Healthcare Claims Data Analytics: The Proven Tool for Maximizing Payer Reimbursement

As a revenue cycle professional or healthcare CFO, you’ve read that 67% of denials have a high potential of being recovered…if only you appeal.

What’s stopping you? How about:

• Lack of experienced staff
• Cost of appeals - $43 to $181 each
• Burdensome manual processes
• Inconsistent payer policy changes that make appeals incomplete or inaccurate, and therefore, pointless.
• Missing documentation and data
• Higher priority tasks
• Lack of visibility  
• Missed appeal deadlines
• Failure to monitor appealed claims

Then, too, reimbursements from one or two payers for one or two treatments may strike you or a staff member as inconsistent. Most likely, they are. Payer underpayments are also rampant in the United States today, draining 1% to 3% of net patient revenue, according to a study published in Becker’s Hospital Review. Other studies estimate that underpayments can be as high as 11 percent of revenue. We work with clients whose underpayments are even higher!

Nearly all healthcare providers are buried under these obstacles. What are the workarounds?

Our clients have had success using healthcare claims data analytics. With 90% of medical practices reporting another year of operating cost increases in an MGMA Stat poll, and the Kaiser Family Foundation now clocking denial rates for ACA insurers at 19%, healthcare organizations must capture as much revenue as possible to escape this margin squeeze.  In the Big Data Era, doing so will require providers to use healthcare analytics to proactively identify and address denial patterns, payer underpayments, and make data-driven decisions that maximize revenue recovery and financial stability. In a sign that physician groups, healthcare MSOs, and organizations understand this imperative, research shows the healthcare analytics market is rapidly expanding. 

What is healthcare claims data analytics?

Healthcare claims data analytics is the process of collecting, organizing, and analyzing vast amounts of claims, denials, and appeals data to uncover patterns, trends, and actionable insights regarding billing, payment, and denial outcomes within the healthcare revenue cycle. 

Unleashing artificial intelligence, machine learning, and automation on this data renders meaningful reports and visualizations. 

Providers use healthcare claims data analytics to break down denials across multiple dimensions — such as denial codes, procedure codes, facilities, providers, and insurers. The patterns and root causes revealed often go unnoticed. For example, if reports reveal frequent denials for a specific CPT code due to missing modifiers, staff can update workflows to ensure proper documentation. 

Healthcare claims data analytics also identify and analyze underpayments. It compares each payment coming in to the contracted rate and then lists all underpayments in user-friendly dashboards. Staff start their day jumping on these underpayments, particularly when they reveal themselves as part of a trend. It sifts through large volumes of claims and remittance data to pinpoint specific services, procedures, or payers that frequently result in underpayments, revealing patterns and root causes such as coding errors, contractual misinterpretations, or payer processing mistakes. 

Moreover, real-time monitoring and automated alerts notify revenue cycle teams immediately when payments fall below expected thresholds, triggering workflows for timely review and appeal. By drilling down into payer-specific trends and contract compliance issues, analytics supports strategic negotiations and process improvements that prevent future underpayments and denials. 

With this level of advanced analysis, you can reduce denial rates, put a stop to healthcare underpayments, and improve workflows so staff achieve higher accuracy.

How healthcare claims data analytics attack obstacles to proactive denial and underpayment oversight

Just reading the tasks healthcare claims data analytics carries out should give you a sense of the work it can shoulder. 

• Lack of experienced staff: Software catches each denial or underpayment that comes in and delegates it to a staff member’s workflow. Analytics software with prioritization capabilities goes a step further by providing guided workflows and standardized templates to support staff and streamline appeals. Expertise built into the software makes pricey consultants less necessary. 
• Cost of appeals: Sure, the work involved in appealing low-value claims and underpayment recovery is a revenue drain. Because data analytics solutions can target high-value, high-success-rate denials for appeals, however, the amounts recovered should outpace the resource time invested. Further, once underpayments are rectified in the payers system, revenue increases persist. 
•  Burdensome manual processes: Automation tools free up staff by handling repetitive tasks like data entry, appeal generation, and tracking.
•  Inconsistent payer policy changes: AI-driven analytics monitor payer behavior and policy updates in real-time, ensuring appeals and underpayment recovery requests align with current requirements.
•  Missing documentation and data: Analytics flags incomplete claims before submission and integrates with EHRs to auto-pull necessary documentation during appeals.
•  Higher priority tasks: Once appealing the high-value denials and underpayments proves itself as cost-effective in limited trials, team members and supervisors recognize its value. Both revenue protection initiatives get a higher priority. 
•  Lack of visibility and need for data analysis: Real-time dashboards highlight denial and underpayment trends, appeal successes, and revenue impact, enabling data-driven decisions. Find all the numbers you need in a few clicks rather than days of juggling spreadsheets. 
•  Missed appeal deadlines: Automated tracking systems send alerts to staff for approaching deadlines and auto-generate appeals to meet payer timelines.
•  Failure to monitor appealed claims: Analytics platforms provide real-time status updates on appealed claims and trigger follow-ups for stalled cases.

