SynthesisJournal of patient-reported outcomes2026
Machine learning in the development and application of patient-reported outcome measures (PROMs) for surgical patients: a systematic review.
Synthesis in Journal of patient-reported outcomes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) and machine learning (ML) are increasingly integrated into healthcare, offering potential advancements in patient-reported outcome measures (PROMs) for surgical populations. Improved PROMs can enhance patient-centered care by accurately capturing patient experiences with minimal burden.
objectiveIn the context of surgery, where recovery trajectories vary widely, this study aims to systematically review the use of AI and ML in the development, application, and prediction capabilities of PROMs in surgical populations, with a focus on psychometric properties and the predictive accuracy of post-surgical outcomes.
methodsA comprehensive search of the PubMed database was conducted from inception until August 2024. Studies were included if they utilized AI or ML in the development, application, or predicting PROMs for surgical patients. Methodological quality was assessed using COSMIN and PROBAST tools, depending on study design. A qualitative synthesis of findings was performed.
resultsTwenty-two studies met the inclusion criteria, with 19 rated as high quality. Six studies focused on developing computer adaptive tests (CAT) PROMs, seven on evaluating psychometric properties, and five on ML for post-surgical outcome prediction. CAT PROMs showed comparable measurement accuracy to traditional PROMs, good to excellent construct validity, and significantly reduced patient burden by reducing the length of questionnaires. ML algorithms, such as logistic regression, random forests, extreme gradient boosting, and neural networks, achieved similar predictive accuracy for post-surgical outcomes, with no single model demonstrating consistent superiority.
conclusionsAI and ML have the potential to improve PROM utilization in surgical care by enhancing efficiency and personalization while maintaining data quality. Clinicians can use AI-driven PROMs to reduce patient burden and integrate ML models for accurate post-surgical outcome prediction, thereby optimizing patient-centered care.
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