Validating Administrative Data Markers for Elder Abuse Identification via Chart Review in a National Health System

This is the authors' abstract. We don't add key points for this paper.

Background Elder abuse (EA) is a serious public health concern that is dramatically under‐documented in electronic health records (EHRs). Although structured EHR markers for EA are commonly used in research to identify EA cases in large cohorts due to their accessibility, their validity for capturing clinical EA concerns is unknown. Participants and Setting Of 3,724,639 Veterans aged ≥ 60 years with an outpatient Veterans Health Administration (VHA) encounter in 2022, 8161 (0.2%) had an EA‐related ICD‐10 code and/or a structured social work consult marker for “abuse/neglect” recorded in the EHR that year. From the 7382 with complete race/ethnicity data, we randomly sampled 105 veterans for review, intentionally oversampling women and racial/ethnic minority groups. Methods We conducted a retrospective chart review, systematically abstracting clinical notes within ±180 days of the structured EA marker to assess the positive predictive value (PPV) with 95% confidence intervals (CIs) against a reference standard of documented active clinical EA concerns in free text notes. Descriptive statistics were used to characterize the sample and documentation patterns. Results Only 14 of 105 veterans (PPV [95% CI] 13.3% [7.5–21.4]) had free‐text documentation of active clinical EA concern: 9 of 77 (11.7% [5.5–21.0]) with a social work consult marker, 4 of 27 (14.8% [4.2–33.7]) with an ICD‐10 code, and 1 of 1 (100% [2.5–100.0]) with both. Among the 91 veterans without documented concern, 20 had documentation of historical abuse only (e.g., military sexual trauma), while the remainder had no clear explanation for the administrative marker. Conclusions Structured EA data markers in the VHA EHR rarely corresponded to free‐text documentation of active clinical EA concern, raising important questions about their validity as proxies for EA identification. These findings underscore the need for developing standardized, clinician‐friendly approaches to EA documentation in EHRs.