Quick answer: The ROI of automating prior authorizations in Epic comes down to a three-factor formula: hours saved per auth × monthly auth volume × loaded staff cost — plus the downstream revenue protected from fewer auth-related denials and faster procedure scheduling. For a multi-specialty group on Epic, where prior auth consumes roughly 13 staff-hours per physician per week, automation that handles the routine majority of requests typically pays back its cost on labor alone within the first year, before counting a single prevented denial.
Start with the number you're trying to beat
The ROI case is your current cost minus the automated state, and most groups have never priced the current state — because prior auth work is smeared across coordinators, billers, medical assistants, and physicians, none of whom log it as a line item.
The industry baseline is well measured. The 2025 AMA prior authorization survey found practices complete about 40 prior auth requests per physician per week, consuming roughly 13 hours of combined physician and staff time — and 40% of physicians now employ staff dedicated exclusively to PA. On the transaction side, the CAQH Index has historically priced a manual prior auth at around $11 in provider labor versus roughly half that for a fully electronic one — and found only 40% of medical PA transactions are fully electronic, which is why the manual cost dominates.
For your own baseline, time a sample: most groups land at 20–45 minutes of cumulative staff touch time per manual auth once you count data gathering, portal entry, and the repeated status checks. Use fully loaded staff cost — salary plus benefits and overhead — or you'll understate the baseline by a third.
The three ROI levers, in order of certainty
PA automation pays through three distinct channels, and a credible business case treats them differently.
Labor time is the defensible floor. Automation removes the data-gathering, submission, and status-checking work from the routine majority of auths; staff keep the exceptions. This lever is nearly certain and easy to measure, so build the payback case on it alone.
Denial prevention is the second lever. Auth-related denials — missing auth, expired auth, mismatched CPT — are among the most preventable denial categories, and they trace directly to follow-up slipping through cracks. Each one costs rework time, and some become write-offs. Model this conservatively (assume automation prevents a third to half of your auth-related denials) and present it as upside.
Throughput is the biggest and least certain lever. An auth that clears in two days instead of nine is a procedure that happens a week earlier — and in a multi-specialty group, the procedures behind auths (imaging, injections, surgeries, biologics) are the high-revenue events. Faster auth turnaround also reduces patient leakage to competitors with shorter waits. Track it after go-live rather than promising it in advance.
A worked example you can rebuild with your own numbers
Take a 20-physician multi-specialty group on Epic — cardiology, GI, orthopedics, and primary care under one roof.
At the AMA-measured average of 40 auths per physician per week, the group runs roughly 3,200 auth requests a month. Suppose Epic's native ePA and connected payers handle 35% cleanly, leaving about 2,080 manual auths a month. At a conservative 25 minutes of cumulative staff touch time each, that's roughly 870 staff-hours a month — at $30/hour loaded, about $26,000 a month, or $312,000 a year, of labor spent on portals, faxes, and status calls.
Now apply automation with an 80% touchless rate on the manual volume. Roughly 1,660 auths process without staff touches; the remaining 420 exceptions take a short, informed review. Monthly staff time drops to roughly 200 hours — recovering on the order of $20,000 a month, or about $240,000 a year, in labor capacity. Hold that against the vendor's pricing at your volume; at this scale the labor line alone typically clears the software cost several times over.
Two modeling disciplines keep this credible with a skeptical partner group: model the touchless rate at 75–85%, never 100%, because real volume includes peer-to-peer reviews and judgment calls; and present labor as the floor with denial and throughput gains tracked as upside.
The hidden costs an honest model includes
Four cost lines belong in the model even though vendors rarely volunteer them.
- Implementation and integration. Connecting to Epic via HL7/FHIR, mapping work queues, and running a parallel-validation period takes 30–60 days of calendar time and some of your team's attention. On Community Connect, add the host system's approval timeline.
- Coverage gaps in your payer mix. No automation covers every payer equally well. Ask vendors which of your top ten payers they handle touchlessly today, and discount the model for the ones they don't.
