Improving Claims Management, Utilization Review, and Care Coordination

This article by Grayson Miller was published by Healthcare IT Today on May 13, 2026, and is reproduced here. Hilla Fogel, PhD, co-founder and CTO of QuantalX Neuroscience, is one of the experts featured.

It is a well-known and well-discussed fact that all of the best decisions are backed by data. In the payer-provider ecosystem, the big areas that use a lot of data-backed decisions are claims management, utilization review, and care coordination. To get a better understanding of the specifics on data analytics’ role, we reached out to our talented Healthcare IT Today Community to ask — how are organizations using data analytics to improve claims management, utilization review, and care coordination across payer-provider ecosystems? The following are their answers.

Lindsay Porter, VP of Coding and Clinical Solutions at AGS Health

Organizations are using data analytics to bring discipline and predictability to claims management. Instead of reacting to denials, we are studying trends across payers, specialties, and locations to pinpoint breakdowns in documentation, coding, or eligibility before a claim goes out the door. Targeted insights help us address root causes, not just rework claims. Clear performance visibility, like clean claim rates, denial categories, and turnaround times, drives accountability across teams.

Similarly, predictive modeling, AI, and integrated platforms are being used in UR to enable real-time, collaborative assessments of medical necessity, automate prior authorizations, predict appropriateness with high accuracy, reduce unnecessary admissions, and minimize administrative friction through shared data views. For care coordination, platforms aggregate clinical, claims, pharmacy, and SDOH data to risk-stratify patients, predict readmissions/adverse events, identify care gaps, flag high-risk individuals, and support proactive interventions (e.g., outreach programs) and better transitions.

Payers benefit from cost control and total cost of care optimization, while providers gain actionable insights for better outcomes and efficiency. When used strategically, analytics strengthens payer negotiations, reduces avoidable write-offs, and protects margin while accelerating cash flow.

Robert Connely, Global Industry Market Leader, Healthcare at Pega

Data analytics in healthcare has evolved. We no longer just analyze claims data for reporting trends and or flag a potential fraud case. Today, we’re using analytics in real time to do things like examining a claim when it is received, automatically determining the complexity of the claim, the Service Level Agreement in place, and the amount of time an adjudicator would need to complete the work. The analytics then prioritize the high-value claims in the adjudicator’s queue to give them time to complete the work.

These simple, high-value uses of AI are saving tens of millions of dollars every year for payers. The same improvement is also used the same way in workflows like prior authorizations, appeals & grievances, customer services, and many other areas.

Ben Maisono, SVP Strategy at Tendo

Data analytics is increasingly being used to connect the dots between utilization patterns, clinical appropriateness, and care outcomes. On the claims side, predictive analytics can flag high-cost episodes or unusual billing trends earlier, reducing friction and helping payers and providers resolve issues proactively rather than through denials and appeals. For utilization review, organizations are applying analytics to ensure services are aligned with evidence-based guidelines, while also identifying where administrative burden can be reduced.

For example, some payers are piloting “gold carding” programs where high-performing providers receive fewer prior authorization requirements. In care coordination, analytics-driven risk stratification helps identify members who may benefit from case management—such as patients with multiple chronic conditions, frequent ED visits, or social barriers impacting adherence. This allows payer and provider teams to collaborate around shared care plans instead of operating in parallel.

David B. Snow Jr., Founder and Head of Value-Based Care at Cedar Gate Technologies, an IQVIA business

Access to analytics for payers and providers is not just a nice thing to have in today’s healthcare environment, it’s a requirement. But not all analytics are equal. Some tools can only look at what happened in the past. Others attempt to look forward and anticipate or predict future needs, but only have access to a small slice of data from the vast array of information for any given patient or entire patient populations, potentially limiting the effectiveness of treatment recommendations.

To truly improve claims management, utilization, and care coordination in a collaborative payer-provider relationship, organizations need more than just analytics. They need an advanced enterprise data management system that offers a single source of truth, bringing together all of the data on every member or patient from hundreds of disparate data sources. That information needs to be cleansed and enriched so everyone trusts the data and can seamlessly access it at any point in the care journey.

Organizations also need predictive tools that can accurately anticipate member and patient needs, offering more insights to reduce unnecessary utilization, drive better collaboration, and eliminate redundancies and waste in care. Finally, the underlying data informing analytics must be transparent, providing drill-down capabilities to see how the software arrived at a specific conclusion or view the data informing predictive algorithms.

Michelle Lichte, Senior Partner, Professional Services at Pivot Point Consulting

Organizations are using analytics to gain tighter control of data, improve governance, and inform smarter financial and operational decisions. Thus, they are using data to unify clinical, financial, and operational data to enable more accurate claims management, denial rate reduction, and proactive care coordination to drive better outcomes with reduced cost. It’s about the ability to pivot and take action.

