Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments.

ABSTRACT: BACKGROUND:Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments. METHODS:We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016-2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performan

SUBMITTER: Irvin JA 

PROVIDER: S-EPMC7195714 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature