Healthcare Payer Intelligence: How Providers Can Make Data-Driven RCM Improvements 

For too long, providers had neither the time nor the inclination to check how well payers paid them. 

But now you’re here. And as the management service organization or physician group CFO, your prime directive is protecting – even increasing – provider revenue. 

Today’s environment of razor-thin margins, rising patient payment responsibility, and private equity healthcare investment (where every provider needs to show reliable revenue) necessitate optimal revenue capture. By keeping payers honest via proactive revenue cycle work, you deliver your ultimate value to your physicians and the communities that depend on them. 

Making assertive revenue capture moves takes buy-in not only from physician leaders and staff but also from your team and your private equity backers. 

The fastest way to win this buy-in is with payer intelligence derived from operational data. 

You stand at the nexus of vast amounts of healthcare data coming from diverse sources, making effective data management and integration not just beneficial, but essential. Review this article to make sure you’re using healthcare payer intelligence to improve revenue. 

What is healthcare payer intelligence?

Healthcare payer intelligence is the collection, analysis, and application of data from insurance claims, billing patterns, and cost trends. Both healthcare provider organization executives and software need data to derive actionable insights across the revenue cycle. Healthcare payer intelligence reveals payer performance, which is the critical wisdom that shapes revenue cycle and operations tactics, payer negotiation stance, and payer mix decisions (as in: maybe it’s better to drop these guys?). 

Payer intelligence fuels any strategic approach that leverages advanced analytics, artificial intelligence, and machine learning to optimize provider operations, enhance decision-making, and improve financial performance in healthcare organizations.

Why healthcare payer intelligence is important for providers

Marshaling and analyzing payer intelligence empowers healthcare provider organization executives to: 

• instantly detect and rectify underpayments
• proactively address contract violations
• seize revenue optimization opportunities
• strengthen negotiating positions with data-driven insights
• rank payers according to reimbursement rate to determine the payer mix

Industry leaders like the Healthcare Financial Management Association (HFMA) and McKinsey emphasize that such visibility is crucial for proactive payer relationship management and revenue cycle optimization. By leveraging analytics, healthcare providers can:

• maximize revenue capture
• minimize revenue leakage
• ensure regulatory compliance
• enhance financial forecasting accuracy

The sheer volume and complexity of payer contracts, coupled with the intricacies of healthcare data, necessitate advanced technological solutions. Automation and AI-powered software have emerged as indispensable tools for achieving real-time contract performance insights.

Providers can further up their game by insisting on real-time visibility. Real-time visibility into payer contract performance enables healthcare organizations to swiftly detect and address revenue cycle issues:

• rapid issue identification: instantly spot underpayments, denials, and other revenue leakage problems.
• proactive payer notification: quickly alert payers to discrepancies, preempting common excuses about missed deadlines.
• accelerated revenue capture: implement immediate corrective actions to minimize the time between claim submission and payment. 

Integration with existing healthcare IT infrastructure results in intuitive dashboards, timely alerts, and predictive analytics that drive rapid decision-making and corrective actions. If you’re using software, lean on their customer success team to shoulder the work. 

Where healthcare payer intelligence use stands today

With many contract management and payer analytics solutions on the market, healthcare providers have their pick of partners. They can even arrange payer intelligence manually if they have adequate staff with adequate training. 

Still, not every healthcare organization is as assertive as it should be when it comes to payer intelligence and performance. 

• 70 to 80% of large health systems use automated RCM platforms like Experian (end-to-end) or MDClarity (point solutions).
• 90% of top-performing medical groups leverage analytics to track payer KPIs like clean claim rates and underpayments. 
• Most providers employ payer scorecards or analytics tools to optimize healthcare payer or contract performance and risk-based reimbursements.
• 60% of hospitals participate in benchmarking programs (e.g., HFMA’s MAP Keys) to align payer contracts with financial goals.
•  30–40% of small, independent practices systematically collect payer data but rely on spreadsheets. 

