ArticleJournal of the American Medical Informatics Association : JAMIA2023
Performance drift in a mortality prediction algorithm among patients with cancer during the SARS-CoV-2 pandemic.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Explainability in context: calibrating appropriate trust and reliance in artificial intelligence.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Performance Drift in a Nationally Deployed Population Health Risk Algorithm in the US Veterans Health Administration.JAMA health forum · 2025Article
- Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.PLoS medicine · 2025Observational
- Importance of model governance in clinical AI models: case study on the relevance of data drift detection.BMJ digital health & AI · 2025Article
- COVID-19 and Rates of Cancer Diagnosis in the US.JAMA network open · 2024Article
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Abstract
Sudden changes in health care utilization during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic may have impacted the performance of clinical predictive models that were trained prior to the pandemic. In this study, we evaluated the performance over time of a machine learning, electronic health record-based mortality prediction algorithm currently used in clinical practice to identify patients with cancer who may benefit from early advance care planning conversations. We show that during the pandemic period, algorithm identification of high-risk patients had a substantial and sustained decline. Decreases in laboratory utilization during the peak of the pandemic may have contributed to drift. Calibration and overall discrimination did not markedly decline during the pandemic. This argues for careful attention to the performance and retraining of predictive algorithms that use inputs from the pandemic period.
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