerns of individuals who are already aware of their health condition.” Lastly, not only can mHealth be used for new and innovative ways to deliver care, it can also be used to maintain continuity of care: As highlighted in the article, The Impact of Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective, ”mHealth apps effectively promote information exchange, storage, and delivery, and they improve the ability of patients to monitor and respond to diseases. They can also be used for training, information sharing, risk assessment and symptom self-management”. As a result of all of the above it appears clear that the potential for public health to leverage mHealth to broaden access, improve care and reduce the total cost of care is attainable in the near term if done correctly. However, as noted in “The digital divide in access to broadband internet and mental healthcare” this is particularly difficult in rural areas, “because rural businesses and homes are located far apart from one another, installing fiber-optic cables across many miles for a small number of paying customers presents internet service providers with the challenge of geographical barriers and a limited profit margin.” However, 5-G and fixed broadband technology may provide a quick and more financially viable potential solution to this issue. Related Reading: Commercial mHealth Apps and Unjust Value Trade-offs: A Public Health Perspective Mobile Health Apps and Health Management Behaviors: Cost-Benefit Modeling Analysis Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective Research on the Impact of mHealth Apps on the Primary Healthcare Professionals in Patient Care Mobile health app users found to be more content with public health governance during COVID-19

Our Take: Mobile Health (mHealth) apps have emerged as a transformative force in the healthcare industry, significantly impacting public health in various ways. These applications leverage the ubiquity of smartphones and the power of digital technology to improve healthcare access, patient engagement, and health outcomes. Addressing privacy concerns, and health inequalities, and ensuring regulatory compliance are essential steps in maximizing their benefits while mitigating potential risks. The ongoing integration of mHealth into healthcare systems holds great promise for the future of public health. Key Takeaways: Almost 40% of Americans aged 65 and older still do not own a smartphone and approximately ⅓ of Americans who have smartphones do not have high-speed internet connection within their homes (Pew Research Center) The least dense areas of the United States pay upwards of 37% more for broadband than the densest centers with the lowest-income households tending not to have a home broadband subscription (Benton Institute for Broadband & Society) 65.6% of Primary Care Health Professional Shortage Areas (HPSAs), which are defined in part by having a provider-to-patient ratio of 1:3500 were located in rural areas (Rural Health Innovation Hub) Almost half (49%) of lower-income households (i.e., those whose annual incomes are $50,000 or less), live on the precipice of internet disconnection in that they could lose connectivity due to economic hardship (Benton Institute) For lower-income households (i.e., those whose annual incomes are $50,000 or less), half (49%) live near the precipice of disconnection in that they have lost connectivity due to economic hardship (Benton Institute for Broadband & Society) The Problem: While Mobile Health (mHealth) apps have made significant strides in improving public health, they also come with several challenges and problems that need to be addressed. First and foremost is unequal access and the exacerbation of existing disparities, often referred to as the “digital divide”. For example, while according to the Pew Research Center, across developed economies, “a median of 85% say they own a smartphone, 11% own a mobile phone that is not a smartphone and only 3% do not own a phone at all” this is not synonymous with broadband access particularly for the underserved and elderly. For example, according to the Pew Research Center, almost 40% of Americans aged 65 and older still do not own a smartphone and approximately ⅓ of Americans who have smartphones do not have high-speed internet connection within their homes. Although many will argue that just having a smartphone will give their owners access to a broadband hotspot, this argument fails to take into account that broadband access via a hotspot is quickly “throttled down” by cellular providers and many of those who own smartphones may not have unlimited data plans necessary to make that a viable option. Moreover, many in rural and underserved areas often pay more for broadband access. For example, according to the Benton Institute for Broadband & Society, the least dense areas of the United States pay upwards of 37% more for broadband than the densest centers with the lowest-income households tending not to have a home broadband subscription, citing price as the problem”. Importantly this could lead to an exacerbation or racial disparities in rural populations which are showing patterns of increases in BIPOC populations. In 1990, one in seven people in rural areas identified as people of color or indigenous, in 2010 one in five rural Americans identified this way. Many of those families also sit at the precipice of what is called “subscription vulnerability” For lower-income households (i.e., those whose annual incomes are $50,000 or less), half (49%) live near the precipice of disconnection in that they have lost connectivity due to economic hardship (during the pandemic), live at or below the poverty line, or say it is very difficult for them to fit broadband service into their household budgets. There is also the problem of low digital literacy and low user engagement for those who do have access. This was particularly evident during the COVID pandemic. For example, an article from WIRED magazine entitled, “Telemedicine Access Hardest for Those Who Need it Most” found that “as many as 41% of Medicare recipients don’t have an internet-capable computer or smartphone at home, with elderly Black and Latinx people the least likely to have access compared to whites”, while another study in JAMA found “approximately 13M elderly adults have trouble accessing telemedicine services, and approximately ½ of those people may not be capable of having a telephone call with a physician due to problems with hearing, communications, dementia, or eyesight, including 71% of elderly Latinx people and 60% of elderly Black people.” Moreover, many apps lack the ability to customize to their users and may be of questionable quality. Most mHealth apps offer a one-size-fits-all approach, failing to adapt to individual user needs, preferences, and goals or limitations. Without personalization, users may not find the app relevant to their specific health concerns, which can lead to disengagement over time. In addition, as the authors note in “Mobile Health Apps and Health Management Behaviors: Cost-Benefit Modeling Analysis”, “It is evident that situational effects create some kind of general perception of risk because they inhibit the effective impact of mobile health apps on lifestyle behaviors, such as weight loss or physical activity [while] some apps may provide inaccurate information or unreliable health advice, potentially putting users' health at risk.” Privacy concerns and the slow pace of passing policies and regulations for data protection adds to consumers’ uneasiness. For example, as we noted in “Health App Regulation Needs A New Direction-The HSB Blog 4/12/22, “while the markets and technology are moving at a rapid pace, policies and efforts around regulation move extremely slowly and have generally lagged behind advancement.” The Backdrop: The impact of Mobile Health (mHealth) Apps on public health occurs within the context of several overarching societal and technological trends that have shaped the healthcare landscape. Understanding this backdrop is essential for comprehending the significance of mHealth apps in improving public health. One of these has been the proliferation of smartphones and users' ability to capture, store and transmit large volumes of health data on these devices. As noted in , “The Impact of Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective” “mobile health apps effectively promote information exchange, storage, and delivery, and they improve the ability of patients to monitor and respond to diseases.” With billions of people carrying smartphones, these devices have become ubiquitous and readily accessible tools for healthcare management and information. The maturation of mHealth also facilitate the delivery of remote care and remote patient monitoring (RPM) allowing care delivery for underserved urban communities as well as broad swaths of rural communities. For example, according to the Rural Health Innovation Hub, 65.6% of Primary Care Health Professional Shortage Areas (HPSAs), which are defined in part by having a provider to patient ratio of 1:3500 were located in rural areas. Given the lack of providers in these areas many countries [including the United States] have begun to use mHealth apps on a large scale to provide consultation, monitoring, and care services for patients.” These mobile health apps, encompass both telehealth, virtual care and RPM allow for the exchange of two-way data between patients and healthcare personnel to realize remote medical consultation, psychological consultation, health education, and obtain medical protection thereby facilitating virtual consultations, monitoring, remote diagnostics and escalation to in-person visits when necessary. Given their ubiquity, and ability to constantly measure users' health data with relatively inexpensive technology, mHealth has demonstrated an ability to help reduce the cost of healthcare delivery. Not only has this been achieved by an increase in the delivery of basic preventive care it has also moved the delivery of care from episodic and reactive to continuous and proactive. As noted in the aforementioned “The Impact of Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective“, “the emergence of mHealth apps has changed the supply mode of health services and brought about benefits for both healthcare providers and recipients. On the one hand, doctors use mHealth apps to process patient information and monitor patient health. On the other hand, individuals use mHealth apps to obtain health information for immediate diagnosis." As a result, these apps can reduce the burden on traditional healthcare systems by enabling remote care and self-management of a number of health conditions. Implications: As noted above mHealth apps have a number of positive implications for the delivery of healthcare and public health. mHealth apps can help promote healthy lifestyles, track fitness and nutrition, and create an opportunity for early intervention. As noted in the article, “Mobile Health Apps and Health Management Behaviors: Cost-Benefit Modeling Analysis", “chronic diseases, but not health crises, often manifest in the form of health management routine. [In situations like this] the use of mobile health apps helps to address the health concerns of individuals who are already aware of their health condition.” MHealth can also provide opportunities for continuity of care in public health, particularly for communities that lack transportation or the ability to take time off from jobs to seek treatments. This can be magnified during times of crisis like pandemics or natural disasters when in-person visits are challenging. As noted in, “The Impact of Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective’, ”Mobile health apps effectively promote information exchange, storage, and delivery, and they improve the ability of patients to monitor and respond to diseases. They can also be used for training, information sharing, risk assessment, symptom self-management, contact tracking, family monitoring, and decision-making [as they were] during the COVID-19 pandemic.“ Perhaps most importantly, mHealth can help reduce costs and address workforce shortages associated with physical infrastructure, including travel, time off and geographic barriers making healthcare more cost-effective for both patients and providers. As the authors note in “Mobile health app users found to be more content with public health governance during COVID-19”, “Smartphone apps can partly eliminate the shortage of medical resources and improve the quality of medical services for high-risk groups and [those] residing in remote locations.” Related Reading: The Impact of Using mHealth Apps on Improving Public Health Satisfaction during the COVID-19 Pandemic: A Digital Content Value Chain Perspective Mobile health app users found to be more content with public health governance during COVID-19 Commercial mHealth Apps and Unjust Value Trade-offs: A Public Health Perspective Research on the Impact of mHealth Apps on the Primary Healthcare Professionals in Patient Care Access to Telemedicine Is Hardest for Those Who Need It Most

