Enhancing Patient Care through Improved Provider Data Quality: An AI - Driven Data Solution

US healthcare systems spend 4.5 trillion annually, based on CMS Data published in the 2022 report [1]. Patient Satisfaction is a crucial aspect of the care journey. Finding the right provider who is 'participating' a. k. a. 'in - network' with the member's insurance plan ensures the right services and benefits to the member. The latest generation of members uses mobile technologies such as 'Apps' to find providers participating in member insurance plans. This means that provider systems and payers should maintain the most accurate and correct information about providers, their specialties, office locations, and hours of operation and accept new patients if they are serving as primary care physicians (PCPs). CMS review found that 52.20% of the provider directory locations listed had at least one inaccuracy. Types of inaccuracies included: 1) The provider was not at the location listed, 2) The phone number was incorrect, or 3) The provider was not accepting new patients when the directory indicated they were [2] i. e., More than half of provider data available to members are either inaccurate or incomplete. The healthcare industry increasingly recognizes the critical role of data quality in enhancing patient care. This scholarly article proposes an innovative framework for improving provider data quality through integrating Artificial Intelligence (AI) and data analytics. By leveraging AI - driven capabilities, healthcare organizations can identify inaccuracies, inconsistencies, and gaps in provider data more efficiently, enabling the cleansing and standardization of data at scale. This approach streamlines provider data management and significantly impacts patient care by ensuring accurate provider information is available for clinical decision - making, care coordination, and patient engagement strategies.