JMIR Publications

ABSTRACT

Background:

Positive economic impact is a key decision factor in making the case for or against investing in an artificial intelligence (AI) solution in the healthcare industry. It is most relevant for the care provider and insurer as well as for the pharmaceutical and medical technology sector. Although the broad economic impact of digital health solutions in general has been assessed many times in literature and also the benefit for patients and society has been analyzed, the specific economic impact of AI in healthcare has been addressed only sporadically.

Objective:

To systematically review and summarize cost-effectiveness studies dedicated to AI in healthcare, and to assess whether they meet established quality criteria.

Methods:

In a first step, the quality criteria for economic impact studies were defined based on established and adapted criteria schemes for cost impact assessments. In a second step, a systematic literature review based on qualitative and quantitative inclusion and exclusion criteria was conducted to identify the relevant publications for an in-depth analysis of economic impact assessment. In a final step, the quality of the identified economic impact studies was evaluated based on the defined quality criteria for cost-effectiveness studies.

Results:

Very few publications have thoroughly addressed economic impact assessment and the economic assessment quality of according AI publications shows severe methodological deficits. Only six out of 66 publications could be included in the second step of the analysis based on the inclusion criteria. Out of these six studies, none comprised a methodologically complete cost impact analysis. There are two areas for improvement: First, initial investment and operational costs for the AI infrastructure and service need to be included. Second, alternatives to achieve similar impact must be evaluated to provide a comprehensive comparison.

Conclusions:

The systematic literature analysis proved that existing impact assessments show methodological deficits, and that upcoming evaluations require more comprehensive economic analyses to enable economic decisions for or against implementing AI technology in healthcare.