AI-Driven Framework for Quantifying Interoperability Debt and Predicting Operational Resilience Fragility in Healthcare Payer Data Architectures
DOI:
https://doi.org/10.47941/ijhs.3840Keywords:
Interoperability Debt, Interoperability Debt Index (IDI), Operational Resilience, Healthcare Payer Systems, Healthcare Data Architecture, Artificial Intelligence (AI)Abstract
Purpose: This study develops and evaluates an artificial intelligence–driven framework for quantifying interoperability debt (ID) within healthcare payer ecosystems and examines its predictive utility as a leading structural indicator of operational resilience degradation. Interoperability debt is conceptualized as the cumulative architectural burden created by hybrid legacy‑plus‑API integration models, brittle point‑to‑point interfaces, and manual reconciliation workflows that remain latent during routine operations but manifest acutely under stress.
Methodology: A cross‑sectional predictive modeling design was applied to simulated and historical transaction patterns across 1,240 enterprise‑grade payer pipelines. The study integrates (a) architectural complexity metrics, (b) hybrid HL7 FHIR JSON and X12 EDI schema interactions, and (c) an XGBoost gradient‑boosted anomaly detection model to identify latent structural friction. The Interoperability Debt Index (IDI) is mathematically formalized as a weighted composite of integration complexity, manual workaround density, legacy exposure, and schema volatility.
Findings: The IDI-enhanced model demonstrated materially higher predictive performance than baseline models. High‑debt pipelines (IDI ≥ 0.75) exhibited a 42% increase in cascading transaction failures and severe latency degradation (≥1,200 ms) under 500% stress‑load simulations. These findings demonstrate that interoperability debt is a statistically robust leading indicator of architectural fragility, outperforming traditional rule‑based monitoring systems.
Unique Contributions to Theory, Practice, and Policy: This study advances operational resilience theory by shifting the analytical paradigm from retrospective downtime monitoring to proactive, structural precursor modeling through a mathematically grounded KPI. For healthcare payer executives, the framework provides an actionable, quantifiable modernization KPI that directly connects backend architectural remediation to measurable reductions in systemic operational risk. For regulatory bodies, it introduces an objective mechanism to evaluate whether federal interoperability mandates are supported by robust, resilient backend data architectures rather than superficial, "check-the-box" API compliance.
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Copyright (c) 2026 Abhilasha Abad
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