Abstract 17253: Concepts Associated With 30-day Rehospitalization From Unstructured Data Among Patients With Heart Failure

Abstract

Introduction: Heart failure (HF) is a diagnosis for which the Centers for Medicare & Medicaid Services mandated public reporting on 30-day risk-standardized rehospitalization rates. Examining risk factors predicting rehospitalization has been widely studied, and most of studies utilized structured data from electronic health records (EHRs) in HF. However, those studies did not use patient-level information that could be captured from unstructured data. Hypothesis: Unified Medical Language System (UMLS) concepts that may be used as factors that could be associated with the likelihood of 30-day rehospitalization among patients with HF by using natural language processing technique. Methods: We obtained notes from patients diagnosed with HF from EHRs from a tertiary hospital between June 1, 2015 and December 31, 2019. We analyzed 14 note types including physician notes, nursing notes and discharge summaries. We used the publicly available natural language processing software MetaMap to extract UMLS concepts that may be used as factors associated with the liekhood of 30-day rehospitalization and the Pandas package in Python for processing and analysis. Results: We identified 350,272 notes among 4,773 patients discharged with HF. The median age of the cohort was 76 years, 54.3% were male, and 89% white, and the 30 day-rehospitalization rate was 12.8%.. A total of 350,272 notes were for 4,164 patients who were not rehospitalized, and 57,212 notes for 609 patients who were rehospitalized. The UMLS concepts identified at a higher rate in notes of rehospitalized patients compared to non-rehospitalized patients included “personal appearance” (7.0% vs 5.5%), “power (psychology)” (3.5% vs 2.5%), and “intrinsic drive” (2.1% vs 1.1%). The UMLS Concepts that were mentioned less in rehospitalized patient notes include “family” (26.3% vs 30.8%), “home environment” (59.9% vs 64%), and “independence” (20.6% vs 24.6%). Conclusions: We identified six UMLS concepts associated with 30-day rehospitalization in patients with HF. Further study is warranted to determine whether incorporation of concepts from unstructured data could improve prediction of 30-day rehospitalization and provide opportunities for patient-level intervention.