You should be unleashing AI-driven automation across the revenue cycle. When you target potential acquisitions, you can demonstrate how you’ve improved revenue without the need for additional hires. See how this women’s health physician group got a crushing amount of work done without bringing on more RCM staff. Consulting firm McKinsey & Co. shares that by using automation and AI, US healthcare stands to save $200 to $360 billion in (mostly) administrative costs.  

Healthcare payer intelligence gathering 2 ways

Manual data collection methods

Most healthcare provider organizations still struggle to find the staff bandwidth to research payer intelligence. Those that do conduct it with these three time-intensive steps: 

1. Claims Analysis: providers review Explanation of Benefits (EOB) statements and remittance advice to track reimbursement rates, denials, and approvals.
2. Contract Review: staff manually examine payer contracts to understand fee schedules and reimbursement terms.
3. Benchmarking: providers use tools like the CMS Physician Fee Schedule Look-Up Tool to compare their rates against Medicare baselines[39].

 Automated data collection and analysis via contract management software

More providers are using AI-driven contract management solutions to pull data from their existing software platforms. They get the payer intelligence data from:

• revenue cycle management 
• payer analytics (often included in the above)
• all-payer claims databases (APCDs) (supplied by some states)
• EMR (often integrated with the above)

RCM software trains its vendor-designed algorithms on industry-wide data to handle common tasks like claims processing, coding accuracy checks, and denial prediction. These algorithms are often standardized to comply with regulations (e.g., HIPAA) and payer requirements. 

To render fully custom results, healthcare organizations often collaborate with vendors to tailor algorithms to their specific workflows, payer contracts, or patient demographics. This partnership creates a tailored approach that delivers the most impactful insights to the healthcare organization. 

KPIs to reveal healthcare payer intelligence and contract performance

Whether you use a manual or AI-driven contract management software solution, it takes concrete, real-time data to prove your hunches to peers and decision-makers and get the changes you want. This data forms the basis of the payer performance KPIs that reveal just how payers serve you. 

Use these KPIs to dive into healthcare contract performance: 

Financial Impact KPIs

These KPIs analyze projected vs. actual revenue by contract, revenue leakage, and payer mix impact on overall revenue.

Reimbursement rate: 

The reimbursement rate is the final payment providers actually receive for their services after accounting for all contractual and financial adjustments. This rate reflects the net result of negotiated discounts with payers, as well as contractual allowances and adjustments. 

While the base payment rate serves as the starting point, the reimbursement rate represents the true financial outcome of the service provided. It's the bottom-line figure that directly impacts a healthcare organization's revenue and financial health.

Benchmarks: 

• Commercial reimbursement rates typically range from 143% (professional services) to 263% (outpatient) of Medicare FFS rates, with an overall average of 190%, according to Milliman. 
• Advantage (MA) payments average 86.8% of hospital costs, while commercial payers reimburse 144.8% 

This said, reimbursement rates vary by state and procedures. 

• Inpatient services:  Commercial rates range from 109% (Hawaii) to 274% (West Virginia) of Medicare. 
• Outpatient services rates vary from 157% (Alabama) to 396% (West Virginia).  
• Professional services: Specialists earn 139% of Medicare nationally, but disparities exist (e.g., 243% in Wisconsin vs. 119% in Kentucky. 

Insight: Reimbursement rates under the above levels can indicate potential underpayments, variations across different payers, and opportunities for contract renegotiation. Furthermore, analyzing reimbursement rates allows healthcare providers to benchmark their performance against industry standards, identify service lines that may be underperforming financially, and make informed decisions about resource allocation and strategic planning to optimize revenue capture.

Action to take: To improve, compare reimbursement rates across payers to determine the value of each to your organization.  

How to: 

• Manually: Use CMS’s PFS Look-Up Tool to find Medicare rates for specific CPT codes, adjusted by geographic location (GPCI). Compare payer-allowed amounts against Medicare baselines. Analyze Explanation of Benefits (EOB) Statements. Track the “allowed amount” (not “paid amount”) for top codes across major payers. Calculate reimbursement as a percentage of your practice’s charges and Medicare rates
• Contract management software:  using AI to flag underpayments and model negotiation targets (e.g., 130% of Medicare).

