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  • Lessons Learned: Digital Mental Health

    Overview: “Digital Mental Health (DMH), involves providing mental health support through messages, videos, programs, applications, games, and other means. As the global demand for mental health services rises and traditional institutions face increasing pressure, Digital Mental Health Interventions (DMHIs) offer a potential solution by addressing issues such as stigma, confidentiality, and lack of motivation.” The use of these tools saw rapidly accelerated adoption due to the COVID pandemic, demonstrating their potential to improve accessibility, offer personalized treatment options, and facilitate early intervention. In fact, many consider the expanded availability of DMH tools to be a proof case for expansion of telehealth and one of the early success stories in that area. Nevertheless, despite these advancements, there remains a pressing need to overcome barriers such as provider shortages, high costs, and disparities in access to ensure that these technologies can effectively supplement traditional care and reach underserved populations. Ongoing efforts are required to optimize communication, enhance data sharing practices, and navigate regulatory frameworks to maximize the impact of digital solutions in mental health care delivery. The Backdrop: Digital behavioral health tools addressing treatment shortages and accessibility in the United States reveals a multifaceted mental health landscape characterized by significant challenges and disparities. For example, according to “Understanding the U.S. Behavioral Health Workforce Shortage” from the Commonwealth Fund, as of 2022, over 160 million Americans live in Mental Health Provider Shortage Areas (HPSAs), with a critical shortage of mental health professionals particularly in rural and underserved urban areas. This shortage exacerbates access issues, with many individuals facing prolonged wait times for mental health services. The economic barrier is also substantial. Access to care data from Mental Health America shows that nearly 60% of adults in need of mental health treatment report cost as a deterrent to accessing services. In addition, despite an estimated 59 million adults in the U.S. reporting a mental illness, nearly half did not receive treatment as noted by the Substance Abuse and Mental Health Services Administration (SAMHSA). The COVID pandemic further underscored the importance of mental health care, with many mental health organizations such as Mental Health America noting a decided increase in mental health conditions such as anxiety and depression due to factors like social isolation and fear of the virus and its consequences. This crisis has highlighted not only the resilience but also the vulnerabilities of the mental health care system, prompting accelerated adoption of digital tools such as telehealth and mobile applications to bridge gaps in care delivery. Amidst these challenges, digital behavioral health solutions offer promising avenues for transforming mental health care delivery. These technologies hold potential to enhance accessibility through remote consultations, provide personalized treatment options through data-driven insights, and facilitate early intervention and preventive care measures. However, their integration necessitates addressing complex issues such as data privacy, regulatory compliance, and ensuring equitable access across diverse populations. While digital behavioral health tools present significant opportunities to address treatment shortages and improve accessibility, their effective implementation requires comprehensive strategies that encompass technological innovation, policy reforms, and community-based initiatives to ensure all individuals have access to timely and effective mental health care services. Lessons Learned: What have been some of the “lessons learned” around increasing the implementation and effectiveness of DMH technology and improving access to mental health treatment from our prior “Our Take” posts over the years? The U.S. faces a mental health crisis, with over half the population in areas lacking providers and adults citing cost as a barrier to treatment. In 2022, nearly half of the adults suffering with a mental illness went untreated per Mental Health America. These challenges highlight the urgent need for more providers and affordable mental health services. More than half of the U.S. population (160M people) live in a Mental Health Provider Shortage Area-HPSA (Commonwealth Fund) Nearly 60% of adults in need of mental health treatment reported cost as a reason for not receiving services according to a 2022 survey (Mental Health America) Approximately 59 million adults reported having a mental illness and nearly half did not receive treatment as of 2022 (SAMHSA) The national average wait time for behavioral health treatment is 48 days, compared to 26 days for new-patient, non-emergent treatment and 21 days for family medicine (National Council for Mental Well Being, Merritt Hawkins) Digital Tools for Behavioral Health Hold Up Post-COVID...But More Needs to Be Done Integrating DMH technologies requires careful attention to communication, data sharing, and addressing regulatory and ethical concerns to maximize benefits and ensure equitable access to effective care. However, if done correctly these solutions offer promising opportunities to transform behavioral healthcare by enhancing accessibility, personalized treatment options, and early intervention. One study found “no statistically significant association between the modality of care (telehealth treatment group versus in-person comparison group) and the one-month change scores on standard assessments of depression or anxiety (BMC Psychiatry) As of March 2023, 160 million Americans live in areas with mental health professional shortages, [and] over 8,000 more professionals [are] needed to ensure an adequate supply (Commonwealth Fund) An estimated 21M adults or approximately 8.4% of U.S. adults had at least one major depressive episode (NIMH) The percentage of need for behavioral services that is actually met nationwide is less than 30% (KFF) Digital Behavioral Health Tools Can Address Treatment Shortages & Accessibility-The HSB Blog 6/24/23 Leveraging digital interventions effectively requires balancing their benefits with practical considerations to enhance mental health care accessibility and efficacy in the future. The COVID pandemic clearly accelerated the adoption of DMH tools and highlighted their potential to address mental health needs, especially in underserved areas. In a 2021 PubMed review of 735 studies there was an increase in positive health behaviors by about 33% after the uptick in telehealth appointments instead of in-person care. Mental illness has a prevalence rate of about 14% and accounts for 7% of the overall global burden of disease according to Current Psychiatry Reports. The pandemic generally precipitated a broad-based increase in mental illnesses such as anxiety and depression often driven by social isolation and fear (both of COVID itself and of its impacts). Assessing the Impact of Telemental Health As We Emerge from COVID-The HSB Blog 6/22/22 Final Thoughts: The adoption of DMH tools in healthcare either on their own or as a complement to in-person treatment,  has shown promise in addressing critical gaps in access and treatment shortages. Our work to date and lessons learned underscore the importance of robust communication channels, secure data sharing practices, and ethical considerations in order to deploy these technologies effectively. Moving forward, ensuring equitable access to mental health services remains paramount, requiring continued innovation and collaboration across healthcare sectors to improve outcomes for individuals facing mental health challenges nationwide. Related Reading: Analyzing the Challenges Facing Digital Mental Health (DMH) Between Aspiration and Reality Understanding the U.S. Behavioral Health Workforce Shortage Access to Care Data 2022 Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis The effects of treatment via telemedicine interventions for patients with depression on depressive symptoms and quality of life: a systematic review and meta-analysis

