The implications of artificial intelligence and machine learning in health financing for achieving universal health coverage

While there is an abundance of publications on the application of artificial intelligence (AI) for different areas in health in general, the topic of AI in health financing has received less attention.

 

Copyright: who.int – “The implications of artificial intelligence and machine learning in health financing for achieving universal health coverage”

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Based on a rapid literature review, this paper provides an overview of the current applications of AI and machine learning (ML) for health financing functions and tasks in research, policy and practice to identify the key issues and implications for achieving universal health coverage.

The paper assesses the type of questions being studied in relation to progress towards UHC. The review presents the broad scope of subjects to which ML approaches are applied, such as the prediction of health expenditure, risk scoring, claims management and fraud detection, identification of households for targeted policies, health needs informed benefit package design, and analysis of the effects of health coverage scheme design on health service utilization.

In the discussion and conclusion, the paper fleshes out the main benefits and the (possible) positive and negative effects on intermediate and final UHC objectives as well as the risks of the application of AI and ML and some of the related regulatory implications. Future topic-and country focused research questions are suggested.

Download the report here.

Original WHO article: www.who.int

Der Beitrag The implications of artificial intelligence and machine learning in health financing for achieving universal health coverage erschien zuerst auf SwissCognitive, World-Leading AI Network.

Source: SwissCognitive