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Special Communication
November 2018

Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

Author Affiliations
  • 1Division of Rheumatology, Department of Medicine, University of California, San Francisco
  • 2Center for Population Health Sciences, Stanford University, Palo Alto, California
  • 3Veterans Affairs Medical Center, San Francisco, California
JAMA Intern Med. 2018;178(11):1544-1547. doi:10.1001/jamainternmed.2018.3763
Abstract

A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning–based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.

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