How healthcare claims data analytics works

Healthcare claims data analytics finds top denial and underpayment opportunities by analyzing claims data to identify patterns, trends, and root causes across multiple dimensions. 

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Here’s how it works:

• Pattern recognition: Analytics tools detect common denial reasons such as coding errors, payer errors, duplicate claims, missing documentation, or lack of prior authorization by examining large volumes of historical claims data.
• Descriptive analytics: This first layer categorizes denials by type, payer, procedure, or diagnosis, revealing which claims are most frequently denied and highlighting payers or service lines with higher denial rates. (Curious? Explore the types of healthcare payer analytics.)
• Diagnostic analytics: Going deeper, analytics pinpoints exact causes behind denials and underpayments — like specific coding mistakes or documentation gaps — allowing providers to address underlying issues rather than just symptoms.
• Predictive analytics: Advanced models forecast which claims are at high risk of denial before submission by analyzing historical trends and payer behaviors, enabling proactive correction and prevention.
• Prioritization based on financial impact: Analytics ranks denials and underpayments by dollar amount, frequency, and likelihood of successful appeal, helping revenue cycle teams focus efforts on the highest-value opportunities to maximize recovery.
• Monitoring payer trends: Continuous tracking of payer-specific denial patterns establishes much-needed payer price transparency. It allows providers to adapt billing practices and negotiate better contracts by understanding which payers deny claims most often and why.
• Automation and AI integration: AI-powered analytics automate detection, classification, and prioritization of denials, reducing manual workload and accelerating identification of top denial opportunities.

By leveraging these capabilities, healthcare organizations can uncover actionable insights that drive targeted interventions, reduce denial rates, identify underpayments, and optimize revenue recovery.

8 concrete benefits of using healthcare claims data analytics 

When providers use analytics to generate customized denials and underpayments reports and analyze trends, their insights directly translate into measurable financial and operational improvements. 

Using analytics results in:

1. Reduced denial rates and higher clean claim submissions
By identifying root causes (e.g., coding errors, eligibility issues, or payer-specific policy gaps), teams can implement targeted fixes to prevent recurring denials. Fewer denials mean lower labor costs and higher net revenue.

2. Better revenue and payer negotiation stance with underpayments identified

When underpayment trends are uncovered, providers gain clear evidence of payer non-compliance with contract terms, enabling them to demand accurate reimbursements and hold payers accountable. 

3. Faster, more effective appeals
Customized reports enable teams to auto-generate payer-specific appeals with the required documentation. An analytics system's workqueue feature assigns denials and underpayments to staff based on expertise (e.g., payer, denial type), streamlining workflows and reducing appeal turnaround times. This increases overturn rates and turnaround time, and recovers revenue that would otherwise be lost.

3. Improved payer negotiations
Analyzing denial patterns by payer (e.g., which insurers deny claims most often and why) provides leverage during contract renewals. For instance, if reports show a payer consistently denies claims for “lack of medical necessity,” organizations can renegotiate terms or demand clearer guidelines, strengthening future reimbursement terms.

Similarly, transparency and detailed insight into payment discrepancies or underpayments empower providers with the concrete data needed to negotiate better contract terms, improve payment accuracy, and ultimately secure higher revenue.

4. Operational efficiency gains
Visibility into denial and underpayment trends by provider, facility, or service line helps pinpoint training gaps or workflow bottlenecks. For example, if one location has higher denial rates due to registration errors, targeted training can resolve the issue. If one location has high underpayment rates, the provider can alert all involved and examine faulty workflows. Automating manual tasks like appeal tracking and deadline alerts also frees staff to focus on complex cases.

5. Revenue recovery and stabilized cash flow
By uncovering underpayments and preventable denials, organizations recover otherwise lost revenue. Analytics also identifies “low-hanging fruit” (e.g., high-volume, easily overturned denials), ensuring quick wins that boost cash flow. 

6. Data-driven decision making
Custom dashboards and reports provide CFOs and RCM leaders with actionable metrics (e.g., denial recovery rates, underpayments by payer, cost per appeal) to allocate resources strategically and justify investments in technology or staff.

8. Enhanced compliance and reduced risk
Tracking denial reasons linked to regulatory requirements (e.g., missing Advance Beneficiary Notices) helps organizations maintain compliance and avoid audits or penalties

Ultimately, data-driven denial management and underpayment recovery transform historically reactive (or non-existent) processes into strategic assets, safeguarding the financial health and sustainability of healthcare providers.

Overcome capitulation to payer denials

As mentioned above, payers and patients only appeal 1% of denied claims. When your appeals come back denied again, why bother? When you advocate to pursue underpayments only to have the c-suite dismiss it as cost-ineffective, you move on to other tactics. 

