Improved medical record prioritization with AI-enhanced audits
Challenge
A leading managed healthcare company faced strained provider relationships and inefficient claim selections due to a one-size-fitsall audit approach, resulting in low medical record (MR) retrieval rates and ineffective resource allocation. With a diverse network of over 30,000 healthcare providers ranging from large hospital chains to independent clinics, the client required innovative solutions to enhance efficiency while maintaining transparency and trust.
The client’s audit process relied on 1,000+ rules and algorithms to identify problematic claims for clinical audits. While this approach uncovered billing errors such as upcoding, unbundling, and authorization mismatches, it also caused significant issues:
01
Strained provider relationships:
Aggressive and frequent audits led to provider pushbacks and resource-intensive disputes and appeals. Providers were deterred from engaging with auditors, affecting long-term partnerships.
02
Ineffective resource allocation:
The client’s uniform audit approach failed to account for differences in provider size, claim volumes, and performance. High-risk claims were under-audited, while low-risk claims consumed valuable resources, leading to low MR retrieval rates (below 60%) and hit rates (13%).
03
Operational inefficiency:
The absence of segmentation in provider audits resulted in prolonged negotiations, technical denials, and delayed care delivery, significantly reducing operational efficiency and recovery rates.
The client required an optimized, data-driven solution to balance audit rigor while maintaining constructive payer-provider relationships and maximizing saving opportunities.
Solution
To address these challenges, EXL designed and deployed a sophisticated machine learning (ML) framework to dynamically optimize claim selection and tailor audit strategies. This innovative solution tailored audit strategies by segmenting providers and optimizing the volume and focus of claims based on specific attributes:
Dynamic segmentation:
The model segmented providers based on 50+ parameters, including provider size, claim volumes, performance metrics (e.g., collectability, MR rates, appeals), historical claim quality, and audit performance (findings and recovery rates).
Targeted auditing:
The framework prioritized outliers and high-cost claims, deploying audits more strategically among high-risk providers while reducing unnecessary audits on low-risk entities.
Continuous learning:
Leveraging auditor and subject-matter expert (SME) insights, the system validated and refined its selections through a feedback loop, continuously improving accuracy over time.
Integrated approaches:
EXL seamlessly integrated the ML framework with existing clinical models and error detection algorithms, establishing a multi-layered and holistic strategy for optimized claim identification.
Balance and collaboration:
By proactively managing audit frequency and strategically focusing on high-impact claims, the framework enhanced provider collaboration while safeguarding against financial leakages.
This solution enabled the client to significantly reduce resource waste while fostering trust and cooperation with its diverse provider network and effectively safeguard against financial leakages.
Results
The implementation of EXL’s dynamic claim-selection framework delivered exceptional outcomes across critical performance metrics, all while fostering better provider collaboration:
Enhanced MR receipt rates:
The MR receipt rate increased from 65% to 78%, a remarkable 13% increase, within 12 months, leading to the acquisition of an additional 38,000 medical records. This demonstrated improved provider responsiveness and collaboration.
Significant financial recovery:
Deploying the framework uncovered $16 million in overpayments across two programs, directly contributing to the client’s financial goals.
Improved audit efficiency:
By focusing audit resources on high-risk providers and claims, the client achieved higher operational efficiency and ROI, effectively optimizing recovery efforts while mitigating provider abrasion.
These outcomes underscore the power of combining ML-driven segmentation with EXL’s strategic expertise to deliver targeted, scalable, and high-impact solutions.
EXL’s partnership with this healthcare client exemplifies the effective use of advanced ML to address complex challenges. By tailoring audits to provider-specific attributes and refining claim selections, EXL’s solution not only improved financial recoveries but also strengthened provider relationships, paving the way for sustainable long-term success.
As the client continues to leverage the framework, future updates and enhancements promise to deliver even greater levels of efficiency and transparency. The collaboration between EXL and the healthcare company is a testament to how innovative technology can transform traditional processes into value-focused, results-driven approaches.