What this calculator helps you estimate
Prior authorization work sits in a difficult operational middle ground. It is administrative work, but it touches clinical policy, patient access, provider relationships, and revenue protection at the same time. When teams are understaffed or request volume climbs faster than capacity, the result is not only higher labor cost. It can also mean longer turnaround times, more handoffs, incomplete submissions, and a larger share of avoidable denials. That is why healthcare organizations often look at automation first in this area: the process is rules-heavy, repetitive in many categories, and expensive when handled entirely by manual review.
This calculator is built for fast business-case modeling. It estimates the return on investment of a blended prior authorization workflow in which an automation platform handles the routine, structured portion of the queue while human reviewers focus on exceptions. In plain language, the tool asks a simple question: if software can take over the straightforward cases, how much reviewer time is freed up, how much denial-related loss might be avoided, and how long would it take to recover implementation and platform costs?
The model does not assume that every request becomes touchless. Instead, it mirrors a more realistic operating design. Some requests are eligible for straight-through processing because the rules are well defined and the necessary documentation is already available. Other requests still need a nurse, pharmacist, or medical director because the case is incomplete, clinically nuanced, or outside the platform's supported pathways. That is why the calculator asks for both automation coverage and an exception rate. Coverage describes how much of the annual volume the platform can attempt. Exception rate describes how often those attempted cases still bounce back to manual review.
To use the calculator, start with your annual request volume and the average number of manual minutes required today. Then enter a fully loaded hourly cost for the staff who perform the work. Next, estimate the share of cases the platform can touch, the share of touched cases that still become exceptions, and the average manual minutes needed for those exception reviews. Finally, add the recurring platform fee, one-time implementation cost, the expected denial improvement on the automated portion of the queue, the average financial impact per denial, and the number of years you want to include in the analysis. The result is a directional ROI view that can support budgeting, vendor evaluation, or scenario planning.
Each input represents a distinct operational choice. Annual Prior Authorization Volume is the total request count handled in a year. Manual Minutes per Case is the current average end-to-end effort before automation. Fully Loaded Hourly Cost should include wages, benefits, overhead, and any internal burdening standard used by finance. Automation Coverage is the percent of the total queue that the platform can process under current rules and integrations. Automation Exception Rate measures how often automated attempts still need a human. Manual Minutes per Exception Review is where you model the fact that leftover work can become more specialized and sometimes more difficult.
The remaining financial inputs convert those operational assumptions into ROI. Annual Automation Platform Cost is the recurring subscription or license spend. Avoided Denial Rate Improvement is the percent reduction in avoidable denials on the automated portion of volume, not a blanket reduction in all denials across the enterprise. Average Net Cost per Denial should reflect lost revenue, rework, appeals effort, and partial recovery behavior in your environment. One-Time Implementation Cost captures setup, integration, training, and change management. Analysis Horizon determines how many years of net annual benefit are included in the total value figure.
When you read the results, focus on the story each output tells. Annual labor savings estimates the reviewer time avoided after accounting for exception work. Annual denial avoidance benefit estimates the financial protection created by more complete and consistent prior auth handling. Net annual benefit subtracts recurring platform cost to show ongoing annual value. Payback period shows how quickly implementation cost can be recovered. Total value over analysis horizon rolls the annual benefit across your chosen number of years and then subtracts the one-time implementation cost. If that last figure is strongly positive under conservative assumptions, you likely have a credible case to explore further.
How the formula works, with a worked example
Under the hood, the math is intentionally simple so that finance, operations, and clinical leaders can understand it together. First, the calculator converts manual minutes into hours. It uses your annual volume and hourly cost to estimate the current labor spend embedded in the baseline process. Then it estimates how many cases are touched by automation, how many of those become straight-through cases, and how many still need a manual exception review. The labor savings figure comes from the time avoided on straight-through cases minus the time still required for exception handling. In other words, automation creates value only when it removes more manual work than it adds back through exceptions.
The existing formula block below is preserved in MathML because it expresses that labor relationship clearly. It is a simplified labor-only view, but it captures the same logic used by the calculator: higher coverage expands the opportunity set, while a higher exception rate gives some of that opportunity back.
Note: the on-page calculator uses the same concepts but computes straight-through and exception volumes explicitly before converting the result into dollars.
After labor savings, the model adds a denial avoidance estimate. That amount is calculated on the automated share of annual volume and multiplies your assumed improvement rate by the average net cost per denial. From there, the calculator subtracts annual platform cost to arrive at net annual benefit. If that annual figure is positive, the tool divides implementation cost by annual benefit to estimate a payback period in months. Total value across the full horizon is then calculated as net annual benefit times the number of years, minus the one-time implementation cost. The calculator does not discount future cash flows, so multi-year totals should be read as nominal planning figures rather than formal NPV analysis.
Using the default assumptions gives a practical example. Suppose an organization handles 185,000 prior auth requests per year, spends 18 manual minutes per case, and values reviewer labor at $58 per hour. If automation can touch 62% of the queue, and 14% of those automated cases still become exceptions taking 11 manual minutes each, a substantial portion of routine reviewer work is avoided. Add a 2.5% improvement in avoidable denials on the automated volume, apply an average net cost of $345 per denial, subtract a $475,000 annual platform fee, and compare the result with a $275,000 implementation cost over three years. The resulting outputs give a concise answer to the question leadership usually asks: does the recurring operational benefit justify the upfront and ongoing spend?
