The Impact of Machine Learning Mortality Risk Prediction on Clinician Prognostic Accuracy and Decision Support: A Randomized Vignette Study

Ravi B. Parikh, William J. Ferrell, Anthony Girard, Jenna White, Sophia Fang, Justin E. Bekelman and Marilyn M. Schapira
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Ravi B. Parikh: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
William J. Ferrell: Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Anthony Girard: Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Jenna White: Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Sophia Fang: Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ, USA
Justin E. Bekelman: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Marilyn M. Schapira: Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Medical Decision Making, 2025, vol. 45, issue 6, 690-702

Abstract: Background Machine learning (ML) algorithms may improve the prognosis for serious illnesses such as cancer, identifying patients who may benefit from earlier palliative care (PC) or advance care planning (ACP). We evaluated the impact of various presentation strategies of a hypothetical ML algorithm on clinician prognostic accuracy and decision making. Methods This was a randomized clinical vignette survey study among medical oncologists who treat metastatic non-small-cell lung cancer (mNSCLC). Between March and June 2023, clinicians were shown 3 vignettes of patients presenting with mNSCLC. The vignettes varied by prognostic risk, as defined from the Lung Cancer Prognostic Index (LCPI). Clinicians estimated life expectancy in months and made recommendations about PC and ACP. Clinicians were then shown the same vignette with a hypothetical survival estimate from a black-box ML algorithm; clinicians were randomized to receive the ML prediction using absolute and/or reference-dependent prognostic estimates. The primary outcome was prognostic accuracy relative to the LCPI. Results Among 51 clinicians with complete responses, the median years in practice was 7 (interquartile range 3.5–19), 14 (27.5%) were female, 23 (45.1%) practiced in a community oncology setting, and baseline accuracy was 54.9% (95% confidence interval [CI] 47.0–62.8) across all vignettes. ML presentation improved accuracy (mean change relative to baseline 20.9%, 95% CI 13.9–27.9, P  

Keywords: machine learning; artificial intelligence; prognosis; palliative care; non-small cell lung cancer (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:6:p:690-702

DOI: 10.1177/0272989X251349489

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