  • Digital Tools for Behavioral Health Hold Up Post-COVID...But More Needs to Be Done

    Our Take: Mental health needs in the U.S. have increased substantially driven in part by the isolation, stress and anxiety of the COVID pandemic. At the same time there has been increased recognition and acceptance of the importance of addressing the mental health needs of patients along with their physical health. However, as treatment rates have increased, existing shortages of behavioral health professionals and high out-of-pocket costs have created even larger gaps in coverage and care. While digital behavioral health services and tools may help reduce the shortfalls of traditional care models impacting access and quality, they may end up exposing other issues. For example, many argue that on their own digital health tools lack the ability to provide comprehensive mental health treatment, particularly for individuals with complex and co-occurring behavioral health needs. In addition, until reimbursement parity is reached for digital health services, tele-behavioral health may end up being relegated to a support for traditional care models rather than a complement or replacement. Key Takeaways: More than half of the U.S. population (160M people) live in a Mental Health Provider Shortage Area-HPSA (Commonwealth Fund) Nearly 60% of adults in need of mental health treatment reported cost as a reason for not receiving services according to a 2022 survey (Mental Health America) Approximately 59 million adults reported having a mental illness and nearly half did not receive treatment as of 2022 (SAMHSA) The national average wait time for behavioral health treatment is 48 days, compared to 26 days for new-patient, non-emergent treatment and 21 days for family medicine (National Council for Mental Well Being, Merritt Hawkins) The Problem: According to the CDC’s Morbidity and Mortality Weekly, “the prevalence of symptoms of anxiety disorder was approximately three times those reported in the second quarter of 2019 (26% versus 8%), and prevalence of depressive disorder was approximately four times that reported in the second quarter of 2019 (24% versus 7%)”. In addition, “suicidal ideation was also elevated; approximately twice as many respondents reported serious consideration of suicide in the previous 30 days than did adults in the United States in 2018 (approximately 11% versus approximately 4%).” While there were certain methodological differences that may not make these numbers directly comparable, the trend does appear to be increasing. During the COVID public health emergency (PHE) State and Federal officials relaxed regulations for behavioral health services, thereby increasing access to treatment, medications, community, and care. While the telemedicine flexibilities have been extended through the end of 2024 and legislation is pending, the shape of such legislation and any unwinding or redesign poses a threat to the expanded care models enabled during the Pandemic. For example, behavioral health services delivered via telehealth are often not covered or are also reimbursed at a lower rate than in-person services and behavioral health providers are also less likely to accept insurance due to low reimbursement rates and administrative burdens. In addition to coverage gaps and low reimbursement rates, high out of pocket costs for behavioral health services are also major barriers to access. As a result of all these factors, according to the 2022 survey nearly 60% of adults in need of treatment reported cost as a reason for not receiving services. In addition, while “Medicaid Expansion was also expected to improve access to mental health care, since Medicaid is the single largest payer for mental health services in the USA, studies have found limited effects. (which may be due to certain populations not being counted or having previously receiving care from sources like community health clinics, etc.)” Background: The opioid epidemic and COVID pandemic have also played major roles in the increase of mental illness and substance use rates. The compounding of loss, grief, unemployment, and loneliness contributed to increases in reported alcohol and drug use, as well as delays in care, cancellations of appointments, and poor access to prescriptions. As noted, forced by the experience of COVID, authorities had to experiment with ways to expand new and existing methods of care. In addition, the expansion of reimbursement for telehealth services during the  pandemic was also broadened to include previously uncovered audio-only services, thereby bringing access to many communities struggling with limited or no broadband access. These flexibilities did appear to increase access. For example,  a recent article in The Journal of Primary Care for Community Health found that “telehealth services for depression, specifically telephone with audio or video access, mitigated technology challenges that included accessing the internet and limited bandwidth for patients with lower incomes.” Also according to SAMHSA, during COVID, the availability of outpatient substance use treatment delivered via telehealth increased by 143% between January 2020 and January 2021. In addition, some of the flexibilities provided under the PHE have now been permanently authorized under certain legislation. For instance, under the Consolidated Appropriations Act of 2023, Federally Qualified Health Centers (FQHCs) and Rural Health Clinics can continue to serve as distant site providers for behavioral health services thus allowing Medicare patients to receive behavioral health services via telehealth. Moreover, Medicare patients will continue to be eligible to receive telehealth services for behavioral/mental health care in their home and audio-only behavioral telehealth services are still allowable. Also, there will continue to be no geographic restrictions for originating sites for behavioral/mental telehealth services and Rural Emergency Hospitals (REHs) will continue to be allowed to be originating sites for telehealth. Nevertheless, persistent workforce shortages, and unequal distribution of existing workforce leave many individuals seeking care without access as well, particularly in rural communities.  