D, Varkala

Background: Inefficiencies in the revenue cycle, such as elevated claim denial rates, extended duration of accounts receivable (A/R), and excessive administrative rework are significant financial challenges for acute-care hospitals. Existing HL7 v2 billing workflows are very manual and error-prone which leads to operational inefficiencies. The solution lies at the intersection of artificial intelligence (AI) and HL7 Fast Healthcare Interoperability Resources (FHIR), enabling automated claim validation and denial prediction. Methods: Retrospective comparative observational study in acute-care hospitals using Epic Professional Billing systems. Revenue cycle performance metrics were obtained for a 12-month pre-intervention period (manual HL7 v2 workflow) followed by a 12-month post-intervention period after AI-enabled FHIR deployment. The primary outcomes were first-pass claim denial rate, accounts receivable length of time to pay, and claim rework. Statistical analysis was conducted by using the chi-square and independent t-tests, where p < 0.05 was considered significant. Results: We analysed 48,392 claims. AI-driven international financial reporting standards (IFRS) validation resulted in significantly improved patient first-pass denial rates which fell 14.4% to 7.4% (p < 0.001), account-receivable trailing days reduced from 41.7 to 28.4 days (p < 0.001) and decrease of claim reworking rate from 18.1% to 8.5% (p < 0.001). They also reduced processing time, and the manual review burden. Conclusion: FHIR, coupled with AI capabilities, streamlines the documentation and authorization processes within the revenue cycle while minimizing denials-based losses through a fully integrated infrastructure creating tangible operational and financial advantages compared to traditional manual pathways. Keywords: Artificial intelligence; HL7 FHIR; revenue cycle management; claim denial reduction; interoperability; healthcare finance; predictive analytics; Epic Professional Billing