Abstract
The field of patient-centred healthcare has, during recent years, adopted machine learning and data science techniques to support clinical decision making and improve patient outcomes. We conduct a literature review with the aim of summarising the existing methodologies that apply machine learning methods on patient-reported outcome measures datasets for predicting clinical outcomes to support further research and development within the field. We identify 15 articles published within the last decade that employ machine learning methods at various stages of exploiting datasets consisting of patient-reported outcome measures for predicting clinical outcomes, presenting promising research and demonstrating the utility of patient-reported outcome measures data for developmental research, personalised treatment and precision medicine with the help of machine learning-based decision-support systems. Furthermore, we identify and discuss the gaps and challenges, such as inconsistency in reporting the results across different articles, use of different evaluation metrics, legal aspects of using the data, and data unavailability, among others, which can potentially be addressed in future studies.
Highlights
There is growing interest and support for the utility and importance of patientreported outcome measures (PROMs) in clinical care
This literature review identifies scientific articles that focus on the application of machine learning methods in the process of predicting short or long-term clinical outcome(s) using PROMs data
Our review identified 15 articles focusing on the utilisation of PROMs for predicting outcomes by leveraging the analytical abilities of machine learning methods
Summary
There is growing interest and support for the utility and importance of patientreported outcome measures (PROMs) in clinical care. PROMs are commonly defined as reports or questionnaires completed by patients to measure their view on their functional well-being and health status [1]. PROMs may capture the patient’s perspective on both social, physical, and mental well-being. Shifting the focus from disease-specific factors towards the patient’s perspective may provide a useful basis for shared medical decision making between a clinician and a patient [2,3]. Recent evidence indicates that shared decision making has a positive impact on the quality of decision making, satisfaction with treatment, and patient–provider experience [4]. Well-informed patients agreeing upon their course of treatment with their caregiver have better outcome and satisfaction [5]
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