Prognostic machine learning models can help identify patients in need of advance care planning (ACP) conversations. However, additional work is needed to understand which factors guide physicians’ comfort with and prioritization of ACP conversations, even after such nudges. In this exploratory secondary analysis of a cluster randomized trial, we examine the relationship between characteristics of internal medicine physicians (eg, training background and practice patterns) and likelihood of ACP conversation following notification by a mortality risk prediction machine learning model.
WrightAAZhangBRayA, et al.Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA. 2008;300(14):1665-1673. doi:10.1001/jama.300.14.1665
2.
DeteringKMHancockADReadeMCSilvesterW. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. Br Med J. 2010;340:c1345. doi:10.1136/bmj.c1345
3.
LundSRichardsonAMayC. Barriers to advance care planning at the end of life: an explanatory systematic review of implementation studies. PLoS One. 2015;10(2):e0116629. doi:10.1371/journal.pone.0116629
4.
RubingerLGazendamAEkhtiariSBhandariM. Machine learning and artificial intelligence in research and healthcare. Injury. 2023;54:S69-S73. doi:10.1016/j.injury.2022.01.046
5.
ChiSKimSReuterM, et al.Advanced care planning for hospitalized patients following clinician notification of patient mortality by a machine learning algorithm. JAMA Netw Open. 2023;6(4):e238795. doi:10.1001/jamanetworkopen.2023.8795
6.
ManzCRParikhRBSmallDS, et al.Effect of integrating machine learning mortality estimates with behavioral nudges to Clinicians on serious illness conversations among patients with cancer: a stepped-wedge cluster randomized clinical trial. JAMA Oncol. 2020;6(12):e204759. doi:10.1001/jamaoncol.2020.4759
7.
SchellJOSchenkerYPiscitelloG, et al.Implementing a serious illness risk prediction model: impact on goals of care documentation. J Pain Symptom Manag. 2023;66(6):603-610. doi:10.1016/j.jpainsymman.2023.07.015
8.
BrajerNCozziBGaoM, et al.Prospective and external evaluation of a machine learning model to predict In-Hospital mortality of adults at time of admission. JAMA Netw Open. 2020;3(2):e1920733. doi:10.1001/jamanetworkopen.2019.20733
9.
WalterJMaJEPlattA, et al.Quality improvement study using a machine learning mortality risk prediction model notification system on advance care planning in high-risk patients brown hospital medicine. J Brown Hosp Med. 2024;3(3):120907. doi:10.56305/001c.120907
10.
MaJKilpatrickKWDavenportCA, et al.Impact of prognostic notifications on inpatient advance care planning: a cluster randomized trial. J Pain Symptom Manag. 2025;70:602-612. doi:10.1016/j.jpainsymman.2025.08.013
11.
LiEHFerrellWKlaimanT, et al.Impact of behavioral nudges on the quality of serious illness conversations among patients with cancer: secondary analysis of a randomized controlled trial. JCO Oncol Pract. 2022;18(4):e495-e503. doi:10.1200/OP.21.00024
12.
AndersonWGKoolsSLyndonA. Dancing around death: hospitalist-patient communication about serious illness. Qual Health Res. 2013;23(1):3-13. doi:10.1177/1049732312461728
13.
SacksOAKnutzenKERudolphMAMohanDBarnatoAE. Advance care planning and professional satisfaction from “Doing the Right Thing”: interviews with hospitalist chiefs. J Pain Symptom Manag. 2020;60(4):e31-e34. doi:10.1016/j.jpainsymman.2020.06.024
14.
ZajoncRB. Attitudinal effects of mere exposure. J Personality Soc Psychol. 1968;9(2, Pt.2):1-27. doi:10.1037/h0025848
15.
TsugawaYKatoHJhaAK, et al.Characteristics of physicians who adopted medicare's new advance care planning codes in the first year. J Gen Intern Med. 2020;35(6):1914-1916. doi:10.1007/s11606-019-05368-x
16.
LiJQianYJungHY. Heterogeneity in billing for medicare’s advance care planning codes among physicians and advanced practitioners in 2016 and 2017. J Gen Intern Med. 2021;36(11):3601-3603. doi:10.1007/s11606-020-06553-z
17.
HsuNCHuangCCHsuCHWangTDShengWH. Does hospitalist care enhance palliative care and reduce aggressive treatments for terminally ill patients? A propensity score-matched study. Cancers (Basel). 2023;15(15):15. doi:10.3390/cancers15153976
18.
KimKHongMKimG. Regional variations in engagement in advance care planning among racially and ethnically diverse older adults. Innov Aging. 2019;3(Supplement_1):S274-S275. doi:10.1093/geroni/igz038.1017
