Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance.

Clicks: 282
ID: 104191
2020
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Abstract
Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.
Reference Key
ho2020ensuringbulletin Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ho, Calvin W L;Ali, Joseph;Caals, Karel;
Journal bulletin of the world health organization
Year 2020
DOI
10.2471/BLT.19.234732
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