Insufficient RCM Staff for Underpayment Recovery? Automation Gets It Done

A 2024 study of 200 healthcare RCM administrative staff published in MedCity News finds that 100% of the respondents believe insufficient RCM staffing depletes their organization’s revenue. 

More days in accounts receivable, lower patient volume, and more denials (now at nearly 14% of all claims submitted) can all be attributed to operating with only a skeleton crew. Until more enter the workforce, you’re forced to accept underperformance as the status quo. 

One simple way to invigorate your revenue, however, is by turning over a relatively simple revenue cycle task – underpayment recovery – to automation. 

Underpayments are widespread in the United States. A study published in Becker’s Hospital Review establishes that underpayments drain one to three percent of healthcare organization revenue annually. A different study put underpayment revenue leakage as high as 11 percent. Some of our clients have shared that their underpayments reach five to seven percent of net revenue. That’s real money left on the table, and they just don’t have the staff to go after it. Large physician groups and management services organizations with multiple locations can’t walk away from this revenue, particularly if they’re considering selling or raising capital in the coming years. 

Healthcare organizations are pushing back and winning, however. 

TeamHealth won $10.8 million dollars in their underpayments case against UnitedHealthcare.  

The 100 Alabama hospitals now suing Blue Cross claim the insurer underpaid them by $5 billion. 

When a 30-location orthopedics group identified their underpayments (for the first time), they recovered $10 million.  

When they do manage to look into underpayments, revenue cycle executives are learning that payers frequently:

•  make processing errors.
•  leave out the annual escalators they agreed to.
•  incorrectly bundle services.
•  fail to include the late fees that occurred when they missed payment timeframes
•  incorrectly interpret services as their carve-outs and reassign them to a secondary payer.
•  join accounts incorrectly. 

When automation can march into your contracts, find underpayments, reveal trends, and even alert you as they occur, you jumpstart your underpayments recovery and improve net revenue. Considering that US healthcare spending will grow 2.5% faster than GDP every year into 2027, you can assume demand for your services will grow. Sweeping in more revenue to accommodate that demand can get you revenue-producing additions like an onsite lab, a new service, or physician. 

This article covers the benefits of automating underpayment identification,  the key features of this software, how to smoothly transition, and more. 

Take a quick, self-guided tour through a powerful contract management and underpayments identification tool that automatically alerts you to payment variances:

Automated underpayment solution use cases

Modern RCM software leverages artificial intelligence (AI) and machine learning to automate underpayment detection. Specifically, these systems carry out: 

• Payment analysis: The software automatically compares actual payments received against expected payments based on contract terms. As payments come in, it flags all underpayments for easy access by staff. Good software can also prioritize which underpayments to pursue first based on the potential recovery amount. 

• Trend analysis: In addition to pinpointing each underpayment, AI-powered systems can identify underpayment patterns. Aggregations do two things: identify large sums and alert you to possible root causes. Knowing that a certain CPT code is vulnerable to an underpayment, staff can explore what’s going on either in-house or at the payer’s and then take steps to rectify it. 

• Contract Management: Automated contract systems ingest and digitize the rates and terms in all payer contracts, putting this data in your control. It alerts you to renewal dates and even the period 60 or 90 days before. With reminders, providers can review proposed payer changes and even put forward the changes they want. Contract management software makes these tasks simple so that staff doesn’t hesitate to carry them out. 

How automated underpayment recovery software boosts revenue

The impact of automation on underpayment detection is substantial. With automation, an organization enjoys: 

• Reduced manual labor hours: Tasks that once took hours can now be completed in minutes. For example, analyzing 25 CPT codes per payer would be intensely laborious if conducted manually, but automation makes it almost effortless. Healthcare now has 30% fewer coders than they need according to the AMA. The ones you can get are expensive. Automation can take over a nearly unlimited amount of work without burnout. It can even work through the night. Many forward-thinking providers avoid having to hire more RCM staff because of the productivity automation affords. 

