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The Driver: Minded, a mental telehealth company, raised $25M in Seed funding with participation from Streamlined Ventures, Link Ventures, The Tiger Fund, Unicorn Ventures, Trousdale Ventures, Gaingels, SALT Fund, and TheFund. Minded is a behavioral telehealth company focused on making access to mental health medication easier and more affordable. The seed money raised will go towards the development of expanding their behavioral health prescription medication platform to other regions of the U.S. In addition, Minded plans to use some of the funding to extend its care umbrella to attention and mood disorders. Key Takeaways: According to the National Alliance on Mental Illness, over 40 million adults in the U.S (19%) have an anxiety disorder and about 21 million adults (8%) had at least one major depressive episode in the U.S. in 2020. Patients can refill their prescription and have it delivered to their homes without having to leave the house. Patients are not required to have a diagnosis prior to using the service but will have to undergo an evaluation by a psychiatrist or nurse practitioner. Depending on the patient's insurance coverage, the cost of medications may be reimbursed (but not the Minded subscription itself). The Story: The origin of Minded comes from CEO and co-founder David Rodnick’s struggles with insomnia and anxiety for 10 years which was treated with medication. During that time, he realized the difficulty to obtain these medications as well as how expensive it was to see a specialist. Rodnick’s solution was to create a platform to increase the accessibility of medications for others, eventually coming upon a solution in the form of Minded. The Minded platform is designed for those 18 years and older who were diagnosed or believe they have anxiety, depression, or insomnia. According to the National Alliance on Mental Illness, over 40 million adults in the U.S (19.1%) have an anxiety disorder and about 21 million adults in the U.S (8.4%) had at least one major depressive episode in 2020. Minded patients take an online assessment to check their eligibility for the program. If eligible, the patient will be matched with a psychiatric specialist for an evaluation via video call. These specialists will prescribe medication depending on the needs of each patient. The patient can then choose whether they want to pick up their medication from a local pharmacy or have it delivered to their house. The platform currently operates on a subscription basis, charging a monthly fee of $65 in addition to the cost of medication. Depending on the patients' insurance provider, the prescriptions may be reimbursed. The Differentiators: One thing that distinguishes the Minded platform is the breadth of the offering. Minded does an initial assessment, matches the consumer with a specialist, sets up appointments, and has specialists that can prescribe medication. The medication can be delivered to the patient's home, a local pharmacy, or go to an online pharmacy. While there are similar companies like Mentavio and BetterHelp that target mental health conditions by offering counseling and therapy via video calls, these companies lack additional services such as prescribing and delivering medication. Not only is Minded offering these services, but they are aiming to make obtaining medication cheaper and more efficient. When these services are combined they make a very complete offering for those in need of treatment and medication for behavioral health issues. The Big Picture: Minded is bringing value through their behavioral care telehealth, prescription, and medication delivery services. Their unique approach to telehealth will make accessing medication for those suffering from anxiety, depression, and insomnia easier. With the use of the platform, patients will have access to a licensed physician at their fingertips, as well as quality medication at the physician’s discretion. Where needed, virtual appointments can decrease person-to-person contact for those who are immunocompromised. Minded is targeted at improving access, efficiency, and reducing costs. In addition, the fact that patients don’t need insurance or a prior diagnosis to sign up removes several barriers to care (prescriptions may be reimbursable depending on the patient's insurance carrier). However, while the program's subscription membership may ease convenience, the $65 monthly fee may be considered expensive by some, particularly many of the underserved who may be in a particular position to benefit from these services due to lack of transportation and a shortage of providers in certain areas. Minded, a telehealth platform specializing in managing mental health medication, raises $25M, Digital psychiatry startup Minded grabs $25M to boost access to mental health medications

The Driver: Avive Solutions recently raised $22 million in series A funding. The company develops next-gen automated defibrillators and a software system to increase the survival rate of patients who suffer from sudden cardiac arrest. Avive Solutions’ product features an alert system with a connected defibrillator device for any potential cardiac arrests in the area. The funding round was led by Questa Capital, Catalyst Health Ventures, Irish Angels, and returning investor Laerdal Million Lives Fund. The company will use the funding to further develop its AED platform. Key Takeaways: Sudden Cardiac arrest is a leading cause of death in the United States, causing approximately 350,000 deaths every year. Every minute a patient does not receive a shock from an AED, their survival chances decrease by 7% to 10% and nearly 90% of out-of-hospital cardiac arrests are fatal. Despite their best efforts, the average EMS response time in the U.S. is eight to 12 minutes and can be more in underserved communities. Avive’s emergency response software platform connects with 911 emergency communications centers through a partnership with RapidSOS The Story: Avive Solutions was founded in 2017 by Sameer Jafri, Rory Beyer, and Mosely Andrews. The initial idea behind the healthcare tech startup was that anyone, regardless of background, should be able to influence a cardiac arrest event positively. Beyer met Sameer Jafri, the founder, and president of the Los Angeles-based Saving Hearts Foundation, a nonprofit focused on preventing sudden cardiac death, at a Conference where they decided their interests aligned and they began to work on product development. While both Rory and Moseley’s background is technical Sameer’s experience with SCA prevention gave them expertise in approaches to improving survival from SCA, resulting in them forming Avive Solutions. The company’s focus is to lower mortality from SCA with their next-gen AED machine and emergency response software platform. The company’s Avive Connect product which weighs less than two pounds is meant to be lighter and less expensive than AEDs from competitors currently found in offices. Avive’s goal is to price its products several hundred dollars less than those on the market now which usually range from $1,200 to $2,500. The Differentiators: As noted, one of the main differentiators for Avive’s product is its size and weight. The Avive Connect weighs approximately two pounds compared to four to seven pounds for current wall-mounted models. In addition, according to Bay Area Inno Avive’s product is designed to operate off a rechargeable cell-phone-sized battery. Key to Avive's mission is to make AED machines more accessible to the public, particularly in underserved areas where survival rates for SCA are only about 10% and can be as low as 1%, according to the company. The chances of survival and life expectancy however rise substantially after people suffering SCA are treated in a hospital, thus the founders desire to get more AEDs in underserved communities. In addition, Avive has partnered with RapidSOS allowing “its platform to seamlessly connect with 911 Emergency Communications Centers (ECCs) and offers incident data-sharing capabilities to providers in an effort to close data gaps that often exist during a cardiac arrest response. ECCs with access to RapidSOS will be able to dispatch Avive Connect AEDs to the location of a cardiac arrest emergency by audibly alerting and displaying a map on the device to navigate bystanders to the location of the person in cardiac arrest” according to the company. This will not only help reduce time until AED use but can dramatically increase survival rates to as high as 40% the company claims. The Big Picture: The idea behind Avive Solutions is to target sudden cardiac arrest and improve survival rates through their AED tech and software. By creating a product that is less expensive, lighter, and easy to use (particularly when combined with the RapidSOS system) survival from SCA can be increased. According to one study deployment of such technology by bystanders could save almost 500 lives per year. This could be particularly important in underserved communities where response times for EMS and distances to appropriate treatment facilities can be higher due to a lack of resources. In addition, Avive has developed a “4-Minute City” program that strategically places Avive’s AEDs throughout a city or county and trains members of the community to use them. As noted by the company, this program can “prepare citizens to step up when it matters most to deliver early intervention for cardiac arrest, in communities where there are often significant disparities in outcomes based on socioeconomic status, race, age, and other factors.” While the company is awaiting FDA approval for the device and there still remain questions of reimbursement and potential insurance coverage, assuming these are overcome in the near term, we believe this device holds significant potential for improvement in care and outcomes. As noted in one study from the University of California, “although highly effective when used for out-of-hospital cardiac arrest (OHCA), ...AEDs often are placed in areas of low risk and limited temporal availability”, Avive Solutions could readily address this issue. Avive Solutions raises $22M for wirelessly connected AED technology, Connected AED company Avive scores $22M