Healthcare claims data analytics helps your team avoid a “learned helplessness” that may have taken over you or your revenue cycle team. Learned helplessness can arise in both personal and work arenas. 

Learned helplessness is a psychological state in which an individual, after repeated exposure to uncontrollable negative events, comes to believe they are powerless to change their situation — even when opportunities to do so exist. This leads to passivity, decreased motivation, and a sense of hopelessness, often persisting even when circumstances change and control is possible.

These examples should help your team recognize the value in appealing denials using healthcare claims data analytics. 

Several leading hospitals and healthcare systems have instituted successful denial appeal programs, leveraging analytics, process improvements, and cross-functional collaboration. Here are notable examples with available dollar amounts where possible:

• Radiology Imaging Associates (RIA) had a highly manual contract management process and a biller who promised thorough underpayment identification but never delivered. The contract management system that RIA brought on included healthcare claims data analytics that not only systematically identifies payer underpayments but also manages complex managed care agreements. Right away, the system identified $1.1 million in validated underpayments from just this one payer.
• When an orthopedics MSO plagued by denials and underpayments implemented contract management software that included healthcare claims data analytics, it identified $10.3 million in underpayments from 7 payers, amounting to 13% of reported revenue. 
•  Minnesota Eye Consultants tired of making sense of contracts via inaccurate, manual means and a PMS with insufficient denial and underpayment functions. With a contract management system fueled by healthcare claims data analytics, they quickly achieved higher revenue without resorting to hiring new RCM staff. 

By analyzing patterns in expected revenue shortfalls, teams can proactively remediate issues, leading to fewer denials and underpayments.

Healthcare claims data analytics software must-have features

Healthcare claims data analytics software is often included as a key component within a healthcare contract management platform. These platforms combine contract lifecycle management with advanced analytics to provide actionable insights that improve contract oversight, compliance, and financial performance. By integrating claims data with contract terms, the software enables providers to monitor reimbursement accuracy, identify denial and underpayment trends, and optimize revenue cycles more effectively.

The following core capabilities ensure that claims data is accurate, actionable, and accessible. Its very reason for being is to enable teams to identify issues early, prioritize high-impact appeals, and make informed decisions that drive financial performance and compliance. Go over each of these features with any software vendor you evaluate. 

• Comprehensive data integration:
  
  • Seamlessly combine data from multiple sources, including EHRs, billing systems, and payer portals.
  • Provide a unified, holistic view of patient and claims information to ensure accurate and complete analysis.
  • Reduce errors caused by fragmented or siloed data.
• Real-time analytics and automated workflows:
  
  • Enable real-time visibility of denial and underpayment trends as they emerge.
  • Offer automated alerts for missed appeal deadlines and prioritize high-value denials and underpayments.
  • Help staff focus on the most impactful appeals while reducing manual workload.
• Intuitive data visualization:
  
  • Provide customizable dashboards and interactive reports.
  • Transform complex claims data into clear, actionable insights for revenue cycle teams and leadership.
  • Facilitate easier interpretation and faster decision-making.
• Compliance and security standards:
  
  • Ensure adherence to HIPAA and other regulatory requirements.
  • Protect patient data privacy and maintain organizational compliance.
• Interoperability and IT Infrastructure Integration:
  
  • Adoption of analytics software means integrations with existing healthcare systems such as practice management platforms, EHRs, billing software, and your healthcare RCM platform.
  • Avoid data silos and streamline workflows across departments.
  • Support flexible APIs and multi-tenant architectures that scale with organizational growth and multiple locations.

By insisting on these core features, healthcare providers can fully leverage claims data analytics to reduce denials and underpayments, accelerate appeals, and maximize revenue recovery.

MD Clarity makes proactive payer management possible at your organization

Healthcare claims data analytics ensures providers harness vast and complex data sets to improve financial performance, operational efficiency, and patient outcomes. With rising operating costs, increasing denial rates, and evolving payer requirements, providers must leverage analytics to identify denial patterns, optimize revenue cycle processes, and proactively prevent revenue leakage. Additionally, analytics delivers the actionable insights needed to adapt swiftly to regulatory changes and market pressures.

MD Clarity’s contract management platform RevFind transforms raw denial data into targeted action plans. Its user-friendly dashboards help providers shift from reactive revenue-leakage firefighting to proactive denial management, turning this process into a strategic asset rather than a cost center.

You can gain control over denials, appeals, contracts, underpayments, and more with MD Clarity’s RevFind. This contract management platform offers clear visibility into denial trends, enabling providers to identify which claims are denied by specific payers or for particular CPT codes, and quantifies the associated potential revenue. This transparency helps teams focus on appealing denials that are most likely to succeed by uncovering common illegitimate denial reasons, such as claims rejected for timely filing or authorization despite proper procedures being followed. Additionally, RevFind reduces the heavy manual workload of appeals by allowing users to track appeal stages, label accounts, and create work queues for batch processing. 

Explore how RevFind can help you improve denial recovery.  Schedule a demo today! 

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