• Katherine R Courtright, Corey Chivers, Michael Becker, Susan H Regli, Linnea C Pepper, Michael E Draugelis, and Nina R O'Connor.
• Department of Medicine at the Perelman School of Medicine, University of Pennsylvania, 303 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA. katherine.courtright@pennmedicine.upenn.edu.
• J Gen Intern Med. 2019 Sep 1; 34 (9): 1841-1847.

BackgroundDevelopment 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.ObjectiveTo assess the clinical impact of triggering palliative care using an EHR prediction model.DesignPilot prospective before-after study on the general medical wards at an urban academic medical center.ParticipantsAdults with a predicted probability of 6-month mortality of ≥ 0.3.InterventionTriggered (with opt-out) palliative care consult on hospital day 2.Main MeasuresFrequencies of consults, advance care planning (ACP) documentation, home palliative care and hospice referrals, code status changes, and pre-consult length of stay (LOS).Key ResultsThe 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 < .001), median (interquartile range) pre-consult LOS decreased by 1.4 days (2.6 [1.1, 6.2] vs 1.2 [0.8, 2.7], P = .02), ACP documentation increased by 38% (23 [17%] vs 37 [28%], P = .03), and home palliative care referrals increased by 61% (9 [7%] vs 23 [17%], P = .01). There were no differences between the control and intervention groups in hospice referrals (14 [10] vs 22 [16], P = .13), code status changes (42 [30] vs 39 [29]; P = .81), or consult requests for lower risk (< 0.3) patients (48/1004 [5] vs 33/798 [4]; P = .48).ConclusionsTargeting 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.

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