Verdict: 🟢 Strong Fit for Stated Use Case

Logic for Verdict: 🟢 = Deployed at major reference sites (Mayo Clinic, Geisinger) for the exact problem described (OR efficiency). While the company is Series A (early-to-mid stage), the workflow value is validated by enterprise adoption, moving it beyond "experimental" status for its core niche.

• Core Function: AI-platform for predicting and optimizing capacity, scheduling, and staffing across OR, Inpatient, and Rehabilitation units.
• Company: Opmed (Founded ~2020; HQ: Boston, MA / R&D: Tel Aviv).
• Ideal For: High-volume hospitals and health systems with complex OR block utilization challenges.
• Pricing: Contact Sales / Not publicly disclosed (Enterprise model).
• Integration: Deep integration with EHRs (Epic, Cerner/Oracle) via HL7/FHIR, flat files, and direct database connections.
• Regulatory / Compliance: HIPAA compliant (stated); Rehab module specifically tuned for CMS/payor rule compliance. SOC 2 status not explicitly authenticated in public documentation.

Operational Utility

Surgical Services (OR & PACU)

• The Problem: OR schedules are rigid; PACUs get bottlenecked.
• The Fix: Predicts case length variance to prevent overbooking. Syncs OR outflow with PACU bed availability to stop patients from being "held" in the OR due to lack of recovery beds.

Rehabilitation (Inpatient Rehab Facilities)

• The Problem: Coordinating PT, OT, and Speech Therapy for one patient is difficult; ensuring those minutes meet strict CMS billing rules is harder.
• The Fix: Automates the "Tetris" of therapy scheduling. It ensures patients get their required 3 hours of therapy (preventing revenue denials) while minimizing gaps between sessions so patients aren't waiting in hallways.
• Key Metric: Early deployments cite a 95% reimbursement approval rate due to better adherence to CMS documentation/time rules.

Inpatient & Nursing (Floors)

• The Problem: Nurse staffing is based on census (number of bodies), not workload (acuity/complexity).
• The Fix: Predicts the actual labor required for the patients on the unit (e.g., "Room 3 needs 1:1 care") and suggests staffing levels weeks in advance, reducing reliance on expensive last-minute agency nurses.

Disclaimer

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