Denials: Insights from Transaction and AR Data

By: Ashley Hunter, MBA, CHFP, CRCR

​Many healthcare professionals rely solely on denied claim data to monitor denials. While this approach is logical, since denial data highlights the reasons for non-payment, services being denied, and responsible payers, it shows only part of the picture. Transaction and accounts receivable data also provide valuable denial insight. 

​​Identifying denial trends is essential for addressing internal process gaps, but it is equally important to understand the broader financial impact these denials pose.

Organizations cannot focus solely on denial prevention; they must also ensure they have the necessary resources to effectively rework and appeal denied claims. Unworked or written-off denials often surface in transaction and AR data, revealing downstream financial losses that may be missed when relying only on denial reports.

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Transaction Data Insights

At a high level, transaction data provides visibility into charge, adjustment, and payment activity. These figures drive key performance metrics, most notably, the collection rate, which measures the proportion of billed amounts successfully collected. While there are multiple ways to calculate this metric, one particularly insightful method is net collection rate, which helps highlight opportunities related to avoidable adjustments. 

Net Collection = Payments ÷ (Charges - Adjustments)

​Unlike the gross collection rate, which measures collections against total charges, the net collection rate accounts for contractual and other adjustments, offering a clearer view of the amount collected vs the amount that should have been collected. Gross collection rates remain valid but tend to reflect overall payment performance and may be distorted by inflated charge structures or inconsistent fee schedules. In contrast, net collection provides a more accurate measure of financial performance by aligning collections with expected reimbursement.

Expected Adjustment % = (Charges - Allowed) ÷ Charges
Actual Adjustment % = Adjustments ÷ Charges

Calculating the expected adjustment percentage is a critical first step in identifying adjustments that fall outside of the standard contractual allowances. This expected adjustment amount should be derived from payer contracts and configured within the billing system to ensure accuracy. Once both expected and actual adjustment percentages are available, they should be compared to uncover potential discrepancies.

A variance between the two—particularly when the actual adjustment percentage exceeds the expected—signals that additional, non-contractual adjustments are occurring. This often points to avoidable revenue loss, such as write-offs stemming from denials, billing errors, or untimely follow-up. Such discrepancies serve as key indicators within transaction data that denials and other preventable issues are directly impacting the organization’s financial performance.

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​AR Data Insights

​At a basic level, accounts receivable (AR) data reflects the total unpaid balances and how long those balances have remained outstanding. The days in AR metric estimates the average time it takes to fully resolve and collect on a claim. Most insurance companies process and pay clean electronic claims within 7 to 14 days, while those requiring paper submission may take closer to 30 days.

​In an ideal scenario where all claims are clean and paid promptly, days in AR would hover around 30. When this metric exceeds 30 days, it indicates delays in resolving claims, often driven by open or unresolved denials. Extended days in AR are frequently associated with a growing portion of AR aged over 120 days, which can negatively impact cash flow and financial performance.

AR Days = AR Balance ÷ Average Daily Charges

After confirming that open denials are affecting accounts receivable, the next step is to uncover the cause. Open denials typically increase AR for two main reasons:

1. The denial types require significant, time-intensive claim rework
   
2. The organization lacks sufficient back-end staffing to manage the denial volume effectively
   

Denial Examples that Require Extensive Rework:

• ​Other Documentation/Information Requests
• Medical Necessity
• Authorization-Related Denials

These denial types are rarely resolved with a simple correction. For instance, medical necessity denials often require the submission of medical records along with a detailed letter of medical necessity, making the resolution process far more labor-intensive than billing-related denials. This denial type, such as a missing provider taxonomy code, can typically be resolved through quick correction and resubmission. If an organization is experiencing a high volume of these complex denial types, AR performance will likely suffer due to longer resolution timelines.

​Another major contributor to elevated AR is inadequate denial-resolution staffing. While staffing resources should not be allocated to AR management by default, a significant rise in denial volume may warrant a reevaluation of FTE deployment. However, this decision should be data-driven; it's important to analyze denial types before increasing headcount. The following list, though not comprehensive, showcases just some examples: 

• Billing or coding errors - Organizations should first explore automation or system configuration changes to reduce manual effort.
• Payer-driven documentation requests - These have become more common in 2025 due to shifting payer policies, and staffing alone may not resolve the issue. Payer trends should be analyzed and escalated to the appropriate provider representatives for resolution.
  

​Ultimately, tailoring denial management strategies based on denial type, root cause, and payer behavior is key to reducing AR and improving cash flow.

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​ The Bigger Picture

​By incorporating transaction and accounts receivable data into denial analysis, healthcare organizations gain a more comprehensive view of financial performance and operational efficiency. Metrics like the net collection rate and days in AR  help identify where denials are negatively impacting financial performance and point to underlying workflows and operational issues that present opportunities for improvement. Ultimately, a data-driven, holistic approach to denial management—one that leverages both denial and financial data—empowers organizations to reduce revenue leakage, streamline workflows, and improve overall cash flow.