Utilization and reimbursement OS for regional cancer networks opening compact upright radiotherapy suites.

1. Existing oncology real estate can now host advanced radiotherapy, so expansion decisions move from greenfield construction to fast retrofit economics.
2. Lower-footprint systems will still face scrutiny because buyers must prove enough reimbursable patients will fill each room.
3. A 57-contract installed-base pipeline is large enough to support a dedicated software layer instead of one-off consulting around each launch.
4. Named adopters at Stanford, Dana-Farber, and McLaren validate the category and create urgency for regional networks that do not want to lose referrals upstream.

Catalyst. Leo's signed contract base and dramatic footprint reduction mean upright radiotherapy is moving from rare flagship buildouts to a broader rollout, which forces cancer networks to solve launch economics and referral operations now.

The startup sells a utilization and access OS built specifically for compact radiotherapy launches. Before purchase or retrofit approval, it connects to EHR, tumor-board, referral, and claims data to estimate eligible case volume, payer mix, and contribution margin by indication and geography. During commissioning, it produces insurer-specific medical-necessity packets, tracks missing records, and routes likely candidates from affiliated oncologists into consult slots. After go-live, it monitors denial reasons, referral leakage, schedule fill, and no-show risk so the new room reaches target throughput without overstaffing. Over time, the company becomes the operating layer between novel radiation hardware vendors, provider networks, and payers.

What's different. OEM deployment teams optimize a single hardware sale, while this company optimizes ongoing room utilization across referrals, authorizations, and multi-site scheduling. Generic hospital BI tools can report volume after the fact, but they do not encode radiation-oncology eligibility rules, payer packet assembly, or launch-specific slot economics. That creates a compounding data moat around which indications, payers, and referral partners actually make compact radiotherapy programs succeed.

Jobs to be done

flowchart LR
  Buyer[Oncology leader] --> Pain[Compact room launch needs volume and payer certainty]
  Pain --> Product[Utilization and access OS]
  Product --> Outcome[Confident capex approval and fuller treatment schedules]

Executive takeaways

• The exact beachhead is real but narrow: Leo has turned upright radiotherapy into a live procurement category, yet the visible rollout universe is still measured in dozens to low hundreds of advanced sites, so the venture case depends on expanding from upright launches into adjacent high-capex radiotherapy programs once the workflow is proven [1][2][15][16][17].
• The bottleneck shifts from civil construction to launch orchestration when compact fixed-beam systems fit inside existing vaults; payer authorization, referral conversion, and commissioning throughput become the real determinants of room ROI [1][3][7][8][22][25][36].
• Hospitals already suffer staffing shortages, prior-authorization delays, and fragmented QA workflows, which makes the pain economically real for a product that removes manual coordination rather than adding another dashboard [20][21][23][25][26][36][38][39].
• Incumbents own planning, treatment records, and enterprise data, but none of them specializes in first-room launch economics across referrals, authorizations, and modality-specific readiness [27][28][29][30][36][38].
• Regulatory risk is manageable while the product remains workflow and evidence infrastructure; it rises materially if the system starts making patient-specific clinical or coverage decisions without clinician review [5][22][32][33][34].

Market definition

Workflow software that de-risks first-room launch economics for compact advanced radiotherapy: forecast eligible patients, build payer-ready authorization packets, and manage slot fill for new upright or fixed-beam treatment rooms [1][3][7][10][22][25].

Customer and buyer

Primary users are radiation-oncology operations leaders, chief medical physicists, and referral/revenue-cycle managers because they own commissioning, readiness, and daily slot utilization; the economic buyer is usually the oncology service-line SVP or CFO because staffing gaps, prior-authorization delays, and underfilled rooms directly change launch ROI [20][21][25][26][36][38][39].

Buying triggers

• Board approval or retrofit kickoff for a compact upright or fixed-beam room turns demand forecasting and payer workflow into immediate operational work. [1][3][7][8]
• A center’s proton referrals hit payer delays or inconsistent coverage, making authorization operations a visible bottleneck before go-live. [22][23][25][26]
• Physics and operations teams realize commissioning, peer review, and QA handoffs will otherwise run across spreadsheets and siloed systems. [20][36][38][39][40][48][53]

Willingness to pay

Budget plausibly comes from launch-readiness, service-line operations, and avoided-denial spend rather than net-new IT. Buyers already absorb staffing shortages, manual prior authorization, and commissioning/QA overhead, so a tool that shortens time-to-utilization can fund itself out of existing room-launch budgets. [20][21][22][25][26][36][38]

Category dynamics

Growth signal 16% projected increase in radiotherapy treatment courses in Europe by 2025

Validation signals

• Leo raised $65M and Stanford has already completed the first compact upright proton treatment, showing the category has moved from concept to live deployment.
• PTCOG reports 133 particle-therapy facilities in operation worldwide and 34 more under construction, so advanced-radiotherapy launch operations are a recurring need, not a one-off event.
• Payer delay and staffing studies show that throughput problems already have measurable operational and clinical consequences.

