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The Driver: R-Zero recently raised $105M in a Series C financing led by investment firm CDPQ with participation from BMO Financial Group, Qualcomm Ventures, Upfront Ventures, DBL Partners, World Innovation Lab, Mayo Clinic, Bedrock Capital, SOSV and legendary venture capital investor John Doerr. The Series C financing brings the total amount raised by R-Zero to more than $170M since its founding in 2020. The company will use the funds to scale deployments of its disinfection and risk modeling technology to meet growing demand across public and private sectors, including hospitals, senior care communities, parks and recreation, other government facilities, and college and corporate campuses. Key Takeaways: According to the company, R-Zero’s technology neutralizes 99.9% of airborne and surface microorganisms R-Zero’s UV-C products cost anywhere from $3K to $28K compared with traditional institutional UV-C technology which can cost anywhere from $60K to $125K Using R-Zero's products results in more than 90% fewer greenhouse gas emissions (GHG) and waste compared to HVAC and chemical approaches For every dollar employers spend on health care, they’re spending 61 cents on illness-related absences and reduced productivity (Integrated Benefits Institute) The Story: R-Zero was co-founded by Grant Morgan, Eli Harris and Ben Boyer. Morgan, who has an engineering background, had worked briefly as CTO of GIST and had been V.P. of product and engineering at iCracked and was previously in R&D in medical devices. Harris co-founded EcoFlow (an energy solutions company) and has been in partnerships and BD at drone company DJI, while Boyer had been an MD at an early-growth stage VC Tenaya Capital. According to the company, the co-founders applied their experience to innovating an outdated legacy industry to make hospital-grade UVC technology accessible to small and medium-sized businesses. Prior to R-Zero these units could cost anywhere from $60-$125K and often lacked the connected infrastructure and analytics necessary to optimize performance and provide risk analytics for its users (ex: how frequently and heavily rooms are being used and when to use disinfection to help mitigate risk). The Differentiators: As noted above, typical institutional ultraviolet (UV) disinfectant lighting technology can be expensive and has the potential to be harmful (hihigh-poweredVC lights can cause eye injuries if people are exposed to them for long periods of time), however, R-Zero has found ways to mitigate these issues. First, as noted in Forbes, its products run anywhere from $28K for their most expensive device the Arc, to the Beam at $5K and the Vive at $3K. Moreover, while the Arc can only be used to disinfect an empty room due to the wavelength of UVC light, the Beam creates a disinfection zone above people in a room while the Vive can be used to combat harmful microorganisms when people are in a room. In addition, according to the company R-Zero’s technology neutralizes 99.9% of airborne and surface microorganisms and does so with 90% fewer greenhouse gas emissions and waste compared to HVAC and chemical approaches. As a result, R-Zero can help improve indoor air quality in hospitals and other medical facilities, factories, warehouses, and other workplaces more efficiently and effectively than outdated technologies. Implications: As noted above, R-Zero’s technology will help hospitals and senior care facilities cost effectively sanitize treatment spaces which can’t necessarily be done with current technology. Moreover, as medical care increasingly moves to outpatient settings the ongoing workplace shortage will challenge these facilities to find ways to keep themselves clean and disinfected and avoid disease transmission. For example, the company claims that their customers have been reducing labor costs by 30%-40%, a number which will likely only get higher given the current labor situation. In addition, even in facilities that have the necessary workforce, it is often difficult to optimize staff time to ensure that offices are sanitized and used to maximum capacity. Utilizing devices like the R-Zero Beam or Vive can allow medium-to-small size facilities to constantly and efficiently be disinfecting rooms, making them immediately available for use.. Also, by removing the burdensome task of having already overworked clinical or janitorial staff spend time sanitizing the rooms, R-Zero’s technology can help improve employee productivity and satisfaction at a time when both are stretched thin. This Startup Wants To Bring Disinfecting UV Light Into “Every Physical Space”, R-Zero Raises $105 Million Series C to Improve the Indoor Air We Breathe, This startup built an ultraviolet device that can disinfect a restaurant in minutes

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

The Driver: Prescribe fit is a virtual/telehealth-based orthopedic health startup. It is specified for patients that are dealing with orthopedic bone and muscle injuries. Prescribe fit raised 4 million in seed funding. The round was led by Tamarind Hill with participation from the Grote Family as well as Mike Kaufman, the former CEO of Cardinal Health. According to the company, proceeds of the funding will be used to aggressively expand the company, as well as broaden and accelerate product development. Key Takeaways: According to the Bone and Joint initiative USA, 124 million Americans suffer from a musculoskeletal disorder On average, 1 out of 4 elderly adults fall each year and over 800,000 people end up in the hospital due to a fall injury per the U.S. CDC Patients who experienced falls had longer hospital stays and were more frequently discharged to other healthcare facilities, instead of their primary residence according to a study by the Hospital for Special Surgery According to an article in the Journal of Medicine, fear of falling often develops after experiencing a fall and developing a fear of falling can cause older adults to avoid physical activity, experience more difficulty with activities of daily living, and become less able to perform exercises. The Story: Originally started as a weight loss coaching startup in January 2020, Prescribe Fit was only able to secure only one client after enduring the shutdown of all non-essential health services during the Pandemic. Co-founded by CEO, Brock Leonti, who previously owned a home health agency for approximately six years, the company worked at that time to help treat obesity and served primary care doctors. While the company was limited to just one client during the Pandemic they were able to test and refine their model as well as a number of treatment models. As part of that the company gleaned a number of insights including how to successfully use remote patient monitoring technology and the need for limited administrative burden on physicians. The Differentiator: Based on its experience and what it had learned during the Pandemic in August 2022 Prescribe Fit transitioned its business model to focus solely on orthopedic practices and the treatment of the root causes of MSK issues. According to Leonti, this includes helping orthopedic patients reduce blood pressure, blood sugar & weight at-home and partnering with orthopedic practices to improve their patients mental acuity, flexibility & endurance. As noted in the Columbus Business Journal, “Prescribe Fit has a team of nurses and care coordinators who meet remotely with patients and “edit” their daily routines so their behavior changes stick.” This includes having patients take pictures of their meals and then having coordinators indicate where they may be able to reduce portion sizes or substitute healthier items in their diets. According to Leonti, this has allowed orthopedic patients to obtain 5.4% average weight loss in just 16 weeks and create personalized at-home health plans resulting in 80%+ of patients staying engaged for 9+ months, both of which help improve MSK issues. Implications: According to the Bone and Joint initiative USA, 124 million Americans suffer from an MSK disorder but will often end up treating the symptoms and not addressing the root cause. In part this is due to the limited availability of orthopedic specialists and other clinicians to address these issues. By connecting these patients via specialists offices with nurses and other case managers who can address specific dietary and behavior issues that are contributing to these conditions (ex: lack of exercise or inappropriate exercise routines) Prescribe Fit is helping improve the quality of care while lowering the cost. Moreover, since patients are being monitored by clinicians using remote patient monitoring (RPM) and chronic disease management tools, physicians are able to create an additional reimbursement stream (while paying Prescribe Fit a management fee). As the U.S. gets older demographically a larger proportion of the population will have to deal with MSK issues that can lead to falls and injuries which can often compound into other issues. By addressing these issues and helping patients strengthen bones and improve muscle tone Prescribe Fit may help reduce the incidence (and cost) of such issues. Health tech startup Prescribe FIT raises $4M in oversubscribed seed round, Weight-loss coaching startup Prescribe Fit doubles with focus on orthopedics

Our Take: AI chatbots such as ChatGPT and tools that are being developed like it have significant promise in revolutionizing the way care is delivered and the way that patients and care providers can connect with each other. Due to their ease of use and equity of access, patients from all backgrounds can receive effective care, particularly in the fields of medical administration & diagnosis, mental health treatment, patient monitoring, and a variety of other clinical contexts. However as ChatGPT, the most advanced publicly available AI yet is still in its beta stage, it is important to keep in mind that these chatbots operate on a statistical basis and lack real knowledge that leads them to frequently give inaccurate information, and make up solutions via inferences based on the data they are trained on, raising concerns as to whether they can be trusted to deliver correct information. This is especially true for patients with chronic conditions who may be putting their health in danger by following chatbots’ advice that could be seriously flawed. Key Takeaways: Chatbots have the potential to reduce annual US healthcare spending by 5-10%, translating to $200-360 billion in savings (NBER) In a study evaluating the performance of virtual assistants in helping patients maintain physical activity, diet, and track medication 79% of participants reported virtual assistants had the potential to change their lifestyle (International Journal of Environmental Research and Public Health) Artificial intelligence solutions in healthcare are easier to access than ever before and care providers are quickly adopting AI chatbots to solve deficiencies in manpower and equity of access for tasks they see as easy to automate. As noted by STATNews, “ChatGPT was trained on a dataset from late 2021, [so] its knowledge is currently stuck in 2021…it would be crucial for the system to be updated in near real-time for it to remain highly accurate for health care” The Problem: With new developments in advanced medicine and technology including artificial intelligence