Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

TLDR

Machine learning promises objective synthesis of EHR data but can introduce biases such as missing data, misclassification, and socioeconomic disparities that may worsen existing health inequities. The paper aims to identify biases in EHR‑based ML decision support and suggest remedies for overreliance, data bias, and lack of clinical relevance. The authors review bias sources in EHR‑derived ML algorithms and propose strategies to mitigate overreliance, data bias, and insufficient clinical information.

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.