As of December 2023, more than half of the U.S. population (160M people) live in a Mental Health HPSA (Health Provider Shortage Area) according to the Commonwealth Fund. Rural counties are more likely than urban counties to lack behavioral health providers and receive care from primary care providers. Individuals in rural counties are also more likely to experience one or more behavioral health conditions. Recent Data from the CDC shows that as of 2020 the highest state and county estimates of depression are observed along the Appalachian and southern Mississippi Valley regions. Commonly used digital health tools include mobile health applications, wearable technology, internet based cognitive behavioral therapy, virtual reality, and machine learning. The use of wearable technology has the potential to reduce limitations in diagnosis and treatment of behavioral health conditions by providing objective measures based on a patient’s true behavior outside of their appointments. However, given that wearable technologies may be subject to user error and comfort may impact adherence and tolerability, the ability to have periodic in-person visits with providers can be essential. Moreover, many digital behavioral health tools often work best when treatment is combined in a hybrid model. For example, internet-based CBT can expand access, maintain engagement, enhance privacy, and reduce costs to both patients and the healthcare systems. Additionally, electronic consultation can offer an opportunity for digital health to support providers, particularly specialists like psychiatrists, who may already have limited availability to see new patients. The ability to do electronic consultation both synchronously and asynchronously (at least between providers) may also reduce wait time for referrals, medication management and support peer learning. Implications: Expanding the integration of physical and behavioral health through the use of tele-behavioral health tools may help meet needs and close access gaps for particularly vulnerable populations. This is particularly important as poor mental health impact has been shown to significantly impact an individual’s daily life and overall wellbeing. According to the World Health Organization, depression is a leading cause of disability worldwide and is tied to multiple chronic illnesses. For example, people with depression have a 40% higher risk of developing metabolic diseases, lower rate of employment and higher risk of substance use than the general population. Similarly, research has found that people with one or more chronic diseases also experience higher rates of behavioral health conditions such as depression, anxiety, and substance use, often exacerbating their conditions. Recent research, including a study from the Annals of Medicine have “provided evidence that treatment via telemedicine interventions were beneficial for depressive symptoms and quality of life in patients with depression.” Thus, access to behavioral health care is essential to the prevention of total morbidity and premature mortality for many individuals with a behavioral health need. Digital health tools have been shown to reduce stigmatizing encounters, transportation costs and other geographic barriers. Additionally, participation in digital mental health interventions such as virtual support groups or group therapy can increase social connection. A recent study on the effectiveness of digital mental health tools in reducing depression and anxiety found them to be moderately to highly effective in low resource settings. However, to successfully implement the integration of tele-behavioral health will require significant organizational commitment as well as a change in training and systems to ensure clinicians are all aligned in ensuring that patients have the capacity to access and utilize platforms for care. In addition, for underserved communities with limited access to clinicians, low digital literacy, poor broadband or other issues, providers and payers will have to be creative to ensure these developments don’t further exclude them from care. This may include looking for public/private partnerships to improve technology access, using asynchronous technology (like store and forward) where necessary or even in some cases just resorting to the old analog telephone if necessary. Digital behavioral health tools present an opportunity to address workforce shortages and supply issues but policy officials and those looking to expand access must remember inability to deploy these technologies will further impact already medically marginalized communities. Related Reading: The effects of treatment via telemedicine interventions for patients with depression on depressive symptoms and quality of life: a systematic review and meta-analysis Health Resources and Services Administration, National Center for Behavioral Health Workforce Analysis, Behavioral Workforce, 2023 National, State-Level, and County-Level Prevalence Estimates of Adults Aged ≥18 Years Self-Reporting a Lifetime Diagnosis of Depression — United States, 2020 Technology-Based Approaches for Supporting Perinatal Mental Health Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis

