Practices that show potential to achieve desirable public health outcomes in a specific real-life setting and produce early results that are consistent with the objectives of the activities and thus indicate effectiveness.

The Potential for Bias in Machine Learning and Opportunities for Health Insurers to Address It

Gervasi, S. S., Chen, I. Y., Smith-McLallen, A., Sontag, D., Obermeyer, Z., Vennera, M., Chawla, R.

Release Date:

Peer Review Study

Data Collection and Analysis
Healthcare Access and Quality
Tools Included
Outside U.S.

Data Collection and Reporting

The authors present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. The authors suggest mechanisms for identifying and dealing with bias, and discuss challenges and opportunities to increase fairness through analytics in the health insurance industry.

Resource Details

Outcomes of Interest

Reduction of Health Disparities

Priority Population(s)

Setting(s) of Implementation

Geographic Area of Implementation

Implementation Period