The Driver: CytoVale Inc announced in November that it raised $84 million in a Series C funding round, led by Northwest Ventures Partners with participating investors, Sands Capital and Global Health Investments Corporation. Funds will be used to bring Ed focused FDA 510(k) Cleared IntelliSep Diagnostic Test from a simple blood draw to hospitals and health systems nationwide to support early detection and diagnosis of fast-moving diseases like Sepsis. To date, Cytovale has raised over $128.6 million in funding in over 9 rounds, with additional investments from Breakout Ventures, Blackhorn Ventures, Dolby Family Ventures, Western Technology Investments, and grants and contracts from the National Science Foundation, the National Institute of Health, and the U.S Health & Human Service Department. Key Takeaways: Sepsis-the body’s overwhelming life-threatening response to an infection contributes to at least 1.7 million adult hospitalizations and at least 350,000 deaths annually in the United States (MMWR, 2023) Mortality rates from sepsis increase at least 8 percent for every hour that treatment is delayed and 80% of sepsis deaths could be prevented if treated in time (AAMC) More than 87% of sepsis cases originate outside of the hospital, so when a patient comes into the emergency department, physicians often face a mystery to solve quickly (Mayo Clinic) Sepsis was both the most frequent (2.2M stays) and the costliest ($41.5 billion in aggregate) of the 10 most common principal diagnoses for inpatient hospital stays (AHRQ) The Story: Founded in 2013, Cytovale is led by a team of scientists, engineers, former physicians, and financiers. Co-Founder and CEO, Ajay Shah, PHD is an expert in cell based diagnostic technologies and comes from a family of physicians. CytoVale, was spun out of the UCLA lab of Dino Di Carlo, the co-founder and scientific advisor to Cytovale. As noted by the San Francisco Business Times in 2014, Cytovale was one of the early recipients of funding from Peter Thiel’s Breakout Labs and has been working since inception to create a quick way to detect disease by using microfluidics to measure the physical properties of cells. Initially the company targeted biomarkers for the early diagnosis of sepsis, a potentially deadly blood infection that is difficult to spot until the infection has reached organs. In 2019, Cytovale was awarded a contract from the Biomedical Advanced Research and Development Authority (BARDA) which announced that it would give the company an initial $3.4 million, with an option for an additional $4.17 million, to advance development of the company's sepsis test, which may be able to diagnose the blood infection in less than 10 minutes. According to BARDA and the CDC, “sepsis kills about 270,000 Americans annually and occurs when there is a faulty immune response to an infection, [which] can cause tissue damage, organ failure, and even death.”  In January of 2023, Cytovale received U.S. Food and Drug Administration (FDA) 510(k) clearance to aid in the early detection of sepsis in adult patients with signs and symptoms of infection who present to US emergency departments (ED). The Differentiators: Patients who visit the ED with Sepsis usually present with fever and chills, low blood pressure, increased heart rate, and difficulty breathing which are caused by bacterial, viral, or fungal infections; these symptoms can mimic other conditions. Current common diagnostic testing includes Blood Tests, Urine Tests, Wound Culture Tests, Sputum Culture Tests, and X-rays, are time-consuming and have the potential to produce false negatives. Cytovale’s IntelliSep,  a biomechanical test, rapidly assesses a patient’s immune activation state using interrogation immune cell morphology and mechanics by applying pressure to the patient’s white blood cells and characterizes their response – which differs between septic and non-septic patients, with results in under 10 minutes. IntelliSep categorizes its results into 3 categories, band 1 through 3, which is based upon the probability of a patient having or developing sepsis within the next three days, with band 3 having the highest susceptibility. Access to pertinent information in a matter of minutes gives physicians the confidence to determine treatment options and reduce poor health outcomes including death. As noted by the company, ”IntelliSep is a groundbreaking diagnostic tool that helps clinicians recognize sepsis and supports critical time-sensitive clinical decisions, providing test results in under 10 minutes. [It is a] first in a new class of ED-focused diagnostic tools that assess host response, and is a simple, fast, and intuitive solution that provides actionable answers directly from a standard blood draw.” Reducing the time to diagnosis is particularly important, especially with Sepsis. For example, according to a 2006 study, mortality rates from sepsis increase at least 8 percent for every hour that treatment is delayed. As explained by the Sepsis Alliance, “the condition is the body’s overwhelming life-threatening response to an infection, which triggers a chain reaction and quickly leads to tissue damage, organ failure, and death. [As a result], as many as 80 percent of sepsis deaths could be prevented with rapid diagnosis and treatment—making early detection critical to improving clinical, operational, and financial outcomes. The Big Picture: In 2017, hospital costs for 35.8 million hospital stays were $434.2 billion, making hospitalization the most expensive healthcare utilization, with Septicemia being the single most costly inpatient condition at an aggregate cost of $41.5B. In addition,  Septicemia was far and away the most expensive condition with a mean cost per stay of $18,700. body’s overwhelming life-threatening response to an infection where there are difficulties distinguishing common infections or other conditions that can mimic sepsis. This can lead to errors, delays, misallocation of medical resources, and overuse of antibiotics, resulting in increased costs to the healthcare system; costs estimated at $62 billion annually on sepsis alone. “We are very aware of the cost constraints on hospitals, and we see IntelliSep offering value of many orders of magnitude greater than its costs' “, stated Shah in a 2021 Forbes interview before receiving FDA 510(K) clearance for IntelliSep. Our Lady of the Lake Regional Medical Center and its Emergency Department, located in Baton Rouge Louisiana- which has the highest rates of sepsis mortality in the United States, was among the first medical facilities to implement the IntelliSep test as its Sepsis protocol as part of a multi-center study. According to the national principal investigator, Dr. Hollis O’Neal, “the test provides hospital staff with information needed to identify and treat septic patients efficient Cytovale Secures $84 Million Series C to Advance Commercialization of Transformative Sepsis Diagnostic Tool; How Cytovale Is Set to Transform the Fight Against Sepsis; Sepsis: The biggest threat you've never heard of

Our Take: The drug development process is a time-consuming and expensive endeavor fraught with failures even with proper planning and execution. With an average of $1-2 billion spent per successful drug and a development period of 10-15 years, the high cost and lengthy timeline are barriers to entry for many drug manufacturers. However, the integration of artificial intelligence (AI) into the drug development process has the potential to modernize the industry. AI can help researchers in a variety of ways, such as by analyzing large datasets, predicting biological processes, identifying new drug targets, and assisting in the design of new drug molecules. Furthermore, AI can assist in data mining, generating regulatory documents, and identifying suitable candidates for clinical trials. The implications of these developments are significant, as AI has the potential to improve the speed and efficiency of drug development, ultimately leading to the production of more effective treatments, although care must be taken to ensure its factual accuracy and validity as an increasing number of companies adopt AI solutions. Key Takeaways: Most drugs take between 10-15 years to be developed at an average cost of $1-2B before receiving [U.S.] approval for clinical use (Chinese Academy of Medical Sciences and the Chinese Pharmaceutical Association) It is estimated that 85% of the human proteome is considered undruggable and finding effective pharmaceuticals to target these proteins is considered exceptionally hard, or impossible (The Cambridge Crystallographic Data Centre) Machine learning methods such as eToxPred correctly predict synthetic accessibility and toxicity of drug compounds with accuracy as high as 72% (BMC Pharmacology and Toxicology) The use of Machine Learning in drug discovery could save approximately $300-400M per drug (U.S. General Accounting Office) The Problem: Drug development is a time-consuming, costly process rife with failures even with strong strategic planning and execution of the process. For example, as noted in a recent article in Acta Pharmaceutica Sinica B (the journal of the Chinese Academy of Medical Sciences and the Chinese Pharmaceutical Association), most drugs take between 10-15 years to be developed, with an average cost of $1-2 billion spent before finally receiving federal approval for clinical use. While in theory during “clinical drug development, a delicate balance needs to be achieved among clinical dose, efficacy, and toxicity to optimize the benefit/risk ratios in patients. [Ideally] a drug candidate would have high potency and specificity to inhibit its molecular target [supplying] high drug exposure in disease-targeted tissues to achieve adequate efficacy at an optimal dose (ideally at low doses), and minimal drug exposure in healthy tissues to avoid toxicity at optimal doses (even at high doses).” However, while this is easy to specify in theory, in practice it becomes difficult to execute. For example, according to the article “analyses of clinical trial data from 2010 to 2017 show four possible reasons attributed to the 90% clinical failures of drug development: lack of clinical efficacy (40%–50%), unmanageable toxicity (30%), poor drug-like properties (10%–15%), and lack of commercial needs and poor strategic planning (10%). Consequently, each of the five stages of drug development 1) discovery & development, 2) preclinical research, 3) clinical research, 4) FDA review & approval, and 5) FDA post-approval drug safety monitoring, require significant funding and resources yet can have precarious returns. As noted in an article in Nature Reviews Drug Discovery, while the probability of going from phase III to launch has risen from 49% to 62% in the periods from 2010-2012 to 2015 to 2017, the probability of a compound going from phase II trials to phase III trials has remained essentially the same at about 25% during this same period. The process is voluminous and requires analysis of large amounts of varying types of data. As noted in a report from the GAO on the benefits and challenges of machine learning in drug development, there are multiple types of data relevant to drug development, including data from biomedical research to better understand the biology of diseases, the pharmacology of potential drugs, the toxicity of known compounds as well as the various forms of patient data necessary to conduct the trial and analyze efficacy. Asa result, drug developers are faced with the task of analyzing ever increasing amounts of data to produce similar declining returns on their research leading them to seek new ways to search for and analyze potential candidates, such as the application of AI to the drug discovery process. The Backdrop: AI has the potential to solve a variety of industry problems and is being used in drug development to rapidly speed up the process of creating and assessing the effects of these novel compounds. While some authors have identified at least 10 ways AI can help in the drug discovery process (see Machine Learning in Drug Discovery: A Review) some of the more common uses that researchers associate with the use of AI in drug development include: 1) helping to find promising new drug candidates in lead and biomarker discovery, 2) data analytics and prediction (ex: classification, clustering, and prediction) of effective candidates for further analysis, 3) using AI (and capabilities like digital twins) to improve the speed and efficacy of preclinical development, 4) the detection and understanding of the potential for adverse effects. For example, by feeding this data to AI tools, which find associations between