Evidence map›Paper›PMID 40889581›Full record

Trial reportJournal of pain and symptom management2025

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

Jessica E Ma, Kayla W Kilpatrick, Clemontina A Davenport, Jonathan Walter, Yvonne Acker, Noppon Setji, Maren K Olsen, Mihir Patel, Michael Gao, Matthew Gardner and 4 more

Abstract readRandomized Controlled TrialPragmatic Clinical Trial
In one paragraph

Trial report in Journal of pain and symptom management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Jessica E MaDepartment of Medicine (J.E.M, J.W., N.S., D.C.), Duke University School of Medicine, Durham, North Carolina, USA; Geriatrics and Extended Care (J.M.), Durham VA Health System, Durham, North Carolina, USA. Electronic address: Jessica.ma@duke.edu.
Kayla W KilpatrickDepartment of Biostatistics and Bioinformatics (K.W.K., C.A.D., M.K.O., M.G.), Duke University, Durham, North Carolina, USA.
Clemontina A DavenportDepartment of Biostatistics and Bioinformatics (K.W.K., C.A.D., M.K.O., M.G.), Duke University, Durham, North Carolina, USA.
Jonathan WalterDepartment of Medicine (J.E.M, J.W., N.S., D.C.), Duke University School of Medicine, Durham, North Carolina, USA.
Yvonne AckerPatient Safety and Clinical Quality (Y.A.), Duke University Health System, Durham, North Carolina, USA.
Noppon SetjiDepartment of Medicine (J.E.M, J.W., N.S., D.C.), Duke University School of Medicine, Durham, North Carolina, USA.
Maren K OlsenDepartment of Biostatistics and Bioinformatics (K.W.K., C.A.D., M.K.O., M.G.), Duke University, Durham, North Carolina, USA; Durham Center of Innovation to Accelerate Discovery and Practice Transformation (M.K.O.), Durham VA Health System, Durham North Carolina, USA.
Mihir PatelDuke University School of Medicine (M.P., S.B.), Durham, North Carolina, USA.
Michael GaoDepartment of Biostatistics and Bioinformatics (K.W.K., C.A.D., M.K.O., M.G.), Duke University, Durham, North Carolina, USA; Duke Institute for Health Innovation (M.G., M.G., M.S., S.B.), Duke University, Durham, North Carolina, USA.
Matthew GardnerDuke Institute for Health Innovation (M.G., M.G., M.S., S.B.), Duke University, Durham, North Carolina, USA.
Jamie GollonBusiness Transformation Office (J.G.), Duke University Health System, Durham, North Carolina, USA.
Mark SendakDuke Institute for Health Innovation (M.G., M.G., M.S., S.B.), Duke University, Durham, North Carolina, USA.
Suresh BaluDuke University School of Medicine (M.P., S.B.), Durham, North Carolina, USA; Duke Institute for Health Innovation (M.G., M.G., M.S., S.B.), Duke University, Durham, North Carolina, USA.
David CasarettDepartment of Medicine (J.E.M, J.W., N.S., D.C.), Duke University School of Medicine, Durham, North Carolina, USA.

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
University of Michigan (UM) HEAL Initiative National K12 Clinical Pain Career Development Program (UM-HCPDP)K12NS130673 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Daniel J Clauw, Steven Edward Harte · 2022 to 2026
$12.5M
NCATS NIH HHS UL1 TR002553NIDDK NIH HHS L30 DK140987NINDS NIH HHS K12 NS130673
6 · The paper itself

Abstract

backgroundA poor prognosis is an important trigger for advance care planning (ACP) conversations, but clinicians often overestimate prognosis.

objectiveTo determine whether ACP note documentation increases by notifying inpatient physicians that a patient is at high risk of mortality.

methodsA 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.

resultsSeventy 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).

conclusionsMachine learning mortality model notifications can motivate physicians to document ACP conversations during a hospitalization.

Indexed as

Advance Care PlanningInpatientsAgedDocumentationElectronic Health RecordsFemaleHospitalizationHumansLength of StayMachine LearningMaleMiddle AgedPrognosisAdvance care planninginpatient medicinemachine learning

Identifiers

PMID40889581
PMCPMC12519415

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.