A Typology of Existing Machine Learning-Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis. J Med Internet Res 2021 Jun 22;23(6):e26391
Date
06/23/2021Pubmed ID
34156338Pubmed Central ID
PMC8277386DOI
10.2196/26391Scopus ID
2-s2.0-85108803335 (requires institutional sign-in at Scopus site) 18 CitationsAbstract
BACKGROUND: Considerable effort has been devoted to the development of artificial intelligence, including machine learning-based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that hold health care organizations responsible for providing high-quality, cost-effective care. Policy analysts, ethicists, and computer scientists have identified unique ethical and regulatory challenges from the use of MLPA in health care. However, little is known about the types of MLPA health care products available on the market today or their stated goals.
OBJECTIVE: This study aims to better characterize available MLPA health care products, identifying and characterizing claims about products recently or currently in use in US health care settings that are marketed as tools to improve health care efficiency by improving quality of care while reducing costs.
METHODS: We conducted systematic database searches of relevant business news and academic research to identify MLPA products for health care efficiency meeting our inclusion and exclusion criteria. We used content analysis to generate MLPA product categories and characterize the organizations marketing the products.
RESULTS: We identified 106 products and characterized them based on publicly available information in terms of the types of predictions made and the size, type, and clinical training of the leadership of the companies marketing them. We identified 5 categories of predictions made by MLPA products based on publicly available product marketing materials: disease onset and progression, treatment, cost and utilization, admissions and readmissions, and decompensation and adverse events.
CONCLUSIONS: Our findings provide a foundational reference to inform the analysis of specific ethical and regulatory challenges arising from the use of MLPA to improve health care efficiency.
Author List
Nichol AA, Batten JN, Halley MC, Axelrod JK, Sankar PL, Cho MKAuthor
Meghan C. Halley PhD, BA, MPH Assistant Professor in the Institute for Health and Humanity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Artificial IntelligenceDelivery of Health Care
Humans
Machine Learning
Quality of Health Care
