Every claim your practice submits generates a trail of structured data. Your practice management system creates it, your clearinghouse processes it, and your payers respond to it with payment decisions that contain detailed reasoning encoded in standardized formats. Most practices use a fraction of what that data can tell them, processing remittances for payment posting and logging denials for follow-up, while the deeper patterns in the data go unexamined.
This is one of the areas where I find the largest gap between what practices have access to and what they are actually using. The information needed to understand why specific payers are behaving the way they are, which procedure types are being scrutinized, and whether a denial trend represents a one-time error or a systemic payer policy change is already sitting in the electronic transaction files passing through your clearinghouse. The question is whether your team has the tools and the process to read it.
The Two Files That Tell the Story
The foundation of EDI-based payer analysis is understanding what the 837 and 835 transaction files each contain, and what becomes visible when you pair them together.
The Claim — What You Submitted
The 837 is the electronic claim file your practice sends to the payer. It contains patient demographics, insurance information, diagnosis codes, procedure codes, dates of service, and the billed amount. Think of it as the complete record of what you submitted and how you coded it. The 837P handles professional claims, the 837I handles institutional claims, and the 837D handles dental claims.[1]
The Response — What the Payer Did With It
The 835 is the Electronic Remittance Advice (ERA) the payer sends back. It documents the payment decision for every claim line, including the amount paid, any adjustments applied, and in the case of a denial or reduction, the specific reason codes explaining why. Those reason codes, known as Claim Adjustment Reason Codes (CARCs) and Remittance Advice Remark Codes (RARCs), are where payer behavior becomes readable.[2]
The analytical power comes from pairing these two files together. Comparing what was submitted in the 837 against what was paid or denied in the 835 allows you to identify the specific claim characteristics that are generating denials, the payers that are applying unusual adjustment patterns, and the procedure and diagnosis combinations that are being scrutinized more heavily than others.[3]
"Creating a process for tracking and trending denials using 835 data can serve as a valuable diagnostic tool and a roadmap for where you need to go to improve your bottom line."
MedCom Solutions, What Are the Differences Between 835 and 837? [3]
What Payer Behavior Analysis Actually Looks Like
The most actionable use of EDI data for most practices is not sophisticated predictive modeling. It is consistent trending of denial reason codes by payer over time, which surfaces the patterns that individual claim-level review cannot see.
Is a specific payer changing its adjudication behavior?
Payers update their coverage policies, documentation requirements, and medical necessity criteria regularly, and they are not always consistent about notifying providers when they do. A spike in denials from a specific payer on a specific procedure type, visible in your 835 trending data, is often the first indication that a policy change has occurred. Catching this pattern quickly allows your team to investigate, update workflows, and address the backlog of affected claims before they age past appeal deadlines.
Are specific procedure and diagnosis combinations generating consistent denials?
When the same CARC code appears repeatedly on the same procedure code across multiple payers, it is usually a coding or documentation issue rather than a payer behavior issue. Distinguishing between these two root causes matters significantly for how you respond. A documentation gap requires a workflow change on your end. A payer policy change requires a payer-specific response, potentially including an appeal strategy and a contract review.
Are you being paid at contracted rates?
The 835 contains the payment amount and the contractual adjustment applied to every claim. Systematically comparing actual payments against contracted rates by payer and procedure code will surface underpayments that would otherwise go undetected. Research from MedCom Solutions suggests that underpayment issues are more common than most practices realize, and that regular 835 reconciliation against contract terms is one of the most direct ways to recover revenue that is being quietly left on the table.[3]
Where are your highest-volume denial categories and are they moving over time?
Tracking denial categories by dollar volume and claim count, quarter over quarter, shows whether your process improvements are actually producing results and where new patterns are emerging. A denial category that has been declining for three quarters and suddenly spikes is worth investigating immediately, because the cause is almost certainly a recent change in either payer behavior or an internal process breakdown.
A Note on 835 Data Consistency
One important operational reality that any team working with 835 data needs to understand is that 835 remittance data is not uniform across payers. The HIPAA standard governs the transaction format, but individual payers have companion guides that define how they use specific fields, and those implementations vary.[3] A denial code that means one thing in a UnitedHealthcare 835 file may be applied differently in an Aetna or BlueCross file. This is one of the reasons why 835 analysis benefits from tooling that is designed for healthcare data specifically, rather than general-purpose analytics that treats all payer files as equivalent.
It is also worth noting that the 835 and the corresponding electronic funds transfer do not always arrive in perfect alignment. Bundled payments covering multiple patients and multiple dates of service in a single deposit require careful reconciliation to ensure that the payment record matches the actual deposit, and that discrepancies are identified and resolved before they create credit balance issues or inaccurate A/R reporting.[4]
A Real-World Scenario
Consider a multi-specialty practice that had been experiencing a gradual increase in denials from one of their largest commercial payers over a period of about four months. The denials were being worked individually as they came in, but no one had identified a pattern because the claims were spread across multiple providers and procedure types.
