Interpretable AI-Enabled Framework for Proactive Revenue Cycle Management: An Evidence-Informed Integrative Review

Revenue cycle management (RCM) is a data-intensive healthcare function spanning patient access, eligibility, authorization, documentation, coding, claims, denials and payment. Current artificial intelligence (AI) evidence is fragmented across adjacent administrative and clinical workflows, making it difficult to translate individual models into a coherent, accountable RCM strategy. This study develops an interpretable AI-enabled framework for proactive RCM using a structured evidence-informed integrative review. The synthesis mapped evidence across administrative health records, claims analytics, automated ICD coding, AI-supported documentation, prior authorization, denial-appeal triage, explainable AI and trustworthy healthcare AI. The evidence base included systematic reviews covering 70 administrative-health-record studies, 137 healthcare-claims fraud studies, 16 AI prior-authorization studies, 73 automated ICD-coding studies using MIMIC-focused literature, and 519 healthcare large-language-model evaluations. The synthesis indicates that AI is most mature as an augmentation layer for documentation and coding, risk scoring, denial prioritization and anomaly detection, whereas direct evidence for end-to-end RCM financial improvement remains limited. The proposed framework integrates four layers—data and interoperability, predictive intelligence, workflow orchestration, and governance/human oversight—and formalizes intervention prioritization using predicted risk, financial impact and intervention cost. The framework provides a practical architecture for human-led, AI-augmented RCM and defines technical, operational, financial and governance outcomes for future prospective validation.Keywords: artificial intelligence; machine learning; revenue cycle management; health informatics; medical coding; claims management; denial prediction; clinical documentation; explainable AI; healthcare finance