Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.

ABSTRACT: Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clin

SUBMITTER: Jung K 

PROVIDER: S-EPMC6512802 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature