Electronic Health Record Mortality Prediction Model for Targeted Palliative Care Among Hospitalized Medical Patients: a Pilot Quasi-experimental Study

Abstract

Background: Development of electronic health record (EHR) prediction models to improve palliative care delivery is on the rise, yet the clinical impact of such models has not been evaluated. Objective: To assess the clinical impact of triggering palliative care using an EHR prediction model. Design: Pilot prospective before-after study on the general medical wards at an urban academic medical center. Participants: Adults with a predicted probability of 6-month mortality of ≥ 0.3. Intervention: Triggered (with opt-out) palliative care consult on hospital day 2. Main Measures: Frequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS). Key Results: The control and intervention periods included 8 weeks each and 138 admissions and 134 admissions, respectively. Characteristics between the groups were similar, with a mean (standard deviation) risk of 6-month mortality of 0.5 (0.2). Seventy-seven (57%) triggered consults were accepted by the primary team and 8 consults were requested per usual care during the intervention period. Compared to historical controls, consultation increased by 74% (22 [16%] vs 85 [63%], P < 0.3) patients (48/1004 [5] vs 33/798 [4]; P =.48). Conclusions: Targeting hospital-based palliative care using an EHR mortality prediction model is a clinically promising approach to improve the quality of care among seriously ill medical patients. More evidence is needed to determine the generalizability of this approach and its impact on patient- and caregiver-reported outcomes.