The Driver: Season Health recently raised $34 million in a series A funding led by Andreessen Horowitz and joined by LRV Health, Company Ventures, Toyin Ajayi, (CEO of Cityblock), along with the founders of Shef, Instacart and MasterClass executives. The company which emerged out of stealth mode earlier this year, developed a platform where it pitched itself as a “digital food pharmacy” where it offers personalized nutrition to patients based on their health needs and has it delivered to their homes. The HIPAA-compliant platform also pairs patients with dietitians where disease management can be discussed based on patients’ preferences of food. Currently, Season Health is available in 7 states and the new funding will be used to build its business development and operations teams, and to support integration and partnerships with food retailers. Key Takeaways: Diabetes cost the U.S. $327B according to a study entitled “Economic Costs of Diabetes in the U.S. in 2017” by the American Diabetes Association Season Health’s dietitians develop meal plans aimed at helping patients manage chronic conditions, such as diabetes and kidney issues A 2010 study found that antioxidant rich diets contributed to improved cell function in heart and blood vessels but only when the antioxidants came from diet (not supplements) The company claims its programs enable better health outcomes for patients and reduce the cost of care for insurers The Story: CEO Josh Hix, co-founded Season’s Health in 2019 in Austin, Texas along with Mustafa Shabib, his co-founder and CTO. Hix was the former CEO of consumer meal kit and delivery business, Plated, and stated that his initial idea on starting this new company stemmed from his belief that “Unhealthy diets are a core reason for nearly 85% of U.S. healthcare costs”. The company is among a number of startups focused on a "food as medicine" approach to better support the management of chronic disease. Under programs like Season’s, given clients receive a more nutrient rich, higher quality, more tailored meal they believe patients receive better health outcomes and lower total healthcare costs. With over 50 employees now, Season also claims to work with leading health systems to support providers in writing evidence-based food prescriptions across a spectrum of clinical conditions and socioeconomic statuses. Currently, Season operates on a self-pay model; patients pay $75 per month as a subscription fee, which includes access to a dedicated registered dietitian, personalized meal recommendations, and concierge ordering. The Differentiators: One of the main differentiators is the tailored nutrition-based meals based on disease status or a desire to improve one’s health. While other meal delivery platforms such as Hello Fresh and Home chef deliver meals, Season Health has dietitians that prepare meals based on individual chronic disease management versus those just based on taste or dietary preference. Along with dietitians, the company also works alongside doctors and nutritionists to meet the proper requirements to help improve disease outcomes. While Season Health competes with companies like Soda Health, Soda’s programs provide debit cards to purchase healthy food and medications as well as transportation and it is not focused on meal planning or matching meals to medical conditions. Along those lines, tailoring meals could be important as according to the Center for Disease Control (CDC), nearly half of adults in the United States (about 47% or 116 million) have Hypertension and almost 11% (34 million) suffer from diabetes. With platforms such as Season Health, the goal is to adjust meal planning and realign it with healthcare with a focus on the nutritional content of meals to reduce disease prevalence and improve outcomes. Season Health and programs like it could potentially help underserved communities reduce the healthcare burden given the higher incidence of chronic disease and the large number of food deserts in those communities. The company is working to have its service covered by most health insurance providers, which would not only provide a reliable source of revenue but would also be beneficial for patients by broadening its reach to those who cannot afford it as a self-pay product. Season is currently working with Geisinger, CommonSpirit Health, and kidney-focused telehealth provider Cricket Health, (which recently merged with Fresenius Health Partners and InterWell Health). They are said to be partnering with Season in order to reach even more patients. The company claims that their focus will be on diabetes and kidney disease while working its way to eventually expand into cancer, and maternity and heart health. The Big Picture: Season Health’s mission is to bring the benefits of food as medicine and improve the lives of millions of people who are struggling with nutrition sensitive conditions. By creating a platform that offers cost effective food all while managing health conditions, Season is combining the convenience of home delivery with better health. Season Health is taking the first step in trying to properly manage health conditions by treating nutrition as the medication for its patients. Given prevention and wellness is generally a better, less expensive way to treat chronic conditions than treating disease after the fact, Season Health may be on the right path to having meaningful impact and ROI in treating disease. Moreover, with the current prevalence of food deserts in underserved communities they may make it possible to address health outcomes on a broad scale as patients adopt a healthier lifestyle. We believe Julie Yoo of Andreessen Horowitz was right on point in a statement accompanying the fundraising when she noted that while “we can readily shop and receive advice for better dietary choices from our doctors, dieticians, health coaches, and even the NYTimes Cooking app...translating that advice into actual healthy food showing up on our dining tables is still a disjunct, highly manual, and costly process." Season Health raises $34 million in Series A funding, Food-as-medicine startup Season Health nabs $34M backed by Andreessen Horowitz, Cityblock's Toyin Ajayi

The Driver: SmithRx, a tech-backed pharmacy management company, recently raised $20 million in a Series B funding round led by Venrock and existing investors including Founders Fund. The company was founded in 2016 and has raised a total of over $37 million in three rounds of funding. The company states that they “have built an evolved client-aligned version of a pharmacy benefit manager (PBM) which promotes 100% pass-through savings to their employer clients thereby maximizing drug savings. The fundraising proceeds will be used to expand their platform to more health systems as well as hire new staff members. Key Takeaways: According to the Drug Channels Institute, the three largest PBMs control nearly 80% of the market, while the top six PBMs handled more than 95% of total U.S. equivalent prescription claims. SmithRx claims to have over 200 integrations with employer benefit brokers and payers and to provide 50% potential drug savings via its platform. The company has developed a Drug Acquisition Platform or DAP - (their own version of a pharmacy benefit manager or PBM) which runs claims through algorithms that look for lower-priced medications. SmithRx charges a flat fee for each claim and states that it uses 100% pass-through pricing with its PBM technology. The Story: Founder and CEO, Jake Frenz, started SmithRx 2016 ”to create a technology-driven and cost-competitive PBM that offers clients a flexible customer-centric product …that enables choice, aligns incentives, and surfaces insights to improve care delivery,”, according to the company’s website. Frenz, who had previously worked at Collective Health where he built the operations organization, and at Anthem where he led teams delivering commercial and Medicare healthcare products, states his vision is to bring a new disruptive business model to the PBM space, one that couples technological innovation and efficiencies with a transparent and auditable pricing program. Pharmacy Benefits Managers are, in essence, the intermediaries of almost every aspect of the pharmacy benefits marketplace, however, they are known to be very vague about their costs and fees. SmithRx claims to reduce total drug costs and improve health outcomes with 100% pass-through pricing with their PBM technology. With ambitions such as these, they seem to be leading the next generation of customer-centric pharmacy benefits with a tech-forward approach that provides full control and transparency to its 1200+ employers served. The Differentiators: While PBMs create complexity and make back-room deals for rebates, SmithRx has created what they call a Drug Acquisition Platform (DAP). According to Axios, the company works with employers and runs claims through algorithms that look for lower-priced medications based on pharmacy distribution, clinical management, rebates, and special programs. As employers spend more and more on prescription drugs and medication, SmithRx offers an alternative to the traditional PBM with no hidden fees. In addition, SmithRx offers mail-order Rx which gives patients a 90-day supply of their prescription delivered straight to their door which the company claims is comparable in cost to a 60 day supply from other suppliers. SmithRx also offers members clinical programs and alternative sourcing through SmithRx Connect which the company claims will make it easier to manage their prescription benefits and can help reduce overall drug spending. The company is currently working with brokers including Alliant, Hays, and Gallagher and has integrations with payers including Aetna, BlueCross BlueShield, and HMA. The Big Picture: SmithRx states it was built on the foundation of aligning incentives and providing a better solution for plan sponsors. In addition, their platform has been using technology to integrate with telemedicine to potentially benefit both patients and clients. According to the company, this has resulted in a client retention rate of 99% and an estimated savings of up to 50%. While there are a number of competitors like Mark Cuban’s Cost Plus Drug Company, Amazon, Hims and Hers as well as Ro, as Axios recently noted, many are restricted in how they work due to PBM rules or avoid PBM’s altogether. Moreover, given that SmithRx charges a flat fee for each claim, it is likely to increase pressure for others to provide greater visibility into their pricing as well as make it more easily auditable than current interactions with the major PBMs. Moreover, by working with brokers and payers, SmithRx has positioned itself alongside those whose incentives in terms of cost and quality are better aligned to their mission and that of their customers. By enabling greater convenience through such things as a prescription card to use at other retail pharmacies, and home delivery that provides discounted prescriptions at your door, services like SmithRx can also help to broaden access to hard-to-reach populations who may lack access if there are no local brick-and-mortar pharmacies. While it remains to be seen which model will best disrupt the PBM market or even if any single model alone will disrupt the PBM market, it is clear that the PBM go-to-market model is ripe for disruption. Scoop: Venrock leads pharmacy startup's $20m refill, Tech-backed PBM SmithRx scores $20M, and more digital health fundings