When they ran a quarterly trend analysis on their 835 data, a clear pattern emerged. The same CARC code, indicating a medical necessity issue, was appearing with increasing frequency on a specific category of evaluation and management visits billed at higher complexity levels. The dollar volume affected was substantial, and the pattern had been building for 16 weeks without anyone connecting the individual denials into a single systemic issue.
Investigation revealed that the payer had updated its documentation requirements for high-complexity E&M visits, requiring specific elements of medical decision-making to be documented in a particular format. The fix involved updating the documentation template used by the affected providers and submitting corrective appeals for the backlog of denied claims with the updated documentation attached. The trend reversed within 60 days of the workflow change being implemented.
Where to Start This Week
You do not need a sophisticated analytics platform to start reading your EDI data more effectively. The starting point is establishing a consistent process for reviewing what your 835 files are already telling you.
Pull a 90-day summary of your top denial reason codes by payer. If your practice management system or clearinghouse does not surface this automatically, your clearinghouse's reporting portal almost certainly has this data available. Seeing your top five CARC codes by payer, sorted by dollar volume, will immediately tell you where the patterns worth investigating are concentrated.
Compare the current 90 days against the prior 90-day period for your largest payers. Stability in denial patterns is generally good news. A denial category that has increased significantly between the two periods is a signal that something has changed, either in your internal workflow or in how that payer is adjudicating claims, and it is worth understanding which one before deciding how to respond.
Select your highest-volume payer and run a payment variance analysis. For that payer, compare the contracted rate for your top ten procedure codes against what was actually paid over the last 90 days. Even a small per-claim underpayment across high-volume codes adds up to material revenue leakage over time, and it is entirely recoverable if it is identified before the contract dispute window closes.
Build a standing monthly review of CARC trending into your revenue cycle calendar. This does not need to be a long session. A 30-minute monthly review of denial code trends by payer, with a clear escalation path when something moves meaningfully in the wrong direction, is enough to catch most systemic payer behavior changes before they compound into a material problem.
The EDI data moving through your clearinghouse every day is one of the most underutilized analytical assets in your revenue cycle. Most practices treat it as a transaction record rather than a source of intelligence about how their payers are behaving. Building the habit of reading it consistently, and acting on what it reveals, is one of the most direct paths to recovering revenue that would otherwise be permanently lost.
That wraps up the first five issues of this series. Each topic we have covered, from denial-resistant workflows and sustainable automation to operational intelligence, A/R reduction, and EDI analysis, connects back to the same underlying principle: the practices that protect and grow their revenue are the ones that build systems designed to catch problems before they become expensive. If any of these topics have raised questions about your own revenue cycle, I would genuinely enjoy the conversation.
- Invene (August 2025). Demystifying Healthcare EDI: The 9 Critical Transactions Explained. Invene is a healthcare technology firm specializing in data interoperability and revenue cycle infrastructure. This article provides a comprehensive technical explanation of the nine core HIPAA EDI transaction sets, including the 837 claim submission formats and the 835 remittance advice, and is one of the most thorough publicly available references on the subject. invene.com →
- Accountable HQ (August 2025). EDI Files in Healthcare: What They Are, Common Transactions, and How They Work. Accountable HQ is a healthcare compliance and billing platform. This article covers the practical application of 835 and 837 files in revenue cycle operations, including how to use CARC and RARC codes for denial root-cause analysis and how to structure an EDI-based analytics workflow. accountablehq.com →
- MedCom Solutions. What Are the Differences Between 835 and 837 and Why Knowing Matters. MedCom Solutions is a revenue cycle consulting firm specializing in charge capture optimization and payment analytics. This article provides practical guidance on using paired 835 and 837 analysis for denial trending, underpayment identification, and payer behavior monitoring, drawing on their operational experience with hospital and health system clients. medcomsolutions.com →
- Streamline Health. Healthcare Claims: The Role of 835s and 837s. Streamline Health is a healthcare analytics company focused on revenue integrity and clinical documentation improvement. This article explains the reconciliation challenges that arise from the mismatch between bundled EFT deposits and individual 835 remittance files, and covers best practices for ensuring accurate payment posting and A/R reporting. streamlinehealth.net →
- UnitedHealthcare Provider Portal. EDI Transactions and Code Sets. UnitedHealthcare is one of the largest commercial health insurers in the United States. Their provider EDI documentation illustrates the payer-specific companion guide variations that affect how 835 data should be interpreted across different payers, including their specific implementation of CARC codes and EFT consolidation practices. uhcprovider.com →
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The ROI platform is built to surface the payer behavior patterns in your 835 data and connect them to the workflow actions that protect your revenue.