AUTOMATION AND RISK MITIGATION IN HEALTHCARE CLAIMS: POLICY AND COMPLIANCE IMPLICATIONS

Abstract

This study addresses a persistent and consequential gap in the healthcare operations literature: although organizations have rapidly adopted digital and automated claims-processing technologies, empirical evidence directly linking specific automation configurations to measurable compliance and risk-mitigation outcomes remains limited. The purpose of this investigation was to quantify how automation maturity within contemporary cloud and enterprise claims environments relates to denials performance, audit exposure, and payment-integrity stability. Using a quantitative, cross-sectional, multi-case design, the study analysed 214 organizations spanning payer, provider, and third-party administrator settings, each representing heterogeneous cloud-native and enterprise claims platforms. Core variables included Automation Maturity, Compliance Posture, Policy Governance Quality, Data Protection Controls, and Risk Mitigation Outcomes, operationalized through standardized composites and objective metrics such as adjusted denial rate, first-pass resolution, audit exceptions, and payment-integrity flags. The analytic strategy incorporated reliability and dimensional validation, multivariable regression, and pre-specified mediation and moderation models, supported by robustness procedures including fractional logit and negative binomial estimators for bounded and count outcomes. Headline findings demonstrate that higher automation maturity was strongly associated with improved risk-mitigation performance (β = .31, p < .001), corresponding to a 2.4-percentage-point reduction in adjusted denial rate and a 2.6-percentage-point improvement in first-pass resolution between the lowest and highest maturity terciles. Compliance Posture operated as a significant partial mediator (a = .45; b = .22; indirect effect = .10), indicating that more mature automation environments tend to reinforce procedural and regulatory discipline. Policy Governance Quality further amplified the effectiveness of automation (interaction β = .14), and organizations with higher maturity exhibited substantially fewer audit exceptions (IRR = 0.86). Taken together, these results clarify the operational mechanisms through which automation delivers measurable value in cloud and enterprise claims ecosystems. For leaders overseeing digital claims transformation, the evidence suggests that automation alone is insufficient; instead, the most reliable reductions in denial friction, audit exposure, and payment-integrity risk emerge when automation is paired with strong compliance routines, explicit governance of business rules and machine-assisted review logic, and disciplined data-protection controls. These organizational capabilities appear to function synergistically, producing more consistent, transparent, and defensible outcomes at scale.