and machine learning tools, care providers are working hard to adopt these systems to healthcare particularly where they have the potential to address an ongoing workforce shortage. Moreover, as populations age, the gap between incidence of disease and treatment options broadens. For example, according to the Journal of Preventing Chronic Disease, in 2018, an estimated 51.8% of US adults had at least one out of the ten most commonly diagnosed chronic conditions, and 27.2% of adults had multiple chronic conditions. As a result, hospitals and physicians (providers) are seeing greater levels of care utilization and a need to connect these patients with care resources that address their conditions and/or prevent the conditions from becoming more severe. In addition, given the inefficiencies and disparities in the delivery of care in the U.S. healthcare system (ex: lack of providers in certain rural areas, long wait time for specialists) technology may be best positioned to address these deficiencies and improve outcomes. Over time as these tools become more sophisticated they can be used for initial triage escalation to clinicians for a high level of care increasing the use and application of human intelligence/experience where it may be most needed. The Backdrop: The advent of AI chatbots holds great promise in changing the way we deliver and manage care, especially for practices that lack the resources to handle large numbers of patients and the amount of data they generate. According to Salesforce, chatbots (coined from the term “chat robot”) is a computer program that simulates human conversation either by voice or text communication, and is designed to solve a problem. Early versions of chatbots were used to engage customers alongside the classic customer service channels like phone, email, and social media. Current chatbots such as ChatGPT, leverage machine learning to continually refine and improve their performances using data provided and analyzed by an algorithm. As noted in WIRED magazine, the technology at the core of ChatGPT is not new “it is a version of an AI model called GPT-3 that generates text based on patterns it digested from huge quantities of text gathered from the web.” What makes ChatGPT stand out is “it can take a naturally phrased question and answer it using a new variant of GPT-3, called GPT-3.5 (which provides an intuitive interface for users to have a conversation with AI). This tweak has unlocked a new capacity to respond to all kinds of questions, giving the powerful AI model a compelling new interface just about anyone can use.“ After creating an account with OpenAI (the open source developers behind ChatGPT), all users have to do is type their query into a search bar to begin using ChatGPT’s services. Although ChatGPT is still in beta, its capabilities are impressive and it has surpassed any previously publicly available AI chatbot to date. Using ChatGPT makes it easy to learn as it can quickly and easily summarize any topic the user wishes, saving hours of research and digging through links to understand a certain topic. It can help people compose written materials on anything they wish, including essays, stories, speeches, resumes and more. It is also good at helping people to come up with ideas, and since AI is particularly good at dealing with volume it can provide a litany of possible solutions to humans looking for those solutions. Perhaps the largest change it will bring lies in computer programming. ChatGPT and other AI chatbots have been found to be particularly good at writing and fixing computer code, and there is evidence that using AI assistance in coding could cut total programming time in half according to research conducted by GitHub. For certain healthcare administrative takes, chatbots seem to have a bright future and can connect patients to their care providers in ways that weren’t possible before. Access to healthcare services is one of the most apparent ways, particularly for those living in rural and remote areas far away from the nearest hospital. Disparities among According to the Journal of Public Health, evidence clearly shows that Americans living in rural areas have higher levels of chronic disease, worse health outcomes, and poorer access to digital health solutions as compared with urban and suburban areas. Not only do individuals living in rural areas live much farther away from their nearest hospital, but the facilities themselves often lack the medical personnel and outpatient services common at more urban hospitals which contributes to the inconsistencies in care and outcomes. Similarly, given the increased administrative burden that accompanies the digitization of healthcare and healthcare records, doctors are increasingly occupied by the deluge of tasks that are more suited to automation than others. For example, certain tasks like appointment scheduling, medical records management, and responding to routine and frequently asked patient questions aren’t always the most effective use of medical professionals’ time and could be handled in a consumer friendly and efficient manner by chatbots like ChatGPT. Given the easy way that users are able to interact with ChatGPT there is also the potential to eliminate some of the traditional barriers to the delivery of healthcare, particularly the one-to-many issue given clinician shortages. However, this will not happen near term and will require some refinement. First, as noted by STATNews, “ChatGPT was trained on a dataset from late 2021, [so] its knowledge is currently stuck in 2021…even if the company is able to regularly update the vast swaths of information the tool considers across the internet, it would be crucial for the system to be updated in near real-time for it to remain highly accurate for health care uses.” In addition, the article quoted Elliot Bolton from Stanford’s Center for Research on Foundation Models, who noted that ChatGPT is “susceptibe to inventing facts and inventing things, and so text might look [plausible], but may have factual errors.” Bearing that in mind, should ChatGPT follow the path of other chatbots in medicine it does have potential in a number of clinical settings, particularly in the field of mental health. Here it is important to note that neither ChatGPT nor chatbots possess the skills of a licensed and trained mental health professional or the ability to make a nuanced diagnosis so should not be used for diagnosis, drug therapy or treatment of patients in severe distress. That said, the study of chatbots in healthcare has been most extensive around mental health with most systems designed to “empower or improve mental health, perform mental illness screening systems, perform behavior change techniques and in programs to reduce/treat smoking and/or alcohol dependence.” [Review of AI in QAS]. For example, a study from the Journal of Medical Internet Research reported that chatbots have seen promising results in mental health treatment, particularly for depression and anxiety. Among other things “participants reported that chatbots are useful for 1) practicing conversations in a private place; 2) learning, 3) making usersfeel better, 4) preparing users for interactions with health care providers, 5) implementing the learned skills in daily life; 6) facilitating a sense of accountability from daily check-in, and, 7) keeping the learned skills more prominently in users’ minds. Similarly, in a literature review published in the Canadian Journal of Psychiatry assessing the impact of chatbots in a variety of psychiatric studies, numerous applications were found, including the efficacy of chatbots in helping patients recall details from traumatic experiences, decreasing self-reported anxiety or depression with the use of cognitive behavioral therapy, decreasing alcohol consumption, and helping people who may be reluctant to share their experiences with others to talk through their trauma in a healthy way. However, as pointed out by Mashable, ChatGPT wasn’t designed to provide therapeutic care and “while the chatbot is knowledgeable about mental health and may respond with empathy, it can’t diagnose users with a specific mental condition, nor can it reliably and accurately provide treatment details.” In addition to the general cautions about ChatGPT noted previously (it was only trained on data through 2021 and it may invent facts and things), Mashable notes three additional cautions when using ChatGPT for help with mental illness: 1) It was not designed to function as a therapist and can’t diagnose, noting “therapists may frequently acknowledge when they don’t know an answer to a client’s questions, in contrast to a seemingly all-knowing chatbot” in order to get patients to reflect on their circumstances and discover their own insights; 2) ChatGPT may be knowledgeable about mental health, but it is not always comprehensive or right, pointing out that ChatGPT responses can provide incorrect information and that it was unclear what clinical information or treatment protocols it had been trained on; 3) there are [existing] alternatives to using ChatGPT for mental health help, these include chatbots which are specifically designed for mental health like Woebot and Wysa which offer AI guided therapy for a fee. While it is important to keep these cautions and challenges in mind, they also provide a roadmap of areas that ChatGPT is likely to be most effective once these issues are addressed. Similarly, chatbots are also good for monitoring patients and tracking symptoms and behaviors, and many are used as virtual assistants in order to ensure patients’ well-being is positive while ensuring they are adhering to their treatment schedule. A study published in the International Journal of Environmental Research and Public Health evaluated the performance of a virtual health assistant to help ensure patients maintain physical activity, a healthy diet, and track their medication. Results revealed that 79% of participants believed that virtual health assistants have the potential to help change their lifestyles. However, some common complaints were that the chatbot didn’t have as much personality as a real human would, it performed poorly when participants initiated spontaneous communication outside of pre-programmed “check-in” times and that it lacked the ability to provide more personalized feedback. Implications: AI-based chatbots like ChatGPT have the potential to address many of the challenges facing the medical community and help alleviate issues faced due to the workforce shortage. As many have noted, a report by the National Bureau of Economic Research stated that chatbots have the potential to reduce annual US healthcare spending by 5-10%, translating to an estimated $200-360 billion in savings. In addition, due to their 24/7 availability, chatbots provide the ability to respond to questions and concerns of patients at any hour addressing pressing medical issues and reaching people in a non-intrusive way. Moreover, as AI technology continues to develop, an increasing number of healthcare providers are beginning to leverage these solutions to solve persistent industry problems such as high costs, medical personnel shortages, and equity in care delivery. Chatbots are perfectly poised to fill these roles and increase efficiency in the process given they can perform at a similar level to humans. Generally, assessments of physician opinions on the use