Take a quick walk through a comprehensive contract management solution that Benchmarking reimbursements against national standards, including Medicare. Providing insights for proactive contract negotiations.

Days in accounts receivable (DA/R): 

The average time it takes to collect payments. 95% of accounts should be coming in in under 30 days.  

Insight:  Aging A/R (45-, 60-, and 90-day buckets) can indicate revenue cycle inefficiencies like issues with claim submission, coding accuracy, or follow-up processes as well as missed filing limits or regulatory requirements. Struggles collecting from patients also underlie long DAR. Get tips on facilitating patient financial responsibility here. The fault could also lie with slow payer processes. 

Actions to take: To shorten time in A/R and escalate collection efforts, research and analyze root causes. Optimize workflows and consider prioritizing claims. 

Net collection rate (NCR)

The percentage of expected revenue collected within bills more than 120 days. The industry benchmark is over 95%.

Insight:  An NCR under 90% can indicate inefficiency in billing, poor payer contract management, patient collection strategies, over-reliance on error-prone manual processes, absence of effective RCM software, compliance issues. It could also reveal poor denial management, inadequate staff skill, and cash flow issues. 

Actions: Collect 100% of copays and even deductibles upfront. Read about how to get provider staff onboard with upfront collections here. Ensure you’re communicating well with patients. Optimize claims management. 

Operational KPIs

Payment accuracy

Payment accuracy tracks the accuracy of payments received compared to contracted rates. This helps identify underpayments, overpayments, and discrepancies in real-time. The industry goal is for payment accuracy to be at 95%-97%.

Insight: A low payment accuracy rate indicates inefficient billing practices, outdated technology or manual processes, and staff training gaps.

Action: To improve a low accuracy rate, providers should implement automated reimbursement solutions, optimize coding practices for accuracy and efficiency, and utilize technology to monitor payment postings in real-time.

Clean claims ratio (CCR)

The percentage of claims that pass all edits without manual intervention. The benchmark is 95% or higher. 

Insight: A low clean claims rate indicates coding errors or inconsistencies, incomplete or inaccurate patient information, low claims staff skill, or lack of proper claim scrubbing processes.

Action: To improve a low clean claims rate, providers should implement automated claim scrubbing software, provide ongoing staff training on coding and documentation best practices, and/or establish a pre-submission review process for complex claims.

First-pass resolution rate (FPPR):

The first pass resolution rate is the percentage of claims paid on first submission, indicating overall revenue cycle management effectiveness.

Insight:  First Pass Resolution Rate reveals financial stability through minimized revenue leakage and rework costs, operational efficiency via claims submission accuracy, payer-specific compliance gaps affecting reimbursement, root causes of denials tied to coding errors or missing documentation.  

Action: To improve this metric, deploy AI-driven claim-scrubbing software for pre-submission error detection, implement continuous staff training on updated coding standards and payer policies, analyze denial trends to address recurring submission issues, collaborate with payers to clarify requirements and reduce discrepancies.

Payment variance (PV)

The difference between the expected reimbursement for a claim and the actual payment received from insurance payers. The benchmark is typically less than 2-3% variance.

Insights: A high payer variance indicates potential contract misinterpretation, outdated fee schedules, coding errors, improper claim adjudication by payers, missed contract terms or carve-outs, ineffective contract management processes, revenue leakage due to underpayments, cash flow disruptions, increased administrative burden for appeals and follow-ups.

Actions: To improve payer variance, healthcare providers should implement a robust contract management system to:

•  track expected vs. actual reimbursements
•  conduct regular audits of high-volume or high-dollar claims 
•  leverage analytics to identify underpayment patterns by payer or service line
• use data-driven insights on payer performance to renegotiate contracts
• develop a streamlined process for appealing underpayments and following up on unresolved variances

Read more about healthcare contract management best practices here. 

Payer Compliance Rate (PCR)

The percentage of claims paid by insurance companies in compliance with their contractual obligations. The industry standard benchmark is typically 95%.

