A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making - Michal Pruski; Simone Willis; Kathleen Withers (BMC Medical Informatics and Decision Making)
Abstract Purpose This review summarises the studies which combined Patient Reported Outcome Measures (PROMs) and Machine Learning statistical computational techniques, to predict patient post-intervention outcomes. The aim of the project was to inform those working in value-based healthcare how Machine Learning can be used with PROMs to inform clinical practice. Methods A systematic search strategy was developed and run in six databases. The records were reviewed by a reviewer if they matched the review scope, and these decisions were scrutinised by a second reviewer. Results 82 records pertaining to 73 studies were identified. The review highlights the breadth of PROMs tools investigated, and the wide variety of Machine Learning techniques utilised across the studies. The findings suggest that there has been some success in predicting post-intervention patient outcomes. Nevertheless, there is no clear best performing Machine Learning approach to analyse this data, and while baseline PROMs scores are often a key predictor of post-intervention scores, this cannot always be assumed to be the case. Moreover, even when studies looked at similar conditions and patient groups, often different Machine Learning techniques performed best in each study. Conclusion This review highlights that there is a potential for PROMs and Machine Learning methodology to predict patient post-intervention outcomes, but that best performing models from other previous studies cannot simply be adopted in new clinical contexts.