patients’ genotypes and phenotypes, researchers are able to discover new biomarkers that allow for patient stratification as well as the identification of biochemically active genomic regions that respond best to certain drugs. As noted in the Journal of Signal Transduction and Targeted Therapy, not only can AI transform and interpret this data into potential biological processes that could be utilized in the pharmacodynamics of a certain drug compound more rapidly than human researchers, it can do so far more accurately than human researchers given AI’s ability to discover patterns and relationships. In terms of designing the drugs themselves, AI tools can be used to save researchers a lot of time. AI that has been trained using advanced biology and chemistry data is assisting in identifying new drug targets and helping to build applicable new drug molecules. A significant problem in the process of drug discovery is the proper identification of genomic regions that could be useful in regard to potential drug targets, and an estimated 80% of the human genome is yet untested or simply undruggable. Understanding and examining large volumes of biological data resulting from the genomics, proteomics and experimental interpretation of a certain drug target is a lofty task to complete with traditional methods, and the complex biological networks are difficult to fully break down and map completely. By analyzing a target’s gene expression, protein-protein interactions, results from clinical trials and disease biology, these AI algorithms can predict if the target is suitable for drug interactions and build molecules with specific properties, activities and toxicities that can help identify suitable candidates as per Research and Markets’ report on AI in drug target discovery and validation. In the preclinical and clinical spheres, AI is rapidly adapting to the needs of researchers in order to set up and analyze the data from necessary experimental trials needed for a drug to receive approval from the FDA and prove its efficacy so that pharmaceutical companies can create a product that works. The development and testing of new drugs creates terabytes to petabytes of biological data at each stage of development, which is ideally suited to AI tools’ ability to work with large datasets. Pfizer, one of the largest pharmaceutical companies in the world, which has been utilizing AI for data mining purposes, have reported that AI runs much faster and more accurately than any human researchers are capable of and provides the added benefit of helping the company to meet regulatory and quality control requirements such as generating the reams of materials necessary to be submitted during the development process. Moreover, as noted in a recent article in Trends in Pharmacological Sciences, outside of the drug development process itself, AI can be used to identify and access the patient records of those who are most likely to benefit from clinical trials, reducing the time to identify suitable trial candidates and improving success rates. This is extremely important to the success and speed of trials given that approximately 48% of trials miss enrollment targets and 49% of patients drop out of trials before completion (thereby making the identification of suitable candidates key to enrolling sufficient numbers to account for this). Additionally, the use of AI in remote patient monitoring solutions such as wearable devices, virtual outpatient services, and more can help to monitor patients and predict adverse health events thereby making pharmacovigilance more effective and cheaper. According to “Artificial Intelligence in Health Care Benefits and Challenges of Machine Learning” from the U.S. General Accounting Office in Drug Development, the use of Machine Learning in drug discovery could save approximately $300-400M per drug. Implications: As noted above, AI has the potential to dramatically speed up the development of drug discovery while simultaneously helping to reduce the cost and improve the efficiency compared to traditional technologies currently being leveraged to find new drug molecules. With increased public interest and popularity concerning AI solutions including but not limited to issues in healthcare, new tools are being developed that are already showing great promise. MIT researchers created a geometric deep-learning model called EquiBind that is an estimated 1,200 times faster than one of the fastest, state-of-the-art computational models. EquiBind outperformed the current state-of-the-art model, QuickVina2-W in successfully simulating the binding of drug molecules to protein-coding genes and saved significant amounts of time that are usually spent in computation using cutting-edge geometric reasoning. This advancement will ultimately allow AI to better understand and apply concepts of molecular physics, leading to better predictions and generalizations fueled by the vast amounts of collected information that is difficult and time-consuming for humans to accurately sift through. EquiBind is only one of the multitude of AI tools being developed for drug research, and as AI continues to improve on previous iterations and synthesize increasingly larger volumes of data, this will translate into far greater efficiency and time savings than can be achieved with current industry standards. In addition, AI will have applications in quality control as machine learning methods are used to evaluate drug candidates for toxicity and side effects. For example, according to an article in BMC Pharmacology and Toxicology, a technology called eToxPred can correctly predict the synthetic accessibility and toxicity of drug compounds with accuracy as high as 72%. Over time as the adoption of AI accelerates in drug development there is the potential for the development of even more personalized medicines tailored to the specific needs and genome of patients. Given the vast amounts of patient data collected and stored by hospitals, insurers, and others in healthcare, and as the industry increasingly digitizes, there is a significant volume of data that is underutilized and which could be informing better care practices, including drug discovery. However, as with the application of AI in any industry, this must be done with ethical considerations in mind and specific policies and protocols in place in terms of data privacy, algorithmic bias, and transparency. AI can identify patients that are most likely to respond positively to a particular drug which could lead to treating individuals sooner, rather than possibly having to wait to participate in clinical trials (once again under the right safety protocols). The idea of more targeted and personalized healthcare remains intriguing, but it must be done with accountability and transparency in mind, so that clinicians and patients understand how and why the algorithms work the way they do. If so, there is the potential to fundamentally change the way we develop new drugs and get these experimental treatments to those who desperately need them more quickly and cheaper than ever before. Related Reading: Artificial Intelligence in Health Care Benefits and Challenges of Machine Learning in Drug Development Why 90% of clinical drug development fails and how to improve it? Artificial Intelligence: On a mission to Make Clinical Drug Development Faster and Smarter Artificial Intelligence for Clinical Trial Design Artificial intelligence model finds potential drug molecules a thousand times faster eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates

Our Take: Artificial intelligence (Ai) can maximize the analytic value of devices that allow for continuous monitoring of cardiac patients such as wearable devices and remote patient monitoring (RPM) and have the potential to dramatically impact cardiovascular care. In the words of “Artificial intelligence in cardiology: fundamentals and applications”, “[AI] is becoming integral to the day- to-day practice of cardiology, including interventional cardiology, electrophysiology and cardiac imaging. AI not only holds great promise in cardiology by improving outcomes, increasing accessibility, and enhancing the efficiency of delivery data collected by these devices but it holds the potential to reduce morbidity and create new treatment protocols. However, it also presents challenges related to data security, regulatory compliance, and integration into existing healthcare systems. Adapting to these changes will be essential for realizing the full potential of AI in cardiology. Key Takeaways: 6.2 million adult Americans have heart failure, with prevalence projected to increase by 46% and direct medical costs escalating to $53 billion by 2030 (CDC, Journal of Managed Care & Specialty Pharmacy) Between 2017 and 2020, almost 128M US adults had some form of cardiovascular disease with total costs of $407.3B (American Heart Association) Despite improvements in the treatment and incidence of heart failure the 1-year mortality rate remains approximately 30%, while the 5-year mortality rises to 40% (Circulation) Cardiovascular disease was the underlying cause of death, accounting for almost 1M deaths in the United States in 2020 (American Heart Association) The Problem: Several challenges exist in the integration of artificial intelligence into the future of healthcare delivery in cardiology. First and foremost is data quality and accuracy given that the quality and accuracy of data collected by wearable devices and other digital health tools can vary widely. For example, as noted in the “Artificial intelligence and heart failure: A state-of-the-art review”, “model accuracy may be compromised if optimal image quality or accurate views are not acquired. Using integrated ECG, echocardiography, and clinical data to develop ML algorithms presents the additional challenge of con-currently processing diverse data formats." In addition, integrating digital health solutions may require significant upfront investments in technology and infrastructure. As the aforementioned, “Artificial intelligence and heart failure: A state-of-the-art review” notes " Implementing AI algorithms in clinical practice requires a comprehensive approach that goes beyond obtaining clearance. Implementing AI algorithms in clinical practice can be costly.” Consequently, healthcare systems, particularly less well funded ones may need to consider various funding options including joint-ventures and partnerships. Like other specialties using AI, the use of AI in cardiology will be heavily regulated due to the risk of poor or inconsistent results. As pointed out by “Artificial intelligence in cardiology: Hope for the future and power for the present”, "Another important aspect is the achievement of robust regulation and quality control of AI systems. As AI is a new and rapidly evolving innovative field, it carries significant risks if underperforming and unregulated." Moreover, ensuring that devices are compliant with existing and evolving data protection laws and medical device regulations will be a significant challenge. The Backdrop: The integration of digital technologies such as wearable devices, mobile apps, and remote patient monitoring with AI has enabled rapid advancements in cardiac care technologies. For example, in the article “Artificial intelligence in cardiology: Hope for the future and power for the present” the authors point out that "the Apple Heart Study showed that the utilization of smartphones was effective in identifying patients with subclinical paroxysmal [atrial fibrillation] AF. Highlighting, it detected 0.5% of patients with possibly irregular pulse, 34% of which were diagnosed with AF confirmed by ECG." Clearly the new combinations of new digital technologies and AI has created new opportunities for monitoring, diagnosing, and treating cardiac conditions. In addition, with demographics and aging populations in many countries the incidence of cardiovascular disease is projected to increase in the coming years. For example, according to PharmaNucleus the market for congestive heart failure was valued at $21B in 2021, and is projected to  reach over $36B by 2030, with a CAGR of 7% per year. Hence, as pointed out in “Artificial intelligence in cardiology: Hope for the future and power for the present”, "the incorporation of ML methodology into the field of [heart failure] HF aims the early detection of those patients most at risk of developing the disease, correct classification of patients based on their personalized risk and prompt intervention which can be beneficial for patients with improvements in morbidity and mortality via early initiation of treatment and secondary care (via shifting treatment and follow up in the community and reducing hospital admissions)." Cardiology is and will continue to be both a global and domestic health challenge. For example, according to 2023 Heart Disease and Stroke Statistics from the American Heart Association, between 2017 and 2020, almost 128M US adults had some form of cardiovascular disease with total costs of $407.3B. Applications of AI models to these disease states could help both diagnose and treat these diseases. As noted in the “Role of Artificial Intelligence and Machine Learning in Interventional Cardiology '',”one study used [support vector machines] SVM to detect potentially life-threatening ventricular arrhythmias. Public access ECG databases were used to train, test, and validate datasets, giving a test accuracy of 96.3%, sensitivity, and specificity of 96.2%. Another investigation classified non-life-threatening ECG beats using a convolutional neural network into 5 classes (nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown).” Moreover, as highlighted in “A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring”, “to convince clinicians, government, and funding agencies to pay for the cost of implementation of AI algorithms, it will be important to demonstrate measurable improvements in clinical outcomes, such as reduced length of hospital stays, morbidity, and mortality rates. Moreover, demonstrating a positive return on investment, such as increased revenue or cost saving can help justify the upfront cost of implementing AI algorithms and encourage investments in implementation of technology." Implications: Continuous monitoring and predictive analytics enable early intervention, potentially preventing cardiac emergencies and reducing the severity of conditions including CHF and hypertension.  