3 RCM Reports You Should Be Reviewing to Monitor Payor Payment Trends

Payor reimbursement behavior is complex and constantly changing. Payors make policy changes regularly and it’s challenging for medical providers to keep track of whether they are being reimbursed in full. Staying on top of payments whether for contracted or non-contracted reimbursements can require significant resources. Yet, if not monitored regularly, organizations will miss out on earned revenue and expend additional resources resolving issues on the backend.

Payor policy changes can result in denials and changes in expected reimbursement amounts. Failure to quickly recognize and adapt revenue cycle management workflow to payor reimbursement behavior changes can result in:

  • Costly appeals
  • Write-offs
  • Reduced or delayed reimbursement
  • Patient billing inaccuracies

Many medical providers have processes in place to monitor and follow up on denials. Fewer, however, have business intelligence (BI) and analytics in place to spot underpayments (claims that are paid at a lower rate than the contracted or expected allowable). Being unable to spot and act on these underpayments in a timely manner means a decline in revenue. Fortunately, there are proactive strategies to catch changes in reimbursement rates earlier in the revenue cycle process.

XiFin’s BI and Advanced Analytics help customers proactively monitor payor adherence to contracted payment rates and trend out-of-network payment rates. This rich data source provides actionable insights that can be incorporated into the RCM workflow for improved collections and efficiency.

The following are three examples of analytics that can be used to monitor changes in payor payment behavior.

1. Expected Price Discrepancy: Expected Vs Actual Allowable 

The Expect Discrepancy Analysis report compares the last 90 days of third-party payor reimbursement with the current expect price tables from the billing system. It analyzes the contract fee schedules loaded in the billing system compared against the actual EOB allowable by payors and procedure. Out-of-network expected allowable can also be loaded into the billing system to track payor payment trends. Data are organized to quickly find the rate variances that most affect your financials.

The report is made up of three sections:

Discrepancy Summary

Illustrates the number of expect price discrepancies found.

Discrepancy Impact

Quantifies the total financial impact of the expect price discrepancies over this period. 

Discrepancy Detail

Summarizes each distinct allowed amount by procedure and compares it against the established expect price and statistical weight.

The report is interactive and enables users to filter on payors to display just discrepancies for that payor.

2. Payor Trend Analysis: Outliers by Allowed Amount

This report provides an evaluation of ordered test and payor pairings with a focus on potential impact. It analyzes all payor-level and procedure-level reimbursement from the past 30 days and compares them to the same payor-level and procedure-level reimbursement for the prior 90-day period.

The purpose of this report is to quickly capture changes in allowed reimbursement amount, particularly where test volume is high. It provides visibility into the changes that are impacting revenue the most. This helps organizations identify payor behavior trends early and prioritize responses to those trends that have the highest potential to affect financial results.

This report is also made up of three sections:

Data Filters

Data can be filtered by time period, payor group, and potential impact, which allows the focus to remain on issues with the highest potential impact.

Top Potential Impact

Displays a summary of changes for a test by payor with the most potential impact.

Change in Allowed Amount and Volume

Displays changes in allowed reimbursement by both revenue and volume.

For example, in the report shown below, the lower right corner of the chart highlights where allowed amounts have gone down and test volume is high. This has the potential to have a negative financial impact and thus warrants further analysis. Hovering over a bubble in this report provides additional detail. In the sample report below, hovering over a bubble in the bottom right shows the details of the change, a $9 reduction in the allowed amount per unit in the last 30 days for this particular test.

3. Detail Highest Level: Ordered Test/Product/Service 

Once a change in payor behavior is spotted, the next step is to identify the source or cause of the change. The Detail Highest Level Product/Service report helps pinpoint the time when the change occurred, which in turn helps identify the cause, such as a date-of-service (DOS)-related change in coding or reimbursement. This report also drills down to the test level to determine the impact per test level CPT.

This report has four sections:

Allowed Amount Trend by Account Period

Illustrates the change in reimbursement month-over-month based on the accounting period for the payor.

Allowed Amount Trend by DOS Period

Displays the change in reimbursement month-over-month based on the date-of-service (DOS).

Allowed Amount by DOS Period (CPT Code)

Highlights specific reimbursement received by CPT code for the payor.

Volume & % Allowed Trend (CPT Code)

Displays both volume and % allowed trends by CPT for the payor.

Actionable Insights

It is all too common for medical providers to be unaware of a change in reimbursement until after month-end close. Gaining visibility into changes in payment as well as what is driving the change in a timely manner is essential to optimizing the RCM process and maximizing reimbursement. Failure to quickly recognize and adapt workflow to payor policy changes can result in reduced or delayed reimbursement and inaccurate patient billing.

RCM systems that incorporate advanced analytics and payor behavior analysis help drive early insight into changes in payor behavior and its impact, as well as determine the root causes. Once the root cause has been ascertained, workflow modifications and/or payor relations efforts can help eliminate these issues. XiFin’s BI and Advanced Analytics provide RCM leaders with the ability to apply a proactive strategy to catch changes in reimbursement rates earlier and adapt to payor changes more quickly to mitigate issues. 

In this 5-minute clip, our SVP of Engineering and Analytics, Jeff Carmichael, shares the types of analytics available to XiFin customers so they can proactively monitor and adapt to payor behavior changes, including:

  • Visibility to changes in payment by test and detail on what is driving the change.
  • Comparative data on payor profitability and profitability by procedure code over time.
  • Submissions considerations including how potential consolidation/CPT changes may impact reimbursement.
  • Ability to leverage analytics to drive workflow adjustments and front-end edits to avoid denials.
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Published by XiFin
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