of chatbots in healthcare indicate the willingness to continue their use, and a study published in the Journal of Medical Internet Research reported that of 100 physicians surveyed regarding the effectiveness of chatbots, 78% believed then to be effective for scheduling appointments, 76% believed them to be helpful in locating nearby health clinics, and 71% believed they could aid in providing medication information. Given current workforce shortages, chatbots can act as virtual assistants to medical professionals and have the potential to greatly expand a physician’s capabilities and free up the need for auxiliary staff to attend to such matters. Although AI chatbot platforms and algorithm solutions show great promise in optimizing routine work tasks, current technology is not yet sufficient to allow independent operation as there are certain nuances that are best addressed by humans. Also, as one research review found “acceptance of new technology among clinicians is limited, which possibly can be explained by misconceptions about the accuracy of the technology.” Along with the opportunities for ChatGPT in healthcare, there are a number of challenges to implementing the technology. According to a study from the Journal of Medicine, Health Care, and Philosophy, since chatbots lack the lived experience, empathy, and understanding of unique situations that real-world medical professionals have they should not be trusted to provide detailed patient assessments, analyze emergency health situations, or triage patients because they may inadvertently give false or flawed advice without the knowledge of the personal factors affecting patients’ health conditions. Some clinicians are worried that they may one day be replaced by AI-powered machines or chatbots which lack the personal touch and often the specific facts and data that make in-person consultations significantly more effective. . While over time AI may be able to mimic human responses, chatbots will still need to be developed so they can effectively react to unexpected and unusual user inputs, provide detailed and factual responses, and deliver a wide range of variability in their responses so that they can have a future in clinical practices. This will ultimately require further developer input and more experience interacting with patients in order to adequately personalize chatbot conversations. Additionally, despite safeguards put in place by developers like the many pre-programmed controls to decline requests that it cannot handle, and the ability to block “unsafe:” and “sensitive” content, an article published in WIRED Magazine noted that ChatGPT will sometimes fabricate facts, restate incorrect information, and exhibit hateful statements & bias that previous users may have expressed to it, leading to unfair treatment of certain groups. The article noted that the safeguards put in place by ChatGPT’s developers can easily be bypassed using different wording to ask a question and emphasized the need for strong and comprehensive protections to prevent abuse of these systems. In addition, there is also the need for data security to ensure patient privacy as all of this data will be fed to private companies developing these tools As the aforementioned Mashable article noted about using ChatGPT for mental health advice, “therapists are prohibited by law from sharing client information, people who use ChatGPT…do not have the same privacy protections.” Related Reading: ChatGPT’s Most Charming Trick Is Also Its Biggest Flaw A Process Evaluation Examining the Performance, Adherence, and Acceptability of a Physical Activity and Diet Artificial Intelligence Virtual Health Assistant The Potential Impact of Artificial Intelligence on Healthcare Spending Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape

The Driver: Array Behavioral Care recently raised $25 million in its series C round. The round was led by CVS Health with participation from existing investors Wells Fargo Strategic Capital, Harbour Point Capital, Health Velocity Capital, HLM Venture Partners, OSF Healthcare, and OCA Ventures. Array Behavioral Care provides therapy and telepsychiatry-based virtual care for patients with behavioral health concerns and mental health issues. This new series of funding will help to scale their brand and platform as well as grow their team and improve their tech. This will help to expand their services overall into brand new markets and help to reach Americans suffering and in need of mental health services and overall high-quality behavioral health care. Key Takeaways: There are over 6,300 areas in America that lack access to mental health provider care with those areas covering over 15O M Americans (VMG Health) The number of Americans stating that they use telehealth services for mental healthcare rose 10%, to 59% between 2020 and 2021 (Cross River Therapy) Approximately, 50% of Americans will be diagnosed with a mental illness at some point in their life and 1 in 25 Americans are currently living with a mental illness (CDC) Close to 25 percent of respondents reported they are not able to receive the treatment they need for their mental health conditions due to lack of services, shortages in psychiatrists, and lack of mental health workers (Mental Health America) The Story: Array Behavioral Care was founded in 1999 in New Jersey by chief medical officer and co-founder Dr. James Varrell. As noted on the company’s website, Dr. Varrell was an early advocate for telepsychiatry and began writing the business plan for the predecessor to Array, InSight Telepsychiatry while working as a psychiatrist, with the help of his friend Geoffrey Boyce (current CEO and co-founder of Array) who wrote the business plan. In 2019 Insight TelePsychiatry joined forces with Regroup Telehealth another telepsychiatry provider in the Chicago, IL area. Together they became the largest telepsychiatry service clinician organization in the country. In January 2021 the companies relaunched under the name Array Behavioral Care. Varrell's vision was to modify and provide better access to telehealth psychiatry care. He also wanted to focus on better access for patients living in underserved communities as well as remote areas around the country. He believes in order to help counter the provider shortage and increasing mental health crisis in the population, his company is necessary because of its focus on providing quality telebehavioral care. The Differentiator: Array Behavioral Care one of the early providers of telemental health and telepsychiatry has been in the business for over 20 years. In fact, according to the company, their first telepsychiatry encounter dates back to 1999 with a rural hospital. Array’s goal is to collaborate with healthcare systems, hospitals, community-based medical care organizations, and insurers. As such, they are able to offer their services across a range of different settings to help reach people in a variety of care settings including hospitals, community health centers, nursing facilities, and substance-use medical centers, among others. The company’s focus it to provide high-standard behavioral health services through increased services, access, and better patient results all through the range of partnerships. Array also provides administrative, operational, and technical support while attempting to take away that burden from doctors so they can focus on patient care and provide telepsychiatry services across all 50 states. The Big Picture: With the rise in social isolation, ongoing changes in societies and economics due to the Pandemic as well as the ongoing threat of endemic COVID itself, mental health conditions such as anxiety, depression, and substance abuse have come into even more focus. Due to these pressures, mental illness has surged in America since COVID began. With the increased mental health crisis there is an increased recognition of the need for mental health resources, psychological help, and additional therapeutic treatment options. According to the Pew Research Center, 41% of adults have reported feeling increased levels of mental health anguish and despair since the outbreak of COVID. In addition, 58% of young adults have also experienced feeling mental health despair and distress in the period from March 2020 to September 2022. In addition, 22% of adults reported not feeling optimistic about the future at all or only on rare occasions. Given the breadth of services, from counseling to telepsychiatry as well as the broad population that they serve (available in all 50 states) Array seeks to be the care option for those individuals who are suffering from mental illness. By partnering with organizations such as CVS and Humana, Array hopes to expand access to its network of over 40,000 doctors, nurse practitioners, pharmacists, and more. Through such partnerships, Array states that it has the ability to provide access to 90 M people across its network. Given the increased incidence in mental illness and the chronic shortage of providers, solutions like Array are required to close this gap. Array Behavioral Care announces $25M funding round led by CVS Health; Array Behavioral Care raises $25M to expand its telepsychiatry business

Our Take: The 3D modeling and bioprinting industry is emerging as a unique and multifaceted solution towards medical training, planning & executing complex surgeries, and creating biologically necessary, personalized tissues and organs for patients. Innovations in this industry are yielding encouraging results in making diagnoses more accurate, improving clinician knowledge, and giving patients better health outcomes because this technology gives care providers easier access to the resources they need to improve care, albeit at prices that are restrictive to most organizations. Additionally, 3D bioprinting is far from the panacea it is purported to be as it is yet impossible to fully print complex, vascularized structures such as fully functioning human organs, limiting care providers to the creation of basic tissue and biomimetic structures that are designed to temporarily fix a patient’s issues while they wait for organ transplants or other treatment. Ethical concerns exist as well, as many people are uncomfortable with the idea of “playing God” so to speak and using pluripotent human stem cells to create any type of organic structure. Regardless, innovation is expected to continue along with market growth and given a few decades, 3D modeling and bioprinting will likely become more common in the healthcare industry. Key Takeaways: 3D bioprinters can cost up to $65,000, with software costing up to $15,000 and high hourly fees to capture and obtain CT scans from a healthcare provider (Frontiers in Pediatrics) 3D modeling provides a number of advantages for training medical students and clinicians and allows for proficiency-based training in a variety of contexts Information technology training of 3D models generated from medical imaging allows for the creation of easily shareable design blueprints, and machine learning has been used to create training databases and digital twins These technologies give rise to ethical concerns around quality, safety, and human enhancement, as well as technical concerns about the lack of suitable biomaterials and the complexity of the biostructures being printed (Journal of Biomedical Imaging and Bioengineering) The Problem: Healthcare providers are always