Further, in an environment where 11 to 40% of RCM staff leave every year, providers using automation face fewer of the costs involved in training a new hire. While underpayment automation’s contribution to revenue varies depending on organization size and complexity, mid-sized healthcare providers can potentially save hundreds of hours annually.  

• Increased accuracy: The ceaseless backlog of tasks that staff shoulders today makes errors common. Automation solves this problem in part by detecting documentation gaps, flagging missing test interpretations, detecting inaccurate Evaluation and Management (E/M) code levels, and pinpointing various other discrepancies. When issues are caught early in the revenue cycle, your clean claims rate improves and your denials drop. 

• Faster resolution: With quick, automated identification, providers can resolve underpayments more rapidly, improving cash flow.

How an automated underpayments solution works

Automation software employs several advanced technologies. 

AI and machine learning algorithms analyze vast amounts of historical claims data to establish expected payments for all CPT codes. It learns from past underpayment cases to enhance detection accuracy over time. When changes to payer policies are fed into it, the technology automatically adjusts policies and fee schedules so that all subsequent expected payments adjust accordingly. 

With the expected payment established, automated underpayments software then compares actual payments received against contracted rates and fee schedules, identifying discrepancies between billed amounts and reimbursements. This capability enables the detection of trends in underpayments across specific procedures, payers, or time periods, as well as the flagging of unusual payment patterns that may indicate systematic underpayments.

Further, real-time monitoring and alerts are integral features of automation software. The system continuously scans incoming payments for potential underpayments, generating instant alerts when payments fall below expected thresholds. It prioritizes underpayments based on the dollar amount and likelihood of recovery, while also triggering automated workflows for the review and appeal of suspected underpayments. By combining these technologies, automation software significantly improves the speed and accuracy of underpayment detection compared to manual processes, allowing healthcare providers to identify and address underpayments more efficiently and ultimately enhancing their revenue capture and financial performance.

Steps for transitioning to an automated underpayments solution

By implementing best practices throughout this transition, organizations can reduce manual errors and shift staff to focus on higher-value tasks. Effort invested upfront will return revenue to you for years to come. Stay on track with these steps: 

1. Assess your current processes and needs:

   - Conduct a thorough audit of existing manual underpayment detection workflows.

   - Map out the current underpayment recovery process step-by-step.

   - Quantify time spent and resources allocated to manual underpayment tasks.

   - Survey staff to identify pain points, bottlenecks, and inefficiencies.

   - Determine must-have features and capabilities for an automated solution.

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2. Select the right automation software:

   - Evaluate integration capabilities with your existing EHR, billing, and RCM systems.

   - Look for AI and machine learning that can analyze claims data and payer behavior patterns.

   - Ensure the solution can scale to handle your claims volume and complexity.

   - Consider cloud-based options for easier implementation and updates.

   - Request demos and trial periods to test different solutions.

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3. Develop an implementation strategy:  

   - Create a phased rollout plan starting with a pilot.

   - Assemble a dedicated implementation team with representatives from IT, RCM, and. operations.

   - Develop contingency plans to address potential issues during rollout.

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4. Provide staff training:

- Leverage the automation solution’s customer service team to train your team. Get reference guides, helpdesk phone numbers, and FAQs from this partner. 

   - Offer role-based training tailored to different user groups (billers, managers, etc.)

   - Provide hands-on practice sessions with the new automated system.

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5. Start with a pilot program:

   - Select a representative sample of claims or a single department to pilot.

   - Establish clear success metrics for the pilot (e.g. underpayment identification rate).

   - Run automated and manual processes in parallel to validate results.

   - Gather extensive user feedback to refine the system before full rollout.

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6. Establish clear performance metrics:

   - Define KPIs like underpayment recovery rate, time savings, and ROI.

   - Benchmark current performance as a baseline for comparison.

   - Set up automated reporting to track improvements over time.

   - Review metrics regularly with leadership and make adjustments as needed.

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7. Integrate with existing systems:

   - Map out all data flows between the new solution and existing systems.