  • Xaira Therapeutics: AI Drug Discovery Optimized for Human Biology

    The Driver: Xaira Therapeutics recently raised $1 billion to apply AI to the discovery and development of new drugs. As the company noted in its press release announcing the funding, Xaira brings together three core elements: advanced machine learning research, expansive data generation to power new models, and robust therapeutic product development. The funding, the 3rd largest biotech funding round since 2010 was led by ARCH Venture Partners and Foresite Capital, joined by F-Prime, NEA, Sequoia Capital, Lux Capital, Lightspeed Venture Partners, Menlo Ventures, Two Sigma Ventures, the Parker Institute for Cancer Immunotherapy (PICI), Byers Capital, Rsquared, and SV Angel, among others. Key Takeaways: For every 200 proteins investigated in research, only one in 200 correlates to causation in human disease and shows potential in drug discovery (Nature) The biopharma industry spends over ten times what it did in the 1980’s on development, after adjusting for inflation but yet has shown virtually no gains in productivity since that time (Congressional Budget Office) The use of Machine Learning in drug discovery could save approximately $300-400M per drug (15%-40%) Xaira's recent fundraising was the 3rd largest biotech funding since 2010 behind only Altos Labs in 2022 and Roivant Sciences in 2017 (Endpoints News) The Story: Xaira was founded based on the technology emanating from the University of Washington’s Institute for Protein Design  and the lab of Dr. David Baker. In July of 2021, Dr. Baker’s lab developed “RoseTTAFold, a software tool that uses deep learning to quickly and accurately predict protein structures based on limited information in as little as ten minutes on a single gaming computer. In December 2022, the Institute for Protein Design released RFDiffusion, “a powerful new way of designing proteins that combines structure prediction networks and generative diffusion models.” According to the lab, the model demonstrated “extremely high computational success using the new method and experimentally tested hundreds of A.I.-generated proteins, finding that many may be useful as medications, vaccines, or even new nanomaterials.” Using these technologies as a foundation, Xaira is going to attempt to create and deploy AI models that have been specifically designed, built and optimized based on human biology for drug development and discovery. According to the company, at first it will target protein based antibody drugs but according to Chemical and Engineering News they are “looking for validated targets that are underserved by the current technology”. The article also noted that, the company came together by a coincidence of timing as both Baker and his co-founder Hetu Kamisetty had been discussing founding a company based upon Baker’s technology while at the same time, two of its backers Bob Nelsen of Arch Ventures and Vik Bajaj of Foresite Capital were looking for investment opportunities in the AI space. In addition to Baker and Kamisetty, who previously worked for Meta, the founding team includes  Dr. Marc Tessier-Lavigne, former Chief Scientific Officer of Genentech and former President of Stanford University as well as Dr. Arvind Rajpal, formerly of Genentech; and Dr. Don Kirkpatrick, formerly of Interline and Genentech. It should be noted that Dr. Tessier-Lavigne resigned from his position at Stanford following an inquiry into research misconduct on papers on which he was the principal author. While the special committee did not find any evidence of “fraud, fabrication, or other intentional wrongdoing” questions were raised about Dr. Tessier-Lavigne’s oversight of the research. Dr. Tessier-Lavigne did subsequently issue or attempt to issue corrections to some of the research cited (two of which were not published due to lack of action on the part of the journal in question). The Differentiators: In addition to the personnel from Baker’s Lab, as noted by Genetic Engineering and Biotechnology News, Xaira is also positioned to benefit from “technologies and personnel from Illumina’s functional genomics R&D effort” which have also been spun out into the company. Moreover, “the proteomics group from Interline Therapeutics, a drug developer whose precision medicine platform is designed to map and modulate protein communities” has also been integrated into the company. Based on the extensive resources required to create and test AI models for drug development, we think Xaira’s strong funding, deep scientific and regulatory bench and integrated technology platform, position them well for success. The Big Picture : According to a 2019 article in the journal Nature, there is an “overall failure rate in drug development of over 96%, including a 90% failure rate during clinical development.” The article went on to add that their conclusion was based on the assumption that there were “10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets”...leading to the conclusion that for every 200 proteins investigate in research, only one in 200 correlates to causation in human disease and shows potential in drug discovery. the discovery of one in 200 protein-to-disease causation in drug discovery. While these numbers may sound shocking, they have been relatively consistent in drug discovery and development. For example, despite the fact that the biopharma industry spends over ten times what it did in the 1980’s on development, after adjusting for inflation, the overall likelihood for approval of a drug candidate (from phase I through approval) ranges from just 24% for a hematology drug to under 4% for a urology drug. As a result, it appears clear that AI technology could be used to both improve discovery and speed to market of new drug candidates. In fact, according to the U.S. General Accounting Office, the use of Machine Learning in drug discovery could save approximately $300-400M per drug (15%-40%). Moreover, since human disease is often the sum of many simultaneous interactions (and not just an isolated single interaction often targeted in current animal models), AI is likely to be successful both intentionally and unintentionally (ex: identifying drug targets that may not be the focus of the original search). Exclusive: In $1B+ bet on AI, biopharma heavyweights back new startup to upend drug R&D, Backed by $1 billion, Xaira Therapeutics is readying AI-generated drugs