searching for novel solutions to solve problems that arise when coordinating and delivering care. Technological innovations drive new changes in the market and introduce new ways to diagnose and treat the issues that patients face, and in the context of medical imaging, there is ample room for improvement. Doctors across the globe currently rely on 2D scans derived from computed tomography (CT) and magnetic resonance imaging (MRI) scans, which essentially translate 3D data into 2D scans which leaves more uncertainty and room for interpretation as radiologists convey the information they gather to clinicians who lack their background and experience. Applying these advancements in 3D modeling empowers advancements in fields such as tissue engineering and regenerative medicine. These technologies which aim to artificially create functional tissues constructs, aim to integrate these new methods and to analyze data, build and edit human tissues, and benefit even more from ongoing advancements in medical imaging, Using 3D modeling in conjunction with organ bioprinting technology that relies on these models have the potential to yield large returns for the healthcare industry and could result in significant changes. The Backdrop: 3D bioprinting is the layer-by-layer printing process of functional 3D tissue constructs using a unique type of bio ink known as tissue spheroids. These spheroids lack biological scaffolds and can easily adapt to the correct geometric shape required to bond with other cells. This in turn causes greater cell-cell interaction, cell growth, cell differentiation, and resistance to environmental factors due to high cell density achieved through bioprinting efforts according to a study from the International Journal of Bioprinting. These biological advancements are accompanied by advancements in 3D digital imaging as well, which allows the data collected to be transformed into the 3D images necessary to print tissue in the first place. Information technology transferring of 3D models generated from medical imaging allows for the creation of design blueprints that let other clinicians replicate similar results given they possess the appropriate technology. In addition, as noted by Procedia Engineering, computer-assisted design software including predictive simulations are utilized in both the printing and post-printing process to assist in optimizing the printability of bio inks and can reduce the number of experimental trials required to bioprint tissues. Computer-aided design (CAD) data is combined with computer numerical control, specialized mechanical technology and material science in order to print biomimetic and complex tissue structures using the traditional 3D printing technique of layered overlay, allowing clinicians to replicate anatomical structures with relative biomechanical accuracy considering the fledgling nature of this technology. As noted in the Journal of Advanced Science, training programs deploy sensors and real-time feedback systems to provide more comprehensive feedback to guide instruction and help to better delineate the typical workflow. In tandem with the sensors and real-time feedback, machine learning is being leveraged to create training databases from large datasets and even digital twins which can be used to assist in the planning and execution phases of complicated surgeries according to the International Journal of Bioprinting. At present, 3D bioprinting is primarily used by the pharmaceutical industry to design in vitro models to test new drugs on animals. This assists in making the experimentation process quicker while minimizing mistakes and maximizing cost savings given animal models are generally considered accurate and reliable tools to determine toxicity and model disease but can be expensive and have ethical issues. This technology can also provide patients with personalized organic tissue and organs designed and created from their own cellular material, significantly lowering the risk of organ rejection. As noted by AAPS PharmSciTech, the growth in demand for human treatment using 3D printed tissues are driven by the medical demands of aging Americans, rising demand for organ donors, ethical concerns around animal testing, clinical wound care, and joint repair and replacement procedures. This growth is significant with an expected compound annual growth rate of 15.8% from 2022 to 2030 per Grand View Research. The applications of the 3D imaging and modeling technologies that enable bioprinting hold great promise in and of themselves. For example, 3D imaging can enable clinicians to perform a variety of complex treatments more easily than before. Using CT and MRI scans, radiologists can create 3D reference models that help surgeons better prepare for their job and visualize new solutions that may be harder to deduce from 2D scans. During one procedure in 2016, Dr. Michael Eames used 3D imaging to recreate a digital twin of a child’s arm, who was suffering injuries from unhealed bones that prevented the rotation of his arm and caused intense pain. Once the orthopedic team created the digital twin of the arm, they could see that it was only necessary to reshape the child’s bones, which was an insight that ended up decreasing the procedure time from 4 hours to 30 minutes and returning 90% arm-range movement to the patient only 4 weeks after his surgery. Compared to similar surgeries not conducted using 3D modeling methods, self-reported postoperative pain and scarring were much lower, ultimately leading to lower costs for both hospital and patient, a shorter recovery time, and greater patient satisfaction according to a press release from Axial3D. Training outcomes for medical students is another important application of this technology, allowing for proficiency-based training in a variety of surgical contexts as an increasing number of training curricula are beginning to adopt simulation as a part of their programs. Basic simulators which help new students hone their surgical skills are available as teaching tools, and some simulators meant for skilled surgeons to perfect their strategy before entering the operating room, are able to fully simulate entire procedures such as joint replacements and fixating fractures, according to an article published in the Journal of Future Medicine. Using 3D simulators ultimately changes the nature of learning and gives students a more individual approach towards their coursework as their hands-on experience will no longer be limited to unwieldy manuals, predetermined lab times, and doctor shadowing. 3D models allow for interactive manipulation, a better understanding of spatial relationships, and the utilization of novel methods of visualization for learning anatomy that trainees have been reported to enjoy more. However, despite the plethora of benefits of this technology, price is a limiting factor if educational institutions wish to print these 3D models. For example, an article in the Journal of the American College of Radiology found that each 3D model cost approximately $3,000 per procedure with an operating cost of over$200,000 per year to run the 3D printing service. Skilled technicians and talented 3D designers are also needed to properly utilize specialized software that can interpret and reimagine 2-dimensional CT and MRI scans in great 3-dimensional detail according to an article published in the Annals of 3D Printed Medicine. Implications: As technological innovation rapidly advances, the 3D bioprinting industry will continue to leverage advanced imaging and modeling techniques in attempts to create exact replications of anatomical structures. The use of 3D modeling in viewing anatomical images makes the process of understanding medical imaging more intuitive, leading to more accurate diagnoses, better surgical planning, better patient and care provider education, and improved health outcomes according to an article from Jump Simulation. Innovations in this field are also leading to greater integration of organisms with technology, such as the creation of extremely complex microphysiological devices that integrate sensors within soft tissue structures, created by the Wyss Institute at Harvard University. This technology can be further adapted to create other vascularized tissues that researchers can use to investigate the effects of certain regenerative medicine and drug testing, with biosensors able to yield more accurate and localized results than with previous technologies, according to the Wyss Institute. Additionally, 3D models created using these new methods of medical imagery can easily be shared with other medical practitioners with access to 3D bioprinting technology to benefit in a similar way. However, this raises questions about privacy and the legality of sharing detailed anatomical models of patients’ organs which will require explicit informed consent. Despite these initial promising indications for 3D bioprinting technology, there are a number of challenges that the technology will need to overcome before broader adoption. Although it has a bright future in the healthcare industry, current technology is simply not enough yet to meet the demands of the modern patient and the price to use it is often unattainably high. In addition, a variety of technical challenges exist, including the need to increase the resolution and speed of bioprinter technology, the inability to recreate the organic cellular density of certain tissues and organs, the lack of suitable biomaterials needed to replicate this technology on a much larger scale as pluripotent stem cells are difficult to come by, and of course the complexity of the biostructures being printed, particularly vascularized tissue that must be properly constructed to avoid necrosis as per an article published in the Journal of Biomedical Imaging and Bioengineering. There are also ethical issues that raise a number of concerns such as equality, safety, and human enhancement as outlined by a study from the International Journal of Scientific & Technology Research. Will patients have equal opportunities to access 3D bioprinting technology regardless of socioeconomic status, and how do insurers plan on covering payment for such services, if at all? Is this new technology safe for humans in the long term, and will medical staff receive sufficient training to use it? Finally, will this technology be ultimately used to build better people and improve their bodies by replacing organs with brand new ones, not to mention the inevitable integration of man and machine that is already being assessed in a variety of clinical contexts? These issues must be addressed by care providers, federal regulators, and the patients that will benefit from 3D bioprinting to assuage concerns and give legitimacy to a promising new technology with the potential to revolutionize tissue engineering, regenerative medicine, and clinical training. Related Readings: 3D Bioprinting of Living Tissues 3D Bioprinting Strategies, Challenges, and Opportunities to Model the Lung Tissue Microenvironment and Its Function 3D Printing Helps Surgeon Restore Child’s Sporting Ambitions and Reduce Surgery Time from 4 hours to Under 30 minutes Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin Role of Three-Dimensional Visualization Modalities in Medical Education Should Society Encourage The Development Of 3D Printing, Particularly 3D Bioprinting Of Tissues And Organs?

