Healthcare Prior Authorization Automation ROI Calculator

About This Healthcare Prior Authorization Automation ROI Calculator

Many payer and provider organizations are racing to automate prior authorization, but the landscape is crowded with vendors touting black-box savings claims. This calculator gives operations leaders a transparent way to compare an existing manual workflow with a blended automation approach, making realistic assumptions about case mix, exception handling, and denial leakage. By entering how many requests your utilization management team processes, the effort per case, and the staffing costs, you can quantify the status quo expense. The tool then models what happens when a portion of those requests flow straight through an automated decision engine, while the trickier cases still require human touch. Because denials often cascade into appeals and patient abrasion, the calculator also allows you to estimate the avoided revenue leakage from a more consistent, timely response. Executives can use the resulting insights to structure pilots, negotiate platform pricing, and set expectations with medical directors.

Prior authorization work is notoriously variable. A cardiology drug may be approved in seconds, while an out-of-network surgical referral triggers hours of chart review. Automation programs typically start with predictable, high-volume benefit categories, then expand as models mature. The calculator mirrors that ramp by letting you dial in the automation coverage and the share of those automated cases that still route back to humans. If your data science team is testing robotic process automation or natural language summarization to speed up document preparation, this tool can show the combined effect when layered on top of medical necessity rules. By experimenting with different coverage levels and exception rates, you can quickly visualize the breakeven point where machine triage pays for itself.

Organizations also face one-time enablement costs, such as integrating prior authorization APIs, mapping benefit policies, and training clinicians to trust the new workflow. The calculator spreads that implementation spend across the analysis horizon so you can compare multi-year value. It recognizes that automation gains may fund further investments, like expanding into inpatient utilization review or scaling nurse advice lines. Because it is focused on return on investment, the model expresses its outputs as labor savings, denial avoidance value, total net benefit, and the number of months until the project pays for itself. This framing helps CFOs decide whether to pursue capital budget approval or seek outcome-based pricing with vendors.

How the Core Calculations Work

The calculator converts all cycle time inputs into hours, multiplies them by the fully loaded hourly compensation rate, and contrasts the manual baseline with the automated future state. The labor savings are straightforward: the portion of cases no longer handled manually multiplied by the original effort per case. For exceptions, it adds back the time reviewers still spend on the subset that cannot be auto-approved, recognizing that complex clinical determinations still demand expertise. Denial savings are calculated using the automation coverage multiplied by the improvement in avoided denials and the average net cost per denial, which includes lost reimbursement plus rework. The total benefit subtracts the annual platform fee to highlight the recurring financial impact.

Payback is expressed in months to align with operational planning cadences. Total value aggregates annual net benefits across the user-selected analysis horizon and subtracts the one-time implementation cost. The model applies the implementation spend immediately, making early payback harder to achieve if the up-front integration work is heavy. By changing the horizon length, finance teams can see how long-term contracts or phased rollouts influence total economics. If you are comparing multiple automation vendors, entering their quoted subscription fee and expected accuracy will show the break-even point in a consistent format.

Implementation roadmaps rarely move in a straight line. The first phase may focus on digitizing intake packets, the second on applying machine learning to clinical rules, and the third on embedding determinations directly into the electronic health record. Each stage requires change management, payer-provider collaboration, and metrics. This calculator supports that journey by letting you model phased adoption—start with a modest coverage percentage, then update the inputs as automation expands. You can document real-world performance and adjust the business case every quarter. Having a living model helps clinical leaders communicate progress to regulators and patient advocacy groups who want to see that automation is improving access rather than erecting new barriers.

The tool also highlights operational nuances that are easy to overlook. When automation straight-throughs more cases, it changes the mix of work left for human reviewers. They spend more time on complex edge cases, which may require higher credential levels or longer review windows. By adjusting the exception minutes input, you can stress-test how staffing mixes need to change. Finance partners can then translate those insights into recruiting plans, incentive structures, and quality assurance budgets. Because the calculator captures both labor and quality levers, it equips cross-functional teams to align on metrics such as turnaround time guarantees, provider satisfaction, and patient access.

Prior authorization modernization is a marathon that touches IT, clinicians, providers, and members. By quantifying the labor, quality, and denial levers in one place, this calculator helps you stage investments, document realized savings, and explain how automation supports equitable access decisions. Use it to pressure test contracts, track actual performance against projections, and communicate progress to finance teams and medical leadership committees.