Social Determinants of Health (SDoH) and Artificial Intelligence (AI) in Healthcare

Social Determinants of Health (SDoH) and Artificial Intelligence (AI) in Healthcare

Social determinants of health (SDoH) are conditions in the environments in which people are born, live, learn, work, play, and age that impacts a wide range of health, functioning, and quality of life trajectories. Individual and community behaviors can influence health costs and outcomes regardless of being outside the control of the health system. Understanding social determinants of health is imperative for strengthening health and minimizing longstanding disparities in healthcare. More specifically, focusing on the health care system concerns of health coverage, quality of care, and medical bills is where Artificial Intelligence (AI) can step in. Focusing on the use of AI to model and understand social determinants of health will accelerate us towards a value-based care model while improving and maintaining health instead of treating illness.

According to the Centers of Medicare and Medicaid Services (CMS) “Health insurance is a critical component of the healthcare industry with private health insurance expenditures alone estimated at $1.1 billion in 2016.” Emerging AI applications typically fall into two categories: cost efficiency and fraud detection. Techemergence explains:

  • Cost Efficiency: Insurance and similar health companies are developing software platforms to recommend preventative healthy habits and behaviors to patients, such as nutrition strategies and exercise. This lowers the cost of waste or loss on preventable healthcare expenditures that could be caused by unhealthy habits.
  • Fraud Detection: Researchers are developing machine learning algorithms to analyze health insurance claims to predict cases of fraud.

AI can handle the enormous datasets that must be investigated and assessed to streamline health insurance protocols. Majority of applications are still early in their implementation process; more case studies should be implemented to prove cost savings potential and ease-of-use for all end-users. Healthcare Informatics explains “Many healthcare leaders say better data and more robust local partnerships will enable scalability and accountability in social determinants of health programs. Moving forward, providers also will need robust technology solutions that focus on workflow integration, bi-directional data exchange and analytics, as well as tools that can help digitally close the loop on community resource referrals.”

Digging deeper into AI and quality of care, more progress has been made in medical centers with automated recognition of patterns in patient data. McKinsey & Company points out “AI applications can help companies to optimize services and lower costs, accelerate processes, and make better decisions.” AI can alter a physician’s diagnostic process and how they will determine a patient issue. It can expose hidden health markers that medical professionals do not observe automatically. It can look at both structured and unstructured data, the results and experience of practitioners from across the health care bionetwork– to identify trends while predicting probable future health problems. Being able to foreshadow potential health issues that can arise may help in cost reduction and change the course of the patient’s life. Real-world applications of AI are being used every day through platforms like Apple Health, 23andMe, and other wearable devices. Having access to these innovative technologies increases preventative care, quality of care, can lower medical bills, and help consumers make smarter decisions on health coverage.

Although there has been expansive progress addressing social determinants of health using AI, many obstacles remain. Implementing a value-based platform, organizations can gather information from intelligent and open workflows that let care teams efficiently find and bridge care gaps. Advances in AI and machine learning can drive medical discovery and build ground-breaking algorithms personalized to the individual patient, improving healthcare.

Softheon offers a comprehensive Decision Support Recommendation Engine that helps individuals compare plan types, rates, deductibles, out-of-pocket maximums, and more. The Decision Support user flow is a Progressive Disclosure Experience wherein the employee shopping for health coverage advances naturally through the question-steps, where only necessary information is displayed and requested at any given time. After selecting from a list of drugs and providers, the consumer is asked some key health-related questions and preferences. These factors, along with industry ratings of each plan, allow for a plan score to be calculated that is custom to the consumer themselves. Additionally, on top of the plan score display on the individual plans, Softheon’s out-of-pocket cost calculator uses the same inputs to calculate an estimated expense that the consumer might expect to spend that plan year.



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