First-Pass Resolution Rate (FPRR)

What first-pass resolution rate measures

First-pass resolution rate measures how many claims are submitted, adjudicated, and finished in a single cycle. "Finished" means the claim reaches a zero-balance status for the payer with no follow-up from your team: payment as expected, correct application to deductible, or a valid non-pay that you do not plan to appeal.

FPRR is stricter than clean claim rate. A claim can be clean, accepted into the payer's system, and still not resolve on first pass if it hits an avoidable denial, partial payment that needs correction, or a preventable edit like exceeding authorized units.

High FPRR tells you that coding, front-desk data, eligibility, and authorization workflows are all working together. Low FPRR means your team is touching too many claims after first submission, which directly adds days and labor cost to collections.

Why first-pass resolution rate matters operationally

Every claim that does not resolve on first pass costs extra touches: follow-up calls, corrected claims, appeals, or rebilling. Those touches show up as extra work for your team, more days in A/R, and more chances to miss timely filing or appeal limits.

In behavioral health, one missed first-pass resolution on a 30-day residential stay or a month of IOP can tie up tens of thousands of dollars. Poor FPRR also hides inside your denial rate and adjustment reports. If your FPRR is low, the same unit of revenue may be cycling through your team two or three times before it lands.

Leaders use FPRR as a quick read on how healthy the upstream process is: scheduling, eligibility, auth, documentation, and coding. Operators use it to find which payers, locations, or service lines are driving the most avoidable rework.

How to read and use first-pass resolution rate

Operationally, FPRR is most useful when it is:

• Tracked by payer, program, and place of service
• Measured on a clear cohort (for example, by claim submission month)
• Connected to denial categories such as eligibility, auth, coding, and medical necessity

If your FPRR is high overall, but low for a single payer or program, you likely have a targeted process issue. For example, a carve-out behavioral health payer that consistently denies days beyond the auth on first pass. If your FPRR is low across the board, you likely have structural problems such as weak benefits checks, missing concurrent authorizations, or recurring coding errors.

Set a target, watch the trend, then drill into the specific denial reasons that block first-pass resolution. The most valuable work is not just raising the percentage, but removing the repeatable failure modes that are chewing up staff time and cash flow.

Beyond the main formula, a few variations are worth knowing. Each answers a slightly different question.

You submit 1,000 primary claims in March for your behavioral health programs.

By the end of the adjudication cycle:

• 860 claims paid as expected on first submission.
• 40 claims received appropriate zero-pay decisions that you do not plan to appeal (for example, applied to patient deductible, secondary responsibility, or non-covered per contract).
• 100 claims either denied incorrectly, underpaid, or hit issues like exceeding authorized days that require corrected claims, auth updates, or appeals.

First-pass resolution rate for March, using the standard claim-count formula:

FPRR = (860 + 40) ÷ 1,000 × 100% = 900 ÷ 1,000 × 100% = 90%

The arithmetic is simple. The insight is that 10% of your March claims will consume extra touches and extra days. If those 100 non-resolved claims are mostly high-dollar residential stays for one carve-out payer, that 10% may represent a much larger share of your A/R headache than the percentage suggests.

• Counting only paid claims as resolved, and excluding valid zero-pays like deductible or coordination of benefits, so FPRR looks worse than reality and you over-focus on issues that do not need fixing.
• Including claims still in process at the payer in the FPRR denominator for the period, which drags down the rate for recent months and makes trend analysis noisy. For example, counting April submissions before the main payers have adjudicated them.
• Treating corrected claims as part of first-pass performance instead of counting the original claim outcome. For instance, logging a resubmitted residential claim that fixed an auth-date issue as resolved on first pass, which hides how often that error happens.
• Ignoring carve-out behavioral health payers that deny days over auth with CO-197 and then get fixed by manual auth extensions. Those claims often get counted as resolved eventually without showing up as first-pass failures tied to poor auth management.
• Mixing primary, secondary, and tertiary claims into one FPRR without segmentation, which makes complex coordination-of-benefits issues look like front-end problems. For example, Medicare secondary claims for patients with employer plans can drag down FPRR unless you isolate primary performance.