  • Ensuring Digital Health Reduces Not Aggravates Inequities in Healthcare

    Our Take: There is immense potential for digital health to empower marginalized communities by increasing access to information and quality care. However, as seen during the COVID pandemic, the design and dissemination of digital health tools can perpetuate existing inequities in our society. Going forward, health equity frameworks must be incorporated into the future design and testing of digital health tools, to ensure that they help bridge the digital divide. Key Takeaways: Disparities in digital access exist among nonmetropolitan households, racial/ethnic minority, and low-income households with over 32% without a desktop or laptop computer with high-speed Internet and 22% without a smartphone (Journal of Rural Health) The average medical visit takes approximately 2 hours, including the actual appointment  (20 minutes), travel, waiting, paying, and completing paperwork (Harvard Medical School) Over 2 million women left the labor force during the height of the pandemic between February and October 2020 (National Council on Aging). individuals earning more than $75,000 annually had a greater rate of growth in having ever used the Internet than those earning less than $20,000 (Pew Research Center) The Problem: People of color, older adults, those with limited English proficiency, and rural and low-income communities have often faced disproportionate access to digital health interventions. For example, according to a 2022 article in the Journal of Rural Health, “Significant disparities in digital access exist among nonmetropolitan households, racial/ethnic minority households, and lower-income households with the authors noting that of over 105M households, over 32% were without a desktop or laptop computer with high-speed Internet, almost 22% were without a smartphone with a data plan for wireless Internet, and 14% were without any digital access.” This is despite the clear benefits that these services provide to these communities which typically lack access to care and as a result often have higher burdens of chronic conditions. Access to digital health can reduce barriers to care due to increased flexibility for childcare or work commitments, comfort, reduced commute time, and transportation costs. Digital health interventions can also increase access to personal health data and health information, increasing patient self-efficacy. However, these digital applications are rarely designed with a focus on communities with an expressed need. For example, both the design and testing of digital health products are not often adapted to frequent users like the elderly with special needs. As pointed out in a recent study entitled “Digital health platforms for the elderly? Key adoption and usage barriers and ways to address them”, “studies argue that certain designs are needed that are more adapted to the conditions of individuals with impairments – for example, those suffering from hearing and visual impairment.” Background: During the COVID pandemic, it was evident that access to digital healthcare and its potential benefits were not universal. In a recent study examining the impact of the COVID pandemic on the use of digital health tools, the authors found that populations with a history of digital exclusion such as older adults (ages 65+), low income, or educational attainment, and racial/ethnic minorities had lower odds of using the internet and a variety of digital health tools including telemedicine during the pandemic. The study also found that individuals earning more than $75,000 annually had a greater rate of growth in having ever used the Internet than those earning less than $20,000 which suggests a widening of the digital use disparity between socioeconomic groups. In addition, digital health tools are often unable to meet the needs of medically marginalized communities. For example, the Department of Labor cites that women often serve as caregivers and primary healthcare decision-makers in their households, therefore it is integral that women have access to the knowledge and tools to satisfy their multiple roles within their communities. For example, according to the National Council on Aging, over 2 million women left the labor force during the height of the pandemic between February and October 2020. Nevertheless, digital tools are often not designed with this population in mind. As noted in a recent article from the Yearbook of Medical Informatics,” working women with children may not be able to adopt advice from stress-relief applications suggesting spending time with family. These unrealistic recommendations can cause guilt or increase stress levels due to a perceived failure to follow recommendations.” This is particularly troubling when one considers that the top two leading causes of death for women are heart disease and cancer which also mimic national mortality trends. Moreover,  digital health solutions that target chronic conditions prevalent among women are rarely funded to meet their actual needs. Many of the sexual and reproductive digital health applications that currently exist also fail to reach priority subgroups with disproportionate inequities in sexual and reproductive health. For example, Black women participate in digital pregnancy services less often, despite the maternal and infant mortality rate remaining the highest in the U.S. Implications: As we move toward a more virtual healthcare delivery system, the lack of a health equity framework in digital healthcare will only continue to exclude marginalized communities from equitable access to care, further exacerbating health inequities in those communities. As noted in a recent article in PLOS Digital Health, “to be truly inclusive, digital healthcare must go beyond digitizing what exists and must pursue digitalization at scale. It is essential that adoption of digital healthcare occurs hand-in-hand with community driven need for the solutions and digital literacy education.” Indeed, another article entitled “The need for feminist intersectionality in digital health” stated that intentional development of inclusive digital health applications is integral to excluding biases from the design of digital health applications. It is imperative that researchers also consider disparities in digital literacy and access to high-speed internet and smartphones in their study protocol. As the article went on to highlight, the adoption of plain language, clear communication techniques, and inclusive content can further support the uptake of digital health applications among diverse communities with differing levels of digital literacy and education. As the aforementioned PLOS article pointed out, to reduce digital inequities, underrepresented populations in the utilization of digital health applications such as women, racial and ethnic minorities, and rural communities need to be included in the design and testing of applications. Community-engaged research designs can also be an avenue to include traditionally underrepresented communities in digital health research and design. Patients, especially from historically medically marginalized communities must be included in developing solutions to meet their healthcare needs. Existing and emerging frameworks exist and are available to help increase our understanding of and address the root causes of health inequities and bias in our health system. These should at least be consulted when developing digital tools to at least avoid exacerbating existing disparities with new developments in digital healthcare. Authors Allison Crawford and Eva Serhal, have developed a Digital Health Equity Framework which considers socio-economic factors, cultural contexts, digital literacy, and access to the health system and availability of quality health services. This framework prioritizes health policy and regulation for digital health intending to address health inequity, as well as community needs, context, and inclusion In addition, in 1989 legal scholar Kimberlé Crenshaw developed a theory of intersectionality that posits that intersections of factors such as race, class, and gender are influenced by larger structural systems such as racism, classism, and sexism. While originally developed to highlight the ways in which prevailing legal and policy conceptions of discrimination overlooked the experiences of Black American  women, it is increasingly suggested as an innovative framework with the potential to advance understanding of, and action on, health inequalities, as noted by authors like Daniel Holman. Clearly, frameworks like the Digital Health Equity Framework and Intersectionality are necessary to help us better assess the root causes of health inequities and ultimately identify solutions to reduce those causes. Incorporating these frameworks early on in the design, production, testing and implementation of digital health solutions can help tailor interventions to specific subpopulations with disparate health outcomes and assess differences in the uptake of digital health applications. Related Reading: Demarginalizing the Intersection of Race an Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics Healthcare inequity and digital health-A bridge for the divide, or further erosion of the chasm? The Impact of the COVID-19 Pandemic on Internet Use and the Use of Digital Health Tools: Secondary Analysis of the 2020 Health Information National Trends Survey The need for feminist intersectionality in digital health

  • The Digital Divide is Real: How Can We Address it Right Now?