The Driver: Enthea, one of the first of its kind to offer psychedelic healthcare insurance plans, recently raised $2 million in its seed round. The round was led by Tabula Rasa Ventures with participation from Mystic Ventures and Mike Cotton, former Meridian Capital group executive. The funding will be used to help expand and launch Enthea’s services into 40 cities across America by the end of 2023. In addition, part of the funding will be used to help patients access and acquire psychedelic health care since it is not currently covered by standard employer-based insurance and thus is only available through self-pay. The funding is expected to expand the market as well as the patient base. It will also help to increase education and health benefits for many who are suffering from various mental health disorders, allowing (alternative and possibly more effective) treatments for many who would normally be unable to afford them. Key Takeaways: 50 million adult Americans are suffering from a mental health illness (~ 20% of the population) with 5% of American adults experiencing a severe mental health illness (Mental Health America) Ketamine is currently approved for off-label use in treatment-resistant depression, major depressive disorder, bipolar disorder, PTSD, and substance use disorder, among others (Axios) 46% of psychologists reported seeing more adolescents from the ages of 13-17 since the COVID pandemic (American Psychology Association) 11.4 million Americans or approximately 5% of adults experienced suicidal thoughts in 2022 and Suicidal thoughts have been on a steady increase each year since 2011-2012 (Mental Health America) The Story: Enthea was founded by CEO and co-founder Sherry Rais, along with co-founders Joshua Barber, Dan Rome, and Keith Lietzke. According to founder Sherry Rais, Enthea’s mission is to help millions of people obtain expansive, easy, and inexpensive access to psychedelic-assisted therapy (PAT) for issues including treatment-resistant depression, anxiety, and post-traumatic stress disorder (PTSD), a novel approach highlighted by emerging research as an effective and evidence-based alternative therapy.., Enthea’s mission it to be “all in'' to provide a service that represents what they refer to as their JEDI values - which stand for Justice, Equity, Inclusion, and Diversity. Initially founded as a not-for-profit company in 2022, Enthea experienced financial difficulties and decided to rebrand as an employer benefit company based on the growing interest from employers for this innovative therapy. Since the rebranding, the company has been able to get the financial backing needed to meet employer demand, as evidenced by the recent fundraising. The Differentiator: Enthea is a pioneer as the first health insurance provider to offer benefits for psychedelic healthcare. They gained notoriety after Dr. Bronner’s, a natural soap brand became among the first U.S. based employers to offer coverage of psychedelic treatments. Dr. Bronner’s decided to add to their mental healthcare insurance plan and offer ketamine-assisted psychotherapy for its employees to help promote mental health awareness and access. According to the company, Enthea provides a turn-key operation that makes it easy for employers to include treatments as part of their health care coverage to employees and their families. Its plans currently cover ketamine-assisted therapy [and the company plans to add]; MDMA- and psilocybin-assisted therapies…as they become FDA-approved in the next several years.” As noted by Forbes, since access to psychedelic-assisted therapy (PAT) is not covered by traditional employer-based insurance plans, treatments have largely been reserved for patients with the financial resources to pay for it out of pocket. Enthea makes these treatments more affordable and accessible by offering standardized, evidence-based care available through specialty providers in its network. As noted by the company, Enthea has developed several core competencies that include: Evidence-based medical policies for psychedelic therapies that are regularly updated based on clinical developments and FDA approvals. Standards of care and credentials across the Enthea Provider Network to assure quality, positive patient experiences, and positive treatment outcomes. Easy treatment authorization and reimbursements to providers, while shielding employers from Protected Health Information. A range of customizable options based on the company's business and personnel needs. Starting at the beginning of 2023, Enthea will offer services in New York City, Austin, Texas, and the Bay Area in California and plans to have services within 20 markets by mid-2023 with a goal of having 40 markets up and running by year-end 2023. The Big Picture: With almost 20% of the adults in the U.S. experiencing a mental illness, equivalent to nearly 50 million Americans and approximately 5% of U.S adults experiencing severe mental illness, it is clear there is a need for additional sources of affordable, convenient mental health treatments across the country. For example, the incidence of adult mental illness ranges from a high of approximately 27% in New Jersey to a low of 16% in Utah. Psychedelic treatments and Enthea, are providing an additional treatment option for the large population of Americans who require mental health services, a great number of whom have not found success with traditional therapies. Adding ketamine-based psychotherapy to employers' health-based insurance plans for patients is an important way to provide evidence-based, affordable treatment to employees. In addition, Enthea may increase the likelihood of attracting the younger generation who are more likely to look for employers that are providing extensive and innovative benefit packages. With emerging research and approvals, psychedelic treatment will just be one more option that can address the mental health crisis in America and reduce the 50% non-treatment rate. Insurance Provider Enthea Offering Psychedelic Therapy Coverage As An Employee Benefit, Dr. Bronner's Offering Its Workers Psychedelic Therapy Coverage

Our Take: Virtual reality (VR) and augmented reality (AR) have emerged from the Pandemic as practical, digital solutions to help providers and patients solve pressing issues uncovered by COVID including the need to scale the delivery of care, improving care for chronic conditions as patients age-in-place and advancing treatment modalities for the mentally ill. Although virtual reality (VR) technology has been mostly associated with the entertainment industry, its success has become evident in the healthcare industry with promising results for not only diagnosing and treating a variety of illnesses but for ushering in the future of truly transformative techniques in medicine. However, challenges remain around existing infrastructure to support VR, software standardization, rapid technological innovation, and the lack of evidence-based VR programs. Consequently, it would be a disservice for the healthcare industry to overlook the promising results to date in the face of these challenges. Key Takeaways: The virtual reality market has a projected compound annual growth rate of over 30% between 2020 and 2025 (PWC) Some major trends in healthcare VR applications include neurological and developmental therapy, pain reduction through distractions, exposure therapy for phobias, and psychological applications (JMIR Biomedical Engineering) VR training improved physicians’ surgical performance by 230% compared to traditional training programs (Harvard Business Review) Barriers to adoption of VR therapy in clinical settings include the lack of evidence-based VR programs, infrastructure to collect and analyze VR data, software standardization, and technological obsolescence. The Problem: The rise in chronic illness seen across the developed world, a shortage of care providers, and the need to provide better care at lower cost all necessitate new, more cutting-edge treatments that improve the experience of the patients receiving care, also improve population health and deliver care more efficiently. In this environment, care providers are increasingly turning towards new technologies such as AR and VR to enable new types of training, remote care, and mental health treatments. As a result, the applications of AR and VR in healthcare and healthcare’s overall movement into the metaverse are growing. The Backdrop: The “Metaverse is a catch-all term that refers to the entire digital and virtual world and is a convergence of physical, augmented, and virtual reality in a shared online space" according to “Overview: Technology Roadmap of the Future Trend of Metaverse based on IoT, Blockchain, AI Technique, and Medical Domain Metaverse Activity”. As defined in the same article, VR is a technology that substitutes one’s vision of the physical world with a digitally produced scene using software and headgear devices [while] AR is a technology that combines the digital and physical worlds. It uses computer vision techniques such as object recognition, plane detection, facial recognition, and movement tracking to recognize real-world surfaces and objects. The term ‘mixed reality’ refers to a combination of augmented and virtual reality.” Virtual reality in the healthcare market has a projected compound annual growth rate of approximately 37% between 2022 and 2028 according to a report by GlobeNewswire. A variety of medical professions are beginning to adopt VR technology in a growing number of ways. Some major trends in healthcare VR applications include but are not limited to; neurological and developmental therapy, pain reduction through distractions, exposure therapy for phobias, and psychological applications, according to JMIR Biomedical Engineering. VR therapy has also seen extensive use in the assessment of clinical outcomes for social functioning, cognition, symptomatology, and general mental health research, and is well suited to identify illnesses and offer consistent, low-cost, and accessible care. While most consumers are familiar with VR in the context of gaming and entertainment, applications like these are increasingly finding their way into the healthcare industry for use by both patients and care providers. For example,”using VR, AR, and MR technology, doctors can create smart digital operation theatres where they can perform virtual live patient operations allowing others to watch and participate for training. In addition, by combining AR and VR technology with artificial intelligence, doctors can face simulated conditions and complications based on their own individual responses. VR has been utilized effectively to increase surgical efficiency and assist in training. As noted in a recent article in the Harvard Business Review, VR training improved participants’ overall surgical performance by 230% compared to traditional training programs. Similarly, the U Conn Health system has been using VR training programs for their orthopedic surgery residents, saving time and money, and partially eliminating the need for comparatively more expensive, one-time use cadavers. VR data can also be used to create detailed virtual models of a patients’ anatomy, allowing physicians to easily view and manipulate these 3D models from different angles. Additionally, it currently plays a significant role to help diagnose and treat physical trauma and other fractures and has been applied in cardiology and neurology to monitor and assist patients which improve patient health outcomes according to a study from the Journal of Clinical Epidemiology and Global Health. During the Pandemic, VR was used successfully to teach emergency medicine by using 360 degree video. New visualization techniques offered by VR technology can help trainees gain a greater understanding of the human body and the operating procedures they will need to know, increasing confidenceand skills before they enter the operating room with actual patients. VR technology has also been used to help promote clinicians’ empathy for patients. According to an article in the Journal of the Medical Library Association, through VR simulations, certain mental health conditions and age-related health problems can be recreated, helping students better understand the illnesses they are treating and increasing empathy with the people they treat. In addition to helping train, a variety of clinicians, AR/VR technology is also found to be very effective in the treatment of conditions that impact the mind and mental health. For example, a systematic review from the Journal of Aging Psychiatry found that VR therapy is effective in both diagnosing and treating psychiatric disorders such as anxiety, phobias, and PTSD, as well as illnesses such as dementia, schizophrenia, and autism. The U.S. military has conducted extensive research into treating veterans’ mental health using VR and the results show positive clinical outcomes observed in preventing, identifying, and treating combat-related PTSD as noted in the Journal of Clinical Psychology in Medical Settings. VR’s potential to assist in mental health treatment is well-established in the healthcare industry. For example, VR exposure therapy