Detecting the Potential for Bias in Healthcare Data.

Abstract

Bias in healthcare, including systematic errors, prejudice, or assumptions involving patient care, is an important issue which can cause disparities in health outcomes. In this review we focus on information bias, specifically measurement bias. This bias includes systematic errors in collecting, recording, or interpreting healthcare data, hence the crucial role of healthcare professionals, researchers, and policymakers. Measurement bias becomes an issue when sensitive attributes are involved, as these biases can impact public health decisions based on inaccurate data. We used a cross-checking validation process to address these concerns and enhance data quality. We compared patient data from two different sources, from UAMS and a commercial data provider, both relating to the same healthcare event, to verify accuracy and Consistency. Our analysis incorporated essential data quality metrics to ensure the reliability of the findings. These metrics include Completeness, Accuracy, Consistency, and Validity. Cross-checking with these data quality metrics allowed us to detect discrepancies and inconsistencies, as well as the overall reliability and validity of the data. Our study highlights the importance of rigorous validation and data quality measures to minimize bias and ensure accurate, reliable conclusions, and it calls for the active participation of the audience in this endeavor.