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Impact of Prognostic Notifications on Inpatient Advance Care Planning: A Cluster Randomized Trial.

Publication ,  Journal Article
Ma, JE; Kilpatrick, KW; Davenport, CA; Walter, J; Acker, Y; Setji, N; Olsen, MK; Patel, M; Gao, M; Gardner, M; Gollon, J; Sendak, M; Balu, S ...
Published in: J Pain Symptom Manage
December 2025

BACKGROUND: A poor prognosis is an important trigger for advance care planning (ACP) conversations, but clinicians often overestimate prognosis. OBJECTIVE: To determine whether ACP note documentation increases by notifying inpatient physicians that a patient is at high risk of mortality. METHODS: A pragmatic cluster randomized trial at an academic medical center from September 2021 to December 2022 randomized attending physicians on the inpatient medicine team. An email and page notification was sent to physicians randomized to intervention group for admitted patients at high risk of 30-day and 6-month death based on a machine learning model. The notification recommended to have and document an ACP conversation in the electronic health record (EHR). The primary outcome was documentation of an ACP conversation during hospital admission by the randomized physician. The secondary outcome was ACP note documented by any clinician during the hospital admission. Healthcare utilization outcomes included length of stay and discharge to hospice. RESULTS: Seventy randomized physicians (35 in each group) cared for 314 unique patients (138 control and 176 intervention) at high risk of mortality. Patients of physicians randomized to the intervention group were more likely to have a documented ACP conversation by the randomized physician compared to the control group (34.7% vs. 19.6%; OR 2.04; 95% CI 1.16-3.59). There was no significant change in ACP documentation by any clinician (52.8% intervention vs. 42.8% control group, OR 1.31; 95% CI 0.81-2.13). CONCLUSIONS: Machine learning mortality model notifications can motivate physicians to document ACP conversations during a hospitalization.

Duke Scholars

Published In

J Pain Symptom Manage

DOI

EISSN

1873-6513

Publication Date

December 2025

Volume

70

Issue

6

Start / End Page

602 / 612

Location

United States

Related Subject Headings

  • Prognosis
  • Middle Aged
  • Male
  • Machine Learning
  • Length of Stay
  • Inpatients
  • Humans
  • Hospitalization
  • Female
  • Electronic Health Records
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ma, J. E., Kilpatrick, K. W., Davenport, C. A., Walter, J., Acker, Y., Setji, N., … Casarett, D. (2025). Impact of Prognostic Notifications on Inpatient Advance Care Planning: A Cluster Randomized Trial. J Pain Symptom Manage, 70(6), 602–612. https://doi.org/10.1016/j.jpainsymman.2025.08.013
Ma, Jessica E., Kayla W. Kilpatrick, Clemontina A. Davenport, Jonathan Walter, Yvonne Acker, Noppon Setji, Maren K. Olsen, et al. “Impact of Prognostic Notifications on Inpatient Advance Care Planning: A Cluster Randomized Trial.J Pain Symptom Manage 70, no. 6 (December 2025): 602–12. https://doi.org/10.1016/j.jpainsymman.2025.08.013.
Ma JE, Kilpatrick KW, Davenport CA, Walter J, Acker Y, Setji N, et al. Impact of Prognostic Notifications on Inpatient Advance Care Planning: A Cluster Randomized Trial. J Pain Symptom Manage. 2025 Dec;70(6):602–12.
Ma, Jessica E., et al. “Impact of Prognostic Notifications on Inpatient Advance Care Planning: A Cluster Randomized Trial.J Pain Symptom Manage, vol. 70, no. 6, Dec. 2025, pp. 602–12. Pubmed, doi:10.1016/j.jpainsymman.2025.08.013.
Ma JE, Kilpatrick KW, Davenport CA, Walter J, Acker Y, Setji N, Olsen MK, Patel M, Gao M, Gardner M, Gollon J, Sendak M, Balu S, Casarett D. Impact of Prognostic Notifications on Inpatient Advance Care Planning: A Cluster Randomized Trial. J Pain Symptom Manage. 2025 Dec;70(6):602–612.
Journal cover image

Published In

J Pain Symptom Manage

DOI

EISSN

1873-6513

Publication Date

December 2025

Volume

70

Issue

6

Start / End Page

602 / 612

Location

United States

Related Subject Headings

  • Prognosis
  • Middle Aged
  • Male
  • Machine Learning
  • Length of Stay
  • Inpatients
  • Humans
  • Hospitalization
  • Female
  • Electronic Health Records