is shown to reduce patients’ fear of heights by 68% according to an article in The Lancet Psychiatry. The same holds true for PTSD where an early study on VR and PTSD found that of 20 service members who enrolled in and completed the study treatment protocol, 75% had experienced at least a 50% reduction in PTSD symptoms and no longer met DSM-IV criteria for PTSD at post-treatment. In the study average, PTSD scores decreased by approximately 50%, depression scores by 47%, and anxiety scores by 36%. In the near term, it is clear that AR/VR technology can make complex and often expensive training procedures easier and can also successfully be applied to certain mental health conditions. Longer-term the technology could be utilized for more remote procedures, like using robotic technology to utilize the skills of more experienced clinicians stationed at major medical centers which can act as technology hubs. Implications: The technological capabilities of VR are advancing at an astonishing rate and new models of VR headsets are being released so quickly that most consumers may be surprised by their latest capabilities. For example, graphical fidelity has improved to the point that it is able to reproduce real-world settings and phenomena to such an extent that a study in the Journal of Environmental Psychology indicated that both the real and the virtually recreated outdoor exposures led to physiological arousal, showed benefits to mood levels, and had the same restorative impact. Already, several VR companies have introduced products that are starting to gain a foothold in the industry. In November 2021, AppliedVR received FDA de novo approval for its EaseVRx product to treat chronic lower back pain thus becoming the first VR provider to receive FDA de novo approval for a pain indication. In September 2022, XRHealth, an Israeli-based company, introduced VR-based NeuroRehab designed for individuals who suffer from the consequences of strokes and brain injuries, where patients are guided through activities aimed to help regain functionality. However, since VR is still a relatively new technology it faces several challenges. These include the lack of commercially available evidence-based VR programs, and rapid innovation in the industry coupled with proprietary issues leading to rapid technology obsolescence. Currently, there are interoperability issues as well as a lack of current infrastructure to support the technology. In addition, many VR programs are still largely therapy-oriented and lack methods to collect and record suitable data for research purposes. The novelty of VR in healthcare means the resources to support it aren’t quite there yet, and care providers need to make the proper investments to best utilize the data they collect. Given the rapid projected rate of growth of the industry, we expect stakeholders will likely be motivated to solve these issues quickly and efficiently as the demand for VR solutions grows. Related Readings: Immersive Virtual Reality in Health Care: Systematic Review of Technology and Disease States How Virtual Reality Is Transforming Healthcare | U.S. Chamber of Commerce Can Simulated Nature Support Mental Health? Comparing Short, Single-Doses of 360-Degree Nature Videos in Virtual Reality With the Outdoors Research: How Virtual Reality Can Help Train Surgeons Nursing students’ views of using virtual reality in healthcare: A qualitative study - Saab - 2022 Virtual Reality Goes to War: A Brief Review of the Future of Military Behavioral Healthcare Using virtual reality in medical education to teach empathy

The Driver: PayZen, a healthcare fintech startup, raised $220 million in a debt and equity financing just one year after raising $15 million in series A funding. PayZen will receive $200 million in a credit facility and $20 million in equity. PayZen brands itself as a get care now, pay later service that enables patients to pay their out-of-pocket medical bills over time with convenience and transparency. This round's $20 million equity constituent was made in part by 7wire Ventures while the credit facility is funded through Viola Credit. This credit will help focus on expanding growth in the market. This new funding will provide PayZen the opportunity to continue to expand and grow while providing their no-cost, inexpensive, pay-over-time service to many Americans allowing them the opportunity to focus on health and less on how to pay for medical billing and service all at one time. Key Takeaways: 70% of providers stated it takes over 30 days to collect a bill over $1,000, while almost one-half of medical practice leaders said that days in A/R increased in the most recent year according to the 2021 Instamed Trends in Healthcare Payments Annual Report Approximately 40% of adults reported that they experienced problems paying off medical bills or they are paying off medical debt that contributed to long-term financial issues like lower credit rating, credit card debt, or depleting their savings according to the Commonwealth Fund 2022 Biennial Health Insurance Survey 41% of adults reported that they have debts from outstanding medical or dental bills due to healthcare expenses according to the KFF Health Care Debt Survey 65% of Hispanics, 60% of Blacks, and 39% of white adults state that on the KFF Health Care Debt survey it is hard to afford medical care costs, especially for the Black and Hispanic populations with lower financial income The Story: PayZen was founded in 2019 by three fintech entrepreneurs, who are veterans who held various roles at fintech startups Beyond Finance and Prosper: Ariel Rosenthal, Itzik Cohen, and Tobias Mezger. The company uses its artificial intelligence (AI) based model to underwrite patients debt and allow them to buy medical care now and pay later. According to CEO Cohen, “because we watched what ‘buy now, pay later’ could do for e-commerce” they realized its potential in healthcare where “medical providers [were] having a hard time, because they [needed] to collect more and more of the bill from the patient.” For example, according to the West Health-Gallup Healthcare Affordability Index, “an estimated 44% of American adults are struggling to pay for healthcare.” In addition, according to a KFF Health Care Debt survey, 35% of adults have delayed dental services, 25% of vision health services, 24% of medical care services, and 18% of hospital services due to increasing and rising costs. PayZen’s goal is to allow patients the opportunity to have medical procedures done without having to worry about cost. The company believes it is providing an affordable choice by using the patient's data to create a payment plan where one bill can be paid over as many as 5 years. This model allows patients to attain health equity without having to make any livelihood sacrifices. This helps many in underserved communities attain health access right now when society is still living in the shadow of a pandemic while attempting to stay healthy overall. PayZen is providing an outlet for Americans that need options. The Differentiator: As noted in Crunchbase, PayZen’s “business model relies on medical providers to pay for the PayZen platform and integrate it into [the providers] own internal systems. The return on the provider’s investment comes by allowing hospitals and doctors to increase payment adherence, and receipt of a larger share of payments while at the same time increasing patient access to care. By using its AI-based platform and a provider's own data on the patient, PayZen is able to custom underwrite a payment plan for patients thereby making it easier for many to afford care now that they don’t have to pay for that entire bill all at once. This is particularly important given the post-COVID challenges of today’s economy, where 8% of Americans are considered “cost-desperate” defined as experiencing three key healthcare financial challenges (unable to pay for needed medical treatment over the prior three months; skipped prescribed medication due to cost over the prior three months and were, unable to afford quality care if it was needed today). PayZen offers a debit card that allows patients to pay for their healthcare bill right before their appointment or for pharmacy costs while transferring the balance into a custom payment plan-based selection that has no secret rates or hidden fees, just zero interest and zero fees. According to the company, PayZen’s model works as they are able to increase collections on the patient’s portion of the balance by 50% from its current rate of less than 20%. The Big Picture: According to the Commonwealth Fund 2022 Biennial Health Insurance Survey, approximately 40% of adults reported that they experienced problems paying off medical bills or they are paying off medical debt that contributed to long-term financial issues like lower credit rating, credit card debt or depleting their savings. This is particularly important for women, low-income Americans, and minority groups who are often underserved and where affordability of healthcare can be an issue. The introduction of innovative payment methods like PayZen makes it easier for many to afford care now that they don’t have to pay for that entire bill all at once or incur additional debt. Patients can spread out medical bills, which can typically be large, in payments over years and be able to attain health equity without having to make any livelihood sacrifices. For providers, using a system like PayZen helps accelerate payments and increase the amount they collect. For example, 70% of providers stated it takes over 30 days to collect a bill over $1,000, while almost one-half of medical practice leaders said that days in A/R increased in the most recent year according to the 2021 Instamed Trends in Healthcare Payments Annual Report. Clearly, finding more effective and affordable methods of payment could benefit both patients and providers. PayZen secures $15M Series A for ‘care now, pay later’ healthcare platform, PayZen Raises $200 Million in Credit, $20 Million in Equity

Our Take: Healthcare providers are in an everlasting quest to increase profit margins and find new sources of funding to ensure financial solvency while providing the highest quality of care possible. Over the last several years, private equity (PE) firms have increasingly been investing in healthcare to help fill the funding gap. Yet this has not been without controversy and to varying effect. While the investment from these firms has helped many deal with the financial strains brought on by COVID and declining reimbursement, addressing many of these strictly from a business standpoint can have less favorable outcomes on patient care. For example, while PE firms are often a resource for the business acumen needed to transform organizations, capital and technology, many opponents argue that the tools they use to help increase efficiency or productivity may hurt health outcomes. In the face of this debate are calls for Federal and State regulators like CMS and HHS, to increase regulatory oversight and hold PE firms accountable for business practices that may adversely impact the care of patients. Key Takeaways: ● 46% of health system & physician group finance leaders reported their organizations were behind their 2022 revenue goals (R1 RCM survey) ● Healthcare providers are increasingly turning to investment from private equity firms to fund their value-based care services, creating an informal industry ● Anywhere from 53% to 68% of the nation’s hospitals will end 2022 with their operations in the red versus the 34% reported in 2019 (Kaufman Hall) ● Private equity investment has grown dramatically over the past decade from $41.5 billion in 2010 to $119.9 billion in 2019 (American Antitrust Institute) The Problem: Since as early as the late 1970’s the rising cost of healthcare has forced the government and others to look at methods to control spending. Following the expansion of care through Medicare and Medicaid in the late 1960’s healthcare costs rose rapidly, due in part to increasing demand due to the expansion of care. Ever since the sustained double-digit increases in healthcare costs of 1967-1984 (when they ranged from 10.2%-15.9% per year) the cost of healthcare and it’s impact on the Federal budget has been a national issue and cost-saving has been a significant concern of the healthcare industry. For example, during that period healthcare costs went from 6% of GDP to 10% of GDP. Beginning in the late 1980’s and early 1990’s a number of efforts were made in both the public and private sectors to rein in healthcare costs. In the early 1990’s health insurance companies tried to control costs through the use of health maintenance organizations, however due to the lack of fully integrated provider networks and the lack of patient choice, led to the failure of HMOs. In 2007, the Institute for Healthcare Improvement (IHI) launched what has come to be known as “the Triple Aim” which as noted by the Commonwealth Fund “was designed to help health care organizations improve the health of a population patients’ experience of care (including quality, access, and reliability) while lowering—or at least reducing the rate of increase