- Exception staffing. The review queue needs a named owner. You're not eliminating the PA function; you're shrinking it and changing its job description.
- The tuning quarter. Month one runs below the steady-state touchless rate while the system learns your payer mix and your team builds trust. Model year-one savings on ten months, not twelve.
A model that includes these survives CFO scrutiny. A model that doesn't gets one hard question in the partner meeting and dies.
The staffing question, answered honestly
The recovered hours rarely become payroll cuts, and the business case should say so up front.
What actually happens in multi-specialty groups: PA coordinators shift to exception handling, denials work, and peer-to-peer scheduling; medical assistants get their clinical support time back; and the chronic understaffing in scheduling and patient communication finally gets coverage without new hires. The financial value is identical — it's capacity you'd otherwise hire for — but the framing matters for how your team receives the change, and for whether your best coordinator (the one with payer knowledge automation can't replace) stays.
This is also where platform choice shows up in the ROI. Automation that connects prior auth to the surrounding workflows compounds the return — Honey Health's Prior Authorization agent, for instance, runs alongside its eligibility and referral intake agents, so an auth starts from a benefits check that's already run rather than from scratch, and the recovered staff hours don't get re-absorbed coordinating between systems.
What to measure after go-live
Lock the baseline before launch, then track five numbers quarterly.
- Touchless rate — share of auths completed with zero staff touches. The headline automation metric; should climb through the tuning quarter toward the modeled 75–85%.
- Staff hours on PA — re-run the time sample at 90 days. This validates the labor line your payback case rests on.
- Auth turnaround time — order date to determination, median and worst decile, by payer. The throughput lever lives here.
- Auth-related denial rate — denials coded to missing/invalid auth, as a share of claims. Should trend toward zero.
- Cost per auth — total spend (software plus exception labor) divided by auth volume, against the manual baseline. This is the single number a CFO can track on one line.
Review at 30, 60, and 90 days with the vendor accountable for the touchless-rate ramp. Groups that treat the first quarter as a shared tuning project, with the data on the table, consistently hit the modeled numbers; groups that treat go-live as done usually leave a third of the return unclaimed.
Frequently asked questions
How do you calculate the ROI of prior authorization automation in Epic?
Multiply hours saved per auth by monthly auth volume by loaded staff cost — that's the labor floor. Then add conservatively modeled denial prevention and track throughput gains after go-live. For multi-specialty groups, where PA consumes about 13 staff-hours per physician per week, the labor line alone usually pays back the software within the first year.
What does a manual prior authorization actually cost?
CAQH has priced manual prior auth at roughly $11 of provider labor per transaction versus about half for fully electronic — but that per-transaction figure understates the touch time in specialty workflows, where cumulative data gathering, portal entry, and status chasing commonly run 20–45 minutes per auth. Time your own sample; it's the most persuasive number in the business case.
How quickly does PA automation pay for itself?
Groups with meaningful volume typically reach payback within two to three quarters on labor savings alone, with denial and throughput improvements following over subsequent billing cycles. Model year one on ten months of steady-state performance to account for the tuning quarter.
Does ROI mean cutting PA staff?
Usually not. Groups redeploy recovered hours into exception handling, denials, peer-to-peer scheduling, and the patient-facing coverage they've been short on. The financial value is the same — capacity you'd otherwise hire — but the honest framing keeps your most experienced coordinators engaged instead of polishing resumes.
What touchless rate should we model?
75–85% of manual auth volume for a well-tuned system, never 100% — real volume includes peer-to-peer reviews, medical-necessity judgment calls, and payer edge cases that should route to humans. Ask vendors for the touchless rate measured on a payer mix like yours, not a demo environment.
Does Epic's native automation change the math?
It shrinks the manual base the third-party automation works on — which is why the model should start with your actual channel split rather than total volume. Native ePA and Payer Platform coverage is real but partial: industry-wide only about 40% of medical PA transactions are fully electronic, so most multi-specialty groups still carry a manual majority worth automating.
