Objective: To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data.
Materials And Methods: We used EPIC-based EHR data from July 2016 to June 2021 and linked it to community-level data from the US Census American Community Survey. We identified predictors of interest within the EHR structured data and applied natural language processing (NLP) techniques to identify patients' social needs in the EHR unstructured data.
Background: Emergency department (ED) visits at end-of-life may cause financial strain and serve as a marker of inadequate access to community services and health care. We sought to examine end-of-life ED use, total healthcare spending, and out-of-pocket spending in a nationally representative sample.
Methods: Using Medicare Current Beneficiary Survey data, we conducted a pooled cross-sectional analysis of Medicare beneficiaries aged 65+ years with a date of death between July 1, 2015 and December 31, 2021.
Background: Social needs and social determinants of health (SDOH) significantly outrank medical care when considering the impact on a person's length and quality of life, resulting in poor health outcomes and worsening life expectancy. Integrating social needs and SDOH data along with clinical risk information within operational clinical decision support (CDS) systems built into electronic health records (EHRs) is an effective approach to addressing health-related social needs. To achieve this goal, applied research is needed to develop EHR-integrated CDS tools and closed-loop referral systems and implement and test them in the digital and clinical workflows at health care systems and collaborating community-based organizations (CBOs).
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