in—the per capita cost of care.” Given the unsustainable rate of increase in healthcare costs, the Triple Aim, along with the passage of the Affordable Care Act under President Obama, helped institutionalize the idea of cost containment in healthcare. As a result of these changes (and others) hospitals have been under increasing pressure in terms of reimbursement and revenues. These pressures have been particularly acute on the revenue side, as both public and private insurers have tried to decrease utilization of expensive inpatient hospital resources. As noted in the American Hospital Association Trendwatch Chartbook, from 2007 to 2018, the hospital payment shortfall relative to costs for Medicare has gone from $21.5B to $56.9B in 2018. Similarly, the hospital payment shortfall relative to costs from 2007 to 2018 has gone from $10.4B to $19.7B, despite a number of states expanding Medicaid under the Affordable Care Act. Along those lines, according to a survey of health system and physician group finance leaders by R1 RCM, 46% of respondents reported their organizations were behind their 2022 revenue goals. In addition, as a result of the supply chain shortages and great resignation many providers experienced during the pandemic, rising supply costs and workforce shortages have further squeezed provider profits. Not surprisingly faced with such a difficult environment providers have looked to cut costs, increase revenues, and boost operations. In addition, a number are eagerly welcoming investment from PE firms eager to attempt to take advantage of demographic shifts in population which they expect will increase demand for care. With ample cash to pour into many providers, many PE funds believe they can reduce administrative costs and waste through scale efficiencies. The Backdrop: PE firms have made aggressive forays into the healthcare industry over the past several decades, and investment does not seem to be slowing down. For example, according to a report from the American Antitrust Institute, from 2010 to 2019 PE investments in healthcare increased at a compound annual growth rate of almost 13% from $41.5 billion to $119.9 billion. This raises numerous questions about the future, the role these investors play in the industry and how to weigh and balance both the positive and negative effects. While there have been a number of high profile cases where private equity investments have led to negative consequences, the answer is actually a little bit more nuanced than that and not all investments or investors can be lumped together. For example, as noted in a January 2019 article in JAMA, “these investments may also benefit patients and bring more efficiency to a system burdened with waste. More research, and likely thoughtful regulation, are needed to preserve the positive effects of private equity in health care while mitigating the negative ones.” There have been successes as well as failures that both sides can point to. The financial windfall provided by PE firms can greatly assist in expanding the acquired hospital’s operations. In May 2021 Health Affairs published a study entitled “Private Equity Investments In Health Care: An Overview Of Hospital And Health System Leveraged Buyouts, 2003–17” which found that hospitals that were acquired by PE firms had larger bed sizes, more patient discharges, and a greater number of medical staff positions and higher operating margins. In addition, PE investment can help providers become more profitable through acquisition, and give them greater access to more resources to invest in resources like IT to succeed in value-based care. According to an article published by the Kenan Institute of Private Enterprise, many PE firms bring increased visibility and analytical tools to their acquired care providers that help them grow. This includes business consulting, systems development, increased supply chain management capabilities, and assistance with mergers and other acquisitions. As the industry consolidates and non-traditional players like Amazon and Walmart enter care delivery, resources from PE can help build provider networks and increase market share. On the other hand, critics point out that PE’s business model is, at its core, fundamentally incompatible with the mission of the healthcare industry. They note that PE companies typically try to deliver at least a 20% to 30% return in profits over a three to seven year investment horizon according to an article by America’s Health Insurance Plans. In contrast, healthcare providers often point to the moral imperative as the driving force of their efforts and not profitability. They note that providers operate with the intention of preventing or treating poor health throughout a patient’s life, sometimes without regard to a patient’s ability to pay for such services. Critics point out that health outcomes are often poorer under privately owned care providers. They argue that you have to look no further than the senior care industry, which the PE industry heavily invested in. According to a study from the National Bureau of Economic Research, they saw a 10% increase in mortality among Medicare patients privately owned nursing homes along with other declines in other measures of patient wellbeing including lower patient mobility, higher prescription of antipsychotic medication, and increased reported pain intensity. In addition to the issue of quality of care, a number of academic journals have raised the issue of increased consolidation and market power in healthcare resulting from PE investment. For example, a study from the Journal of Medical Economics posits that PE investment increases market consolidation, places pressure on hospitals to increase revenues through overutilization of services and upcoding, and puts greater scrutiny on doctors to ensure financially viable decision making while exposing them to certain policies like gag orders and noncompete agreements that may harm their future career prospects. Many argue that the focus on short-term revenue and market consolidation effectively undermines competition in the healthcare industry, destabilizing markets in the pursuit of financial arbitrage. Through overutilization of care and upcoding, patients are subject to unnecessary treatments and higher than expected medical bills as they are charged for services they did not use or didn’t need to begin with. PE investors point to the dire financial straits of many providers and that patients would receive no care at all if they did not step in. For example, as noted in a September 2022 article in Fierce Healthcare, “anywhere from 53% to 68% of the nation’s hospitals will end 2022 with their operations in the red versus the 34% reported in 2019, according to new industry projections released Thursday by Kaufman Hall on behalf of the American Hospital Association (AHA).” In addition, a recent article in the Journal of Healthcare Management Science noted that on average, “21 hospitals [have closed] annually between 2010 and 2015, with 47 closures in 2019 alone. [This] trend of closures has accelerated as hospitals have experienced financial hardship during the COVID-19 pandemic, and it is likely that even more hospitals will close in the near future.” In addition to a number of other effects, they found “when faced with increased demand due to such closure, remaining hospitals in the market tend to respond by a ‘speed-up’ behavior: they increase their service speed and spend less time per patient (on average), instead of accommodating the additional demand by reducing their bed idle times. Speed-up behavior can harm care quality, as it entails cutting some necessary and value-added care steps.” Clearly, if the alternative to taking PE money to sustain operations is closure, those consequences are not ideal either. Given these choices, in some cases it has been up to the government to step in to ensure that, intentional or not, care and pricing are not adversely affected. For example in 2020 the No Surprises Act was passed which provided legal protections against surprise medical bills for patients who get emergency care from out-of-network providers and curbed predatory billing practices. These protections included the establishment of an independent dispute resolution process to negotiate out-of-network payment costs, an appeal process patients can use to fight against certain decisions on the behalf of their health plans, and mandating good faith estimates of medical services for uninsured patients (some of which are the subject of continuing litigation). Although this is an important step to curbing excessive billing that privately owned care providers engage in, critics continue to argue that more needs to be done to fight against what they view as predatory business practices. Implications: While clearly the implications of PE investment in healthcare are the subject of raging debate, there can be no doubt that in an industry which routinely estimates the cost of waste in healthcare that “[ranges} from $760 billion to $935 billion, accounting for approximately 25% of total US health care spending,” increased efficiency is warranted. While many studies have indicated the connection between poor health outcomes and care providers that are owned by PE firms, given the unsustainable pace of healthcare spending, healthcare needs to be operated more like a business going forward. With various new business models and an increasing number of non-traditional new players entering the field, care delivery has got to get more consumer friendly and effective. Moreover, medical staff at successful PE health centers may not agree with these assessments as PE has been shown to help increase charge-to-cost ratios and operating margins, leading to higher pay and job security that comes with profitability as noted in an article from JAMA. During a time when revenues are low following the pandemic for most hospital systems, PE can provide an essential lifeline for struggling healthcare organizations in need of stimulus, introduce new, potentially effective operating practices, and prevent employees from losing their jobs. In addition, clearly the government must play a more active role in setting the guard rails. While the authors were referring strictly to hospital closures, in their study, their recommendation that "close monitoring of market competition can also mitigate the adverse effects…for the rest of the delivery system. This is especially important in light of recent increases in vertical integration, and other similar activities that might negatively impact the healthcare sector.” This is important in terms of PE investment as well as their caution that “policymakers should be aware of the complex nature of these policies, and note that enacting them may also result in unintended consequences such as hospitals being incentivized to reduce the system capacity.” While many critics have pointed to consolidation of the industry and market power, this is happening among both payers and providers as well as naturally, hence PE firms may be enabling those in less robust financial conditions to secure their place in a consolidating market. Moreover, as noted in “Private Investments in Healthcare: What CFOs Need to Know”, hospitals or other healthcare organizations have to be willing to sell. They note that “top factors for an organization being willing to sell are: 1) a health system is struggling financially overall; 2) it has an innovative product or service but needs financial assistance to boost it; 3) the health system has a difficult time meeting compliance requirements; 4) or the practice owner or partner is retiring.” Hence without PE funding they would need to seek other means to address these issues, not all of which would be attractive either. When you add to this the additional investment in technology required to support AI and data analytics required for effective value-based care, this situation only intensifies. Regardless of the source of capital, healthcare will become more of a business and have to become more efficient and responsive in order to rein in unsustainable cost growth and meet the demands of modern consumers. Related Reading: Association of Private Equity Acquisition of Physician Practices With Changes in Health Care Spending and Utilization Does Private Equity Investment In Healthcare Benefit Patients? Evidence From Nursing Homes Healthcare Revenue Falling Short of 2022 Goals for Many Providers Private Equity in Healthcare: What the Experts Want You to Know Private Equity Investments In Health Care: An Overview Of Hospital And Health System Leveraged Buyouts, 2003–17 Private Investments in Healthcare: What CFOs Need to Know

