Abstract 16364: Physician Documentation Behaviors in Electronic Health Records as a Potential Source of Noise for Early Detection of Heart Failure

Abstract

Introduction: Electronic health records (EHRs) are a potentially rich source of data for developing predictive models for early detection of heart failure (HF). But, EHR documentation can vary both because of patient health and variation in clinical practice and behavior among physicians. Hypothesis: The “noise” contributed by variable physician behaviors, such as differences in the frequency and detail of documentation, could potentially confound predictive models for detection of HF. In this study, we characterized the documentation behaviors of primary care physicians (PCPs) in an effort to better understand this potential source of noise. Methods: We used longitudinal EHR data of a stratified random sample of 5,187 patients who were: 1) ≥50 years of age, and without a history of HF. PCPs (n=144) were identified and paired with the patients that they had treated for a minimum of 6 months. We derived 28 measures to characterize PCP behaviors - documentation frequencies of assertions and denials of selected Framingham HF signs and symptoms (FHFSS) in office visit encounter notes adjusted for patient comorbidities. Hierarchical clustering analyses (HCA) were performed on PCP documentation behaviors. Results: Based on HCA analyses, PCPs were clustered into 3 groups with distinct documentation behaviors. Group 1 PCPs (n=63) documented 10 out of 15 assertions, and 11 out of 13 denials of FHFSS significantly more frequently than Group 3 (n=20); while Group 2 PCPs (n=61) have significantly more frequent denial documentation behaviors than the other two (see Figure 1). No significant differences were found among patients’ chronic, episodic and cardio metabolic chronic disease counts (comorbidities) in each of the groups (p<0.05). Conclusions: This study identified PCP groups with distinct documentation behaviors unrelated to patient complexity. This source of noise and potential confounder should be taken into account for predictive modeling.