Insights: A low payer compliance rate indicates potential issues with:

• contract interpretation
• outdated fee schedules (read how to automate payer fee schedule management.) coding errors
•  improper claim adjudication by payers
•  missed contract terms or carve-outs
•  ineffective contract management processes
•  revenue leakage due to underpayments
•  cash flow disruptions
• gaps in staff knowledge of payer policies 
• inconsistencies in claims submission processes

Actions: To improve payer compliance rate, healthcare providers should implement a robust contract management system to track expected vs. actual reimbursements, conduct regular audits of high-volume or high-dollar claims, and provide staff training on payer-specific billing requirements and contract terms. 

To carry out these tasks, it takes a dedicated team to establish:

•  compliance analysis and appeals
• analytics to identify underpayment patterns by payer or service line
• a streamlined process for appealing underpayments and following up on unresolved variances, 
• a comprehensive denial management process to identify root causes and track outcomes.

Gross collection rate (GCR)

The percentage of total charges that are collected; this is typically calculated for a specific time period. The industry standard benchmark for GCR is typically around 95%.

Insights: A low gross collection rate may indicate issues with:

•  charge capture accuracy
• inefficient billing processes
• outdated fee schedules
• lack of upfront collections
• poor patient communication regarding financial responsibility
• discrepancies between contracted rates and actual collections 
• gaps in staff training on proper charging and coding practices

Actions: To improve a low gross collection rate, healthcare providers can take one or more of these actions:  

• implement a robust charge capture system to ensure all services are accurately billed
• conduct regular audits of charging practices and fee schedules
• enhance upfront collection processes for copays and deductibles
• provide clear financial counseling to patients regarding their responsibilities
•  optimize claims submission processes to reduce errors and denials. 
• offer flexible payment options to increase collection
• provide ongoing staff training on proper charging, coding, and collection practices, analyze payer mix, and adjust contracting strategies 
• implementing a patient portal for easier bill pay and financial communication.

Denied claims rates 

Denied Claims Rate

The percentage of claims denied by payers over a specified period, with an industry-standard benchmark typically around 5-6% or lower.

Insights: A high denied claims rate indicates:

• payer authorization issues 
• potential issues with coding accuracy
•  incomplete documentation
•  patient information errors
•  misalignment with payer policies
•  ineffective pre-submission claim review processes
•  revenue leakage due to lost or delayed payments
•  potential gaps in staff knowledge of payer requirements

Actions: To improve a high denial rate, healthcare provider organizations should implement: 

• accurate patient verification and insurance eligibility checks
• automated pre-authorization processes
• thorough documentation practices that support medical necessity
• stringent timelines for claim submission to avoid untimely filing 
• Regular, analytics-fueled internal audits to identify and correct common errors 
• analytics to identify patterns in denials by payer or service line
• a thorough contract management system to ensure compliance with payer agreements

The above KPIs, when monitored in real-time through specialized software solutions, allow healthcare organizations to proactively manage their contracts, optimize revenue capture, ensure compliance, and make data-driven decisions to improve overall financial performance.

Act on healthcare payer intelligence with MD Clarity support

MD Clarity supports providers as they clean up healthcare organization revenue cycles across the United States. 

AI- and automation-driven contract management software solution RevFind automates expected reimbursement calculations using historical data, payer contracts, and fee schedules 

RevFind centralizes and digitizes all payer contracts in one platform, allowing for comprehensive analysis of payment data against contracted terms. The system automatically compares every payment received to the contract terms and flags any discrepancies between actual payments and expected reimbursements based on contract stipulations. 

Key features of RevFind's automated reimbursement calculations work hard:

1. Digitizing and consolidating fee schedules and contracts into a single, accessible platform.
2. Automatically compare each payment against contract terms.
3. Flagging discrepancies and notifying staff of underpayments.
4. Benchmarking reimbursements against national standards, including Medicare.
5. Providing insights for proactive contract negotiations.

By automating these processes, RevFind eliminates the need for manual calculations and spreadsheet-based tracking, reducing errors and improving efficiency in revenue cycle management. This automation allows healthcare providers to optimize their reimbursements and identify opportunities for revenue enhancement. 

To experience how RevFind can help you get the payer health intelligence you need to optimize revenue at your acquisitions, schedule a demo today.

‍