The wealth of patient data collected through digital health tools can fuel research and innovation in cardiology, leading to the development of new therapies and diagnostic methods. For example, collecting data via “Apple’s Siri, Amazon’s Alexa, and Google Assistant. Voice is more convenient and faster than typing on keyboards. [These] AI-assisted virtual assistants can process the input of multi- modal data and present them to the cardiologist in a meaningful manner.” Moreover, by giving patients access to real-time health data they can actively participate in their care. Empowering them in this way can improve adherence to treatment plans and encourage patients to make necessary lifestyle changes. However, as outlined in “Artificial intelligence in cardiology: Hope for the future and power for the present'', “an ethical platform is required for the responsible delivery of [any] AI project. This necessitates cooperation from all the team members of the multidisciplinary team, in order to maintain a culture of responsibility and execute a governance architecture that will adopt ethical practices at every point in the innovation and implementation lifecycle.” Related Reading: Artificial intelligence in cardiology: Hope for the future and power for the present Role of Artificial Intelligence and Machine Learning in Interventional Cardiology Artificial intelligence in cardiology: fundamentals and applications A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring

' The Driver: Twin Health recently raised $50 million in a series D financing to continue to expand its personalized Whole Body Digital Twin technology services, a dynamic model of an individual’s unique metabolism that is used for reversing and preventing chronic metabolic diseases like diabetes, for the company’s employer and health plan partners. Funding for this round was led by Temasek with support by ICONIQ Growth, Sofina, Peak XV, and Helena. According to Crunchbase, Twin Health has raised a total of $248.5 million in funding of four rounds since being founded in 2018. Key Takeaways: People diagnosed with diabetes account for one out of four health care dollars spent in the United States, with spending on insulin increasing from $8 billion in 2012 to over $22 billion in 2022 (American Diabetes Association) Diabetes was the 4th most frequent cause of inpatient hospital stays with a mean cost of approximately $12,000 and total inpatient costs of almost $8B (AHRQ) Direct medical costs associated with diabetes care increased by 7% between 2017 and 2022, with Black Americans with diabetes paying the most in direct health expenditures (American Diabetes Association) As of 2021, 38.4 million people in the United States are living with diabetes with 29.7M people having been diagnosed while an estimated 8.7 million or over 20% remain undiagnosed (American Diabetes Association) The Story: Twin Health was founded in 2018 by CEO Jahangir Mohammed, an engineer and founder of Jaspar (a company that created a “switch on” for Internet of Things technology which enabled communication between a diverse set of objects, which resulted in the proliferation of twin technologies) and VP of Research Dr. Maluk Mohamed, and computer science engineer CTO Terrence Poon. Dr. Mohamed’s son had undergone a liver transplant and Dr. Mohamed conducted research on how sensors could be implanted in patients to facilitate improved post-transplant monitoring and thus reduce the amount of immunosuppressive drugs given to patients post-transplant. Separately, Jahangir Mohammed realized that about 40% of his family members were type 2 diabetic and wondered if twin technology (ex: a virtual copy of the human organs, tissues, cells or micro-environment that constantly adjusts to variations in the online data and can predict the future response of the human being or organ that it is meant to correspond to) could be applied to treat type 2 diabetes. Familial healthcare issues brought together the two founders to collaborate and create Whole Body Twin, which is a digital representation of one’s metabolism, which gives doctor’s access to thousands of data points through wearable sensors, clinical lab parameters, and self-reported preferences. The Differentiators: According to the company, the company’s Whole Body Digital Twin™ is a digital representation of each person’s unique metabolism and delivers precise, personalized guidance about foods, sleep, activity, and breathing through the easy-to-use app. This is combined with a dedicated care team that monitors each patient's sensor data, offers personalized recommendations to improve responses to metabolic conditions like diabetes. Unlike some competitors, Twin Health has been able to validate it’s results through randomized controlled trials. Twin Health completed the first randomized controlled trial for reversing chronic metabolic disease using Digital Twin health technology. Results showed that people diagnosed with type 2 diabetes with digital twin intervention had significantly better HbA1c levels than type 2 diabetics under standard care, with a 2.9% reduction from 9.0 to 6.1 within 6 months. After 1 year of digital Twin Health  intervention, there was a 72.7% remission rate in people with type 2 diabetes. After the first 6 months of intervention, patients had an average weight loss of 16.6 lbs., reducing BMI >30. The Big Picture: As noted in “Digital twin in healthcare: Recent updates and challenges”, “the development of the technologies of big data, cloud computing, virtual reality, and the internet of things (IoT) has laid a technical foundation for the application of digital twin and thus provided clinicians and researchers with a more detailed dimension with which to study the occurrence and development of diseases and to conduct more precise diagnoses and treatments. A digital twin can simulate dosage effects or the device response before a specific treatment and thus indicate whether the medical device or treatment is appropriate for patients and improve the treatment of patients with different causes of disease” thereby helping reduce the cost of development while improving the effectiveness of treatments. As noted above, this can be particularly relevant for diabetes where treatment costs can be staggering. For example, according to the American Diabetes Association’s Economic Report, the total annual costs for diabetes in 2022 were $412.9 billion, which includes $306.6 billion in direct medical costs and $106.3 billion in indirect costs. In addition, the report found that people diagnosed with diabetes account for one out of four health care dollars spent in the United States, with spending on insulin increasing from $8 billion in 2012 to over $22 billion in 2022. With treatments like Twin Health’s “Whole Body Twin” dramatic reductions in cost can be achieved. For example, according to the study conducted by the company, there was a 71% reduction in the use of high-cost medication with data from the study predicting a cumulative annual per patient savings of almost $8,000 due to the improvement of BMI, HbA1C, and blood pressure. In addition, treatments such as these can help patients reduce or eliminate the need for supplemental insulin entirely with Twin Health demonstrating that after a 90-day follow-up, patients discontinued the use of insulin, and about half stopped taking metformin. However, while digital twins and precision medicine hold great promise they must ensure they address the issues of data security, data privacy and data interoperability to achieve their full potential. Twin Health multiplies funding with $50M for metabolic disease-reversing tech; ADA: Twin Health’s AI tech leads to Type 2 diabetes remissions, study finds

While we were working on an upcoming project on the Social Determinants of Health (SDOH) we came across the following article entitled "Coping with the stress and uncertainty of chronic health conditions" in the November 2023 issues of American Counseling Association’s magazine, Counseling Today. We felt it was one of the most encompassing and thoughtful looks at ensuring we treat both the physical and mental health side of those with chronic conditions and reprint it here with permission. Living with a chronic health condition can be physically and emotionally stressful. Imagine waking up in the morning to searing pain because of reoccurring migraines or experiencing vision and speech problems and mobility challenges because of multiple sclerosis. Common types of chronic pain or illness include low back pain, cancer, arthritis, fibromyalgia, diabetes, heart disease, amyotrophic lateral sclerosis (also known as Lou Gehrig’s disease), Alzheimer’s disease and dementia. No matter the type of chronic condition, they all have the potential to be unsettling, which often causes people to seek professional help. Dakota Lawrence, a licensed professional counselor-mental health service provider who specializes in chronic pain, chronic illness and trauma, says many clients come to counseling when the pain is disrupting their lives and they feel things are “falling apart.” For example, the pain may cause some clients to be unable to perform work duties or make them withdraw from a sports team at school. According to a 2020 report by the Centers for Disease Control and Prevention, 20.4% of adults were living with chronic pain in 2019, and 7.4% of adults had chronic pain that frequently limited life or work activities. Lawrence says some clients think the pain or illness “can be fixed” by taking a break from stress, undergoing surgery or engaging in physical therapy. When people are in pain or sick, they tend to think that there is a single clear cause that can be treated or cured, he explains. But repeated doctor visits and medical tests often do not lead to clear results and proposed treatments may not offer much relief. Clients may also take sick leave from work or even change jobs out of concern that they are burnt out and that stress is the cause of their illness, but then they notice that their pain or illness does not disappear despite the respite. “It’s only when [they’ve] gone from doctor to doctor and run out of answers that they tend to wind up in therapy,” Lawrence says. When clients do come to counseling, his main goal is to help the client return to living a meaningful life with their pain or illness as well as the uncertainty that can go along with it. No clear answers One stressor that often comes with a chronic health condition is not having a clear understanding or explanation of what is going on with the body. Alicia Dorn, a licensed clinical professional counselor in Columbia, Maryland, says often clients have been struggling with a chronic condition since childhood without ever having a medical diagnosis or a clear understanding of what kind of health issue they are dealing with or its origin. The reason for this, she says, is that the medical professionals who treated them as children often assumed they were simply experiencing growing pains or overreacting, so they did not conduct additional diagnostic testing. Sometimes an unsupportive family, limited resources or little information about what has caused the person’s symptoms can delay a diagnosis in childhood and adulthood, notes Dorn, who specializes in chronic illness and chronic pain. She says this leaves many adult clients