Evidence map›Paper›PMID 42539303›Full record

ArticlePalliative medicine reports

Using a Machine-Learning Algorithm to Identify Palliative Care Needs in a Primary Care Population: A Pilot Study.

Mairead M Bartley, Jordan C Karow, Rachel M Wiste, Ethan P Heinzen, Patrick M Wilson, Gabriel O Demuth, Shusaku W Asai, Curtis B Storlie, Rachel D Havyer

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Article in Palliative medicine reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Mairead M BartleyDivision of Community Internal Medicine, Geriatrics, and Palliative Care, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0003-1142-2797
Jordan C KarowDivision of Community Internal Medicine, Geriatrics, and Palliative Care, Mayo Clinic, Rochester, Minnesota, USA.
Rachel M WisteDivision of Community Internal Medicine, Geriatrics, and Palliative Care, Mayo Clinic, Rochester, Minnesota, USA.
Ethan P HeinzenRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0009-0000-6281-2934
Patrick M WilsonRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA.
Gabriel O DemuthRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA.
Shusaku W AsaiRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA.
Curtis B StorlieRobert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA.
Rachel D HavyerDivision of Community Internal Medicine, Geriatrics, and Palliative Care, Mayo Clinic, Rochester, Minnesota, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Context: Early palliative care can improve end-of-life outcomes, but referrals to palliative care specialists can be delayed in the primary care setting. Objective: This pilot study assessed the effect of a machine-learning algorithm on time to palliative care in a primary care population. Methods: Patients (aged ≥18 years) were eligible if they were empaneled with a primary care provider (PCP) from July 20, 2020, through May 30, 2021. The algorithm evaluated their health records and presented patients who were predicted to have the greatest need for palliative care. Records were then reviewed by palliative care specialists, and patients were randomized in a stepped-wedge fashion to have a referral notification sent to their PCPs if unmet palliative care needs were verified. Time-to-event outcomes were evaluated with Poisson regression models. Results: Of the 127,080 patients evaluated, 934 had their health records presented for review. Some patients were repeatedly presented by the algorithm (total presentations: 1592). In the intervention arm, PCPs were prompted to order a palliative care consultation for 142 patients. The time to 0.1% of the population receiving a palliative care consultation was 60.9 days for the intervention arm versus 71.8 days for the control arm (probability of a shorter time with the intervention, 0.88). Conclusion: A machine-learning algorithm to identify palliative care needs was successfully integrated into a primary care practice. More work is needed to improve the workflow.

Indexed as

information sciencepalliative medicineprimary health care

Identifiers

PMID42539303
PMCPMC13422726

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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 OpenQuestion graph.