Evidence map›Paper›PMID 39348179›Full record

Trial reportJournal of the National Cancer Institute2025

Automated patient selection and care coaches to increase advance care planning for patients with cancer.

Michael F Gensheimer, Winifred Teuteberg, Manali I Patel, Divya Gupta, Mahjabin Noroozi, Xi Ling, Touran Fardeen, Briththa Seevaratnam, Ying Lu, Nina Alves and 10 more

Abstract readMulticenter StudyRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of the National Cancer Institute, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

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

Corrections and comments

5 · Who and what money

Authors and funding

20 authors.

Michael F GensheimerDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-4897-3843
Winifred TeutebergDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Manali I PatelDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-9943-5762
Divya GuptaDepartment of Medicine, Northwestern University, Chicago, IL, USA.
Mahjabin NorooziDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Xi LingDepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-8357-7267
Touran FardeenDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Briththa SeevaratnamDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Ying LuDepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-7698-8962
Nina AlvesStanford Health Care, Palo Alto, CA, USA.
Brian RogersStanford Health Care, Palo Alto, CA, USA.
Mary Khay AsuncionStanford Health Care, Palo Alto, CA, USA.
Jan DeNofrioDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Jennifer HansenStanford Health Care, Palo Alto, CA, USA.
Nigam H ShahDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-9385-7158
Thomas ChenStanford Health Care, Palo Alto, CA, USA.
Elwyn CabebeStanford Health Care, Palo Alto, CA, USA.
Douglas W BlayneyDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Alexander D ColevasDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Kavitha RamchandranDepartment of Medicine, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-9033-819X

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Vittorio Sebastiano · 2007 to 2026
$71.4M
NCI NIH HHS P30 CA124435
6 · The paper itself

Abstract

backgroundAdvance care planning and serious illness conversations can help clinicians understand patients' values and preferences. Data are limited on how to increase the number of these conversations and what their effects are on care patterns. We hypothesized that using a machine learning survival model to select patients for serious illness conversations, along with trained care coaches to conduct the conversations, would increase uptake in patients with cancer at high risk of short-term mortality.

methodsWe conducted a cluster-randomized, stepped-wedge study on the physician level. Oncologists entered the intervention condition in a random order over 6 months. Adult patients with metastatic cancer were included. Patients with a less than 2-year computer-predicted survival and no prognosis documentation were classified as high priority for serious illness conversations. In the intervention condition, clinicians received automated weekly emails highlighting high-priority patients and were asked to document prognoses for them. Care coaches contacted these patients to conduct the remainder of the conversation. The primary endpoint was the proportion of visits with prognosis documentation within 14 days.

resultsWe included 6372 visits with 1825 patients in the primary analysis. The proportion of visits with prognosis documentation within 14 days was higher in the intervention condition than in the control condition: 2.9% vs 1.1% (adjusted odds ratio = 4.3, P < .001). The proportion of visits with advance care planning documentation was also higher in the intervention condition: 7.7% vs 1.8% (adjusted odds ratio = 14.2, P < .001). For high-priority visits, the advance care planning documentation rate in intervention visits was 24.2% and in control visits was 4.0%.

conclusionThe intervention increased documented conversations, with contributions by both clinicians and care coaches.

Indexed as

Advance Care PlanningMentoringNeoplasmsPatient SelectionAdultAgedCommunicationFemaleHumansMachine LearningMaleMiddle AgedPrognosis

Identifiers

PMID39348179
PMCPMC12990145

What OpenQuestion holds

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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 OpenQuestion graph.