    Our Take: National trends in telehealth use show the popularity of telemedicine has not waned since the COVID-19 pandemic. However, inequities in home broadband access disproportionately impact vulnerable communities in need of telehealth access such as older adults, racial and ethnic minorities, low income, and rural communities. These communities share a higher burden of chronic conditions and higher likelihood of mortality from leading causes of death such as cancer and chronic lower respiratory disease. Adequate home broadband access has emerged as a vital social determinant of health, impacting access to high quality healthcare, health information and social services. Broadband access also intersects with other social determinants such as access to education, social support, and employment, further impacting health outcomes and overall wellness. Aspects of inadequate broadband access such as the lack of high-speed internet or smartphone dependence significantly impacts the quality and timeliness of virtual medical care. Despite the promise of additional government investment in broadband under the Infrastructure Investment and Jobs Act, lack of adequate home broadband access will leave historically disenfranchised, and underserved populations with existing health disparities behind as healthcare continues to move toward the virtual model while access is being built out. Key Takeaways: More than 25% of the U.S. population lacks adequate home broadband access, with that number rising to 57% for those earning less than $30,000 per year and  73% for those living in tribal and/or rural areas (Pew Research Center) Counties where patients required the highest level of care and had the highest acuity (as measured by Hierarchical Condition Categories-HCC risk score) had 50% higher telehealth utilization vs. counties with patients who had the lowest acuity (Journal of Telemedicine and Telecare) 28% of Americans in households earning less than $30,000 per year rely on a smartphone for internet access compared to only 4% of individuals in households earning $100,000 or more (Journal of Telemedicine and Telecare) Rural Americans are more likely than their urban counterparts to die prematurely from the five leading causes of death: heart disease, cancer, unintentional injury, chronic lower respiratory disease, and stroke (CDC) The Problem: Broadband access in the home has become increasingly important since the COVID-19 pandemic as telehealth services become common practice and even preferred in some populations such as communities where telehealth can decrease transportation costs, and other barriers to healthcare. However, as noted in a recent article in the Journal of Telemedicine and Telecare, high-quality telehealth visits require adequate broadband access with the authors finding that counties with the highest median household income had 35% higher telehealth utilization as compared to the counties with the lowest. Moreover, according to a 2024 Pew Research Center Study entitled, Americans’ Use of Mobile Technology and Home Broadband, 95% of adults having annual household income of at least $100,000 say they have broadband access compared with only 57% of adults in households making less than $30,000 per year. The Backdrop: With research indicating that 28% of Americans in households earning less than $30,000 per year rely on a smartphone for internet access compared to only 4% of individuals in households earning $100,000 or more it is clear that the digital divide is real. However these disparities in care and access to care are real. example, counties where patients required the highest level of care and had the highest acuity (as measured by Hierarchical Condition Categories-HCC risk score) also had 50% higher telehealth utilization as compared to the counties with patients who had the lowest acuity according to the above referenced Journal of Telemedicine and Telecare article. Given an HCC risk score predicts how likely chronic health conditions are to affect long-term health outcomes, this suggests that the communities with the highest burden of chronic conditions were also more reliant on telehealth during COVID-19. Similarly, we can conclude that these communities are also more vulnerable to adverse health outcomes when they experience inadequate broadband access. Implications: It is generally well established that communities more likely to have poor internet access may also experience a decline in healthcare visits, inconsistent telehealth usage, as well as a decline in the quality of telehealth visits if they use them at all. For example, according to the CDC, rural Americans are more likely than their urban counterparts to die prematurely from the five leading causes of death: heart disease, cancer, unintentional injury, chronic lower respiratory disease and stroke - all of which are conditions that can be addressed through digital technologies like remote patient monitoring. Perhaps the bigger question is while we wait for these larger infrastructure investments in widespread broadband to occur, what can we do right now? 1) Fixed Wireless: One proposed solution is wider adoption of fixed wireless. As noted by Verizon, fixed wireless access (FWA) has the potential to provide these communities with access to affordable high-speed internet despite the lack of infrastructure. FWA “is a type of 5G or 4G LTE wireless technology that enables fixed broadband access using radio frequencies to send high-speed signals that offer data transfer to and from consumer devices instead of cables.” 2) Coverage of Audio Only Telehealth: Additional support and coverage for audio only telehealth could help bridge the gap. For example, During the COVID-19 pandemic, the Public Health Emergency (PHE) granted broader access to telehealth services including audio-only services for Medicare and Medicaid recipients. An analysis by HHS entitled “ Updated National Survey Trends in Telehealth Utilization and Modality (2021-2022)” showed persistent disparities in accessing video telehealth services by education level, age, race, and ethnicity. Individuals who identified as Hispanic or Latino, Black, and Asian were found to be more likely to use audio-only vs video telehealth services than White respondents. While the Consolidated Appropriations Act, 2023, provided an extension for some flexibilities for providers to bill through December 31, 2024, there is currently no mandate for payment parity for audio-only reimbursement. 3) Public/Private Partnerships and Subsidies: Finally, additional subsidies, perhaps enabled by public private partnerships, should be considered to increase smartphone access. As noted by the 2024 Pew Research Center study, adults ages 65 and older are less likely to even own a smartphone. Consequently, the establishment of state and local programs and partnerships that subsidize the cost of digital devices for individuals/populations that cannot afford these devices can help reduce or eliminate the financial burden of providing individuals with even limited internet access through smartphones. This would be particularly true for those who are most financially burdened such as unhoused individuals and could serve as the hub for enabling a number of other services. While disparities in digital literacy would still impact the quality of telehealth for individuals reliant on their smartphones, they could be combined with training and other services to improve literacy and connectivity. Not only could this education be used to promote equity, payers, providers and care teams could be engaged to connect people with coverage and ensure they were making use of the most appropriate and cost-effective sites of care. As our healthcare system continues to move toward a more digitized, virtual model of care, currently underserved communities and populations with existing health disparities can be brought into the system in a more effective, convenient, and patient-friendly way that reduces barriers while improving the cost and quality of care. Related Reading: Americans’ use of Mobile Technology and Home Broadband A Socio-Ecological Approach to Addressing Digital Redlining in the United States: A Call to Action for Health Equity Updated National Survey Trends in Telehealth Utilization and Modality (2021-2022) Audio-Only Telehealth Post-PHE -- Medicare, Medicaid, and Private Payers

