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J Gen Intern Med. 2019 Sep;34(9):1841-1847. doi: 10.1007/s11606-019-05169-2. Epub 2019 Jul 16.

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

Author information

1
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.
2
Palliative and Advanced Illness Research (PAIR) Center at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. katherine.courtright@pennmedicine.upenn.edu.
3
Predictive Healthcare at Penn Medicine, University of Pennsylvania, Philadelphia, PA, USA.
4
Center for Evidence-based Practice to Clinical Effectiveness and Quality Improvement (CEQI) at Penn Medicine, University of Pennsylvania, Philadelphia, PA, USA.
5
Department of Medicine at the Perelman School of Medicine, University of Pennsylvania, 303 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
6
Palliative and Advanced Illness Research (PAIR) Center at the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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 < .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).

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.

KEYWORDS:

palliative care; prediction model; triggers

PMID:
31313110
PMCID:
PMC6712114
[Available on 2020-09-01]
DOI:
10.1007/s11606-019-05169-2

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