Population health & analytics · Operating room operations AI
Opmed
Opmed makes AI software that forecasts surgical case length and demand and generates optimized OR schedules and block allocations, now extended to staffing and other hospital operations.
Strong fit
- Hospitals that want AI to build better OR schedules and quarterly block allocations
- Perioperative teams that want software-only tools with no cameras in the room
- Systems with several surgical sites that want to balance cases across them
Weak fit
- Hospitals that want video-based review of what happens in the OR
- Buyers who want a long list of U.S. reference customers
- Buyers who need a published price list
Bottom line
Opmed makes AI software that forecasts surgical demand and case length and builds alternative OR schedules and block allocations for planners to choose from. It now pitches the same engine for staffing and other hospital operations. It fits perioperative teams that want scheduling optimization from data they hold today, without installing cameras.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Mor Brokman Meltzer, Avi Paz, and Baruch Barzel founded Opmed during the COVID-19 pandemic. Brokman Meltzer, the chief executive, earned a PhD at Bar-Ilan University, a university near Tel Aviv, on the complexity of work plans. Paz, the chief technology officer, was a senior developer and cloud architect in the health division of Microsoft's research center in Israel, and Barzel heads a lab on complex network dynamics at Bar-Ilan. Opmed's engine flags cases whose time is over or under estimated, accounts for anesthesia and turnover time, and generates alternative schedules that weigh staff, equipment, surgeon preferences, and add-on cases. Planners can also build quarterly and yearly block allocations.
Opmed raised a $15 million Series A in May 2024 from NFX, Grove Ventures, Secret Chord Ventures, Sir Ronald Cohen, and Unbox Ventures. At the time it had about 30 employees, 20 of them in Israel. Implementation takes two to four weeks, and Opmed's published figures include a typical payback of three to four months and a 10 percent gain in OR utilization from its block plans. Its models draw on more than 20 million surgical records. Its customers include Geisinger, AtlantiCare, and Herzliya Medical Center in Israel, and Brokman Meltzer moved to Boston to build the U.S. business.
Compare LeanTaaS when a hospital wants block and infusion capacity tools from one vendor, Qventus when patient flow and discharge planning matter as much as the OR, Aimbient or Apella when the hospital wants cameras that measure what happens in each room, Proximie when remote surgical collaboration matters too, and Artisight when nursing units need sensors as well.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Aimbient | 7.9 | 6.4 |
| LeanTaaS | 7.8 | 7.2 |
| Apella | 7.6 | 7.2 |
| Proximie | 7.6 | 6.1 |
| Opmed | 7.3 | 8.2 |
| Qventus | 7.2 | 6.8 |
| Artisight | 7.1 | 6.6 |
Pricing
| Item | Detail |
|---|---|
| Model | Custom contract quoted by Opmed. |
| What usually drives cost | Number of ORs and sites, which modules are used (daily schedule optimization, block allocation, staffing), and integrations. |
| What to ask in diligence | The price per OR or per site, implementation fees, and how pricing changes when modules beyond the OR are added. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Opmed to function | |
| OR scheduling lead | Planners choose among the generated schedules. |
| Historical case data from the EHR | Predictions depend on past case times. |
| Block release and allocation rules | The engine must follow local policy. |
| Surgeon and anesthesia leadership support | Block changes affect their time. |
| Baseline utilization numbers | Shows whether utilization rises. |
| What will maximize your value | |
| Start with the daily schedule | Quick wins build trust with planners. |
| Move to quarterly block allocation next | That is where utilization gains are claimed. |
| Review prediction accuracy each month | Keeps case-time estimates honest. |
| Deal-breakers | |
| No access to historical case data | |
| No scheduling lead | |
| Wants video-based OR review | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Data connection and setup | 2-4 weeks |
| 2 | First optimized schedules | 1-2 months |
| 3 | Block allocation and more sites | 3-6 months |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.