Our Take: There are several basic issues and challenges in deploying AI that all clinicians and administrators should be aware of and inquire about to ensure that they are being properly being considered when AI is being implemented in their organization. Applications of artificial intelligence in healthcare hold great promise to increase both the scale of medical discoveries and the efficiency of healthcare infrastructure. As such healthcare-related research and investment has exploded over the last several years. For example, according to the State of AI Report 2020, academic publications in biology around AI technologies such as deep learning, natural language processing (NLP), and computer vision have grown over 50% a year since 2017. In addition, 99% of healthcare institutions surveyed by CB Insights are either currently deploying (38%) or planning to deploy AI (61%) in the near future. However, as witnessed by recent errors discovered surrounding the application of an AI-based Sepsis model, while AI can improve quality of care, improve access and reduce costs, models must be implemented correctly or they will be of questionable value and even dangerous. Key Takeaways: According to Accenture’s “Artificial Intelligence: Healthcare’s New Nervous System” report, AI for health is expected to grow at a 40% CAGR through 2021. Researchers working to uncover insights into prescribing patterns for certain antipsychotic medications found that approximately 27% of prescriptions were missing dosages. Even after doing work to standardize and label patient data, in at least one broad study almost 10% of items in the data repository didn’t have proper identifiers. Academic publications in biology around AI technologies such as deep learning, natural language processing (NLP), and computer vision have grown over 50% a year since 2017. The Problem: While it is commonly accepted that computers can outperform humans in terms of computational speed, in its current state many would argue that artificial intelligence is really “augmented intelligence” defined by the IEEE as “a subsection of AI machine learning developed to enhance human intelligence rather than operate independently of or outright replace it.” Current AI models are still highly dependent upon the quantity and quality of data available for them to be trained on, the inherent assumptions underlying the models as well as the human biases (intentional and unintentional) of those developing the models along with a number of other factors. As noted in a recent review of the book “I, Warbot” about computational warfare by Kings College, AI lecturer Kenneth Payne, “these gizmos exhibit ‘exploratory creativity'-essentially a brute force calculation of probabilities. That is fundamentally different from ‘transformational creativity”, which entails the ability to consider a problem in a wholly new way and requires playfulness, imagination and a sense of meaning.” As such, those creating AI models for healthcare need to ensure they set the guardrails for its use and audit its models both pre and post-development to ensure they conform to existing laws and best practices. The Backdrop: When implementing an AI project there are a number of steps and considerations that should be taken into account to ensure its success. While it is important to identify the best use and type with any kind of project, given the cost of the technical talent involved, the level of computational infrastructure typically needed (if done internally) and the potential to influence leadership attitudes towards the use and viability of AI as an organizational tool, it is even more important here. As noted above one of the most important keys to implementing an AI project is the quantity and quality of data resources available to the firm. Data should be looked at with respect to both quality (to ensure that it is free of missing, incoherent, unreliable, or incorrect values) and quantity. In terms of data quality, as noted in “Artificial Intelligence: A Non-Technical Introduction”, data can be: 1) noisy (have data sets with conflicting data), 2) dirty (have data sets with inconsistent and erroneous data), 3) sparse (have data with missing or no values at all, or, 4) inadequate (have data sets that have contained inadequate or biased data). As noted in an article in “Extracting and Utilizing Electronic Health Data from Epic for Research”, “to provide the cleanest and most robust datasets for statistical analysis, numerous statistical techniques including similarity calculations and fuzzy matching are used to clean, parse, map, and validate the raw EHR data.” which is generally the largest source of healthcare data for AI research. When looking to implement AI it is important to consider and understand the levels of data loss and the ability to correct for it. For example, researchers looking to apply AI to uncover insights into prescribing patterns into second-generation antipsychotic medications (SGAs) found that approximately 27% of the prescriptions in their data set were missing dosages and even after undertaking a 3-step correction procedure, 1% were missing dosages. While this may be deemed an acceptable number it is important to be aware of the data loss and know this information in order to properly evaluate if it is within tolerable limits. In terms of inadequate data, ensuring that data is free of bias is extremely important. While we have all recently been made keenly aware of the impact of racial and ethnic bias on models (ex: facial recognition models trained only on Caucasians) there are a number of other biases which models should be evaluated for. According to “7 Types of Data Bias in Machine Learning” these include: 1) sample bias (not representing the desired population accurately), 2) exclusion bias (the intentional or unintentional exclusion or certain variables from data prior to processing), 3) measurement bias (ex: due to poorly chosen measurements that create systematic distortions of data, like poorly phrased surveys); 4) recall bias (when similar data is inconsistently labeled), 5) observer bias ( when the labelers of data let their personal views influence data classification/annotation), 6) racial bias (when data samples skew in favor of or against certain ethnic or demographic groups), 7) association bias (when a machine learning model reinforces a bias present in a model). In addition to data quality, data quantity is as imperative. For example, in order to properly train machine learning models, you need to have a sufficiently large number of observations to create an accurate predictor of the parameters you’re trying to forecast. While the precise number of observations needed will vary based on the complexity of the data you’re using, the complexity of the model you want to build, and the impact of the amount of “statistical noise” generated by the data itself, an article in the Journal of Machine Learning Research suggested that at least 100,000 observations are needed to train a regression or classification model. Moreover, it is important that numerous data points are not captured or sufficiently documented in healthcare. For example, as noted in the above-referenced article on extracting and utilizing Epic EHR data for study based on research at the Cleveland Clinic in 2018, even after doing significant work to standardize and label patient data, “approximately 9% [1,000 out of 32,000 data points per patient] of columns in the data repository” were not using the assigned identifiers. While it is likely that methods have improved since this research was performed, given the size and resources that an institution like the Cleveland Clinic had to bear on the problem, it indicates the larger size of the problem. Once the model has been developed there should be a process in place to ensure that the model is transparent and explainable by creating a mechanism that allows non-technologists to understand and assess the factors the model used and what parameters it relied most heavily upon in coming to its conclusions. For example, as noted by the State of AI Report 2020, “AI research is less open than you think, only 15% of papers publish their [algorithmic] code” used to weight and create models. In addition, there should be a system of controls, policies, and audits in place that provide feedback as to the potential errors in the application of the model as well as disparate impact or bias in its conclusions. Implications: As noted in “Artificial Intelligence Basics: A Non-Technical Introduction” it’s important to have realistic expectations for what can be accomplished by an AI project and how to plan for it. In the book, the author Andrew Taulli references Andrew Ng, the former Head of Google Brain, who suggests the following parameters; an AI project should take between 6-12 months to complete, have an industry-specific focus, should notably help the company, doesn’t have to be transformative, and, have high-quality data points. In our opinion, it is particularly important to form collaborative, cross-platform teams of data scientists, physicians, and other front-line clinicians (particularly those closest to patients like nurses) to get as broad input on the problem as possible. While AI holds great promise, proponents will have to prove themselves by running targeted pilots and should be careful not to overreach at the risk of poisoning the well of opportunity. As so astutely pointed out in “5 Steps for Planning A Healthcare Artificial Intelligence Project: “artificial intelligence isn’t something that can be passively infused into an organization like a teabag into a cup of hot water. AI must be deployed carefully, piece by piece, in a measured and measurable way.” Data scientists need to ensure that the models they create produce relevant output that provide context and the ability for clinicians to have a meaningful impact upon the results and not just generate additional alerts that will go unheeded. For example, as Rob Bart, Chief Medical Information Officer at UPMC noted in a recent presentation at HIMSS, data should provide “personalized health information, personalized data” and should have “situational awareness in order to turn data into better consumable information for clinical decision making” in healthcare. Along those lines, it is important to take a realistic assessment of “where your organization lies on the maturity curve”, how good is your data, how deep is your bench of data scientists and clinicians available to work on an AI project in order to inventory, clean and prepare your data. AI talent is highly compensated and in heavy demand. Do you have the resources necessary to build and sustain a team internally or will you need to hire external consultants? How will you select and manage those consultants, etc.? All of these are questions that need to be carefully considered and answered before undertaking the project. In addition, healthcare providers need to consider the special relationship between clinician and patient and the need to preserve trust, transparency, and privacy. While AI holds a tremendous allure for healthcare and the potential for it to overcome, and in fact make up for its underinvestment in information technology relative to other industries, all of this needs to be done with a well-thought-out, coherent and justified strategy as its foundation. Related Readings: Artificial Intelligence Basics: A Non-Technical Introduction. Tom Taulli (publishers site) Artificial Intelligence (AI): Healthcare’s New Nervous System An Interdisciplinary Approach to Reducing Errors in Extracted Electronic Health Record Data for Research 5 Steps for Planning a Healthcare Artificial Intelligence Project

