Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 1, 2019
Date Accepted: Dec 19, 2019
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
A Systematic Review of Economic Impact Studies of Artificial Intelligence in Healthcare
ABSTRACT
Background:
Positive economic impact is a key decision factor for or against the investment in an artificial intelligence (AI) solution in the healthcare industry. It is most relevant for the care provider, insurer as well as the pharmaceutical and medical technology sector. Although the broad economic impact of digital health solutions in general has been assessed many times in literature, the economic impact of specifically 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 if 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:
Only very few publications thoroughly addressed economic impact assessment, and the economic assessment quality of according AI publications shows severe methodological deficits. Specifically, only six out of 66 publications could be included in the 2nd step of the analysis based on the inclusion criteria. Out of these six studies, no study 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 in favor or against implementing AI technology in healthcare.
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