  • Lessons Learned: SDOH & Chronic Conditions- The HSB Blog 7/12/22

    Overview: Given we have written approximately 75 Our Takes over the last two years we thought it might be helpful over the course of the summer to look at “Lessons Learned” from our posts. As such, this summer we will be looking at our lessons learned on the broad range of digital health on topics we’ve written about including Artificial Intelligence; RPM and Virtual Care; Value-Based Care, and Mental Health (among others). This week we look at Social Determinants of Health (SDOH) and chronic conditions with a focus on how digital health can make a difference. The Backdrop: Chronic Health conditions are one of the leading causes of death and illness worldwide. According to the CDC, approximately 85% of adults over 55 have at least one chronic health condition, and 60% have at least two chronic conditions. As we age, naturally we are increasingly prone to falling ill and are more susceptible to chronic illness resulting in increased spending on healthcare treatments. Chronic conditions can have major impacts on healthcare, patient livelihood, employee productivity, mental health outcomes, and much more. Chronic illnesses like diabetes and obesity could lead to absenteeism and missed work days over long periods of time causing economic strain on employers. Unfortunately, due to uneven distribution of care among certain socio-economic, and geographic groups, certain racial and ethnic groups are at higher risk for certain chronic conditions. However, recent advances in digital health may enable chronic conditions to be dealt with early in the patients' journey by deploying tools that empower patients to monitor and act on or change certain behaviors thereby impacting health conditions. For example, remote patient monitoring (RPM) technology has become increasingly popular and is being used to help treat patients with heart conditions, high blood pressure, cancer, and respiratory diseases. During the Pandemic, digital tools like telehealth measures allowed clinicians to continue to monitor and track patients who were either unable or unwilling to come into the office. Wearable devices such as smart watches or smart apps can aid in monitoring chronic conditions such as hypertension and provide patients with useful and actionable information about their personal health indicators without ever having to leave the comfort of their homes. These technologies can be particularly helpful with certain chronic conditions like cardiovascular disease, where lifestyle modification and self-management can be critical to improving outcomes. These are some of the very many ways in which chronic health conditions can be mitigated by digital health. What follows below are our insights, we hope you enjoy them. Lessons Learned: That said, what were some of the “lessons learned” on chronic health from some of our prior Our Take’s? While digital tools can help the elderly remain independent and age in place, close attention has to be paid to the technology gap and specific, age-appropriate training is required. For example, according to AARP’s “Home and Community Preference Survey” conducted in 2021, “77 percent of adults 50 and older want to remain in their homes for the long term — a number that has been consistent for more than a decade.” In addition, according to a recent article in Mobi entitled “Seniors aren't tech-averse. We're just not designing for their needs.”. The article noted, “Digital health companies that design user-friendly services or products for the elderly could mitigate adverse health-related outcomes and worsened chronic conditions linked to usage barriers by addressing the age-related barriers. These technologies can be particularly helpful with certain chronic conditions like cardiovascular disease, where lifestyle modification and self-management of chronic conditions are critical to improving outcomes. Age Related Barriers to Digital Health Remain-The HSB Blog 5/24/22 https://www.healthcarebullpen.com/single-post/age-related-barriers-to-digital-health-remain The Digital Divide and Broadband Access Must Be Addressed to Make Broad-Based RPM a Reality. A 2020 study in the Journal of the American Medical Informatics Association found that “areas with limited broadband access also had higher rates of chronic diseases such as obesity and diabetes, resulting in a double burden where those with the lowest connectivity have the highest need”. Data from the National Telecommunications and Information Administration indicate that only 12% of those with an annual household income of $25,000 or less used the internet to communicate with their healthcare provider compared to 40% of those making $100,000 or more in 2019. Hispanic, American Native/Alaska Native, and African Americans had the lowest rate of internet use for health-related activities, trailing White and Asian Americans. Biden’s Plan to End Cancer Won’t Succeed Without Social Infrastructure-The HSB Blog 6/7/21 https://www.healthcarebullpen.com/single-post/biden-s-plan-to-end-cancer-won-t-succeed-without-social-infrastructure-the-hsb-blog-6-7-21 Telehealth Could Magnify Inequity For Those Who Lack Access-The HSB Blog 2/16/21 https://www.healthcarebullpen.com/single-post/telehealth-could-magnify-inequity-for-those-who-lack-access-the-hsb-blog-2-16-21 Digital Tools Could Significantly Increase Engagement, Utilization, and Compliance A 2019 JAMA article found, that chronic health conditions lower employees’ productivity and increase the number of missed workdays. Employee absenteeism caused by high blood pressure, diabetes, smoking, physical inactivity, and obesity, incur an annual cost for employers of $36.4 billion. A study by NCQA found that “telehealth facilitates access to healthcare for individuals who might otherwise skip or avoid important services. It also allows care delivery more quickly and efficiently in lower-cost settings. [The report] also found evidence that telehealth can help reduce more costly urgent and emergency department (ED) care, as well as the use of costly and often overused services such as imaging.” Digital Wellness Programs Could Be Key to Engagement and Utilization-The HSB Blog 10/18/21 https://www.healthcarebullpen.com/single-post/digital-wellness-programs-could-be-key-to-engagement-and-utilization-the-hsb-blog-10-18-21 Are Patient Satisfaction and Outcomes Better with Telehealth?-The HSB Blog 3/29/21 https://www.healthcarebullpen.com/single-post/are-patient-satisfaction-and-outcomes-better-with-telehealth-the-hsb-blog-3-29-21 Healthtech Could Meaningfully Address Disparities in Health for the Underserved (assuming broadband access is addressed). For example, data from the CDC indicates that “integrating community health workers (CHW) into the healthcare system [who are often empowered by digital technology] will reduce the burden placed on strained resources and overworked clinicians. It will also help improve health outcomes when used for addressing chronic health conditions [many of which disproportionately impact people of color and are preventable]. In 2021 HealthcareITNews reported, “studies have shown that minority patients routinely receive inferior care because they may be bouncing between hospitals and clinics and also have higher rates of chronic illnesses like diabetes and hypertension, which research indicates can be better addressed by digital technologies.” A 2021 article in the Central European Journal of Medicine entitled, the “Role of new digital technologies and telemedicine in pulmonary rehabilitation” noted that “the continuous monitoring for chronic respiratory conditions can produce the expected efficacy needed for the lower occurrence of systemic side effects and effectively determine the appropriate number of doses for inhalation therapy." Community Health Workers Will Reduce Disparities & Improve Outcomes-The HSB Blog 4/19/21 https://www.healthcarebullpen.com/single-post/community-health-workers-will-reduce-disparities-improve-outcomes-the-hsb-blog-4-19-21 Enhancing Telemedicine Can Close The Infant and Maternal Mortality Gap-The HSB Blog 3/8/21 https://www.healthcarebullpen.com/single-post/enhancing-telemedicine-can-close-the-infant-and-maternal-mortality-gap-the-hsb-blog-3-8-21 Digital Tools Can Improve Efficiency & Effectiveness of Respiratory Therapy-The HSB Blog 6/7/22 https://www.healthcarebullpen.com/single-post/digital-tools-can-improve-efficiency-effectiveness-of-respiratory-therapy-the-hsb-blog-6-7-22 Final Thoughts: Given all the dramatic change we’ve seen in the past 2 years, in large part as a result of COVID and the near-instantaneous embrace of digital care, our lessons learned focus on these factors: chronic care is moving from discrete, point-in-time monitoring to real-time continuous monitoring but is not yet there, at a minimum the pandemic has demonstrated that digital tools will serve as a significant adjunct to in-person care for many underserved communities and may in fact open new avenues for access; providers and payers must pay attention to and design plans to deal with technological issues, cultural barriers and issues around health literacy; remote-patient monitoring, virtual care, and even hospital-at-home models should be designed by disease state and therapeutic category, not one size fits all.

