Data Quality Management in Healthcare: Challenges, Solutions, and Best Practices

Abstract

Healthcare is one of the most data-intensive sectors, with patient records, clinical trials, public health surveillance, and research generating vast amounts of information that directly impact patient safety, clinical decision-making, and health policy. However, healthcare data quality remains a persistent challenge, with studies documenting widespread issues across multiple dimensions including accuracy, completeness, consistency, and timeliness. This review examines data quality management in healthcare, analyzing the unique challenges of healthcare data, including the complexity of clinical information, the diversity of data sources and formats, the sensitivity of patient data, and the high stakes of quality failures. We review the evidence on data quality in healthcare, drawing on studies from multiple settings and countries, and analyze the causes of quality problems, including training gaps, process weaknesses, and governance deficiencies. We examine solutions that have been implemented, including WHO's Data Quality Review toolkit, electronic health record validation, and quality improvement initiatives. We propose a comprehensive framework for healthcare data quality management that addresses technical, organizational, and ethical dimensions.