Prediction tools to prioritise hospitalised adult patients at risk of drug related problems: An umbrella review

Daria S Gutteridge, Dona Babu, Jacquelina Stasinopoulos, Annabel Calder, Michael Bakker, Sally Marotti, Bronwin Patrickson, Karen Macolino, Craig Martin, Lijun Zhao, Niranjan Bidargaddi and Janet K Sluggett

PLOS Digital Health, 2026, vol. 5, issue 8, 1-25

Abstract: Risk prediction tools assist pharmacists to identify and prioritise hospitalised patients at risk of drug-related problems (DRPs) requiring clinical review. This umbrella review identifies existing risk prediction tools, summarises their performance, and highlights factors important for prioritising hospitalised adults at risk of DRPs for clinical review. A systematic search of three bibliographic databases from January 2010 to March 2024, identified systematic reviews that qualitatively or quantitatively examined patient- and/or medication-related factors in risk prediction or prioritisation models or tools for adult inpatients at risk of DRPs. Extracted data included citation, review type, number and date range of primary studies, population, setting, outcomes, and key risk factors. Risk prediction tools were summarised by country, sample size, performance metrics (discrimination, sensitivity, calibration), number of risk factors, and external validation. All extracted data was checked by a second reviewer. The standardized Joanna Briggs Institute critical appraisal instruments were used to assess the quality of eligible studies. Twenty systematic reviews met our inclusion criteria, of which 18 were of sufficient quality for synthesis. Selected articles covered 171 unique primary studies and 32 risk prediction tools, of which 12 demonstrated acceptable or good discrimination of which four also had good calibration. Two tools showed the highest potential for future clinical use based on their performance metrics and external validation data but would benefit from further validation in diverse populations. Commonly reported risk factors for DRPs were psycholeptic medications (reported in 13 systematic reviews), age, comorbidities and reduced renal function (n = 12 reviews). Based on the reviews included, no tools appear to be yet suitable for routine clinical practice due to limited external validation, calibration and unclear risk factor definitions; however, some show promise for further development and testing. Data on existing risk prediction tools and risk factors provides a foundation for refining or automating current models, for instance via machine learning approaches that can iteratively incorporate risk factors to ease use and improve prediction of DRPs within specific clinical settings.Author summary: Medications are essential for treating illness, but they can also be a source of harm, particularly in hospitals and during care transitions. Pharmacists can intervene to reduce risk but often have limited time and need better ways to identify who is most at risk of drug-related problems and requires closer clinical review. This study identified and evaluated existing research on tools designed to predict the risk of medication-related harm in hospitalised adults. We reviewed published summaries of previous studies to understand how these tools were developed, how well they performed, and which patient or medication-related factors were commonly applied. We found many different tools; however, based on the included reviews, none are currently ready for routine use in everyday clinical care. However, several showed promise, especially those that had been tested in more than one patient population. The commonly reported risk factors for drug-related problems were age, multiple medical conditions, reduced kidney function, and use of specific high-risk medications (i.e., psycholeptics). Our findings provide a strong foundation for improving existing tools or developing new automated approaches, including those using artificial intelligence (AI), to better support health professionals and improve patient safety in hospital settings.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001583

DOI: 10.1371/journal.pdig.0001583

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