Our Take: Digital health organizations' data-driven and personalized approach to employee wellness programs design will improve employee engagement by shifting more control of health management to employees. Digital health and technology products like wellness software, smartphones, apps, and virtual services, have provided organizations and businesses with enormous support in improving employees’ health and well-being. The COVID-19 pandemic has shifted the fragmented approach of traditional wellness programs by introducing centralized and personalized digital health solutions in managing work stress and physical and mental health. Digitized solutions are particularly significant because the COVID-19 pandemic made remote working the new norm with accompanying spikes in work stress and mental health-related issues. However, the impact of wellness programs on saving cost, regardless of its form, traditional or digitized, remains unclear. Key Takeaways: According to the CDC, “work stress is the leading workplace health problem and a major occupational health risk, ranking above physical inactivity and obesity.” 82% of large firms and 53% of small employers in the United States offered a wellness program, amounting to an $8 billion industry. 80% of health care costs borne by employers are a result of preventable health conditions. According to a 2020 Aetna International survey, employees believe technological innovations and digital tools could further help them to improve their health. The Problem: Chronic health conditions lower employees’ productivity and increase the number of missed workdays. For employee absenteeism caused by high blood pressure, diabetes, smoking, physical inactivity, and obesity, employers incur an annual cost of $36.4 billion. Consequently, strategic and successful companies establish wellness programs to reduce health-related absenteeism, increase employee productivity and mitigate costs. While most businesses have embraced wellness programs as an essential component of their recruitment and retention strategy, a number of factors have limited employee engagement levels. According to a Harvard Business Review article entitled, “Why People Do - and Don’t - Participate in Wellness Programs” in a survey of 465 full-time employees in companies with established wellness programs, a lack of information and privacy concerns were the most prevalent reasons for nonparticipation in wellness programs. Employees are less likely to engage if they feel that the wellness program is intrusive or a channel for monitoring their health. Improved employee involvement in the wellness program decision-making is crucial for its effectiveness because driving lifestyle changes require individual interest and commitment. With the COVID-19 pandemic, employers faced a different dynamic for employee wellness management, with most employees working from home. Studies and surveys reported high rates of burnout, mental distress, and increased substance use rates for employees working remotely; essential workers faced an even higher risk. ​​The Backdrop: There are a number of issues that should be evaluated when looking at the role and efficacy of employee wellness programs. First, the early case for workplace wellness programs hinged on their ability to help employers manage employee health costs by reducing absenteeism, enhancing productivity, and reducing the overall cost of care. Driven by research that demonstrated many of the unhealthy lifestyle behaviors linked to reduced productivity are modifiable by behavior changes, wellness programs' claim of providing solutions that enhance workers' productivity gained in popularity. However, evidence that clearly shows that wellness programs reduce costs was limited. For example, a 2018 report published in the Journal of the American Medical Association (JAMA) stated that while 82% of large firms and 53% of small employers in the United States offered a wellness program, the report found no significant difference in the control and treatment groups when considering the impact of healthcare spending and utilization. Secondly, enticing employees to take part in employee wellness programs has long been an issue, with rates of engagement generally staying at low levels. This is due in part to the fact that some employees consider workplace wellness programs to be intrusive and would prefer to manage their wellness from the comfort of their homes. Interestingly, many organizations are less supportive when employees opt out of formalized wellness created by the organization and often choose not to support or reimburse more personalized options that employees might pursue (ex: purchasing equipment for exercise at home priced comparably to gym memberships). While there are clear issues with monitoring usage and correlating effectiveness, well-designed self-management programs might be cheaper and more impactful for certain employees than taking advantage of a company's wellness benefits. Finally, there is the issue of an employer's ability to reach the family decision-maker when it comes to wellness programs. For example, in the case of married employees when companies employ the male spouse, it is typically the female member of the household that makes the decision whether or not to engage in wellness programs. For example, a study by the Optum unit of United Healthcare women's uptake in workplace wellness programs was prompted more by physical appearance while 40% of the men engaged because they were prompted by their family. Furthermore, the study observed that mailers to the homes of employees that targeted female spouses had the potential of increasing the uptake rate of the male employees. As a result, many believe digital wellness programs have the potential to vastly alter employee engagement with easier usage, greater customization, and improved ability to target users. All of which could lead to improved utilization. Implications: Digital health applications for employee wellness have the potential to dramatically improve deidentified data collection, enhance workflow, increase productivity, and reduce the financial costs of employee health risks. The digital health wellness programs have filled a gap by providing one-stop solutions for employees that eliminate the hurdle of navigating numerous unrelated solutions offered by many traditional wellness programs. Even companies traditionally thought of as non-technology companies have become involved in digitized wellness program innovations. For instance, Peloton, an exercise equipment company, developed a corporate wellness program providing employees with subsidized access to its digital fitness membership. Similarly, Blue Shield of California saw an increased uptake in employees' engagement with its personalized digital wellness program. The organization reported that at the beginning of the pandemic, there was a significant increase in the organization’s employee engagement and a tenfold increase after the initial outbreak. Another sweet spot is that digitized wellness programs collate and analyze data to provide personalized solutions that motivate employees’ engagement resulting in higher levels of success. For example, ongoing data collection allows providers to tailor solutions to changes in employees' lifestyles or health conditions. This can be particularly important in the area of mental health. As was broadly reported, during COVID-19 lockdown, there was a spike in the rate of anxiety, depression, and substance abuse as a result of many factors related to the pandemic. When this was combined with the shortage of mental health practitioners in the U.S, digitized solutions for mental health became an effective and expedient way to narrow this gap. Companies including Modern Health, Lyra, and Ginger all have mental health applications targeted at the employer wellness space. Nevertheless, there remains the issue of effectiveness and return on investment. As noted in “Effect of a Workplace Wellness Program on Employee Health and Economic Outcomes”, when the authors looked at the impact of workplace wellness programs in the retail industry, they found no significant differences in clinical measures of health, health care spending, and utilization. Making wellness programs more effective requires a different approach that focuses on creating a personalized experience, informed outreach, and providing individualized incentives for improved engagement. Digitized wellness programs that focus on designing solutions that reduce cost while leveraging its unique approach of improving employee engagement through customized and centralized solutions for employees have the potential to drive this change. Related Readings: The Impact of Digital Health Interventions on Health-Related Outcomes in the Workplace: A Systematic Review Effect of a Workplace Wellness Program on Employee Health and Economic Outcomes The Digital Health Dilemma: Is Technology Keeping Workers Healthy or Making them Ill? Digital Wellness Programs with Key Elements may be the Answer

Our Take: Telebehavioral health (TBH) and Telepsychiatry (TP) are increasingly gaining traction as a means of integrating behavioral health into primary care. With the tumultuous past two years and present time residual effects of the Pandemic, this may be the best time for the rise of telebehavioral healthcare. TBH and TP may allow for more accessible and affordable care with equal or better outcomes than in-person care, especially for diagnosis and treatment. Integration of TBH and TP is an important step in the reduction of disparities and a means of vouching for equity in different communities while consolidating health information on an online database and platform. The range of TBH and TP services comes in a magnitude of different approaches which allows for many levels of support and managed care coordination either synchronous or asynchronous (interaction in “real-time” and interaction at one’s pace, respectively). Patients have the advantage of their information being shared between their primary care physicians (PCPs) and telebehavioral health practitioners in a timely manner and with coordination to provide the best form of treatment. Key Takeaways: Psychologists reported increases in treating anxiety disorders, depressive disorders as well as trauma-and stress-related disorders according to the American Psychological Association’s 2021 COVID Practitioner Survey, TBH and TP may allow for more accessible and affordable care with equal or better outcomes than in-person care, especially for diagnosis and treatment There will be a 20% drop in the supply of psychiatrists by 2030, and the number of child psychiatrists is believed to already be insufficient to fulfill present and future demand Telepsychiatry is [now] a covered insurance benefit for 87 million people according to Array Behavioral care a telepsychiatry provider. The Problem: Since the Covid 19 pandemic, not only has the need for behavioral health services increased, but telebehavioral health care has shown to be in even more demand. For example, in the American Psychological Association’s 2021 COVID Practitioner Survey, psychologists reported “increases in treating anxiety disorders (84%, up from 74%), depressive disorders (72%, up from 60%), and trauma-and stress-related disorders (62%, up from 50%).” Moreover, telepsychiatry “is [now] a covered insurance benefit for 87 million people and counting” per Array Behavioral care which claims to be the largest provider of telepsychiatry services. In addition, due to