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A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2 - Data from Non-Wearables

Speaker(s)

Lee W1, Schwartz N2, Bansal A2, Khor S2, Hammarlund N1, Basu A2, Devine B2
1University of Washington, SEATTLE, WA, USA, 2University of Washington, Seattle, WA, USA

Presentation Documents

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 through March 2021, and randomly chose 20% of the identified studies for the sake of manageability. Studies that included at least one HEOR-related MeSH term and applied an ML technique. 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%). While electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (e.g., random forests, 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.

Code

MSR57

Topic

Methodological & Statistical Research

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics

Disease

No Additional Disease & Conditions/Specialized Treatment Areas