Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer
Overview
Authors
Affiliations
Purpose: Patients with metastatic cancer benefit from advance care planning (ACP) conversations. We aimed to improve ACP using a computer model to select high-risk patients, with shorter predicted survival, for conversations with providers and lay care coaches. Outcomes included ACP documentation frequency and end-of-life quality measures.
Methods: In this study of a quality improvement initiative, providers in four medical oncology clinics received Serious Illness Care Program training. Two clinics (thoracic/genitourinary) participated in an intervention, and two (cutaneous/sarcoma) served as controls. ACP conversations were documented in a centralized form in the electronic medical record. In the intervention, providers and care coaches received weekly e-mails highlighting upcoming clinic patients with < 2 year computer-predicted survival and no prior prognosis documentation. Care coaches contacted these patients for an ACP conversation (excluding prognosis). Providers were asked to discuss and document prognosis.
Results: In the four clinics, 4,968 clinic visits by 1,251 patients met inclusion criteria (metastatic cancer with no prognosis previously documented). In their first visit, 28% of patients were high-risk (< 2 year predicted survival). Preintervention, 3% of both intervention and control clinic patients had ACP documentation during a visit. By intervention end (February 2021), 35% of intervention clinic patients had ACP documentation compared with 3% of control clinic patients. Providers' prognosis documentation rate also increased in intervention clinics after the intervention (2%-27% in intervention clinics, < .0001; 0%-1% in control clinics). End-of-life care intensity was similar in intervention versus control clinics, but patients with ≥ 1 provider ACP edit met fewer high-intensity care measures ( = .04).
Conclusion: Combining a computer prognosis model with care coaches increased ACP documentation.
Comparison of 1-year mortality predictions from vendor-supplied academic model for cancer patients.
Gensheimer M, Lu J, Ramchandran K PeerJ. 2025; 13:e18958.
PMID: 39959833 PMC: 11827575. DOI: 10.7717/peerj.18958.
Artificial intelligence across oncology specialties: current applications and emerging tools.
Kang J, Lafata K, Kim E, Yao C, Lin F, Rattay T BMJ Oncol. 2025; 3(1):e000134.
PMID: 39886165 PMC: 11203066. DOI: 10.1136/bmjonc-2023-000134.
Brown C, Khan S, Parekh T, Muir A, Sudore R J Intensive Care Med. 2024; :8850666241280892.
PMID: 39247992 PMC: 11890205. DOI: 10.1177/08850666241280892.
Equity in Using Artificial Intelligence Mortality Predictions to Target Goals of Care Documentation.
Piscitello G, Rogal S, Schell J, Schenker Y, Arnold R J Gen Intern Med. 2024; 39(15):3001-3008.
PMID: 38858343 PMC: 11576666. DOI: 10.1007/s11606-024-08849-w.
Bitterman D, Gensheimer M, Jaffray D, Pryma D, Jiang S, Morin O JCO Clin Cancer Inform. 2023; 7:e2300136.
PMID: 38055914 PMC: 10703125. DOI: 10.1200/CCI.23.00136.