A unified health algorithm that teaches itself to improve health outcomes for every individual: How far into the future is it?. (January 2022)

• Record Type: Journal Article
• Authors: Laroia, Gaurav; Horne, Benjamin D; Esplin, Sean; Ramaswamy, Vasant K
• Is Part Of: Digital health. Volume 8(2022)
• Publication Date: 2022-01
• Subjects: Artificial intelligence -- digital health -- behavior change -- personalized medicine -- machine learning -- lifestyle change -- precision nudging -- choice architecture -- reinforecment learning; Medical care -- Data processing -- Periodicals; Medical informatics -- Periodicals; 362.10285
• DOI: 10.1177/20552076221074126
• Languages: English
• ISSNs: 2055-2076

Abstract

The single biggest factor driving health outcomes is patient behavior. The CHR Model (County Health Rankings Model) weights socioeconomic factors, lifestyle behaviors, and physical environment factors collectively at 80% in driving impact on health outcomes, to the 20% weight for access to and quality of clinical care. Commercial determinants of health affect everyone today and unhealthy choices worsen pre-existing economic, social, and racial inequities. Yet there is a disproportionate focus on therapeutic intervention to the exclusion of shaping patient behaviors to improve healthcare. If the recent pandemic taught us a critically important lesson, it is the imperative to look beyond clinical care. According to the Centers for Disease Control and Prevention (CDC), long-standing systemic health and social inequities put various groups of people at higher risk of getting sick and dying from COVID-19, including many racial and ethnic minority groups. The virus was simply more efficient in detecting such vulnerabilities than the guardians of these physiologies. These insights from the pandemic come at the heel of a confluence of three major accelerants that may radically reshape our approaches to hot-spotting vulnerabilities and managing them before they manifest in a derangement or disease. They are the recent strides in behavioral economics and behavior science; advances in remote monitoring and personal health technologies; and developments in artificial intelligence and data sciences. These accelerants allow us to imagine a previously impossible vision—we can now build and maintain a unified health algorithm for every individual that can dynamically track the two interdependent streams of risk, clinical and behavioral.