Author
Abstract
The AI-powered physician-insurance data mapping platform represents a transformative solution for healthcare revenue cycle challenges, specifically addressing the persistent problem of network-related claim denials. By combining natural language processing for insurance contract interpretation, FHIR-compliant integration with electronic health records, and machine learning for discrepancy resolution, the platform creates a comprehensive verification ecosystem that validates physician participation in insurance networks with unprecedented accuracy. Key innovations include predictive prior authorization capabilities that identify requirements early in the patient journey and patient-facing transparency tools that provide real-time coverage verification and personalized cost estimates. Implementation outcomes demonstrate substantial improvements across financial performance metrics, operational efficiency, workforce optimization, and patient satisfaction dimensions. The solution's impact extends beyond immediate financial benefits to enhance clinical workflow efficiency and patient experience through proactive financial communication. Future development pathways focus on integrating social determinants of health data to optimize coverage opportunities, implementing blockchain-based verification audit trails for dispute resolution, and expanding to real-time multi-payer claim adjudication. The case illustrates how thoughtfully designed artificial intelligence applications can simultaneously address financial sustainability objectives and patient-centered care principles in modern healthcare delivery.
Suggested Citation
Sai Krishna Sandilya Bapatla, 2025.
"AI-Powered Physician-Insurance Data Mapping: A Case Study in Reducing Revenue Leakage,"
Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(7), pages 550-559, June.
Handle:
RePEc:alk:jcsts0:v:7:y:2025:i:7:id:10318
DOI: 10.32996/jcsts.2025.7.7.61
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:alk:jcsts0:v:7:y:2025:i:7:id:10318. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.