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A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2—Data From Nonwearables

Published:August 18, 2022DOI:https://doi.org/10.1016/j.jval.2022.07.011

      Highlights

      • Health data became available in a higher volume and speed in health economics and outcomes research (HEOR), with the advancement of generating and storing the data. Advanced machine learning (ML) techniques have become easier than before, helping researchers answer questions that heretofore were beyond the capabilities of traditional modeling approaches. There has been a rapid increase of ML applications in numerous research areas, but little has been studied in HEOR.
      • We provide a high-level overview of how ML has been applied with nonwearable health data (eg, electronic medical records or administrative claims). Despite the enthusiasm around the diverse roles that ML can potentially play in HEOR, the applications were mainly focused on predicting clinical outcomes, mostly using electronic medical records data, with the aim of supporting providers’ clinical practice. There has been relatively limited use of administrative claims data with the aim of predicting economic outcomes.
      • By understanding how and when ML can be applied to nonwearable health data in HEOR, researchers in this field can better use health data at hand, generating new real-world evidence. Our review also shows remaining opportunities and room for improvement for broader use of ML in HEOR, including predicting economic outcomes by leveraging claims data, which can help inform the cost-effectiveness of interventions.

      Abstract

      Objectives

      Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

      Methods

      We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.

      Results

      We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).

      Conclusions

      The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

      Keywords

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