In a report dated 24 September 2026, TechTarget said Oracle Health had announced five AI revenue cycle capabilities, including prior authorization and appeals management, to be embedded in its revenue cycle solutions in the coming months.
A study of one large insurer's Medicare Advantage claims found that denial rates varied by service and provider type, with the most denials for laboratory services and hospital outpatient providers (Health Affairs, 2022). This payer-side study concerns medical-necessity coverage denials, not hospital-wide initial-denial performance or the effectiveness of denial-prevention AI.
Make the baseline match the intervention
We would require a pilot proposal to specify which measure it intends to change. In our view, the evaluation should measure the claims an intervention is intended to affect, against an agreed comparison. Do not make the all-payer denial rate the sole test of success or include outcomes the intervention cannot plausibly change.
For each endpoint, require a written numerator, denominator and definition of a preventable denial. Specify whether the endpoint concerns initial denials, final unresolved denials, claim counts or dollars. Treat those choices as the local evaluation contract, rather than assuming that everyone means the same thing by “denial reduction.”
Keep the operational and financial decisions separate. Ask whether the intervention prevented the targeted denial, what staff work it required and what financial result finance can substantiate. We would not accept a change in denial counts as sufficient evidence of cash benefit, or an improvement in appeals recovery as the primary test of initial-denial prevention.
Require measurement before go-live, not a new project by default
We recommend approving go-live only after revenue cycle analytics and finance validate a baseline by payer, denial reason and service line, and agree a comparison plan that accounts for concurrent workflow and payer-policy changes. Use existing reports if they pass validation. Do not automatically commission a separate data-readiness project or require a comprehensive root-cause analysis before a bounded test. Do not require a new prospective observation period if historical data pass validation.
Validate the proposed baseline against underlying claims and remittance records. Reconcile totals with finance, document exclusions and check that historical and pilot cohorts use the same definitions and have comparable time for adjudication. Treat the initial historical window and reporting interval as practical starting points; adjust them for claim volume, adjudication time, baseline variability and the evaluation method.