For years, payment integrity was built around simple assumptions: errors can be fixed after payment. In today’s environment of rising medical costs and accelerating claims volume, that approach isn’t sustainable. Outdated post-pay recovery forces health plans and providers to rework claims that could have been proactively validated and corrected – wasting time, draining resources, and eroding trust.
The scale of the challenge is immense. One-third of payment integrity resources are still consumed by administrative work, even as the industry contends with persistent waste and abuse totaling over $200 billion in annual losses. Vendor sprawl and slow, manual legacy operations compound this burden, driving delays, high contingency fees, and repeated documentation requests that frustrate providers, even when claims are ultimately paid.
Forward-thinking plans are now “shifting left.” By applying clinical and coding validation earlier in the payment lifecycle, they can prevent errors before they occur, improve accuracy, reduce administrative friction on both sides, and strengthen payer-provider collaboration. While retrospective review remains essential for complex claims and audits, integrating prospective and retrospective review approaches drives efficiency, transparency, and faster, more consistent payments.
Payment integrity is shifting left: From reactive to proactive
In practical terms, shifting left means moving payment integrity levers earlier in the cost continuum: applying clinical and coding validation earlier, before payment, rather than leaning only on burdensome, time-consuming post-payment workflows. Innovative health plans see the value in shifting left for myriad reasons, including rising costs, pressure on medical loss ratios, and the need to scale their expert in-house teams without adding headcount. Pre-pay data mining also plays a role in this shift, serving as a process to apply rules for identifying payer issues that can be fixed without additional documentation.
The distinction between pre-pay and post-pay is important. Pre-pay approaches prevent overpayments and rework earlier in the payment lifecycle, enabling plans to act on intervention opportunities sooner and pay providers faster and more accurately, especially with scalable automation. Post-pay recovery, while generating savings on paper, often creates provider friction through delayed payments, repeated requests for medical records, limited transparency, and missed opportunities to educate providers for future claims.
What’s more, health plans that operationalize a shift-left approach through clinical intelligence and responsible, domain-specific AI can see meaningful improvements in reimbursement accuracy, operational efficiency, and provider-network collaboration. When applied at the point of claim validation, domain-specific AI, trained on reimbursement methodologies, can proactively help identify claims in which billed diagnoses or services are not supported by objective clinical evidence or are misaligned with established policies and criteria, before payment is issued. Rather than relying on keyword detection, this intelligence evaluates clinical documentation in context, enabling plans to route only the highest-risk claims for targeted review and provider outreach before payment.
Building provider trust with AI purpose-built for payment integrity
Payment integrity often struggles with inefficient, fragmented workflows that create friction for both plans and providers. Vendor dependency, lack of transparency, unclear rationales, and inconsistent audit logic turn payment teams to heavily manual work and ultimately escalation. These inefficiencies drive delays, drain already limited resources, and make it difficult to identify and address root causes, leaving reimbursement unpredictable and recurring errors unresolved.
Modern payment integrity that harnesses clinically intelligent, domain-specific AI and automation looks different. By using AI that understands the nuances of medicine and healthcare payments – and has built-in oversight to ensure trust, traceability, and transparency – payment integrity can automatically surface root-cause patterns and defensible decision logic that support payer-provider collaboration and clearer, earlier interventions.
Beyond enabling faster, more proactive interventions, clinical intelligence allows in-house teams to dramatically scale complex validation operations, expanding the capacity of reviewers and coders without adding headcount, while reducing bottlenecks and focusing human expertise where it matters most.
Connecting payment integrity to pre-service intelligence with clinically trained AI
Shifting left works by leveraging upstream clinical insights–captured during processes like prior authorization and inpatient clinical review–to inform downstream payment integrity. Utilization management and payment integrity share the same goals: ensuring appropriate care, supporting timely, accurate payments, and controlling the cost of care. But these systems have historically lived in siloes: rich clinical data like patient health history, physician notes, authorization determinations, and coverage policies lived in one lane, while claims review and payment integrity operated in another. The result is duplicated clinical reviews, delayed interventions and payments, low-yield results, and provider abrasion.
Cost containment should follow the care continuum, not departmental boundaries. By connecting these clinical insights across settings, timelines, and payment models, AI-powered automation can surface gaps, inconsistencies, or missing documentation before they trigger payment errors or technical denials.
The highest-fidelity signal for payment accuracy is created before a claim is even submitted. Using this signal and clinical intelligence today – through upstream validation and transparent rationales – reduces rework and disputes and supports more consistent, accurate, and faster payments, setting the stage for better provider trust and collaboration downstream.
AI adoption and clinical validation
As providers also adopt AI-powered tools to better support clinical documentation, coding, and appeals workflows, the coding landscape grows increasingly complex, which introduces the growing industry trend of “AI ping-pong” between health plans and providers. This shift is showing up across utilization management and payment integrity, creating a real need for teams to be able to efficiently and precisely evaluate AI-driven evidence for clinical and payment decisions.
While AI can improve documentation quality and capture previously missed information, it can also inadvertently drive higher coding intensity and more AI-generated appeals. For example, comparable conditions are increasingly billed at higher levels over time, and AI-driven documentation tools may capture more comprehensive–but sometimes extraneous–information during patient encounters. Left unaddressed, this can create friction, increase costs, and introduce additional administrative and regulatory complexity.
Across the healthcare continuum, health plans need a new approach to validate coding intensity and clinical support. Precision, domain-specific AI can evaluate evidence quality in context, rather than relying solely on keyword detection from general-purpose AI models. At the same time, keeping expert reviewers and coders in the loop ensures human judgment is applied where needed. When paired with clear policies and strong clinical governance, this approach supports defensible decisions, improves alignment with providers, and reduces unnecessary friction.
Shifting left in payment integrity is building newfound trust, efficiency, and transparency
Shifting left offers health plans a practical, high-impact way to address rising costs and administrative burdens. By using AI-powered intelligence to shift clinical and coding validation earlier in the healthcare continuum, and unlocking upstream clinical data to inform downstream payment workflows, organizations can proactively prevent errors, reduce downstream churn and friction, and support more predictable, accurate, and timely payments.
In practice, this means building a single source of truth consistent across care and payment lifecycles. Teams unify data and clinical context across utilization management and payment integrity workflows, sharing clinical rationale, documentation standards, and decision logic across the continuum, so fewer claims fall into contradictory workflows that trigger denials, audits, or appeals.
It also requires stronger pre-pay workflows. Prompt-pay expectations leave little room for ambiguity, which is why modern programs push for clearer documentation and tighter workflow discipline, not just faster automation. Once those foundations are in place, patterns identified early, like recurring authorization-to-claim misalignments, can inform provider education, optimize workflows, and streamline complex coding and validation processes.
The goal is a payment integrity program that scales efficiently, reduces unnecessary provider friction, and strengthens trust across the healthcare ecosystem. Providers can spend less time navigating administrative hurdles and more time on complex cases, while health plans achieve more consistent and accurate cost containment operations.
Photo: Sakchai Vongsasiripat, Getty Images
Monique Pierce has 25+ years of experience building and leading payment integrity teams. She has worked for both start-up regional health plans including Oxford Health and Devoted Health, as well as for large national health plans like UnitedHealthcare. Monique also has extensive leadership experience in the vendor partner space, with Optum, SCIO Health Analytics, and EXL. She is known for her vision, ability to execute, and passion for metrics. In her role at Cohere Health, Monique drives strategies to improve payment integrity by designing and developing solutions that intelligently bridge the gap between prior authorization and claim reconciliation.
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