Regulatory & technical constraints

• Any cross-site benchmark product must handle PHI and consent/retention rules across HIPAA and GDPR contexts.
• If the product influences clinical eligibility or coverage decisions beyond workflow support, MDR/FDA exposure increases.
• Upright programs still need reproducible setup, image guidance, and treatment-planning translation to avoid introducing new QA risk.
• Payer coverage policies for proton and advanced radiotherapy remain variable, so authorization content must stay indication-specific.

Competition is fragmented rather than direct: OIS/TPS vendors dominate treatment records and scheduling, Epic-class systems hold enterprise workflow and referral data, OEMs help the first install go live, and in-house physicists plus PMOs bridge the gaps. The white space is a neutral control plane for pre-launch demand certainty and post-launch ramp execution [27][28][29][30][36][38][39].

Why incumbents do not win by default

• Treatment planning and oncology information systems. Varian, Elekta, and RaySearch own core treatment workflows, but they do not win by default because pre-launch demand certainty, payer packet assembly, and referral-ramp execution sit outside their center-of-gravity.
• Enterprise EHR workflow. Epic controls the broadest patient and referral data plane, yet it is not built around modality-specific commissioning, upright eligibility logic, or room-level launch economics.
• OEM deployment services. Leo- and Mevion-led deployment work can help a single room go live, but OEM services are vendor-specific and do not naturally become a cross-network benchmark or payer-operations dataset.
• In-house physics and PMO stack. Spreadsheets, binders, and ad hoc QA routines are flexible for first sites, but they scale poorly when peer review, authorization, and throughput need an auditable multi-site playbook.

This company should start as a vendor-neutral launch and utilization control plane for U.S. regional cancer networks opening their first compact upright radiotherapy room. The best first customer is a 3-6 campus network with one retrofit-ready vault, more than 20 community oncology referral partners, and a board-approved go-live inside 18 months. The immediate pain is not treatment planning or EMR replacement; it is proving enough reimbursable patients will arrive, packaging payer documentation, and filling early treatment slots before a newly built room looks underutilized. The wedge should stay narrow around retrospective eligible-volume forecasting, indication-specific authorization packet assembly, referral-to-consult routing, and first-90-day ramp dashboards for one room. That scope matches the buying trigger because budget appears when the capital project or retrofit starts and the sponsor must lock staffing, payer-readiness, and referral-ramp assumptions before commissioning ends. The strongest advantage is a neutral dataset on which indications, payers, referral partners, and launch playbooks actually convert into authorized treatment starts across sites and modalities. The biggest disconfirming risk is that the visible upright installed base stays too small or too academic, forcing the company to prove the same workflow works for compact proton and MR-linac launches sooner than planned. Researched market sizing is modeled and currently modest, and the U.S. regional-network share inside Leo’s disclosed 57 contracts is still unclear, so the first 12 months must validate budget ownership, forecast accuracy, and pilot-to-production conversion before a larger financing case exists.

Problem

• Compact upright radiotherapy lowers construction burden, but cancer networks still risk misallocating capital if they cannot prove enough authorized patients will fill the first room.
• Historical case review, prior authorization, referral coordination, QA handoffs, and go-live slot management still sit across spreadsheets, OEM services, payer portals, and siloed hospital systems, which slows launch and hides avoidable utilization leakage.

Solution

• Deploy a read-first launch OS that mines historical cases, referral flows, and payer mix to forecast eligible volume and contribution by indication before the room opens.
• Turn that forecast into daily operations by assembling payer-ready authorization packets, routing likely candidates from community referrers, and tracking first-90-day slot fill, denials, and leakage after go-live.

Why we win

• The company owns the pre-launch decision window where one avoided underfilled room changes service-line ROI materially and where incumbent OIS, EHR, and OEM tools are least specialized.
• Each deployment compounds cross-site data on indication eligibility, payer friction, referral conversion, and ramp benchmarks that a single health system, OEM, or generic BI layer does not centralize today.

Milestones

flowchart LR
  Wedge[Single-room launch wedge] --> MVP[Forecast plus authorization MVP]
  MVP --> Proof[Forecast accuracy and ramp proof]
  Proof --> Expansion[Multi-room and adjacent-modality expansion]

Founding team

Experiment roadmap

Risk assessment

What must be true

• At least a few U.S. regional networks with first-room launch timelines inside 18 months will buy a neutral workflow layer before go-live.
• Historical case, referral, and payer data must support forecast accuracy tight enough to influence staffing and capex decisions.
• Authorization and referral orchestration must improve time-to-start or denial performance enough to justify per-room software spend near the modeled workflow budget.
• Buyers must prefer vendor-neutral launch control over waiting for OEM dashboards or incumbent EHR and OIS extensions.
• The workflow must transfer into compact proton or MR-linac launches, or the beachhead will remain too small for venture-scale returns.