feeling worried and concerned about having to convince medical professionals that they have a condition that needs immediate attention. Lawrence, co-owner of a private practice in Murfreesboro, Tennessee, says the frustration of not having a formal diagnosis only leads some clients to discover that “there’s not any clear answer as to why this [the chronic condition] is happening and whether it will get any better.” And those who do receive a diagnosis face another challenge: coming to terms with living with a life-altering condition. A diagnosis can be scary, Dorn admits, “because it’s something that likely will not have a cure, and it will change how they live for the rest of their lives.” For example, the chronic condition may mean some clients will have to deal with “persistent suffering,” which can make it harder to live the type of life they want, she says. And for many people, a medical diagnosis can also bring their own mortality into question. Changes to self-identity and daily life Chronic conditions can affect every aspect of a client’s life — work, school, family, friends, recreational activities and even the way they view themselves. Lawrence says before clients discover they have a chronic condition, they may see themselves as strong, independent and able to take care of themselves and the people in their lives. However, the physical and mental limitations that can come along with chronic conditions can alter the client’s identity and leave them feeling lost and unsure of who they are, he says. For example, clients may find that they are not able to do simple things, such as mowing the lawn, playing with their children or enjoying certain social activities with friends. Chronic conditions can also lead to relationship problems. The ability to be physically mobile and connect emotionally with other people in meaningful ways can fluctuate from day to day, Lawrence says. In addition, a relationship with a spouse or partner may have become strained because their significant other is beginning to feel more like a caregiver than a life companion or romantic partner, he adds. Some clients report that the physical pain and depression they experience makes them feel less sexually active and less inclined to sleep or eat regularly, adds Ryan Ibarra, a licensed professional counselor (LPC) at Foothills Neurology, a medical group practice in Arizona that specializes in providing behavioral health treatment for neurological disorders. Research also shows that living with chronic pain or a chronic illness can make people more likely to struggle with mental health disorders, such as depression, anxiety, posttraumatic stress disorder, suicidal ideation and grief. Of the people who took a Mental Health America screening, those with chronic health conditions were at higher risk for a mental health condition. This includes 79% of people who struggle with chronic pain, 75% of those with heart disease and 73% of people with cancer. Ibarra, who specializes in chronic diseases, says clients who have chronic health conditions may also report struggling with fatigue, stomach issues, sleep problems and panic attacks. Dorn says clients often come to therapy because they need help figuring out if they will be able to make the adjustments that will enable them to maintain a measure of stability in their lives. “Every day, clients are reminded of a condition that they didn’t ask for [and] that wasn’t necessarily their fault but is making it much more difficult to be the person they want to be,” she explains, noting that clients are often focused on managing their health and may pretend they are feeling “OK” for those around them. Assessing for chronic health conditions Because some clients may have experienced trauma and may not feel comfortable disclosing their chronic condition in session, particularly if it is not visible, counselors should assess for chronic ailments during the intake process. Lawrence recommends clinicians ask about the client’s health history using a checklist of physical health conditions (such as diabetes, fibromyalgia and cancer) or physical health symptoms, (such as pain, chronic fatigue and dizziness). Counselors can ask clients simple and direct questions, he continues. For example, they can say: When was the last time you saw a health care provider? Are there any current or previous medical diagnoses that are causing significant stress? What do you do in your free time and what activities give your life meaning? On a scale of 0 to 10, how engaged have you been with these activities in the last six months? How many hours of sleep do you average a night? What did you eat yesterday? How often do you get sick? Once or twice a year? Once every few months? Every few weeks When you get sick, how long does the illness typically last? On a scale of 1 (almost never) to 7 (almost always), how often are you in pain? And how intense is the pain on a scale of 0 to 10? Tameeka Hunter, an assistant professor in the Psychology and Counseling Department at Palo Alto University in California, says it is important that clinicians ask about the presence of chronic illnesses and disabilities, but they shouldn’t assume that chronic conditions are the “problem” or presenting concern. Counselors also need to be aware of their own implicit and ableist biases before working with this population, Hunter adds. She recommends counselors use the Implicit Association Test, developed by Project Implicit Research at Harvard University. “It measures the strength of associations between concepts and evaluations or stereotypes to reveal an individual’s hidden or subconscious biases,” explains Hunter, an LPC and certified rehabilitation counselor who lives with a disability. (For more, see the sidebar “How ableism affects people with chronic health conditions.”) Counselors can also review the American Rehabilitation Counseling Association’s Disability-Related Counseling Competencies to learn the specialized skills needed to effectively serve clients with chronic health conditions and disabilities, she says. Noticing and regulating emotional responses Mindfulness-based therapeutic approaches and acceptance and commitment therapy (ACT) can help clients living with chronic pain or illness gain an awareness of the thoughts, emotions and bodily responses that can be a part of their condition or the result of additional life stressors, Dorn says. Doctor’s appointments can be one source of stress or anxiety. Initial appointments with a new provider, general appointments, follow-ups with a specialist and appointments for test results or a potential diagnosis can all create anxiety for clients, Dorn explains. “Some clients fear being told nothing is wrong when they feel unwell, being dismissed by a provider or feeling they have no autonomy over their body and care,” she adds. “This is a form of medical gaslighting that makes navigating the health care system a scary endeavor for clients.” Dorn recommends using mindfulness and ACT techniques with clients who may feel anxious or nervous about going to the doctor for an appointment. For example, counselors can ask clients a series of questions that encourage them to gently observe the thoughts, emotions and body sensations that may come up as they prepare for the visit, she says. These questions can include: What worries come to mind when you think about the appointment? How do these worries show up in your body right now? If you could put all the emotions you feel about the appointment into words, what would they be? What could help you feel more supported and heard during your appointment? What questions or observations would you like to discuss with your doctor? How can you show your body compassion when you’re feeling worried during the appointment? Dorn says she also prepares a plan with the client that includes what to do the night before, the day of, during and after the appointment. She walks them through deep breathing exercises and body scans to practice calming their nervous system and she discusses how clients can advocate for themselves as they navigate the health care system. Counselors can also encourage clients to bring a family member or friend with them to the appointment, so they feel supported and heard, Dorn adds. Dialectical behavior therapy (DBT) is another approach that clinicians can use to help clients develop emotional regulation skills, Lawrence says. He suggests counselors use Check the Facts, a DBT skill that helps clients notice and evaluate their emotional response to a situation. This exercise consists of six reflective questions that help clients determine whether the event itself, their interpretation of the event or a combination of both is causing their emotion. “The goal is to help clients identify their emotions, describe the situation or trigger that caused it as objectively as possible and separate the assumptions, presumed threats, cognitive distortions and catastrophic thinking that may be projected into the situation,” Lawrence explains. He says this DBT exercise also helps clients recognize when their response is ineffective in helping them navigate the situation. For example, a client’s emotion (such as anger, sadness or anxiety) may fit the situation, but the intensity of the emotion may be out of balance. Sometimes an emotional response such as anxiety can be helpful for people living with chronic illness. The key, he says, is to realize when the response becomes problematic. A client with an autoimmune disorder, for instance, may need to be hypervigilant when they go to the doctor’s office to make sure their hands are clean and that they keep an appropriate distance from others who may be sick, Lawrence says. “But if the intensity of their anxiety grows to the point where a client begins to isolate at home and miss their doctor appointments, then we’ve got a problem that can be just as bad for their health.” “Clients run into problems with their emotions when they try to avoid feeling the emotion all together or when the intensity of their emotion is driven by other factors, such as genetics, beliefs, thought distortions, etc.,” he stresses. This can lead to a disproportionate, and often ineffective, response. By using emotional regulation skills such as Check the Facts, clients can learn to better understand their emotions and make sure they are using emotions in functional, adaptive ways, he says. The importance of validation Clients with chronic pain and chronic illness can often feel alone and invalidated and they may even experience medical trauma in the process of trying to find a diagnosis. Dorn, who lives with a chronic illness, says this kind of trauma results from a series of stressful events that are related to a client’s health and make it difficult to feel safe in a medical environment. For example, in some cases, medical providers can be insensitive and write clients off as people who are seeking drugs or are being dramatic, Lawrence notes. Some medical providers may even tell clients that the chronic condition is “all in their head,” he says. But even when medical providers do believe clients have a chronic condition, Lawrence says that seeking a medical answer for the cause or to alleviate suffering can mean invasive procedures or surgeries that don’t always pay off or may further complicate the matter. “The persistent invalidation of their lived experience and invasive exploration of their body can result in medical trauma for some clients,” he notes. Whether it’s a toxic relationship with a doctor or a scary medical experience, clients can often show signs that are similar to posttraumatic stress disorder, Dorn adds. As a result, clients may avoid medical appointments or refuse to talk about their health issues. They may also develop increased worries about their condition and a mistrust of medical professionals. According to Dorn, medical trauma and gaslighting can lead to heightened chronic health symptoms and even a decline in a client’s overall physical or mental health if they don’t get the support they need. The counselors interviewed for this article say that what is often most helpful for clients living with a chronic health condition is to work with a clinician who validates their lived experience and helps them advocate for their own well-being. Ibarra sometimes shares the following hypothetical story with his clients: Imagine entering a room filled with hundreds of people and someone asks, “How many of you struggle with depression and anxiety?” Almost everyone in the room will probably raise their hand. Now imagine someone asks, “How many people struggle with chronic back pain or epilepsy?” Fewer hands would go up, which shows that living with chronic pain or illness is often a more isolated journey. Sharing this story “helps validate the client when they are feeling alone and like no one understands,” he says. “It makes them feel seen by me as their therapist.”