The Driver: Oxford Medical Simulation, a UK-based startup, recently raised $2.4 million for its virtual reality (VR) based healthcare training company. Oxford uses VR simulation based on hundreds of AI-controlled patient scenarios for training. The funding round was led by ACF investors and private investor Dr.Nicolaus Henke, the former chairman and former CEO of McKinsey’s global healthcare practice. This additional funding will help to grow and expand their services across the world so many more healthcare professionals may be trained on their equipment. The funding will help to provide realistic training using simulation to medical professionals like nurses and doctors, to help better patients' health and well-being. Key Takeaways: According to the Association for Talent Development, healthcare workers spend 34% less time on training a year than other industries. Nurse shortages are predicted to hit 1.1 million by the end of 2022, per the American Hospital Association The elderly population is expected to grow from 54M in 2019 to about 80M in 2050 with a commensurate demand for increased healthcare services, putting further strain on the healthcare system according to HHS The healthcare system is expected to need 445K home health aides, 29K nurse practitioners, 95K certified nursing assistants, and over 98K medical/lab technologists and technicians by 2025 based on data from Mercer. The Story: Oxford Medical Simulation was founded in the U.K, in 2017 by Michael Wallace and Jack Cotton. Their simulation software uses a VR model simulation mixed with artificially intelligent patients in order to train practitioners on different scenarios. Not only does this allow clinicians to see different conditions, but It also allows healthcare workers to train in various situations like emergencies, procedural, patient management, and mandatory exercises. The company is using its training to help address the growing shortage of healthcare workers on a global basis while simultaneously decreasing the time it takes to train newly hired staff. This is increasingly necessary as we seek to address the workforce shortage brought on by an aging healthcare workforce, increased demand from an aging population combined with an increase in chronic diseases among this older population. Oxford’s mission is to offer productive, streamlined training for incoming healthcare professionals to improve the result of interventions and the quality of outcomes. In addition, according to an article in explorebit, studies by the NHS have shown “that pressures resulting from these [workforce] shortages will also compromise the competency of newly trained staff, worsening the situation”. The Differentiator: Not only does Oxford’s product offer more realistic training scenarios, but it can also deliver them at a faster rate and more cost-effectively than traditional training. For example, according to the company, overhead is significantly higher for mannequin-based simulation which can cost approximately $400 just to deliver a single simulation. Using the company’s technology, students can access libraries of medical emergencies that allow them to simulate the management of conditions such as sepsis, heart attacks, diabetic emergencies, seizures, and anaphylaxis. In addition to being cost-effective, immersive VR is instantly scalable, allowing institutions to deliver more simulation experiences to their learners more efficiently. For example, while conventional training centers deliver 200 simulations per month, Oxford’s VR-based training can deliver up to 6,000 per month. Because VR simulation is repeatable and can be used without faculty supervision –engaging clinical experiences can be provided using fewer valuable resources. The Bigger Picture: Oxford Medical Simulation's intention is to help medical and healthcare hospitals, centers, and companies deliver exceptional care with the help of their platform which uses simulation to transform the way we look at training in healthcare. By speeding up the process through AI services, new healthcare staff members are able to utilize and learn through non-invasive and simulated methods where there is no danger to patient safety or life. This enables clinicians who normally would be required to be taken away from patient care to remain focused on patients while enabling students to learn or relearn certain techniques without the direct supervision of a clinician. Tools like OMS will be sorely needed to help address the workforce shortage in healthcare. For example, according to an article from the Keck School of Medicine at USC, 34 % of nurses reported saying that they would be leaving their jobs by the end of 2022 and 44% of nurses stated that burnout and stress were the reasons behind their choices. With the help of Oxford, they can help provide some relief and ease burnout by alleviating some of the stress of additional hours required to train new staff while burned out and understaffed. The need for new staff to be properly trained is more urgent now than ever and will only worsen in the near future as healthcare continues to lose staff. Oxford Medical Simulation secures €2.4 million to tackle the healthcare staff crisis with better training; Staff Shortages Choking U.S. Health Care System; A Public Health Crisis: Staffing Shortages in Health Care

Our Take: The remote patient monitoring (RPM) industry is growing rapidly with significant implications for healthcare as a whole. RPM has been proven to help lower hospital readmission rates, and unnecessary care utilization, and decrease overall costs for a variety of chronic illnesses. As a result, providers are increasingly looking for ways to deploy RPM technologies and take advantage of a number of new reimbursement schemes that have been put into place. While RPM devices such as blood pressure cuffs and glucose monitors have been around for a number of years, many of these new devices are incorporating new technologies such as artificial intelligence, and touchless sensor systems and integrating seamlessly with mobile devices like cell phones, tablets, and laptops already in the home. As a result, RPM is becoming an increasingly attractive option for patients and care providers looking to save time and money on routine follow-up care which can easily be moved to an outpatient setting through the deployment of continuous, accurate, instant physiologic measurements. Key Takeaways: Chronic disease accounts for 40% of all deaths in America according to the Journal of the American Medical Association (JAMA), with heart disease, cancer, stroke, COPD, and diabetes representing the top five contributors to mortality For heart failure and COPD patients in particular, RPM saved almost $3K per patient according to an article published in the Journal of Digital Medicine. Remote patient monitoring has significantly helped lower hospital readmission rates and mortality for a variety of chronic illnesses, including diabetes, hypertension, cancer, COPD, and congestive heart failure. Advanced medical devices have proven successful in predicting upcoming episodes of illness for both COPD and heart failure patients according to articles published in the Journal of Respiratory Medicine Case Reports and the Journal of Current Cardiology Reports, respectively. The Problem: There is a growing need for technology that enables the monitoring of patients in an efficient, convenient and effective way, particularly for lower-income individuals who may not be physically close to or able to obtain care easily. Chronic diseases are both prevalent and costly, with nearly 45% of all Americans suffering from at least one chronic disease according to a study published in the International Journal of Environmental Research and Public Health. Moreover, chronic disease can have a high clinical toll, accounting for 40% of all deaths in America according to the Journal of the American Medical Association (JAMA), with heart disease, cancer, stroke, COPD, and diabetes representing the top five contributors to mortality. As an increasing number of Americans are forced to deal with chronic conditions in their everyday lives and the impact of rising medical costs become an even greater burden on patients’ minds, there is a renewed focus on developing resources and services that are both affordable and effective to monitor health. The Backdrop: To help facilitate this, the Centers for Medicare, and Medicaid Services (CMS) issued new guidelines and regulations in the aftermath of the Pandemic that helped eliminate some barriers to coverage of RPM services and loosened reimbursement eligibility, providing greater access for patients who could benefit, according to a report published by the Association of American Medical Colleges. Along these lines, RPM services have shown great promise in reducing hospital readmission rates and mortality for numerous chronic illnesses. For example, according to a study published in the Journal of Telemedicine and e-Health, diabetes patients with greater levels of RPM participation were found to have lower blood sugar levels at the conclusion of the program. In addition, 81% of hypertension patients enrolled in RPM programs achieved their blood pressure goal by 7 weeks on average as noted in a study from the Journal of Clinical Cardiology. Similarly, a study published in the Journal of the American Society of Clinical Oncology found cancer patients enrolled in home monitoring programs were 58% less likely to be admitted for unplanned medical emergencies than those not enrolled in these programs. Finally, advanced medical devices have proven successful in predicting upcoming episodes of illness for both COPD and heart failure patients according to articles published in both the Journal of Respiratory Medicine Case Reports and the Journal of Current Cardiology Reports, respectively. Remote patient monitoring has also proven to be a very cost-efficient method for follow-up care and to improve the cost-effectiveness of checkups for recovering patients. For example, for every 500 high-risk Medicare patients suffering from multiple chronic conditions, health systems can save an estimated $5.2 million annually using RPM devices and software according to an article published in HealthcareITNews. A literature review published in the Journal of Value in Health had similar conclusions, finding that a review of 34 economic evaluations of RPM programs for chronic disease saved significant amounts of money in hypertension, heart failure, and COPD treatment. In fact, for heart failure and COPD patients in particular, RPM saved almost $3K per patient according to an article published in the Journal of Digital Medicine. As reimbursement eligibility for these services expands and new technologies such as artificial intelligence are combined with smaller and faster processors the applications and effectiveness of RPM are bound to increase. Implications: As care continues to move out of facilities driven by the need to bend the cost curve, competition, and the increasing desire of seniors to age in place, it is clear that there is growth potential in the RPM market. As the number of elderly Americans increases, especially those with chronic disease conditions that necessitate frequent monitoring, the demand for both medical devices and RPM software will continue to multiply. Moreover, as both consumers and providers become more comfortable and accustomed to the data these solutions provide, additional proof points will be developed. Many high-tech medical devices combine several of the functions that traditional vital sign or patient monitors fulfill in the hospital, because of technologies and features borne from competition that frequently yields new innovations. New software allows patients to easily access their health information and send it to their care providers. AI technology can then be applied to these newly created data sets to do population health and predictive analytics leading to improved health outcomes. For example, Cadence Health uses AI and machine learning to predict future exacerbating health events using real-time data collected from medical sensors and devices according to an article published in Athena Health Marketplace. Of course, CMS’ guidelines and regulations also play a substantial role in the speed of this adoption, and should CMS decide that more patients should be reimbursed for their RPM medical devices and software enrollment by relaxing eligibility for reimbursement, RPM could even more dramatically impact the process of delivering follow-up and outpatient care. Moreover, while there has been some debate over the difference between consumer-grade and medical-grade devices, a study from the Journal of General Internal Medicine found that home-based measurements collected with such devices as blood pressure monitors have been found to be more reliable and accurate than measurements recorded at medical clinics and kiosks. If the demand persists and the clinical data support their effectiveness, the market competition and innovation will continue and ultimately lead to huge benefits for patients and the hospitals they are treated in alike. Related Reading: 2021 Medicare Coverage of Remote Physiologic Monitoring (RPM) An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health Clinic, Home, and Kiosk Blood Pressure Measurements for Diagnosing Hypertension: a Randomized Diagnostic Study Economic Evaluations of Remote Patient Monitoring for Chronic Disease: A Systematic Review How remote patient monitoring improves care, saves money for chronic care | Healthcare IT News Impact of remote patient monitoring on clinical outcomes: an updated meta-analysis of randomized controlled trials