the national shortage of telebehavioral clinicians available for in-person care, many providers have been attempting to bridge the gap by delivering care through online platforms that can help make care more accessible and provide the same quality of care. For example, according to the Health Resources & Services Administration (HRSA), there will be a 20% drop in the supply of psychiatrists by 2030, and the number of child psychiatrists is already believed to be insufficient to fulfill present and future demand. As a result, many have turned to telebehavioral health to fill the void. Similarly, the urgent need to broaden the delivery of mental health care has been recognized by health insurance providers who have not only supported TBH and TP but have paved the way by collaborating with providers to integrate TB into primary care and creating/joining programs to accelerate telebehavioral services. In 2020 when Association for Health Insurance Plans (AHIP) joined Psych Hub, a platform that created a space to speak on and gain educational insight on mental health, substance use/abuse, and suicide prevention. Psych Hub's Scientific Advisory Board, which identifies ideas to enhance mental health care delivery, includes a number of AHIP member health insurance carriers. These solutions are a means of establishing quality metrics to improve quality of care and “integrating evidence-based practices throughout the continuum of care.” This is of high importance more specifically for teens and adolescents who may have gone through setbacks and are experiencing social isolation, anxiety, and depression due to school closings, graduation cancellations, and other burdens linked to the Pandemic. The Backdrop: Telemedicine continues to show exceptional outcomes for patient care with a plethora of different approaches and models. According to a study entitled “Use of Telemental Health Services During and After COVID-19”, “the survey findings support the continued use of telehealth services offered by mental health providers and organizations, as respondents indicated a desire to use these services more following the pandemic.” TP and TBH have shown benefits and have been sought after by many patients who are unable to receive care in person or unable to use in-person care due to lack of transportation, an inability to take time off from work, or other caretaker responsibilities. However, while the delivery of behavioral health services digitally increased during COVID, one area that continues to need additional progress is the integration of behavioral health services into primary care services. There are a number of telebehavioral health models for care delivery including the Collaborative model, the Integrated model, and the Stepped Care model. All of these have provided a framework to make TBH services viable and feasible to integrate into primary care. The Collaborative Care model is a systematic approach that links care managers and psychiatrists with primary care physicians. This model allows for flexibility and engagement with patients to encourage management self-management and treatment adherence. Outreach encounters are maximized through the collaboration of many practitioners to ensure diagnosis and care are effectively coordinated and handled in a timely manner. The Stepped Care approach model uses more intensive care when less intensive treatments appear insufficient in primary care. It can be used in TP for the treatment of anxiety and depression and other mental health illnesses. In the Integrated based model, “a team of primary care and BH clinicians work together with patients and families using a systematic and cost-effective approach to provide patient-centered experiences to address health behaviors, medical illness, life stressors and crises, mental illness, substance, and ineffective patterns of health care utilization.” The integrated approach is not as extensive timewise and is driven to resolve behavioral healthcare issues while not heavily relying on technology (not evaluated for TBH or TP). Implications: The integration of TBH and TP into primary care has brought about many positive outcomes for both clinicians and patients. The flexibility of practitioners working from home and the access patients have for mental health care online removes any barriers that are present with in-person care such as transportation, convenience or exposure to disease such as COVID. The coordination and collaboration of primary care physicians with mental health providers enables more patient addition points of entry to the health system and a smooth transition to delivering a diagnosis and treatment in a timely manner. It is also a more cost-effective means of providing patient-centered care by cutting costs of additional fees or referrals and facilitating inclusive value-based care. However, one issue is the current and projected shortage in the behavioral health workforce even with the advent of telebehavioral health solutions. While telebehavioral health can help extend strained resources, it will not completely close the gap. In addition, even with the current increase in the use of digital tools some practices are not fully equipped to integrate TBH or TP into their practice structure or make system-level changes due to limiting factors such as technological capabilities. Some PCPs do not have the skill set needed to use the appropriate based evidence-based therapies and intervention techniques to work alongside behavioral practitioners since the specialties have always been handled separately. It is imperative that policymakers provide PCPs with additional training and resources to allow for a smooth transfer of TBH and TP into their practice by implementing integrated care models to yield appropriate improvements in quality care, particularly at times of stress like during the Pandemic. Related Reading:: Telebehavioral Care Improves Access to Mental Health Clinicians AHIP: Next Steps Toward Primary, Behavioral Healthcare Integration Provider- and Practice-Level Competencies for Integrated Behavioral Health in Primary Care: A Literature Review An Update on Telepsychiatry and How It Can Leverage Collaborative, Stepped, and Integrated Services to Primary Care

Our Take: According to the article, “America’s Health Literacy: Why We Need Accessible Health Information”, only 12% of adult Americans demonstrate limited (proficient) health literacy. This impacts overall health outcomes and healthcare expenditures due to a fragmented education system in the US where health education is not standardized. The cycle of poor health literacy contributes to the prevalence of preventable diseases. Integrating Community Health Workers (CHW) into the healthcare system will reduce the burden placed on strained resources and overworked clinicians. It will also help improve health outcomes when used for addressing chronic health conditions (chronic conditions are usually preventable). Key Takeaways: Community Health Workers will: Cost-effectively address the need for healthcare professionals Implement preventative care more closely, reducing the burden on healthcare services Efficiently improve health outcomes, especially for chronic conditions Close gaps between doctors and patient, acting as a mediator Explain medical shorthand to patients while supporting them with potential social services Improve health literacy in a sustainable manner The Problem: Social determinants of health allude to which populations often face extraneous barriers when accessing healthcare. We often overlook the barriers to receiving and complying with treatment plans even when, finally, inside the hospital room. It often starts with the rigid communication between healthcare providers and patients who often do not correctly understand the medical jargon given the poor health literacy in America. In addition to the poor health literacy noted above, in many communities, the formal medical community itself is not the primary means by which healthcare information is dispersed and providers may not be those most trusted to deliver that information. Moreover, even in communities where health literacy may be strong, they may not have good access to services or supports based on their geographic location. The Backdrop CHW’s are defined by the American Public Health Association as frontline public health workers who are trusted members of a community and who have an unusually close understanding of the community served. This relationship allows CHWs to serve as a liaison with the community to facilitate access to services and improve the quality and cultural competence of service delivery. A community health worker also builds individual and community capacity by increasing health knowledge and self-sufficiency through a range of activities such as outreach, community education, informal counseling, social support, and advocacy. CHWs also function as cultural translators. For example, if English is not the patient’s first language or if Western medicine is not commonly practiced in their culture; patients are less likely to comply with or adhere to their treatment plans. Language is an extension of a culture where the semantics of certain words are accessible to in-group native speakers and where the meanings of certain words get lost in translation. In situations like this, CHWs can step in and improve communication. As patients often interpret symptoms and share information according to how they understand the question and their health, doctors may not end up with an accurate picture or truthful assessment of a patient’s health. As a result, there is a need for certain interventions and clarifications to bridge this gap. In addition, at times, healthcare professionals may not have the skills, personality, or luxury of time when presenting highly complex information to demonstrate cultural competence as their focus is the medical problem itself. No matter what the cause, as demonstrated during the Coronavirus pandemic, health inequities exist across demographics and geographic populations. Moreover, telehealth and digital tools allow providers to leverage services like these. For example, telehealth comes with supportive data infrastructure and can help make access easier for the rural population. The data infrastructure, often in the form of digital health tracking devices or applications supported by smartphones can now be leveraged to tackle poor health outcomes on a larger scale. More importantly, these tools can be programmed to give us sustainable positive health outcomes. (Example: Ginger, telemental health) Implications: CHWs will help make healthcare more accessible for consumers while promoting health literacy and fostering trust in the healthcare system. Medical mistrust is a cultural phenomenon that is shared within groups. As consumers of healthcare services, patients are often at the risk of mistreatment, mismanagement, or a simple lack of understanding in terms of clinical care and disease prevention. This can result in underutilization or poor application of healthcare services. Add in the historical implications of hospital malpractices and lack of general equity and the medical mistrust is likely to follow amongst consumers. Small interventions like these can ultimately improve health literacy and have big impacts. For example, the Mississippi Delta Center implemented CHWs in a program aimed at helping treat heart disease in 18 BIPOC neighborhoods. Studies of the program reported that patients with hypertension who were enrolled in this program reported a decrease in both systolic and diastolic blood pressure as well as an improvement in cholesterol levels. The studies also found a median change in health care costs, post CHW intervention of $82 per person/per year (with the range being -$415 to $14). In addition, health education and health literacy must be comprehensive because health is a multidimensional subject where all aspects of health are interconnected. The lack of health literacy and self-efficacy across populations is one of the main drivers behind America’s high mortality rates, low life expectancy, and the highest rates of preventable deaths. Ultimately, persistence and effective health interventions will lead to less adverse health outcomes. Related Reading: America’s Health Literacy: Why We Need Accessible Health Information Integrating Community Health Workers on Clinical Care Teams and in the Community Exploring Four Barriers Experienced by African Americans in Healthcare: Perceived Discrimination, Medical Mistrust, Race Discordance, and Poor Communication Health Disparities: A Barrier to High-Quality Care

