The numbers below are scoping targets, stated as assumptions - not observed results. Every engagement starts by measuring your actual baseline. Health systems deploying this solution typically target 25-40% reductions in claims denials within 90 days - for a multi-site physician group or ambulatory surgery network with $50M-$150M in annual patient revenue, the modeled recovery is $200,000 - $600,000 in annual revenue. Prior authorization is scoped to move from multi-day queues toward same-day completion, which shortens patient care delays. Medical coding teams typically target 15-20% efficiency gains as pre-validated encounter data eliminates rework cycles, and days in A/R are targeted to compress by 6-10 days, improving cash flow predictability and easing working capital strain.
The gains compound over 12 months as the AI model learns your payer-specific rules, coding patterns, and data quality quirks. Months 1-3 focus on denial reduction and speed; months 4-9 are when your team can redeploy recovered capacity toward revenue cycle work that drives incremental margin - payer contract analysis, coding appeals, care pathway design. The working assumption for a mature deployment is that only 10-15% of entries still need human review by month 12. Before any of these numbers mean anything, run them against your own denial rate, A/R days, and claim volume - that baseline measurement is where every engagement begins.