OPTIMIZING HEALTHCARE DATA GOVERNANCE: ENSURING ACCURACY, INTEGRITY, AND ACCESSIBILITY FOR ENHANCED DECISION-MAKING

Abstract

In the era of digital transformation, healthcare organizations are getting dealing with huge volumes of data emanating from different sources such as EHRs, medical devices, patient portals, genomic sequencing, wearable technologies, among others. Handling such data responsibly requires appropriate data governance so that maximum value can be extracted for improvement in clinical and operational decision-making. However, data governance in healthcare also presents specific challenges, such as information silos, inconsistent quality of data, privacy and security, difficulty in complying with regulations, paucity of resources, and resistance to change due to conventional organizational culture. This article delves into best practices for healthcare data governance, focused on key elements: data accuracy, integrity, and accessibility. It considers means of developing strong data governance frameworks that importantly outline roles and responsibilities, define policies, and describe procedures. Data standardization and interoperability are discussed, showing the adoption of industry-recognized standards like HL7 and FHIR in order to attain interoperability through seamless data exchange across different systems.