A useful way to interpret the model is to compare the operational shape of a fully manual queue with a partially automated one. The table below does not change the calculation; it simply explains why the numbers move the way they do when you change the inputs.
| Dimension | Manual PA | With automation |
|---|---|---|
| Reviewer workload | Every request consumes full manual handling time, so staffing pressure rises almost linearly with volume. | Routine requests can move straight through and reviewers spend more of their time on incomplete or clinically nuanced cases. |
| Turnaround time | Queue speed depends heavily on headcount, scheduling, and backlog discipline. | Covered pathways move faster, while exceptions still receive manual attention where judgment is needed. |
| Cost structure | Labor cost scales with demand and complexity. | Some cost shifts into recurring platform spend, but marginal volume can often be absorbed with less incremental labor. |
| Denial risk | Process quality varies more because completeness, documentation, and timing depend on human consistency. | Rules and documentation checks are more standardized, which can reduce avoidable denial exposure on supported pathways. |
Assumptions, scenario planning, and practical notes
ROI estimates in this area are only as strong as the assumptions behind them, so it helps to anchor the inputs in real operating data whenever possible. Start by segmenting volume into meaningful buckets such as pharmacy versus medical prior auth, inpatient versus outpatient, or high-volume service lines like imaging, oncology, cardiology, and specialty drugs. A single average time per case can be useful for fast planning, but it can also hide important variation. If you have queue analytics or time studies, use a weighted average or run the calculator more than once for different workstreams.
What should I use for fully loaded hourly cost? Use the all-in hourly rate for the people who actually perform the work, including benefits, overhead, and supervisory burden if those costs are part of your internal planning standard. If several roles touch the workflow, you can either use a blended average or run separate scenarios and add the results together. The more heterogeneous the staffing mix, the more useful it becomes to split the analysis rather than rely on a single blended number.
How should I estimate automation coverage and exception rate? Coverage should reflect the share of requests the platform can realistically attempt under your current rules, documentation patterns, and integration maturity. It is not the same thing as straight-through success. A platform may be able to ingest many requests but still hand some of them back because the required documentation is missing, a clinical edge case appears, or the policy logic is incomplete. Exception rate captures that handback behavior. If you are early in evaluation, start conservative, especially if vendors are presenting best-case numbers from highly standardized pathways.
How do I interpret denial improvement? Think of it as a reduction in avoidable denials tied to prior auth process quality on the automated portion of the queue. Examples include fewer incomplete submissions, more consistent criteria application, faster response timing, and better documentation capture. It is not a claim that all denials disappear. If you are unsure, set the value to 0% first and evaluate the case on labor savings alone. Then add denial improvement later as a sensitivity test once you have denial reason codes, appeal data, and a credible estimate of how much of the denial pool is truly process driven.
Why can exception review time matter so much? In real operations, automation often removes the easy work first. That means the remaining manual queue can become denser with nuanced, incomplete, or high-risk cases. If that is likely in your environment, use a larger value for manual minutes per exception review than you would for the original average manual case. Doing so will make the model more honest. A tool that looks attractive only when exception work is unrealistically easy probably needs closer scrutiny.
Should labor savings be treated as cash savings? Sometimes yes, but often not immediately. Many organizations first realize the benefit as capacity: less backlog, less overtime, better turnaround, or the ability to support growth without proportional hiring. Over time, that capacity can translate into direct cost avoidance, but the timing depends on staffing policy, attrition, redeployment, and broader utilization management priorities. When presenting the output to leadership, it helps to describe labor savings as either budget reduction or redeployable capacity, whichever better reflects how your organization actually manages headcount.
What is often missing from the math? The model focuses on direct financial effects and intentionally leaves out harder-to-price benefits such as provider experience, member access, audit readiness, policy consistency, and lower staff burnout. It also ignores discounting, future contract renegotiation, and any gradual ramp-up in automation performance. Those omissions do not make the calculator less useful; they simply mean the result should be read as a directional planning figure rather than a full corporate-finance model.
If you are preparing materials for finance or an executive steering committee, a three-scenario approach usually works best. Keep platform and implementation costs fixed, then vary only the assumptions with the most uncertainty. That typically means coverage, exception rate, denial improvement, and occasionally average denial cost. A scenario set like the one below is often enough to turn a calculator output into a credible decision memo:
- Conservative: lower coverage, higher exception rate, minimal denial improvement.
- Expected: pilot-based assumptions that reflect normal operating conditions after stabilization.
- Aggressive: high coverage, lower exception rate, stronger denial benefit once integrations and workflow redesign are mature.
For best accuracy, gather a short data pack before finalizing your assumptions: annual volume by category, current average handle time, staffing mix and burdened rates, denial reasons tied specifically to prior auth, appeal and overturn behavior, and any pilot metrics showing straight-through rates or exception drivers. Even two to four weeks of clean queue data can materially improve the credibility of the ROI estimate.
If you are also estimating adjacent clinical operations, the telehealth vs in-office visit cost calculator can help frame visit-channel economics. For a broader automation portfolio discussion, many teams pair this tool with an RPA ROI calculator or a workflow automation business case so leadership can compare opportunities across revenue cycle, utilization management, and administrative operations.
Automation ROI Summary
Annual labor savings: $0
Annual denial avoidance benefit: $0
Net annual benefit after platform spend: $0
Payback period: 0 months
Total value over analysis horizon: $0
These figures are directional planning estimates and do not include discounted cash-flow analysis or qualitative benefits such as member experience and provider satisfaction.
Optional mini-game: route the PA queue
This arcade-style mini-game turns the calculator's core idea into a quick routing challenge. Clean requests should move into Automation, while DOC? and EDGE requests belong in Review. API cards are bonus opportunities that add time when routed correctly. The mix of cases shifts with your current automation coverage and exception rate inputs, so you can feel the operational difference between an easy straight-through queue and a messy exception-heavy one.