Open diligence questions

• How many of Leo's disclosed 57 contracts are U.S. regional-network launches with go-live inside 18 months rather than academic flagships?
• Which pre-go-live data sets can the startup actually access across historical cases, referral logs, payer outcomes, and scheduling in a typical customer?
• Who owns launch-readiness software budget in practice across oncology operations, revenue cycle, the capital project office, and the CFO?
• What measurable KPI is strong enough to displace OEM services or internal spreadsheets across forecast accuracy, authorization turnaround, denial reduction, and room-fill rate?
• How much additional product work is required to support compact proton or MR-linac launches beyond the upright use case?

Model sanity

• Revenue engine. Revenue is driven by converting $75K paid pre-go-live pilots into $175K/year per-room production contracts, scaling from 3 rooms at Y1 end to the research.yaml-modeled Y3 SOM of 12 rooms.
• Must go right. At least 2 of the first 3 paid pilots must convert to annual production contracts within Y1 (the BP's own milestone) to keep the room ramp, gross margin trajectory, and cash runway on the base-case path.
• Model breaks if. If pilot-to-production conversion falls to the BP's killCriteria boundary of under half, the downside scenario shows Y3 revenue falling ~33% and cash turning negative before Y3 end, forcing a bridge round.
• Next-round proof. Closing 2+ production contracts, validating one adjacent-modality (compact proton or MR-linac) prototype, and publishing a customer-owned ramp case study by month 18-24 is the proof set that justifies raising a seed round on top of this $2.0M pre-seed.

Scenarios

Sensitivity

flowchart LR
  Target[Target: 3-6 campus network, 1 retrofit-ready vault] --> Pilot[Paid pre-go-live pilot: $75K]
  Pilot --> Convert{Converts to production?}
  Convert -->|~67% Y1| Production[Annual per-room ARR: $175K]
  Convert -->|~33%| Churned[No conversion: pilot-only]
  Production --> Rooms[Live rooms: 3 Y1 to 12 Y3]
  Rooms --> Revenue[Recognized revenue]
  Revenue --> GrossProfit[Gross profit at ~70% target margin]
  GrossProfit --> Cash[Operating cash balance]
  Cash --> NextRound[Seed round at 12-24mo milestone]

Flags: TAM/SAM/SOM in research.yaml are explicitly labeled modeled estimates (isEstimate:true), so the entire Y3 revenue ceiling this model targets (12 rooms, $2.1M SOM) inherits that uncertainty rather than being empirically observed. · Gross margin is negative in 5 of the first 9 Y1 months because pilot-phase delivery labor outweighs thin pilot-fee revenue; the 70% target margin is a steady-state assumption that has not yet been proven in a live production room. · CAC ($125K/room) and post-production monthly churn (1.5%) are heuristic estimates with no BP/research-sourced empirical basis, since the company has not yet closed a single paid pilot -- both should be replaced with actuals after the first 2-3 pilot conversions. · The model assumes no additional financing beyond the $2.0M pre-seed; cash falls to a ~1-month buffer by Q3Y3-Q4Y3, meaning a seed round must close by roughly month 30 (well before the 36-month mark) or the company must slow hiring. · Revenue concentration risk is high: losing even 1 of the first 2-3 converted anchor rooms would materially change the Y1-Y2 growth trajectory, echoing the BP's own risk that per-room budgets and long enterprise sales cycles may not support venture-scale economics. · Y2/Y3 headcount and overhead figures are interpolated linearly between Y1-end and Y3-end snapshots rather than driven by a granular hiring plan, so quarter-to-quarter opex step-changes in reality may be lumpier than shown here.

• Slow adoption pace. If upright radiotherapy deployments slip or contracts convert slowly, the initial installed base may grow more slowly than the company plans. Mitigation: Build the first product so it also supports compact proton, MR-linac, and other oncology-room launches that share the same utilization and authorization problem.
• OEM channel squeeze. Hardware vendors may try to bundle basic deployment dashboards and limit access to customer relationships. Mitigation: Own the cross-site payer, referral, and utilization workflows that span multiple vendors and sit outside the OEM's natural product boundary.
• Clinical-trust gap. Radiation-oncology teams may resist software recommendations if patient-eligibility logic feels opaque or overly automated. Mitigation: Start with transparent rules, clinician-in-the-loop review, and launch-specific ROI dashboards that prove the system's predictions against live outcomes.