The Driver: Eleos Health recently announced that it raised $40M in a Series B funding, which was led by Menlo Ventures, and with participation from F-Prime Capital, Eight Roads, Arkin Digital Health, Samsung Next, and ION. Eleos Health has raised a total of $68M and plans to use the proceeds to fuel the company's product development plans - which include new and enhanced AI solutions for group therapy sessions, compliance automation, case management, concurrent documentation, and value-based care support as well as accelerate their hiring efforts Key Takeaways: 47% of the U.S. population live in a Mental Health Workforce Shortage area and it’s  been estimated that in 2021, 57.8M or 1 in 5 adults in the U.S. are living with mental Illness (Psychology Today & NIMH) In 2022, there were 196,000 employed psychologists compared to 816,900 physicians in the United States, with a 6% projected growth by 2030 (Bureau of Labor Statistics) The prevalence of mental illness is higher among young adults (18-25) vs. adults aged 26-49 years and older (50+), yet  the percentage of young adults who receive mental health services is lower than both groups (NIMH) While approximately 1 in 10 people may need to use an out-of-network medical specialist, nearly 3 in 10 need to use an out-of-network therapist or prescriber to meet their mental health needs (Colorado Behavioral Healthcare Council) The Story: Eleos was founded by CEO Alon Joffe, COO Dror Zaide, and CTO/CSO Alon Rabinovich all of whom served together in the Israeli military, where they saw first-hand the effects of PTSD and have witnessed the administrative barriers clinicians face. As noted by Joffe, "we started Eleos after personally witnessing the immense administrative barriers facing clinicians - through our own experiences and those of our loved ones. The providers will always be the heroes of the care story — we're just here to help them write it better and faster." According to the company, Eleos Health is a breakthrough clinical application for voice AI, operating ambiently in the background of behavioral health clinician-patient conversations (with full patient consent and permission). This allows Eleos to accurately interpret, analyze and document behavioral health conversations, thereby reducing the operational burden on providers while unlocking objective insights into evidence-based care and the therapeutic alliance. The Differentiators: Eleos CareOps automated technology uses a voice-based Natural Language Processing (NLP), a behavioral health-specific large language model, that interprets, analyzes, and documents conversations between patient and provider during a session. This strategy enables providers to focus on patient care and effective diagnosing, by identifying empirically supported interventions and intervention strategies directly from the session, rather than focusing on administrative work. As noted by the company, given that 30 million Americans are living with untreated mental health conditions, even using the most optimistic math illustrates that the current supply of 600,000 active licensed U.S. therapists will still fall far short of meeting patient demand. As a result, the U.S. needs to dramatically expand its behavioral health workforce of existing mental health care providers who are currently reporting burnout rates as high as nearly 80% among some (ex: psychiatrists). By contrast, using the Eleos system, the company claims 90% of notes can be generated within 24 hours, in a HIPAA-compliant format that reduces insurance denials due to late submission. Moreover, the company states on average, it can cut clinician documentation time by 40% and year-to-data it has analyzed more than 3 million minutes of therapy sessions saving more than 300 days of documentation time across all providers and organizations. The Big Picture: According to the American Psychological Association, the percentage of psychologists reported not being able to meet the demands of increased workload has steadily risen from 30% in 2020 and 41% in 2021, to 46% in 2022. Given the shortage of providers, systems like Eleos Health which saves documentation time and increases patient-facing time are desperately in need. For example, while approximately 1 in 10 people may need to use an out-of-network medical specialist, nearly 3 in 10 need to use an out-of-network therapist and prescriber to meet their mental health needs. Not only can paperwork burden lead to burnout as noted above but as noted by the Colorado Behavioral Healthcare Council its various components can lead to almost 50% of mental healthcare professionals being dissatisfied with the time spent on paperwork due to: duplicate processes, lengthy admission processes and less time with their clients. Clearly, while generative AI and AI-based voice assistants will need to have very specific privacy and data controls, they can be a vital component of workforce effectiveness in mental health. Eleos Health: Behavioral Health AI Leader Company Raises $40 Million, The Week’s 10 Biggest Funding Rounds: Enable And Autonomous Vehicle Startup May Mobility See Big Bucks

Our Take: Artificial intelligence (AI) has the potential to transform the field of anesthesiology by improving patient safety and patient outcomes. It has the potential to contribute significantly to the advancement of healthcare practices, offering innovative solutions to address critical issues in the field of anesthesiology. Pilot AI projects have already been successfully utilized to classify high-risk surgical patients, continuously administer medications throughout surgery, and aid in clinical decision making. Further opportunities exist for AI to transform the field of anesthesiology by improving the safety of critical procedures like tracheal intubation and nerve blocks. With the current shortage of anesthesiologists, a forecasted increase in the demand for surgical procedures, and an increasingly sick patient population, AI may allow anesthesiologists to provide safe high-quality care for these complex patients and decrease the risks associated with invasive procedures. Despite the promising outlook, the incorporation of AI into anesthesiology requires a deliberate and responsible strategy. The implementation of AI technology should be done carefully to navigate legal, ethical, and safety concerns. Key Takeaways: In 2020, workplace staffing shortages affected 35.1% of US anesthesiologists, a figure that rose to 78.4% in 2022 (Anesthesiology) Experts project approximately a 100% increase in Americans aged 50 years and older with at least one chronic disease by 2050 leading to an increase in patient complexity and surgical demand (Frontiers in Public Health) An automated machine learning model that analyzed a dataset of over 1M patient encounters effectively identified patients at high risk of postoperative adverse outcomes, showcasing the potential for AI to enhance risk prediction in surgical settings (Journal of the American Medical Association) Anesthesiologists successfully used a closed-loop system to maintain blood pressure within 10% of a target range for >90% of the case time in patients undergoing abdominal surgery (Journal of Personalized Medicine) The Problem: Artificial intelligence has emerged as a transformative force in the field of anesthesiology, presenting numerous applications that could greatly improve patient care. From optimizing preoperative patient conditions to calculating the risk of adverse events during the perioperative period, AI may also soon perform routine procedures while augmenting closed-loop systems for automated medication and fluid delivery. Despite the many benefits of utilizing AI in the field of anesthesiology, multiple critical issues must be tackled before anesthesiologists can fully embrace and trust this technology. First and foremost, the adaptability and efficiency of AI in anesthesiology may be impeded by safety considerations. Notably, Ethicon Inc’s Sedasys employed an AI model to administer propofol – an anesthetic and sedative medication – aiming to achieve mild to moderate sedation during gastrointestinal (GI) endoscopic procedures. This closed loop general anesthesia system maintained a continuous propofol infusion to sustain sedation throughout a GI procedure, automatically adjusting the infusion based on vital signs. Due to safety measures, the Sedasys machine was unable to increase the dose of propofol if the patient were to become “light” during a procedure – a significant limitation that decreased the adaptability of this technology. Additionally, Sedasys’ safety mechanism of administering fentanyl followed by a 3-minute waiting period before propofol administration compromised efficiency, particularly given the swift completion of most diagnostic upper GI procedures within 5 to 10 minutes. Secondly, the integration of AI in anesthesiology raises medical legal concerns. Medicine, being a highly complex field with nuanced variables, relies on human judgment for clinical decision-making. The intricacies that human anesthesiologists consider, especially in novel clinical situations, may not be fully appreciated by AI systems. In the event of an anesthetic error during a surgery involving AI technology, determining responsibility becomes a complex matter — whether it lies with the anesthesiologist, the AI-developing company, or the hospital. Moreover, issues of patient consent arise, as individuals may not fully comprehend the implications, both positive and negative, of AI technologies and may harbor concerns regarding the security and privacy of their health data. Lastly, a crucial challenge in the utilization of AI in anesthesia pertains to the quality of data it relies on for training and learning. While systems are generally trained on data from electronic health records, patient monitors, and anesthesia machines, these diverse data sets as well as methods of data acquisition, often subjectively documented by clinicians, pose challenges in ensuring the accuracy, consistency, and completeness of the database. Consequently, faulty training data can lead to AI systems making incorrect judgments, potentially resulting in adverse health outcomes. For instance, an AI system may erroneously flag a patient as high-risk before an operation, prompting invasive monitoring during a surgical procedure which introduces associated risks. All these concerns contribute to the hesitation among anesthesiologists to fully embrace AI technology. These are further compounded when practitioners' concerns about job security and the fear of “black box” AI systems making treatment recommendations are added into the clinician’s calculus. The Backdrop: A recent report from the American Society of Anesthesiologists reveals that almost 40% of anesthesiologists plan on early retirement, while about a quarter have already reduced or plan to reduce their working hours. In 2020, workplace staffing shortages affected 35.1% of US anesthesiologists, a figure that rose to 78.4% in 2022. Amid escalating production pressures from private equity firms, financial struggles in hospitals, and alarming rates of burnout among anesthesiologists, AI emerges as a strategic tool that can reduce the stress that anesthesiologists face by helping them provide high-quality care and improve patient safety. Demographic shifts, such as increased life expectancy and the aging "baby boomer" generation, forecast a surge in demand for surgical procedures in the near future. For example as noted in “Retooling for an Aging America: Building the Health Care Workforce”, the authors note, "older patients use two-to-three times as many medical services as younger patients, and the number of people over age 65 will increase by almost 50%, just in the next 10 to 15 years alone”. A 2022 Frontiers in Public Health paper predicts a 99.5% increase in Americans aged 50 years and older with at least one chronic disease by 2050. As surgical volume and complexity rise, anesthesiologists face increasing obstacles in ensuring patient well-being throughout the entire surgical process. AI, through the use of risk calculators, can assist by identifying high-risk patients based on preoperative variables, allowing for optimized resource allocation and patient preparation. A noteworthy example comes from a 2023 Journal of the American Medical Association paper, where researchers employed an automated machine learning model, analyzing a dataset of over a million patient encounters. This model effectively identified patients at high risk of postoperative adverse outcomes, showcasing the potential for AI to enhance risk prediction and resource optimization in surgical settings. Another machine-learning model published in the journal Anesthesiology was able to successfully predict a significant risk factor for adverse perioperative outcomes: post-induction hypotension, defined as low blood pressure in the first 20 minutes after administering anesthetic medication. Moreover, researchers from Japan published a 2021 paper in the Journal of Intensive Care describing the use of a deep-learning AI model capable of classifying intubation difficulty by analyzing face images of patients. Tracheal intubation, a critical step at the beginning of surgical cases, demands precision to avoid complications such as airway damage, bleeding, and prolonged deoxygenation. The AI model exhibited an 80.5% predictive value, providing anesthesiologists with valuable information to prepare advanced techniques and equipment ahead of “difficult airway” situations, significantly