The Driver: Recently BioIntelliSense entered into a strategic collaboration with Renown Health, a not-for-profit integrated healthcare network of hospitals and care facilities to provide remote patient monitoring (RPM) for both in-hospital and at-home patient care. Based in Golden, Colorado with research offices in Redwood City, CA BioIntelliSense has raised a total of $83M over three funding rounds which included investors such as 7wire Technology Partners, Royal Philips, Fresenius Medical Care North America. TripleTree Holdings, UCHealth, and the CU Healthcare Innovation Fund. BioIntelliSense has developed 2 wearable devices called the BioButton and the BioSticker. While both are FDA-cleared wearable medical devices, the BioSticker enables at-home continuous monitoring of vital signs and symptoms that can be directly associated with COVID for 30 days on a single device, without recharging while the BioButton, which debuted in summer 2020, lasts for up to 90 days and was launched during the COVID pandemic to help support reopening efforts of schools and offices. Key Takeaways: BioIntelliSense’s devices are measuring 20 different physiologic and biometric parameters and then transmitting that data to the cloud and then to a doctor or hospital or nurse, according to the company. The company’s BioButton and BioSticker are designed to be discreetly worn on the upper left chest for effortless remote data capture and a simplistic “stick it on and forget it” patient experience Capable of early detection of health conditions and continuous post-surgery monitoring of vitals to support the recovery process in the comfort of your home Both devices can detect skin temperature, heart and respiratory rate at rest, gait analysis, coughing frequency, sleep, body position, steps, and activity levels. The Story: BioIntelliSense was founded in 2018 by Dr. James Mault (current CEO), a former cardiac surgeon at UCHealth in Colorado, and Co-founded by David Wang, founder of Striiv Corp, who has many years of experience in product execution and innovation. Through Mault’s background in health IT and the medical device industry, BioIntelliSense was able to launch just before the Pandemic and since then, has gained a lot of traction due to the increased demand for telehealth and telemedicine. For example, UCHealth was an early supporter of the startup, striking a partnership with BioIntelliSense months before the start of the pandemic to monitor patients at home before and after a procedure. The hospital system is currently using BioIntelliSense’s devices to monitor patients for deterioration so as to intervene early. Other partnerships include one with Renown Health to enhance in-hospital and at-home care. Differentiators: BioIntelliSense is leveraging a patient-centric approach that captures multiple dimensions of patients’ health over time. Unlike the frequent visits with a primary care physician or hospital, the devices can passively track vitals 24/7 while simultaneously uploading that real-time data to the network for analysis. For patients recovering from health conditions such as COVID, the care team would be able to view daily and weekly trends while suggesting interventions according to their care plan if conditions were to worsen. According to the company, BioSticker and BioButton work via adhesive sensors that are used to monitor patients’ heart rate, respiratory rate, and skin temperature among other features prescribed by a clinician. As noted in an article in ColoradoBiz, the devices are “measuring 20 different physiologic and biometric parameters and then transmitting that data to the cloud and then to a doctor or hospital or nurse or someone trying to take care of you.” According to CEO Mault, given the growing healthcare workforce crisis, routine patient monitoring has become more expensive and less frequent, especially during the ongoing public health emergency. With the BioButton and BioSticker, vital signs monitoring can be provided for each patient far more frequently and at a fraction of the cost. Devices like BioIntelliSense which appear to offer convenience and cost-effectiveness are likely to be beneficial for both the healthcare system and its patients, especially given the current shortage of healthcare workers. Medicare and other insurers reimburse healthcare providers for the cost of BioIntelliSense’s technology. Through the platform’s advanced analytics clinicians may now have access to high-resolution patient trending and reporting to enable medical-grade care from in the hospital to the home, and all points in-between. The Big Picture: BioIntelliSense seems to be bridging the gap between quality care and prevention strategies that may be most beneficial to underserved communities. Their platform is integrating personalized technology where proactive, preventive care work to head off disease and illnesses so as to take a preemptive approach before symptoms actually manifest. If tools like this were integrated into hospitals and clinics, it could potentially change how patient care is delivered facilitating a reduction in hospitalizations, emergency department visits, and shorter hospital stays, which would create cost efficiencies for health systems. Through the platform's data sets and analytics, and with a team of engineers and data scientists who have expertise in wearable sensor development, the company has the potential to help transform care delivery and remote monitoring while expanding to regions with little or no access to healthcare facilities. BioIntelliSense lands $45M for remote patient monitoring device, Tech Startup: BioIntelliSense

Our Take: On Thursday, February 24th, CMS announced it was ending the GPDC ACO program and replacing it with the Accountable Care Organization (ACO) Realizing Equity, Access, and Community Health (REACH) Model effective 1/1/2023. While the new program attempts to increase the role of ACOs in helping to achieve health equity and the amount of provider ownership in ACOs a number of fundamental challenges remain the same. In addition, given the program was only extended until the end of the previous GPDC many analysts have speculated that this will not be the last in a likely series of changes to value-based care programs in Medicare’s ACO programs. Key Takeaways: Approximately 41% of Medicare ACOs are still on one-sided risk models down from approximately 80% in 2018 Under REACH providers must develop a “Health Equity Plan” “that must include identification of health disparities and specific actions intended to mitigate” them as well as a method to collect “demographic and social needs data.” The number of Medicare beneficiaries appears to have plateaued at approximately 11M in 2022 which is down slightly from a peak of 11.2M in 2020 Healthcare spending remains out of control in the U.S. and consumed almost 20% of GDP in 2020, an increase of over 7% of GDP in the past 20 years The Problem: While Accountable Care Organizations (ACOs) were originally envisioned by Dr. Elliot Fisher at the Dartmouth Institute as a way to improve quality and costs by sharing accountability for a patient’s care among all providers along the health care continuum, in practice actual implementation has been more difficult. As noted, Fisher believed accountability was the key to implementing what today we call value-based healthcare whereby providers, including hospitals and physicians, are paid based on patient health outcomes, instead of volume with a goal of reducing the dramatic growth costs while improving quality. Originally put into practice by the Pioneer ACO Model in Medicare as part of the Affordable Care Act (ACA), ACOs have gone through a number of iterations including the Medicare Shared Savings Program (MSSP) and the Next Generation ACO Model as administrations and public policy officials have tried to improve performance and address challenges that have arisen as the models have been implemented. In April 2019 the Trump Administration introduced the Direct Contracting (DC) program. According to the HHS in its announcement, the program was an attempt to get providers who had dropped out of other ACO pilots to reengage with the program as well as to get them to take on so-called two-sided risk. (Please see “The Backdrop”). In addition, the Trump administration’s goal was to incorporate elements of Medicare Advantage (MA) and other private sector initiatives into the DC model. Implementation of DC was set to begin on April 1, 2021. On April 15, 2021, CMS announced that it would stop accepting applications for January 1, 2022. Among other things many were concerned that DC would effectively force beneficiaries to enroll “Medicare beneficiaries into a managed-care like a plan”, it would give create incentives for profit-making entities (ex: MA insurers and private equity groups) to skimp on patient care, and would not adequately address the needs of the underserved. The Backdrop: As described in “Origins and Future of Accountable Care Organizations” which details the evolution of ACOs, “by moving away from strict FFS payment arrangements toward more accountable, value-based reimbursements, providers can be incentivized to more efficiently improve the cost and quality of care.” In early Medicare models such as Medicare’s MSSP, policy officials believed that providers would move through three stages of a payment continuum, taking on more financial risk (and reward) as they took on responsibility for a higher degree of care. Initially, it was believed that providers would need to gain experience by sharing risk in so-called one-sided contracts which had limited downside to learn how to manage patient populations while being shielded from the risk of loss. Eventually, it was believed as providers gained experience managing risk, they would want to capture more reward and move towards what is termed two-sided risk