Background: In healthcare, analysing patient-reported outcome measures (PROMs) on an aggregated level can improve and regulate healthcare for specific patient populations (meso level). This mixed-methods systematic review aimed to summarize and describe the effectiveness of quality improvement methods based on aggregated PROMs. Additionally, it aimed to describe barriers, facilitators and lessons learned when using these quality improvement methods. Methods: A mixed-methods systematic review was conducted. Embase, MEDLINE, CINAHL and the Cochrane Library were searched for studies that described, implemented or evaluated a quality improvement method based on aggregated PROMs in the curative hospital setting. Quality assessment was conducted via the Mixed Methods Appraisal Tool. Quantitative data were synthesized into a narrative summary of the characteristics and findings. For the qualitative analysis, a thematic synthesis was conducted. Results: From 2360 unique search records, 13 quantitative and three qualitative studies were included. Four quality improvement methods were identified: benchmarking, plan-do-study-act cycle, dashboards and internal statistical analysis. Five studies reported on the effectiveness of the use of aggregated PROMs, of which four identified no effect and one a positive effect. The qualitative analysis identified the following themes for facilitators and barriers: (1) conceptual (i.e. stakeholders, subjectivity of PROMs, aligning PROMs with clinical data, PROMs versus patient-reported experience measures [PREMs]); (2a) methodological—data collection (i.e. choice, timing, response rate and focus); (2b) methodological—data processing (i.e. representativeness, responsibility, case-mix control, interpretation); (3) practical (i.e. resources). Conclusion: The results showed little to no effect of quality improvement methods based on aggregated PROMs, but more empirical research is needed to investigate different quality improvement methods. A shared stakeholder vision, selection of PROMs, timing of measurement and feedback, information on interpretation of data, reduction of missing data, and resources for data collection and feedback infrastructure are important to consider when implementing and evaluating quality improvement methods in future research.
Bibliographical noteFunding Information:
We would like to thank our consortium members P.J. van der Wees, M.M. van Muilekom, I.L. Abma, L. Haverman, C.T.B. Ahaus, H.J. van Elten, M. Leusder, H.F. Lingsma, N. van Leeuwen, E.S. van Hoorn and T.S. Reindersma for their contribution. We would also like to thank W.M. Bramer, biomedical information specialist from the Erasmus MC.
© 2022, The Author(s).