JMIR Publications

ABSTRACT

Background:

Considerable effort is devoted to development of artificial intelligence, including machine learning-based predictive analytics (MLPA), for use in health care settings. 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 MLPA in health care. However, little is known about the types of MLPA health care products available on the market today or what their stated goals are.

Objective:

To better characterize available products, we identified and characterized claims about products currently in use in U.S. 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 that met our inclusion and exclusion criteria. We used content analysis to generate MLPA product categories and to 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 five categories of predictions made by MLPA products based on the 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 analysis of the specific ethical and regulatory challenges arising from the use of MLPA to improve healthcare efficiency.