  • Health in Her HUE-Culturally Competent Care for Women of Color

    The Driver: Health in Her Hue, recently raised $3M in seed round funding led by Seae Ventures, which brings the total funds raised to date to $4.2M, since being founded in 2018. This round also included participation from Johnson & Johnson Impact Ventures, Morgan Stanley Inclusive Ventures Lab, Genius Guild, HBCU Founders Fund, Stanford Impact Fund, and a select group of angel investors. The company will use the proceeds from the round to expand its products and programs, including the Care Squad Program, which provides culturally tailored health education classes, and launching of a new product which will enable members to ask clinical experts health questions through video chat. Key Takeaways: Non-Hispanic Black women are three to almost four times more likely to die while pregnant or within 1-year postpartum than their non-Hispanic white counterparts (CDC) While Black women make up only 13.6% of the U.S. female population they have a higher prevalence of many health conditions, including heart disease, stroke, cancers, diabetes, maternal morbidities, and obesity and are much more likely to die of chronic conditions like diabetes (Black Women’s Health Study) According to the company 67% of Health in Her HUE’s members  are more likely to engage with existing EAPs which reduces stress and other health related issues but employee absenteeism and lost corporate revenues as well (Health in Her HUE) Over 60M women in the U.S. are living with some form of heart disease and with the leading cause of death for non-Hispanic Black women accounting for 50,000 deaths per year (CDC) The Story: Health in Her Hue Founder and CEO, Ashlee Wisdom, grew up in a New York City neighborhood where social inequity was and still is prevalent. After working as Assistant Director of Grants Management & Development, she noted that a substantial percentage of the population served are Black and Brown people who have higher rates of diabetes and heart diseases yet did not have health insurance or proper access to care. As a result, in 2018 Wisdom formed Health in Her HUE to connect women of color with culturally sensitive and responsive healthcare providers and content to support them with accessing better care to improve outcomes. She stated that the overall vision for the digital platform “is to be the first touch point for Black women and women of color when it comes to their health. What that means is that if a woman has a question related to her health, her first instinct is not going to WebMD or Google, but it’s Health in Her HUE’s content and resource library where she can find videos and articles on that condition” to make informed decisions on her health. The Differentiators: Health in Her Hue offers three approaches to provide personalized and equitable health to women of color through 1) Community, 2) Content, and 3) Connection. Their Community Forums have virtual care squads where women meet twice per month in a peer support group setting to discuss specific health topics. In terms of content, their curriculum is designed to be culturally sensitive for Black women and women of color by clinicians with expertise and experience on a specific health topic, which is a combination of written content, videos, and activities for participant engagement with each other. The evidence-based content library has articles and research on healthcare topics including Breast Health, Chronic Diseases, Mental Health, Oral Health, Pregnancy and Parenting, Reproductive and Sexual Health, Nutrition and Fitness, Skin and Hair Health, and LGBTQ+ Care. These topics are written by healthcare professionals who are either experts or have experiences with the health issue themselves. With an estimated 1,300 healthcare providers across sixty specialties, the digital platform has a provider directory that 13,000 members have access to. In terms of connection, Health in Her HUE’s peer groups are selected based on a member’s preferences and lived experiences and are guided by trained facilitators. Providers must self-opt in to join Health in Her Hue, to ensure alignment with the company’s mission. The Big Picture: Studies have shown that African Americans are significantly more likely to experience premature discontinuation of psychiatric treatment for depression as compared to non-Hispanic Whites. This can be even more severe when one considers the general underuse of EAP programs. For example, according to the National Business Group on Health, 97% of large employers offer EAPs, yet in 2018, only 5.5% of employees used EAPs. However targeted programs like Health in Her HUE’s can help address this disparity. For example, according to the company 67% of Health in Her HUE’s members  are more likely to engage with existing EAPs (employee assistance programs), which not only reduces  elevated levels of stress and other health related issues but reduces employee absenteeism and lost corporate revenues. In addition, women of color often experience higher prevalence of many chronic conditions and experience difficulty in finding providers that can provide culturally competent care. As noted in a study from Boston University, while Black women make up only 13.6% of the U.S. female population they have a higher prevalence of many health conditions, including heart disease, stroke, cancers, diabetes, maternal morbidities, and obesity and are much more likely to die of chronic conditions like diabetes. It is important to note that while the company states that providers undergo a rigorous onboarding process which includes a health interview, we did note several negative reviews on the company’s site including one where patients felt as if the physician did not listen to their needs at a time of vulnerability, during pregnancy and active childbirth. Given there is already a significant mistrust of providers from minority communities, especially Black Americans, due to the historical disparities in care experience we would like to see more active monitoring of provider reviews from patients for a probationary period to ensure that the providers remain strongly aligned with their mission.

  • mHealth and Public Health Another Look: Vast Potential to Improve Delivery of Public Health

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

  • mHealth: Challenges Remain to Enable Providers to Address Public Health-The HSB Blog 11/5/23

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

  • CytoVale a 10-Minute Test for “The Biggest Threat You Never Heard Of”

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

  • 4 Ways AI Could Revolutionize The Future of Drug Development…No Chat Needed-The HSB Blog 2/16/23

    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

  • Using AI in Cardiology to Soothe the Heart

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

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