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

Journal

Journal of the National Cancer Institute
ISSN: 1460-2105
Titre abrégé: J Natl Cancer Inst
Pays: United States
ID NLM: 7503089

Informations de publication

Date de publication:
30 Sep 2024
Historique:
received: 03 05 2024
revised: 01 07 2024
accepted: 23 09 2024
medline: 30 9 2024
pubmed: 30 9 2024
entrez: 30 9 2024
Statut: aheadofprint

Résumé

Advance care planning/serious illness conversations can help clinicians understand patients' values and preferences. There are limited data on how to increase these conversations, and their effect 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 cancer patients at high risk of short-term mortality. We conducted a cluster-randomized stepped wedge study on the physician level. Oncologists entered the intervention condition in a random order over six months. Adult patients with metastatic cancer were included. Patients with <2 year computer-predicted survival and no prognosis documentation were classified as high-priority for serious illness conversations. In the intervention condition, providers received automated weekly emails highlighting high-priority patients and were asked to document prognosis for them. Care coaches reached out to these patients to conduct the remainder of the conversation. The primary endpoint was proportion of visits with prognosis documentation within 14 days. 6,372 visits in 1,825 patients were included in the primary analysis. The proportion of visits with prognosis documentation within 14 days was higher in the intervention condition than control condition: 2.9% vs 1.1% (adjusted odds ratio 4.3, p < .0001). 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 < .0001). In high-priority visits, advance care planning documentation rate in intervention/control visits was 24.2% vs 4.0%. The intervention increased documented conversations, with contributions by both providers and care coaches.

Sections du résumé

BACKGROUND BACKGROUND
Advance care planning/serious illness conversations can help clinicians understand patients' values and preferences. There are limited data on how to increase these conversations, and their effect 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 cancer patients at high risk of short-term mortality.
METHODS METHODS
We conducted a cluster-randomized stepped wedge study on the physician level. Oncologists entered the intervention condition in a random order over six months. Adult patients with metastatic cancer were included. Patients with <2 year computer-predicted survival and no prognosis documentation were classified as high-priority for serious illness conversations. In the intervention condition, providers received automated weekly emails highlighting high-priority patients and were asked to document prognosis for them. Care coaches reached out to these patients to conduct the remainder of the conversation. The primary endpoint was proportion of visits with prognosis documentation within 14 days.
RESULTS RESULTS
6,372 visits in 1,825 patients were included in the primary analysis. The proportion of visits with prognosis documentation within 14 days was higher in the intervention condition than control condition: 2.9% vs 1.1% (adjusted odds ratio 4.3, p < .0001). 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 < .0001). In high-priority visits, advance care planning documentation rate in intervention/control visits was 24.2% vs 4.0%.
CONCLUSION CONCLUSIONS
The intervention increased documented conversations, with contributions by both providers and care coaches.

Identifiants

DOI: 10.1093/jnci/djae243 PMID: 39348179
pubmed: 39348179
pii: 7796562
doi: 10.1093/jnci/djae243
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Auteurs

Michael F Gensheimer (MF)

Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.

Winifred Teuteberg (W)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Manali I Patel (MI)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Divya Gupta (D)

Northwestern University, Chicago, IL, USA.

Mahjabin Noroozi (M)

Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.

Xi Ling (X)

Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.

Touran Fardeen (T)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Briththa Seevaratnam (B)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Ying Lu (Y)

Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.

Nina Alves (N)

Stanford Health Care, Palo Alto, CA, USA.

Brian Rogers (B)

Stanford Health Care, Palo Alto, CA, USA.

Mary Khay Asuncion (MK)

Stanford Health Care, Palo Alto, CA, USA.

Jan Denofrio (J)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Jennifer Hansen (J)

Stanford Health Care, Palo Alto, CA, USA.

Nigam H Shah (NH)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA.

Thomas Chen (T)

Stanford Health Care, Palo Alto, CA, USA.

Elwyn Cabebe (E)

Stanford Health Care, Palo Alto, CA, USA.

Douglas W Blayney (DW)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

A Dimitrios Colevas (AD)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Kavitha Ramchandran (K)

Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.

Classifications MeSH