   - Conduct thorough testing of integrations before going live.

   - Implement data validation checks to ensure accuracy and consistency.

   - Set up alerts for any data syncing issues.

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8. Implement robust quality assurance:

   - Establish an ongoing auditing process to verify automated results.

   - Conduct regular spot checks of a sample of claims.

   - Set up a feedback loop to continuously refine automation rules.

   - Monitor for any unusual patterns that may indicate issues.

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9. Foster a culture of continuous improvement:

   - Schedule regular check-ins with staff to gather feedback.

   - Empower users to suggest process improvements.

   - Stay updated on industry trends and new features to optimize the system.

   - Celebrate wins and improvements to maintain staff buy-in.

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10. Maintain payer relationships:

    - Proactively communicate any changes in underpayment processes to payers.

    - Work collaboratively with payers to resolve systemic underpayment issues.

    - Use data from the automated system to support contract negotiations.

    - Balance aggressive underpayment recovery with maintaining positive payer relationships.

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The above list contains many elements, but each one is simple. Don’t forget that you have your underpayments automation solution team backing you up. 

KPIs for automated underpayment identification and recovery

Once you have your automated underpayment solution integrated with your other systems, you will need to gather important metrics to demonstrate that this effort is cost-effective. Always document baseline measurements before you begin any new initiative. 

Impress peers and CFOs with these performance metrics:

• Underpayment recovery rate: The percentage of identified underpayments successfully recovered. 
• Average recovery amount: The average dollar value of recovered underpayments.
• Time to recovery: The average time taken from underpayment identification to successful recovery. Watch payers and how fast they respond. If you have a lagger, consider entering that information into its “hassle factor.” You can bring that up during contract negotiations. 
• Underpayment identification accuracy: The percentage of correctly identified underpayments versus false positives.
• Appeal success rate: The percentage of appealed underpayments that result in additional payment.‍
• Payer-specific recovery rates: Break down your recovery rates by individual payers to identify problematic contracts or relationships.
• Root cause analysis: Watch those underpayment trends. The most common reasons for underpayments should inform process improvements.
• Cost savings: Compare the labor costs of manual underpayment detection and recovery to the cost of implementing and maintaining automated solutions.
• Revenue increase: Measure the additional revenue recovered due to improved identification and faster processing of underpayments.
• ROI calculation: Use the formula: ROI = (Gain from Investment - Cost of Investment) / Cost of Investment. For underpayment automation, this could be: (Additional Revenue Recovered + Cost Savings - Cost of Automation) / Cost of Automation.
• Productivity metrics: Track the number of claims processed and underpayments identified per staff member before and after automation implementation.
• Error reduction: Measure the decrease in errors and resulting improvement in clean claim rates due to automation.

By focusing on these key areas, healthcare providers can effectively measure the impact of their underpayment recovery efforts, justify investments in automation, and continuously improve their processes to maximize revenue recovery.

What will your automated underpayments recovery buy your organization? 

…on top of the satisfaction of holding your payers’ feet to the fire, that is.

The lack of well-trained RCM staff amdist the growing volume of claims make manual underpayment detection an overwhelming task for many healthcare organizations. Automation may mean an initial investment, but given the years of improved revenue that result, this upfront effort pays off. Assertive contract management is new to many physician groups, MSOs, and practices, but when backed by data, you can make and win your case with confidence.

We named our automated underpayments solution, RevFind because it has found robust revenue for our customers. It streamlines the process of evaluating payer performance for practices, physician groups, and managed service organizations (MSOs). This technology quickly identifies top-performing and underperforming payers, recognizes payment trends, and detects payer underpayments.

The system centralizes all agreements in a digital format within a single, accessible location. It also implements automated alerts for critical contract dates such as expiration, renewal, and exit deadlines, ensuring timely action. Users can customize notifications to provide advance warning, such as 90 days prior to these key dates, facilitating proactive contract negotiations.

Schedule a demo to see how RevFind provides insights into payment discrepancies and the contract improvements that help you boost net revenue. 

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