Earlier this year, HBI hosted a roundtable discussion on analyzing and managing denials. This topic is of constant interest in HBI’s membership community, but best practices often vary by organization. For example, geography and size can shape payer and patient mix, staffing structures, and available technology—ultimately steering how revenue cycle leaders choose to handle denials.
To foster meaningful comparisons, the roundtable included leaders from five similar organizations—each use Epic and have an annual net patient revenue over $1B:
- Organization A: Nine-hospital system in the Midwest
- Organization B: 12-hospital system in the Midwest
- Organization C: Three-hospital system in the West
- Organization D: 14-hospital system in the South
- Organization E: Seven-hospital system in the West
Several key strategies emerged from this conversation, which other revenue cycle leaders may find useful in their own organizations:
- Map payer denial remittance codes to internal categories that identify ownership and preventability
Organization A: Analysis focuses on initial denials. Payers’ remittance codes are mapped to internal reason codes and categorized using Epic Clarity (a database tool). Categories specify a broad denial reason (e.g., authorization, medical necessity), which department owns the account, and whether the denial was preventable.
Organization B: Denials are mapped to reason categories in a crosswalk using Epic reporting, but analysis focuses on denial write-offs.
Organization C: Remittance codes are mapped to internal categories using Excel and Structured Query Language database management. The internal crosswalk’s hierarchy begins with a broad denial reason (e.g., authorization) and increases in granularity until it reaches the responsible department, as well as whether it was preventable.
Organization D: Denial management and appeals are handled by hospital patient financial services staff, and the department leader also oversees a team dedicated to denial trending and prevention. Denial remittance codes are mapped in an internal crosswalk categorized by denial reason, department, and whether the denial could have been prevented.
Organization E: Denial remittance codes are similarly crosswalked, then categorized by responsible revenue cycle area. Each area operates a denial task force that investigates root causes and creates strategic action plans for denial prevention. Effectiveness is measured in terms of both initial denials and final write-offs.
- Visualize denials to identify trends over time and establish a reporting cadence
Organization A: To help revenue cycle leaders identify trends, Clarity is linked to QlikView, a data visualization tool. Denial trends are discussed during director meetings, in which leaders investigate root causes and make adjustments to categorization in Clarity.
Organization B: Hospital and clinic revenue cycle leaders review weekly, monthly, and quarterly denial reports. The weekly version provides a quick assessment of recent write-offs; the monthly version broadly communicates facility-specific denial trends; and the quarterly report—once implemented—will include service line-specific denial analysis.
Organization C: Denial trends are visualized using Tableau dashboards, which is a strategy HBI has seen employed at many organizations.
Organization D: In partnership with a vendor, Waystar, revenue cycle leaders have compiled regular denial reports and are also working to better integrate reporting and trending into Epic.
Organization E: During daily huddles, denial analysts report the organization’s top denial reasons and responsible departments, month-to-date and year-to-date updates on adjustments, and progress on monthly denial prevention plans. The organization uses Epic Clarity and SAP Webi (a web-based reporting tool) to allow for self-service denial reporting, which staff in each clinical department can access.
- Expand benchmarking beyond traditional measures
Participants generally monitor the same baseline metrics—initial denials and denial write-offs as a percentage of gross or net revenue. However, expanding analysis, such as by measuring the internal costs of overturning a denial and the percentage of initially denied claims won on appeal, can yield more actionable insights.
Organization A: The organization tracks measures associated with working denials (e.g., the costs of claims rework and success rates of overturning denials). Revenue cycle leaders also compare the prevalence of denial reason codes and associated adjustment codes in Epic, which can indicate areas for improvement and highlight successes. For example, a high percentage of initial denials with authorization reason codes, but a low amount of final write-offs for those accounts, could indicate strategies for overturning authorization-related denials are working.
This example shows the importance of moving beyond only measuring write-offs to assess the effectiveness of denial follow-up and appeal. Additionally, this can also reveal further areas for improvement; if authorization-related initial denials remain common, despite being relatively easy to overturn, they represent an avoidable cost to the organization that could be addressed by stronger denial prevention efforts.
Organization B: Revenue cycle leaders link denial reason codes with adjustment codes to monitor trends in write-offs. Leaders are also working to expand analysis of year-over-year write-off trends.
Organization C: Revenue cycle leaders are expanding denial analysis to account for the lag between billing a claim and receiving a denial. For example, leaders at many organizations divide denied revenue in a given month by the total revenue billed during that same month, but the denials being received are likely on claims billed at least one or two months earlier, meaning the denials and billed revenue are not from the same sample of claims.
Hypothetically, if billed revenue drops one month but denials remain steady, initial denial performance is skewed and looks as if it has deteriorated when it truly has not. One strategy could be to calculate a rolling average and include more than one month of denial and charge data, which can smooth out some of those fluctuations and present a more accurate portrait of denials.
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