Behavioral health programs tend to have long episodes and per-diem billing. That structure makes first-pass failures much more expensive. One missed first-pass resolution on a 28-day residential claim or a month of PHP can hold up tens of thousands of dollars and tie up your auth and billing staff for weeks.

Concurrent authorization is the classic behavioral health trap. The claim is clean and coded correctly, but the billed units run past what is currently authorized. The payer adjudicates the authorized units, then denies the remaining days with CO-197. The claim did not need a coding fix. It needed auth units extended before billing so the days lined up.

To protect FPRR, you need billing tightly tied to active authorization limits. That means:

• Aligning per-diem days with remaining units before releasing a claim
• Stopping billers from "using up" future units early just to get a claim out
• Watching for level-of-care changes (for example, residential to PHP) so you are not billing under the wrong auth bucket

In carve-out arrangements, keep a separate eye on FPRR for the behavioral health vendor versus the medical payer. Many teams find that almost all first-pass failures sit in the carve-out bucket because concurrent auth and benefit rules are more restrictive.

AI can help with first-pass resolution rate by catching preventable issues before claims go out and by classifying what broke when claims fail on first pass. Agents can read eligibility responses, auth data, and payer rules in context, then flag claims where billed days will exceed authorized units, where coverage is missing, or where coding patterns are likely to trigger denials.

Supabill's claims-scrubbing agent can hold state on payer-specific policies, including concurrent auth rules, and compare each behavioral health claim to active authorizations and benefits before submission. A denials agent can read every 835, tag CO-197 and similar denial patterns, and roll them up into clear first-pass failure reasons by program and payer. The human RCM lead still owns judgment calls, payer relationship work, and appeal strategies, especially when clinical nuance or contract interpretation is involved.

How is first-pass resolution rate different from clean claim rate?

Clean claim rate measures how many claims pass your edits and the payer's front-end edits without being rejected. First-pass resolution rate goes further. It measures how many claims are fully finished on the first submission, including payer adjudication, without any rework, corrected claims, or appeals. A claim can be clean but still fail first-pass resolution if, for example, the claim exceeds authorized units or the payer underpays and you decide to appeal. Source

What counts as a "resolved" claim for first-pass resolution rate?

A claim is resolved for FPRR when the payer has adjudicated it to a final status and your organization does not plan any further action. That includes claims paid exactly as expected, claims where the patient is correctly responsible (for example, applied to deductible), and claims that are appropriately zero-paid under the contract with no intent to appeal. Denials that you will fight, underpayments that will be adjusted, and claims needing corrected resubmission should not be counted as resolved on first pass. Source

Should denials for exceeding authorized units (CO-197) be counted as first-pass failures?

Yes. When a payer denies some or all units with CO-197 because the billed days exceed the active authorization, the claim failed first-pass resolution. Even if you later extend the auth and get paid via a corrected claim, the original outcome reflects a process miss in auth management. For behavioral health programs where concurrent authorization is common, tracking CO-197 as a first-pass failure category is one of the fastest ways to improve FPRR. Source

How often should a behavioral health organization measure first-pass resolution rate?

Most groups track FPRR monthly by submission cohort, then review trends at least quarterly. Behavioral health teams benefit from slicing it by level of care and payer, because residential, PHP, IOP, and outpatient often have very different authorization rules and denial profiles. Avoid reading FPRR weekly unless your payers adjudicate very fast, because you will be including many claims that are still in process. Source

Is a 90% first-pass resolution rate realistic for behavioral health providers?

A first-pass resolution rate above roughly 90% is a common target in HFMA MAP Keys, but behavioral health providers with heavy concurrent authorization and carve-out business may find that hard to hit without focused work on auth and benefits. Start by measuring your true baseline, then tackle the top two failure modes, often CO-197 for auth issues and eligibility problems. Raising FPRR even a few percentage points in a residential-heavy program can free up significant cash and staff time. Source

• HFMA — MAP Keys