Hospital Readmission and Social Risk Factors Identified from Physician Notes

Abstract

Objective To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30‐day readmissions. Study Setting A multihospital academic health system in southeastern Massachusetts. Study Design An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics. Data Collection/Extraction Methods All‐payer claims, EHR data, and physician notes extracted from a centralized clinical registry. Principal Findings All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD‐9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk‐adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01). Conclusions The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

Advancing quality in sepsis management: a large-scale programme for improving sepsis recognition and management in the North West region of England

“…However, these variables did not predict readmission rates maybe because risk of readmission is determined by social as well as medical factors 16. Information on other recognised prognostic criteria such as Sequential Organ Failure Assessment Score (SOFA) were not available 17.…”

State variation in the characteristics of Medicare‐Medicaid dual enrollees: Implications for risk adjustment

“…For the latter two characteristics, we constructed binary measures denoting whether respondents were unmarried or had a high school education or less (vs some postsecondary education or higher). Education and marriage have well‐documented relationships with health and outcomes of care, including hospital readmissions and mortality …”

Meaningful Information in the Age of Big Data: A Scoping Review on Social Determinants of Health Data Collection for Electronic Health Records

“…(30). Some studies specified that a combination of both structured and unstructured SDOH data were contained in EHRs (12,31,32).…”

“…No health system had a fully automated collection process, although automation was relevant to collection processes through the use of digital devices for gathering patient information. While this review did not specifically focus on practices beyond collection, our collected articles addressed health care staff's preference for free-text notes over questionnaires (12), as well as the possibility for Natural Language Processing (NLP) algorithms to efficiently and reliably identify SDOH data (29,32,34). One study revealed that data extracted from clinical notes were more comprehensive than data extracted from screening questionnaires (32).…”