Concept prototype 01 · synthetic data · not affiliated with Forus

Will ops break at 10x?

A capacity crystal ball: forecast the human-review queue under growth scenarios, find the month it breaks, and rank the automations that buy it back.

What this model assumes (edit the sliders to match your book). Calibrated on a commercial + Medicare-Advantage specialty-Rx queue (biologics and infusibles in chronic disease: rheum, derm, GI, neuro) where a prior authorization (PA) sits between the written script and the first dose. Starting point: ~3,000 active prescribers, ~6 PA-requiring scripts each per month, a ~22-minute manual case. Not calibrated for buy-and-bill oncology, pharmacy-benefit-only generics, or a Medicaid-FFS book. Those have different case-minutes and turnaround rules and need re-grounding before deployment.
22-min case ≈ CAQH 2024 manual-PA effort (24 min). Reviewer throughput below is a labeled internal assumption; see Sources & method.

Scenario controls

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month the queue breaks (>85% utilization)
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projected median time-to-therapy @ month 12
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monthly PA volume @ month 12
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reviewers needed @ month 12 (no new automation)

Projected median time-to-therapy, next 18 months

Queue model: stage utilization ρ drives wait non-linearly (W ∝ ρ/(1−ρ)). The cliff is the point: wait time is fine at 75% utilization and catastrophic at 92%. The dashed line is the one-day brand promise.

Why this matters

Growth is the stated #1 priority, and the brand promise (therapy in ~1 day, not a week) is a queueing promise. At 10x provider growth, the human-in-the-loop review layer is the binding constraint, and headcount planning that looks fine on averages fails on the utilization curve. This model makes the break-point visible 6 to 9 months before it happens, and prices the two levers (hiring vs. automation) in the same units.

Sources & method
All figures here are synthetic but calibrated to the cited real-world ranges below. No real Forus, patient, or payer data is used. Where no credible public anchor exists, the number is labeled as an internal assumption rather than asserted as fact.

What is grounded:

  • 22-minute manual case: calibrated to CAQH's measured manual prior-authorization effort of ~24 min (16 min via payer portal). CAQH Index Report 2024. caqh.org/…/CAQH_IndexReport_2024_FINAL.pdf
  • ~6 PA-requiring scripts / prescriber / month: order-of-magnitude consistent with the AMA Prior Authorization Physician Survey (~39 to 40 PAs per physician per week across all drugs and services; the specialty-Rx subset is a fraction of that). AMA, 2024-2025 eds. ama-assn.org/…/prior-authorization-survey.pdf
  • One-day promise vs. the pre-Forus baseline: biologic time-to-therapy averages ~42 days (insurance approval ~21.5d + specialty-pharmacy fill ~20d); a required PA adds ~4 days at median and ~23 when first denied. Burton et al., J Allergy Clin Immunol 2019/2021 (PMID 33404389); Arthritis Care & Research 2020 (PMC7062557). The chart's "pre-Forus week" and "1-day promise" lines bracket this real range.
  • Why the queue is the binding constraint: 93 to 95% of physicians report PA-associated care delays; AMA PA Survey 2024-2025. Same URL as above.

What is a labeled assumption (no credible public anchor):

  • Reviewer throughput: modeled at 7.5 productive review-hours/day x an 85% effective-capacity factor (approx. a handful of 22-min cases per reviewer-day). Per-reviewer PA quotas are not published by PBMs or UM vendors; this is an internal planning assumption you would replace with measured service-time telemetry in production.
  • Utilization cliff at 85%: standard queueing result (W ∝ ρ/(1−ρ)); the specific 85% threshold is a planning convention, not an industry-published number.

Figures are illustrative for a demo. Cited ranges are payer- and disease-specific (much of the denial/turnaround literature is Medicare-Advantage-specific) and would be re-grounded against Forus's own telemetry before any operational use.

Updated 2026-06-14 · v1.1