How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. (Preprint)

Abstract

Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have actually been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions to increase the likelihood of successful implementation, but recommendations about their use are lacking.

The aim of this study was to develop and apply a framework that positions best-practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments.

The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Hereafter, economic evaluations were used to determine critical thresholds and guide investment decisions.

Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs resulted in an unfavorable cost-effectiveness ratio for potential users. In the intensive care, analytics reduced mortality and per-patient costs when used to identify infections (-0.5%, -€886) and also to improve patient-ventilator interaction (-3%, -€264). Both analytics hold the potential to save money but the return on investment for developers of analytics that identify infections strongly depends on infection rate; a higher rate implies greater cost-savings.

We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.