Marie Mitri, Director of Business Development at Navina

Organizations are increasingly using analytics to identify risk, documentation gaps, and utilization patterns earlier in the care process. By analyzing clinical, claims, and encounter data together, they gain a more complete view of patient status and network performance. This supports earlier intervention, reduces administrative burden, and improves reporting accuracy. In risk-bearing arrangements, linking analytics to source clinical documentation strengthens audit readiness and confidence in reported performance. The strongest results occur when analytics drive decisions during the encounter rather than serving solely as retrospective reporting tools.

Cathy Hartman, Chief Healthcare Solutions Officer at Paradigm

While data analytics is transforming claims management and utilization review processes across the payer-provider ecosystem, its most meaningful impact is emerging in care coordination. This is where integrated data, predictive modeling, and personalized insights are directly influencing outcomes and cost of care. Over the last decade, health plans and providers have made significant advancements and investments in data quality and integrative analytic tools, resulting in more actionable, real-time insights.

Through these investments and advancements, organizations can aggregate multiple data sources to enhance personalization, enabling a more individualized care experience for health plan members. Enhanced analytics and modeling support informed outreach and engagement strategies, ensuring the right benefit programs are reaching the right members at the right time, while stronger measurement methodologies further demonstrate the value of benefit programs for members and plans.

At the same time, machine learning continues to improve the speed and accuracy of claims processing and payment integrity, supporting value-based models and prospective claims adjustments. Emerging AI and language processing tools are also streamlining – and in some instances automating – authorization workflows, reducing administrative costs, and accelerating decision-making. Together, these advancements are creating a more connected payer–provider ecosystem, where real-time data and predictive insights enable more personalized, coordinated, and cost-effective care delivery.

Ashley Basile, Chief Product Officer at Availity

Organizations are increasingly using analytics to connect clinical and administrative data to make workflows smarter, ultimately shifting decisions upstream and with more context. Whether it be claims, utilization, or care coordination, this means identifying gaps before a service is delivered. To successfully realize the full value of analytics though, it must be embedded directly into workflows.

Elevsis Delgadillo, SVP, Customer Success at KeenStack

Organizations are already using predictive analytics to forecast utilization and cost trends, identify high-risk populations, and detect care gaps earlier. In the revenue cycle, analytics support collections and denial management, while referral and leakage tracking improve coordination across systems.

Donovan Campbell, CEO at Medbridge

Regulatory and reimbursement changes surrounding patient-reported outcomes and CMS Access are continuing to evolve the way health systems report (and benefit from) data analytics in 2026 and beyond. Now that most systems have surmounted the challenge of unifying themselves around an EHR, we are seeing them tackle the next challenge: finding system-wide, EHR-integrated platforms that engage patients outside of their four walls, collect additional longitudinal data on outcomes, and make practicing care easier and more cost-effective by using that data to provide insights back to clinicians on how to better tailor their care to each unique patient that walks through their doors.

Monte Sandler, Chief Operating Officer at WebPT

Providers are using analytics to “shift left,” meaning they address potential billing issues before a claim is submitted. By analyzing historical payer responses and denial trends, they can build proactive workflows into scheduling, registration, documentation, and coding. AI enables organizations to automate that decision logic at scale, which increases first-pass claim acceptance rates and accelerates reimbursement. The result is less rework and faster collections across the revenue cycle.

Hilla Fogel, Ph.D, Founder & CTO at QuantalX Neuroscience

Organizations are increasingly leveraging advanced data analytics to integrate clinical and claims data, creating real-time visibility across the payer–provider ecosystem. Predictive models help identify high-risk patients, prevent avoidable admissions, and support proactive care coordination. In claims management, analytics are used to detect billing anomalies, reduce denials, and streamline adjudication workflows. Shared dashboards enhance transparency around quality metrics, utilization patterns, and contract performance. Together, these capabilities reduce administrative costs, accelerate reimbursement cycles, and improve patient outcomes, particularly within value-based care models where coordination and performance tracking are critical.

Vibhor Mathur, Digital Transformation Advisory Consultant at Emirates Health Services

The healthcare ecosystem is undergoing a massive transformation by leveraging data analytics and AI to enhance efficiency, reduce costs, and improve patient outcomes. Vast amounts of healthcare data are now an actionable insight, by integrating disparate data sources, including claims data, EHRs, and other clinical information, to create a comprehensive, longitudinal view of the patient. This ‘unified ‘data view allows care teams to identify gaps in care, such as missed screenings or medication non-adherence, and proactively address them

Julie Scherer, President and Chief Solutions Officer at Motive Medical Intelligence

When applied at the physician level, clinical data analytics deliver powerful insights that can drive meaningful changes in clinical behavior and improve individual performance, as physicians ultimately control the cost and quality of care. Healthcare organizations are using this actionable clinical performance intelligence to evaluate practice patterns, identify clinicians with high volumes of wasteful care, strengthen utilization review, build trusted networks, gold-card clinicians, and target deeper review where it’s needed. These systems have the power to reduce utilization and care coordination costs for payers, improve time and resource efficiency for provider organizations, and align with care standards for enhanced patient care.

Such great insights to consider here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you think organizations are using data analytics to improve claims management, utilization review, and care coordination across payer-provider ecosystems? Let us know over on social media, we’d love to hear from all of you!