From the March 2017 issue of HealthCare Business News magazine
3: Population health
With the health care industry moving toward outcomes-based reimbursement, population health management initiatives are no longer optional. Machine learning applications enable hospitals to support data-driven population health management initiatives by enabling predictive patient population risk assessment, care gaps identification and prescriptive patient intervention guidance.
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Hospitals rely on patient risk scores to help physicians achieve the best possible outcomes while optimizing the use of scarce resources. With machine intelligence, clinicians can assess patient risk more effectively than they could with a patient questionnaire assessing readmission risks, which can be highly subject to interpretation. Machine intelligence approaches can incorporate current risk scores alongside a wide variety of patient clinical, financial and socioeconomic data to assess patient subpopulation risk for future disease states, cost, utilization and other types of risk from the patients’ and health system’s perspective.
By using machine learning to support population health management initiatives, hospitals and health systems benefit from being able to better understand quality indicators and identify ways to improve care outcomes while managing financial risk against emerging value-based reimbursement schemes. Mt. Sinai recently used machine intelligence to analyze clinical and genomic data from a population of type 2 diabetes (T2D) patients. The unbiased machine intelligence analysis identified heretofore unknown three distinct subtypes of T2D patients with distinct clinical and genomic characteristics. This advanced population stratification capability will inform the design of precision treatment regimens.
Next: Patient monitoring and telehealth