A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1—Data From Wearable Devices

• Published in: Value in health Vol. 26; no. 2; pp. 292 - 299
• Main Authors: Lee, Woojung, Schwartz, Naomi, Bansal, Aasthaa, Khor, Sara, Hammarlund, Noah, Basu, Anirban, Devine, Beth
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2023
• Subjects: Economics, Medical; health economics and outcomes research; Humans; Internal Medicine; Machine Learning; Mental Health; Outcome Assessment, Health Care; Public Health; wearable data; Wearable Electronic Devices; machine learning; health economics and outcomes research; wearable data
• ISSN: 1098-3015; 1524-4733; 1524-4733
• DOI: 10.1016/j.jval.2022.08.005

With the emerging use of machine learning (ML) techniques, there has been particular interest in using wearable data for health economics and outcomes research (HEOR). We aimed to understand the emerging patterns of how ML has been applied to wearable data in HEOR. We identified studies published in PubMed between January 2016 and March 2021. Studies that included at least 1 HEOR-related Medical Subject Headings term, applied an ML, and used wearable data were eligible for inclusion. Two reviewers abstracted information including ML application types and data on which ML was applied and analyzed them using descriptive analyses. A total of 148 studies were identified from PubMed, among which 32 studies met the inclusion criteria. There has been an increase over time in the number of ML studies using wearable data. ML has been more frequently used for monitoring events in real time (78%) than to predict future events (22%). There has been a wide range of outcomes examined, ranging from general physical or mental health (24%) to more disease-specific outcomes (eg, disease incidence [19%] and progression [13%]) and treatment-related outcomes (eg, treatment adherence [9%] and outcomes [9%]). Data for ML models were more often derived from wearable devices with specific medical purposes (60%) than those without (40%). There has been a wide range of applications of ML to wearable data. Both medical and nonmedical wearable devices have been used as a data source, showing the potential for providing rich data for ML studies in HEOR. •The widespread use of wearable devices resulted in a constant flow of individual data related to one’s daily activities and certain biometrics. Machine learning (ML) has gained increasing attention in managing large amounts of complex wearable data. Health data collected from wearable devices, benefited by the advanced analyzing technologies such as ML, will likely generate meaningful knowledge in health economics and outcomes research (HEOR).•Our study describes how ML has been applied to data collected from wearable devices in HEOR, which has not been studied yet. ML has not only been applied to monitor general health status but also to monitor or forecast outcomes specific to certain types of disease or treatment. Most studies using devices that were not necessarily designed for medical purposes (eg, smartwatches) have started to be published in relatively recent years.•Our findings suggest a potential for the application of wearable data, coupled with ML techniques, to be expanded to disease- or treatment-specific research in HEOR. The detailed description of the emerging patterns of ML applications with wearable data can address uncertainties in how and when to use ML with wearable data among HEOR researchers, potentially generating additional real-world evidence that can inform treatment and reimbursement decisions.