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Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis

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Abstract

Purpose

To investigate the roles, challenges, and implications of using patient-reported outcome measures (PROMs) in predicting the risk of hospital readmissions.

Methods

We systematically searched four bibliometric databases for peer-reviewed studies published in English between 1 January 2000 and 15 June 2023 and used validated PROMs to predict readmission risks for adult populations. Reported studies were analysed and narratively synthesised in accordance with the CHARMS and PRISMA guidelines.

Results

Of the 2858 abstracts reviewed, 23 studies met predefined eligibility criteria, representing diverse geographic regions and medical specialties. Among those, 19 identified the positive contributions of PROMs in predicting readmission risks. Seven studies utilised generic PROMs exclusively, eleven used generic and condition-specific PROMs, while 5 focussed solely on condition-specific PROMs. Logistic regression was the most used modelling approach, with 13 studies aiming at predicting 30-day all-cause readmission risks. The c-statistic, ranging from 0.54 to 0.84, was reported in 22/23 studies as a measure of model discrimination. Nine studies reported model calibration in addition to c-statistic. Thirteen studies detailed their approaches to dealing with missing data.

Conclusion

Our study highlights the potential of PROMs to enhance predictive accuracy in readmission models, while acknowledging the diversity in data collection methods, readmission definitions, and model evaluation approaches. Recognizing that PROMs serve various purposes beyond readmission reduction, our study supports routine data collection and strategic integration of PROMs in healthcare practices to improve patient outcomes. To facilitate comparative analysis and broaden the use of PROMs in the prediction framework, it is imperative to consider the methodological aspects involved.

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Data availability

The data supporting this systematic review are derived from studies publicly available in the literature, which are cited in the references section; detailed search strategies are provided as supplementary material.

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Acknowledgements

We would like to thank Ursula Ellis who supported the literature search process by providing invaluable feedback on our search strategy. Maggie Yu would like to acknowledge the support provided by the Canadian Institutes of Health Research (CIHR) through the Canada Graduate Scholarship—Master’s program, which supports Maggie’s academic training.

Funding

The authors received no specific funding in preparation of this manuscript.

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Authors and Affiliations

  1. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada

    Maggie Yu & Nick Bansback

  2. Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada

    Mark Harrison

  3. Centre for Advancing Health Outcomes, University of British Columbia, Vancouver, BC, Canada

    Maggie Yu, Mark Harrison & Nick Bansback

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Concept and design: MY, NB. Data curation: MY, NB. Data analysis: MY, MH, NB. Methodology: MY, MH, NB. Validation: MY, MH, NB. Writing (first draft): MY. Writing (review and editing): MY, MH, NB. Project administration: MY, NB. Supervision: MH, NB.

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Correspondence to Nick Bansback.

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Nick Bansback is a member of the EuroQol group.

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Yu, M., Harrison, M. & Bansback, N. Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis. Qual Life Res (2024). https://doi.org/10.1007/s11136-024-03638-8

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