where they would share in any savings compared to a benchmark as well as in any loss. Finally, the theory went, as providers perfected their expertise they would want to move towards a partial or full-capitation model, where they would effectively be paid a flat fee per member per month for all of their care (at a rate that theoretically would provide savings to plans and payers like Medicare). However, while the number of Medicare beneficiaries in ACOs has increased fairly significantly from just under 5 million in 2014 to 11 million in 2022, this number is down slightly from its peak of 11.2 million in 2020. In addition, and perhaps more importantly from a cost-savings standpoint, the overall savings achieved by ACOs in Medicare and the number of providers choosing to take on the additional risk and move into two-sided risk arrangements are still relatively modest. While this is a major point of contention, studies tend to indicate that to date overall savings from Medicare ACOs have not been large (see Implications). In addition, until recently the percentage of providers that have chosen to take on downside risk was relatively low. For example, according to CMS as of 2018, only 17% were taking downside risk and while this number recently increased to 59%, as recently as 2021 it stood at only 37%. As a result of all of the concerns surrounding care being compromised by for-profit entities as well as the somewhat lackluster performance of ACOs last week, the Biden administration announced a halt to DC and a rebranding to the ACO Realizing Equity, Access, and Community Health Model (REACH). Among other things, CMS noted ACOs must have a greater focus on addressing healthcare inequalities by requiring participants to develop a “Health Equity Plan” “that must include identification of health disparities and specific actions intended to mitigate the health disparities identified” as well as “collect beneficiary-reported demographic and social needs data.” Also, CMS will allow nurse practitioners to order additional services to improve access including cardiac rehab, home infusion care, and hospice care. In order to address concerns about beneficiary care being compromised or beneficiaries being forced into plans, “participating providers generally must hold at least 75% of the governing board voting rights” and the new application process will consider whether providers have a “demonstrated strong track record of direct patient care and a record of serving historically underserved communities with…quality outcomes.” In addition, the REACH model is going to change the risk adjustment process to “mitigate potential inappropriate risk score gains” and also reduce the discount rate for global ACOs in 2024-2026 (with the lower discount rate effectively reducing costs to providers). Implications: Healthcare spending has long been out of control in the U.S. and consumed almost 20% of GDP in 2020, an increase of over 7% of GDP in the past 20 years. While value-based care and ACOs have intuitively and logically seemed to make sense, they have never really lived up to that promise in practice. For example, according to Brad Smith, former Director of the Center for Medicare and Medicaid Innovation at CMS, while CMS has launched 54 value-based payment models, only 5 of which have yielded significant savings. As a result, according to Axios, ACOs have saved Medicare only 0.5% of fee-for-service Medicare spending. The REACH rebrand is yet another attempt to tweak some of the issues that have hindered ACOs but it is unlikely to spur long-term success. First, the REACH program does not address a fundamental challenge that many ACOs had faced in prior models, lengthening the transition period from upside-only to downside risk. Many studies have shown that providers drop out of programs like Medicare’s MSSP model when required to take on downside risk. Moreover, most studies indicate better performance when beneficiaries are assigned to the same ACOs for longer time periods. In addition, an overwhelming number of studies have shown that ACOs who serve high-need and underserved populations tend to be underresourced themselves and thus tend to underperform, failing to achieve savings. This model does not do anything to provide additional resources to poorly resourced providers thereby helping them better serve patients and achieve savings. Similarly, while the REACH plan does make some changes to risk adjustment, it only modestly addresses some of the issues around risk adjustment. First, many studies of Medicare ACOs have shown that Medicare’s risk adjustment mechanisms for ACOs fail to properly adjust for the very intensive and specialized care that many in this group demand, leading to inappropriate reimbursement. In addition, while CMS did adjust the risk adjustment mechanisms from what existed in the DC model, many critics contend that the risk adjustment mechanisms need to be more finely tuned to avoid inadvertent profits. This has long been an issue in MA and something that likely will require more study and more transparency. Lastly as noted in “All-Payer Spread Of ACOs And Value-Based Payment Models In 2021: The Crossroads And Future Of Value-Based Care”, “CMS needs to take specific actions to demonstrate its continued support for value-based payment”, and although this rebrand is a step in the right direction the fact that the program will only run until 2026 will give some providers pause about CMS’ commitment and likely lead them to question whether or not to participate. Since Medicare only accounts for a portion of most payers and providers' revenues and patients will have many types of insurance over their lifetimes, “CMS needs to identify opportunities for multipayer ACO models.” More importantly, given the growth and relative size of MA compared to Medicare ACOs (MA is more than twice the size) providers need to work with payers to find ways they can incorporate value-based payment mechanisms into innovative MA plans. Related Readings: All-Payer Spread Of ACOs And Value-Based Payment Models In 2021: The Crossroads And Future Of Value-Based Care Origins and Future of Accountable Care Organizations Comparing GPDC to the ACO REACH Model comparison chart CMS All-Payer Spread Of ACOs And Value-Based Payment Models In 2021: The Crossroads And Future Of Value-Based Care” CMS Innovation Center at 10 Years — Progress and Lessons Learned

The Driver: Trialjectory recently raised $20M in its series A funding round bringing the total the company raised to $27.7M. The round was led by Invest Partners ( a private equity firm that invests in start-up technology and software companies) and was joined by JAL Ventures, Contour Venture Partners, TIA Ventures, Rho Ventures, and Connecticut Ventures. Trialjectory is an AI-based clinical trial matching platform that uses self-reported clinical information to facilitate the clinical trial search and enrollment by cancer patients and their physicians. The company was founded by Noem Geva and co-founder Tzvia Bader. Trialjectory works alongside all sectors of health care to improve the recruitment process. The money raised in the latest funding round will go towards upgrading the AI technology for their clinical trial matching platform. Key Takeaways: A meta-review of 310 clinical trials conducted between 2003 and 2016 found that non-Hispanic whites were more likely to be enrolled in clinical trials than African American or Hispanic and Latino participants Trialjectory's app allows patients to enter information related to their specific cancer to find the best matched clinical trials and has matched more than 50,000 patients to clinical trials. 35% of patients on Trialjectory are people from underrepresented backgrounds and of that 35%, 60% are African American, 30% are Hispanic or Latino Trialjectory anticipates working with pharma companies and trial sponsors to help recruit patients The Story: For CEO and cofounder Tzvia Bader, cancer treatment hits close to home. In December of 2013, Bader was diagnosed with Malignant Melanoma, a type of skin cancer that develops from pigment-producing cells. Bader also lost his mother to a battle with Non-Hodgkin’s Lymphoma, another type of cancer that affects white blood cells. After participating in three clinical trials, the evidence of cancer in Bader’s body was reduced. During his cancer journey, he realized that the clinical trials enrollment process was not patient-friendly, made it difficult to enroll in trials and challenging to access treatment options. With this in mind and years of developing technology companies, Bader decided to team up with Noem Geva to create an AI platform to help address this issue. The end result was the Trialjectory platform which attempts to connect patients in need of advanced cancer treatments and those running trials for cutting-edge therapeutics. The platform allows patients to input their health information, then the AI matches them to open clinical trials. Pharmaceutical companies use it to recruit patients to their own clinical trials. The collaboration between patients, physicians, and pharma companies makes clinical trials more accessible for all. The platform is free for patients, and the company anticipates its revenue stream will come from being sold to drug companies and hospitals. The Differentiators: What sets TrailJetory apart is its ability to break down the barriers to enrolling in clinical trials and to increase enrollment of the underserved. For example, as noted in the book “Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary” patients often encounter challenges such as lack of encouragement, inconvenience, fear of getting no treatment, and difficulty with eligibility criteria. By helping match patients to trials with minimum effort Trialjectory helps reduce these frictions. In addition, while data indicate the Caucasian White males were more likely to be enrolled in oncology trials than Blacks, LatinnX, or women, data supplied by the company indicate that 35% of patients on Trialjectory are people from underrepresented backgrounds, and of those 60% are African American, and 30% are Hispanic or Latino. In the words of cofounder XXXX “This unique approach continues to successfully remove the barriers and biases that historically prevented cancer patients from accessing advanced treatment options.” The Big Picture: Trialjectory is taking an automated approach to both connecting patients with clinical trials and increasing the ranks of the underserved for patients diagnosed with cancer. Their platform is furthering the use of technology in healthcare to make life easier for patients, doctors, and clinical trial sponsors. If this platform works as intended, it will potentially benefit patients and pharmaceutical companies. Patients will have better access to treatment options for their specific cancer. For the company’s holding clinical trials, the platform will increase and diversify patient enrollment helping to speed trails and increase the efficacy of trial results. In addition, as personalized medicine and therapeutically focused companies like Trialjectory create support communities, and other mechanisms to support patients in their care journey it will improve the quality and effectiveness of dialogue with clinicians. Trialjectory scores $20M to match cancer patients to clinical trials; Trialjectory on track to match 50K cancer patients with clinical trials this year: 35% are from underrepresented groups