improving patient safety. This development aligns with the broader trend of integrating AI into robotic intubation systems, exemplified by machines like the Da Vinci surgical system and the Kepler intubation system, which show promise in automating and enhancing the safety of intubation procedures. With further developments in AI, it is possible that artificial intelligence may one day be integrated into these robotic intubation systems allowing for safe and automated procedures to be performed. As shown by a 2021 paper published in the journal Clinical Anatomy, AI has even augmented ultrasound-guided regional anesthesia procedures by aiding in the identification of anatomical structures on ultrasound. In the study, 97.5% or more of the expert anesthesiologists agreed that the AI-assistant would assist in confirmation of anatomical structures on ultrasound for less experienced practitioners. Another key issue that anesthesiologists face is a phenomenon called “alarm fatigue.” “Alarm fatigue” refers to an increase in a health provider’s response time or decrease in response rate to an electronic alarm alert from a medical or patient monitoring device due to the excessive frequency of alarms. This is especially concerning for anesthesiologists who hear many alarms for blood pressure, heart rate, heart rhythm, oxygen saturation, temperature, and more. Even during high-risk cardiac surgeries, 80% of alarms were deemed useless. In an article published in the Health Informatics Journal entitled “Machine learning in anesthesiology: Detecting adverse events in clinical practice,” the authors propose the possibility of AI systems that can be used to generate meaningful and reliable alarms which can mitigate “alarm fatigue.” Lastly, closed-loop systems integrated with AI that can deliver medications to induce anesthesia in patients, show promising outcomes. AI closed-loop systems may eventually be used to control other factors throughout a surgical case as well – blood pressure, neuromuscular blockade, vent management, and pain control. For example, a 2022 paper published in the Journal of Personalized Medicine described the successful use of a closed-loop system to maintain blood pressure within 10% of a target range for >90% of the case time in patients undergoing abdominal surgery. Another 2020 article in the journal Anesthesiology found that closed-loop systems had a positive impact on delayed neurocognitive recovery and outperformed manual control by anesthesiologists in managing anesthetic medication, fluids, and ventilation variables. Implications: Artificial intelligence holds substantial promise for enhancing patient safety and outcomes in the field of anesthesiology. AI enables anesthesiologists to adopt a proactive approach by identifying high-risk surgical patients and optimizing patient preparation. During a surgical procedure, customized and targeted alarms can help reduce "alarm fatigue," and the integration of robotic-AI devices may improve the safety of procedures performed by anesthesiologists such as tracheal intubation and ultrasound-guided regional anesthesia. These measures collectively improve patient outcomes while avoiding adverse health events, paving the way for the future of anesthesiology. As the demand for surgical procedures rises with an aging population and an increase in chronic diseases, anesthesiologists stand to benefit from AI to navigate the complexities of patient care and aid in clinical decision-making. AI may even play a role in helping to alleviate the aforementioned shortage of anesthesiologists anticipated over the next several years. For example, AI may enable anesthesiologists to supervise less credentialed but highly capable clinicians such as nurse anesthetists to broaden care all without compromising patient care quality. The integration of AI into the field of anesthesiology holds the potential to improve care and lower costs all while helping the evolution of healthcare practice. Related Reading: Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations Artificial intelligence and anesthesia: A narrative review A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia

The Driver: Ventricle Health recently announced that it has raised $8M in seed funding in a round led by RA Capital Management alongside Waterline Ventures and other investors. The company currently supports accountable care organizations (ACOs) in the mid-Atlantic, Texas, Ohio and Florida, with plans to announce more markets soon. According to the company the proceeds of the financing are intended to finance the acceleration of the national delivery of their value-based home care model for heart failure patients in collaboration with value-based care provider groups and payers. Key Takeaways: 6.2 million adult Americans have heart failure, with prevalence projected to increase by 46% and direct medical costs to reach $53 billion by 2030 (CDC, Journal of Managed Care & Specialty Pharmacy). 50% of Americans with cardiovascular disease do not have access to a cardiologist with an average wait time for consultation of 26 days (Ventricle Health). Hospitalization costs are by far the largest costs for heart failure and mean costs per hospitalization for heart failure ranged from $10,737 to $17,830 (Journal of Managed Care & Specialty Pharmacy) Despite improvements in the treatment and incidence of heart failure the 1-year mortality rate remains approximately 30%, while the 5-year mortality rises to 40% (Circulation) The Story: The company was founded in 2021 by heart failure cardiologist, Dr. Dan Bensimhon, and a team of veteran heart failure clinicians. Bensimhon, Chief Medical Officer at Ventricle, is a leading board-certified cardiologist and medical director in the Advanced Heart Failure & Mechanical Circulatory Support Program at Cone Health. He is joined by CEO Sean O’ Donnell, who has a background in value-based care delivery and digital solutions. O’Donnell was previously President and COO of Consumer Health Services at the retail-based physician care clinics for Duane Reade and Rite Aid Pharmacies. According to O’Donnell, “the use of emerging technologies enables a hospital-at-home experience, detecting early signs of disease and implementing evidence-based protocols at a fraction of the cost. The aim of the company is to also build the most proactive, engaging, and impactful provider network for cardiac care in the U.S.” The Differentiators: As noted by the company, access to cardiologists for heart failure patients is a particularly crucial issue. For example, they note the average wait time to secure a cardiology appointment in the U.S. is 26 days which can have a significant impact on follow up care and readmissions. Ventricle attempts to address this issue by following a care model that is “anchored around well-established guideline-directed medical therapy (GDMT) pathways” which as noted in the journal Drugs, is “the cornerstone of pharmacological therapy for patients with heart failure with reduced ejection fraction (HFrEF) and consists of the four main drug classes…being used in conjunction.” As the article points out, “there is an underutilization of GDMT, partially due to lack of awareness of how to safely and effectively initiate and titrate these medications.” Ventricle attempts to overcome this by providing patients access to cardiology care appointments from their home in as little as three days. This should help reduce the 30-day readmission rate from CHF which ran as high in one study as 24.4% for those with reduced ejection fraction and was approximately 23% for all-cause readmission. The company believes its home-based and virtually enabled care model can reduce the overall average annual cost of heart failure care by at least 30-50%. The Big Picture: According to the CDC, heart failure costs the nation an estimated $30.7 billion in 2012. By 2030, heart failure spending is expected to exceed $70 billion. As noted above, by allowing CHF patients to access clinicians earlier, Ventricle could substantially help reduce costs particularly those for hospitalizations. For example, according to a 2022 study in the Journal of Managed Care and Specialty Pharmacy, hospitalization costs are by far the largest costs for heart failure. For example, the article pointed out that the costs per hospitalization for heart failure (HHF) ranged from $10,737 to $17,830 (mean) and charges per HHF ranged from $50,569 to $50,952 (mean) based on an analysis of data between January 2014 and May 2019. Ventricle Health’s virtual-based enabled care model aims to detect early signs of disease with the implementation of evidence-based protocols at a fraction of the cost, thereby reducing the cost of heart failure care by 30-50% according to the company. As noted in a number of studies, “clinical trials have demonstrated that self-management interventions, including face-to-face patient education, telephone case management, and home visits can improve self-care adherence and reduce the risk of HF-related hospitalizations.” all of which Ventricle appears to reimagine and deliver virtually. This can not only increase access to care but there is a potential to reduce the utilization of hospital and emergency services, which could result in a dramatic reduction of healthcare costs. Cardiac care company Ventricle Health garners $8M and more digital health fundings, Startup founded by Cone Health cardiologist, Ventricle Health, raises $8 million in seed funding

The Driver: Ilant Health recently raised $3M in initial funding backed by a number of angel investors including Nick Loporcaro, President & CEO of Global Medical Response; Brandon Kerns, CFO of CareBridge, Russell Street Ventures, and Main Street Health; Matt Klitus, CFO of Lyra Health; Ivah Romm Founding CEO of Cityblock Health and current Cityblock board member; Dr. Sylvia Romm, Founder of Sounder Health and David Werry, Co-founder & President at Well. Key Takeaways: According to a recent analysis by Trusit Securities, the market for weight management employer solutions is projected to be nearly $700M by 2024 with the potential to grow to $6B-$B over the longer-term Approximately 42% of the U.S. population has obesity1, with more than 200 diseases associated with this condition (Milliman) Real-world analysis of GLP-1 obesity treatment conducted by two PBMs found that [only] 32% of members on GLP-1 treatment were persistent at one year, and [only] 27% of those” stayed on therapy for the following year (Prime Therapeutics & Magellan Rx Management) 19.3% of U.S. youth (aged 2–19 years) were classified as obese, 6.1% had severe obesity, and 16.1% were overweight (NHANES) The Story: Ilant was founded by Elina Onitskansky, the former senior vice president and head of strategy at Molina Healthcare, after years of her own struggles with her weight and weight loss. As Onitskansky noted on the firm’s website, “I [had] tried just about everything out there – from working with nutritionists to diets and meal replacements to daily exercise and personal training to weight loss resorts…I would lose weight only to see it come back and then some.” It was only after she referred herself into a bariatric procedure that she had sustained success and decided to found Ilant. As Onitskansky noted to Fierce Healthcare, prior to that she often felt unheard or judged by doctors who “assumed she had yet to try basic changes like diet or exercise” often facing a refrain of “why didn’t I try eating more salads” walking more? Or …just try harder?” Moreover, as she noted on Ilant’s website, even after she had lost the weight, Onitskansky was “ashamed [to tell people she] hadn’t been strong enough to ‘do it on her own’ because it “was hard to overcome years of shame and stigma”. As a result, Ilant was born “out of the desire to use [her] experience as a healthcare executive and as an obesity patient to improve care for others.” The Differentiators: . What distinguishes Ilant’s model is that its goal is to be a “single front door” for patients to assess and access obesity treatments that they access through employers and health plans not direct to consumers. For patients, Ilant seeks to deliver holistic, individualized, evidence-based, integrated treatment. This involves evaluating treatments via what the company calls Ilant Metabolism Matters, to match clients to the right treatment. According to the company, this evidence-based algorithm accounts for the “medical, behavioral, and social determinants of health considerations'' of patients and evaluates treatments along the entire treatment spectrum from “intensive behavioral therapy to pharmacotherapy (including all potential medications, not just GLP-1s), to bariatric surgery”. Ilant combines these services with access to doctors trained in obesity, mental health professionals, nutritionists, and others to treat them holistically and help them succeed. For employers and payers, Ilant applies an analytics engine it terms Ilant Rapid Returns, that addresses what it says is the historic undercoding of obesity and impact of obesity treatment. As noted in the aforementioned Fierce Healthcare article, this helps match “individuals to the treatment most likely to drive outcomes and value for them” while taking into account the physical, emotional and social factors that may impact patients. Ilant intends to work with commercial, Medicare and Medicaid insurers but has not announced any partnerships to date. In addition, according to the company they intend to pursue a value-based care approach where they take both one-sided (shared savings) and two-sided (shared savings and loss) on patients. The Big Picture : According to data from the CDC and Milliman, approximately 42% of the U.S. population has obesity1, with more than 200 diseases associated with this condition. Moreover, according to data from two recent studies, the average cost of care was 100% higher for obese patients with obesity than for nonobese with the health care costs related to obesity accounting for 21% of total national healthcare spending in the United States. This problem is of particular concern when it comes to youth and adolescents. Data from the National Health and Nutrition Examination Survey (NHANES) indicated that 19.3% of U.S. youth (aged 2–19 years) were classified as obese, 6.1% had severe obesity, and 16.1% were overweight. While the recent publicity and study data around the glucagon-like peptide-1 (GLP-1) drugs for diabetes have raised hope that they could be prescribed as a solution for weight loss, these drugs are not a panacea for those struggling to deal with weight loss issues. For example, as noted in Milliman’s report “Payer Strategies for GLP-1’s for Weight Loss”, these drugs “ must be taken consistently and long-term to achieve and maintain weight loss benefits and patients who discontinue use after a few initial doses or are inconsistent with their dosing will likely not see any material health benefits…”. The study went on to note “a recent real-world analysis of GLP-1 obesity treatment conducted by two pharmacy benefit managers (PBMs) found that [only] 32% of members on treatment were persistent at one year, and [only] 27% of those” stayed on therapy for an additional year. Given data such as this it does appear that while GLP-1s may be appropriate for some, due to their side effect profiles and persistency issues, they are unlikely to be appropriate or effective for many. As a result, we believe that there remains a large and robust market for solutions like Ilant Health that create a continuum of treatment options and provide broad-based support for patients. According to a recent analysis by Trusit Securities, the market for weight management employer solutions is projected to be nearly $700M by 2024 with the potential to grow to $6B-$9B over the longer term. In addition, while employers and payers will undoubtedly have to cover GLP-1 drugs for certain patients, we do expect them to require patients to pursue other treatment protocols like those offered by Illant and competitors before approving GLP-1 usage as a last resort. While weight loss management is a crowded field, with competitors ranging from publicly traded WW, to Vida, Noom and Wondr Health, we do believe that a substantial market opportunity remains for clinically proven, evidence-based weight management companies. Obesity treatment startup Ilant Health launches with $3M, Value-based obesity treatment provider Ilant Health launches out of stealth

Our Take: Artificial intelligence (AI) has the potential to alleviate physician burnout significantly by reducing the amount of time spent on bureaucratic tasks like documentation and reviewing old medical records. AI that utilizes large language models (LLMs), speech recognition, and natural language processing (NLP) can help transcribe conversations between physicians and clinicians into formatted clinical notes and prepare clinical summaries of a patient’s medical history. This is especially crucial as physicians spend increasing amounts of time utilizing the electronic health record during patient visits and after-hours for documentation purposes. In light of a projected shortage of 124,000 physicians by 2034 and heightened levels of burnout post-pandemic projected by the Association of American Medical Colleges, leveraging AI to minimize bureaucratic burdens is an essential next step. The use of AI to reduce physician burnout through decreasing bureaucratic burdens can allow physicians to dedicate more time to addressing patient concerns, leading to improved patient satisfaction scores and health outcomes. Key Takeaways: Physician burnout costs the U.S. approximately $4.6B/yr. due to reduced hours, physician turnover, and expenses of finding and hiring replacements (Harvard Business School) 60% of physicians agree that bureaucratic tasks, including note writing, are the top contributor to physician burnout (Medscape) Physicians spend almost 50% of their time on the electronic health record (EHR) and desk work with 1-2 hours of after-hours work each night dedicated to EHR tasks (Annals of Internal Medicine) AI utilizing voice-enabled technology saved clinicians 3.3 hours per week and reduced the amount of time physicians spent reviewing old notes by 60% by producing a clinical summary (AAFP) The Problem: Artificial intelligence utilizing LLMs can play a significant role in reducing physician burnout, especially by assisting with the burden of clinical documentation. The advent of new tools from vendors such as Augmedix, Regard, Nuance, and Botco.ai allows healthcare organizations to significantly reduce the administrative burden on physicians. For instance, Nuance’s Dragon Ambient eXperience (DAX) software does this by recording the physician-patient encounter and transcribing it into a formatted clinical note through the utilization of speech recognition and NLP. However, several challenges exist that need to be addressed before further integrating this new technology into the workday of physicians. One issue is privacy. These novel tools require access to a patient’s protected medical record and will also consolidate new medical information during the patient’s visit. Therefore, there is a potential risk to patient privacy rights, and mistrust in these systems may hinder implementation. Moreover, patients may be wary of sharing information, fearing legal repercussions due to recorded data. According to a global survey by UIPath, only 44% of respondents from the baby boomer generation hold favorable views on AI in the workplace. Physicians, too, may be wary of this new technology as they fear for their job security and that recorded audio may be used against them in malpractice cases. For example, a recent survey of 1,500 physicians by Medscape found that only 19% of physicians would be comfortable using voice technology during a patient consultation. Another pressing issue is the possibility of error and misinformation. Language generation models can produce inaccurate information which is particularly concerning when it comes to incorrect medical information. “Hallucination” is the term used to describe when an NLP model conjures false information. This phenomenon is more pronounced when multiple languages are used during a clinical encounter or when the technology infers information that the patient did not explicitly verbalize. AI can also omit facts from the patient visit. Dr. Shravani Durbhakula, a pain physician and anesthesiologist at the Johns Hopkins School of Medicine, expressed her reservations, stating, “The major concerns I would have here is I’m not sure the computer would be smart enough to know what is important [enough] to pull out into the note.” She stated the world-class hospital does not use [ambient intelligence tools] to automate clinical notes. “You could miss critical information." Lastly, artificial intelligence trained on large datasets of text may inadvertently reflect biases present in the training data, perpetuating medical bias. When it comes to note writing, this can be seen in the form of emphasizing certain diagnoses or symptoms for different patient demographics. For example, a New England Journal of Medicine study highlighted this bias when a generative AI model, GTP-4, ranked panic and anxiety disorder higher on its list of potential diagnoses for female patients as compared to male patients. Furthermore, when GTP-4 was asked to generate clinical vignettes of sarcoidosis, the model described a black woman 98% of the time, reflecting a significant bias in its output. The Backdrop: Physician burnout is a huge source of burden on the healthcare system. It can lead to increased medical errors, lower quality of care, worse patient outcomes, and higher attrition rates. For physicians themselves, it can lead to increased rates of depression, substance abuse, suicide, and overall work dissatisfaction. Nearly two-thirds of doctors experience symptoms of burnout following the pandemic according to results published in the Mayo Clinic Proceedings. A major contributor to physician burnout is the increased administrative burden placed on physicians, namely in the form of documentation required for electronic health record systems (EHRs). A perspective piece published in The New England Journal of Medicine found that for every hour spent on patient interaction, physicians spend an extra 1-2 hours completing notes, ordering labs, prescribing medications, and reviewing results, all without extra compensation. In another paper published in Annals of Internal Medicine, the authors discovered that physicians devote almost 50% of their time to the EHR and desk work, allocating an extra 1-2 hours nightly to EHR tasks. 60% of physicians agree that bureaucratic tasks, including note writing, are the top contributor of physician burnout as per a report published in Medscape. AI presents a promising solution to significantly reduce the time spent on documentation. An American Academy of Family Physicians (AAFP) report found that AI leveraging voice-enabled technology saved clinicians 3.3 hours per week thereby helping to reduce burnout. Furthermore, the AI was able to reduce the amount of time physicians spent reviewing old notes by 60% through creating clinical summaries. For example, in one case study, Regard’s CEO found that their AI tool reduced measures of burnout by 50% and reduced documentation time by 25%. Augmedix’s website similarly boasts a 40% improvement in work-life satisfaction and a 3-hour per workday reduction. Despite the burden of bureaucratic tasks like documentation, this is increasingly important in the United States healthcare landscape where the government ties reimbursement to the quality of the medical record. Without proper documentation, physicians do not get paid for the services that they provide patients. The AAFP report found that AI integration resulted in a 25% increase in diagnoses sent to insurance companies that were previously unrecorded in the EHR. Beyond the financial incentive of proper documentation, it also serves to facilitate communication with other healthcare providers, reduces risk management exposure, and captures value-based case metrics. Conversely, inadequate documentation can lead to adverse treatment decisions, expensive diagnostic studies, repeated studies, unclear communication, inappropriate billing, and poor patient care. While proper documentation is necessary, physicians face significant challenges due to the physician shortage in the United States and the high volume of patients. AI stands as a potential tool to mitigate the aforementioned shortfall of physicians by alleviating physician burnout and attrition. A 2021 JAMA Network Open study found that an AI tool extracting relevant patient health data and presenting it alongside the patient record reduced EHR use time by 18%, a promising tool in reducing burnout. Nuance’s DAX software also showcases promising outcomes, claiming to reduce documentation time by 50% and reduce feelings of burnout and fatigue by 70%. Implications: Physician burnout is a pressing issue that needs to be addressed, especially with nearly two-thirds of doctors experiencing symptoms of burnout according to the New York Times and the impending physician shortage. In addition, physician burnout has significant economic consequences. For example, a study by Harvard Business School found that the economic toll of physician burnout is staggering, amounting to approximately $4.6 billion annually in the United States alone. This financial burden arises from reduced physician work hours, physician turnover, and expenses associated with finding and hiring replacements. Artificial intelligence can play a significant role in reducing physician burnout by reducing the amount of time physicians spend on documentation, the top contributor of physician burnout. This reduction in burnout can lead to a noticeable improvement in the quality of patient care, enabling physicians to dedicate more time to patients without distraction and consequently improving healthcare outcomes. Furthermore, minimizing documentation-related work during after-hours, often termed “pajama time,” can help mitigate medical errors, a significant concern when physicians' recall and alertness may be compromised. As AI integration has been found to increase documentation of previously unrecorded diagnoses, physician reimbursement may also become more accurate. While addressing concerns of privacy, error, misinformation, and biases are essential, AI services focused on enhancing the documentation experience are continuously evolving and are poised to play a pivotal role in alleviating physician burnout. As these AI-driven technologies progress, they are bound to enhance the healthcare landscape, ultimately benefiting both healthcare providers and patients. Related Reading: Doctors turn to imperfect AI to spend more quality time with patients AI alleviates burnout, reduces documentation time by 72% in primary care Artificial Intelligence And Its Potential To Combat Physician Burnout Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records