Our Take: Lack of available and high-quality health data for black, indigenous, and people of color (BIPOC), as well as language barriers in healthcare settings, will make it impossible to reduce racial disparities in healthcare unless these issues are resolved. Not only is this type of data essential for ensuring quality care for everyone, but these hurdles are a reflection of system inequities and the ongoing disconnect between providers and patients of different backgrounds that desperately need to be addressed. However, while challenges surrounding accountability for improved data collection and analysis around BIPOC populations continue to persist, efforts to improve these initiatives and dismantle these disparities are on the rise as evidenced by including such efforts in quality measurement scores. This is a start, but more needs to be done. Key Takeaways: According to HEDIS data in 2019, an astounding 94% of commercial health plans reported incomplete ethnicity data The Healthcare Effectiveness Data and Information Set (HEDIS) holds plans responsible for addressing gaps in treatment and outcomes among their patient populations Blacks were almost twice as likely to have undiagnosed kidney disease as Whites according to “Racial and Ethnic Disparities of Chronic Medical Conditions in the USA” Barriers to effective and equitable healthcare can result from social, cultural, and linguistic differences between patients and clinicians The Problem: Persistent racial and ethnic disparities within our healthcare system and the inability to get solid data around the sources and impacts of these challenges have impeded our ability to understand how certain social determinants of health (SDOH) affect the quality of healthcare and health outcomes. These SDOH which the CDC defines as “conditions in the places where people live, learn, work, and play that affect a wide range of health and quality-of life-risks and outcomes” can have profound impacts on the health and effectiveness of certain treatment protocols. For example, according to the Robert Wood Johnson Foundation, SDOH can drive as much as 80% of health outcomes. Although acknowledgment of basic acknowledgment of these factors is increasing, the policy maker’s ability to understand specifically what created and factored into such issues is still in its nascent stages. For example, although the historic lack of effort and resources put forth to examine and address such disparities is one factor, understanding how and why specific geographic regions vary in levels of disparities in care is equally as important. For example, the efforts of many states to collect and analyze the impact of COVID on communities of color has been a meaningful step. Prior to that, there were inconsistent policies in state collection of racial and ethnic data and classifications around illnesses and diseases. Prioritizing the collection of valid and accurate data from all racial and ethnic groups will aid in understanding the missteps and limitations in the data that’s collected. Furthermore, many communities' language barriers or cultural differences often factor into the lack of data collection and analysis. For example, many racially and ethnically diverse populations are not able to participate in research that is only being undertaken in a limited number of languages, limiting their ability to understand the purpose of the research and preventing them from understanding the importance of their participation and data in the research. Reaching out in their own language, and explaining the purpose, benefits, and importance of data collections could help. Along these lines, addressing the regulations and standards governing the collection of data around racial and ethnic populations, known officially as the Standards for the Classification of Federal Data on Race and Ethnicity, is sorely needed as these standards were last updated almost 25 years ago. The Backdrop: In order to understand the differences in health outcomes for diverse populations, it is necessary to advocate for reliable and valid resources to conduct research and examine data to draw conclusions and make policy recommendations. Unfortunately, the lack of reliable, high-quality data, inadequately updated data standards, and an inability to collect culturally relevant data have led to a void of effective answers. Healthcare organizations first need to be able to identify any disparities that exist in particular populations as well as why they exist before being able to address them. For example, if the community being examined is a diverse Black or Latino population, understanding the type of barriers that exist to provide additional services and resources would improve the ability to steer resources and treatment to their needs. In addition, attitudes towards the healthcare system and sources of health information could decrease the difficulty associated with recruiting the various populations into research studies which could increase the diversity of such studies and acceptance of their conclusions. It is also important to note the impact that changes in economic conditions can have on such communities over time. Certain communities may be more sensitive to small changes in economic conditions as they often live with a much smaller safety net than other populations. For example, the Pandemic caused striking and greater shifts in financial conditions and quality of life for many in the BIPOC community as compared with white adults. According to “Disparities in Health and Health Care: 5 Key Question and Answers”, “about six in ten Hispanic adults (59%) and about half of Black adults (51%) said their household lost a job or income due to the pandemic, compared to about four in ten White adults (39%)”. In addition, diverse and underserved populations typically experience a higher prevalence of undiagnosed diseases compared to white populations. For example, in a study entitled “Racial and Ethnic Disparities of Chronic Medical Conditions in the USA”, the authors noted “all minorities were more likely to have undiagnosed diabetes compared to Whites” and “Blacks were almost twice as likely to have undiagnosed kidney disease as Whites”. Identifying and diagnosing these problems will become increasingly important as people of color become an increasing portion of the population given that people of color are forecast to make up more than half of the population by the year 2050. To better adapt healthcare resources, advocacy for and communication with these targeted populations is necessary. While historically healthcare interventions may have fallen short due to the lack of good data and the inability to effectively design strategies that would benefit these populations, as people of color grow demographically, healthcare equity needs to be the top priority for social, economic, and humanitarian reasons as untreated disease strains challenge us ethically as well as fiscally. Leveraging resources rolled out during the Pandemic like the CDC COVID Data Tracker or Emory University; COVID-19 Health Equity Interactive Dashboard to better collect and disseminate granular health data on BIPOC populations could strengthen connections and instill trust in those communities. Implications: The need for interventions to target gaps in health data and in care for minority populations is vital as is the need for sustainability of these initiatives. Quality measurement systems have been one method of encouraging health care systems to fill the information void where data on current disparities exist. For example, the National Committee for Quality Assurance (NCQA) has started “implement[ing] a stratification by race and ethnicity in its health plan quality measure set, the Healthcare Effectiveness Data and Information Set (HEDIS), to hold plans accountable for addressing disparities in care and outcomes among their patient populations.” This is due to the fact that according to HEDIS data in 2019, an astounding 94% of commercial health plans reported incomplete ethnicity data. This demonstrates how incomplete information on inequities in healthcare data is and as such how difficult it would be to address issues within this data even if they are measured. Nevertheless, when providers and health plans have been able to measure such data and take action to address it they have found success. For example, when health plan Health Net, LLC put forth efforts to combine patient-level data, provider data, and public mapping information in order to improve the rates of Mandarin-speaking Chinese members to encourage them to get their cervical cancer screenings, there was a 4% increase in the cancer screening rates. While improved data collection and analysis will help address disparities, it will take time. In the meantime policy officials, providers and plans should explore other approaches near-term. Multilevel interventions that encompass individual patients, family and friends, organizations and providers, and policy and stakeholders may be an effective method to see a shift in the healthcare system. The Centers for Population Health and Health Disparities have developed several multilevel interventions to bridge gaps in access to quality of services for cardiovascular disease and cancer. In addition, language and communication barriers also need to be addressed in order to improve the care given from provider to patient. It is critical for providers and healthcare systems to recognize that populations who are culturally and linguistically diverse need communication solutions to better the overall experience when dealing with clinicians. While delivering culturally competent care means more than just translating something written in English just implementing language translation can be a step in the right direction. For example, one study showed that the use of online translation tools such as Google Translate and MediBabble in hospitals and clinics “increased the satisfaction of both medical providers and patients…and improved the quality of healthcare delivery and patient safety.” Achieving high-quality care for diverse populations will be an effective means in treatment and satisfaction of medical providers and patients. However, it will not be complete until we measure, analyze and address care in a culturally competent manner. According to the American Hospital Association, this will require acknowledging the importance of culture, incorporating the assessment of cross-cultural relations, recognizing the potential impact of cultural differences, expanding cultural knowledge, and adapting services to meet culturally unique needs. Related Readings: A Data-Driven Approach to Addressing Racial Disparities in Health Care Outcomes Disparities in Health and Health Care: 5 Key Questions and Answers The Challenges of Collecting Data on Race and Ethnicity in a Diverse, Multiethnic State In Focus: Reducing Racial Disparities in Health Care by Confronting Racism A New Effort To Address Racial And Ethnic Disparities In Care Through Quality Measurement