Population health & analytics · Operating room operations AI
Apella
Apella uses cameras and computer vision in operating rooms to record surgical case events automatically, write them to the EHR, and predict case duration for scheduling.
Strong fit
- Hospitals that want to fit more surgical cases into their current operating rooms
- Perioperative leaders who want case events recorded automatically instead of typed in by nurses
- Health systems extending the same tools into interventional radiology, cardiology, and endoscopy
Weak fit
- Ambulatory surgery centers with a few rooms and simple schedules
- Hospitals that will not allow cameras in operating rooms
- Buyers who need a published price list
Bottom line
Apella is a San Francisco company that uses cameras, computer vision, and machine learning in hospital operating rooms. It records up to 14 surgical case events automatically, writes them back to the electronic health record, and uses the data to predict case length and plan schedules and staffing. Hospitals choose it when they want more cases out of the rooms and staff they have.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
David Schummers co-founded Apella and is its chief executive. He started his healthcare career in finance, covering medical technology companies, and later joined the surgical robotics company Auris Health in 2014 as its first commercial executive, before Johnson & Johnson bought it for $5.7 billion in 2019. Apella puts cameras in operating rooms. Its software recognizes up to 14 events in each surgical case, writes those times back to the EHR, and shows staff in real time which rooms are running behind. Horizon, a newer product, predicts case duration and room use before the day begins, so schedulers can book rooms more accurately.
Apella raised $80 million in Series B equity and venture debt in January 2026, led by HighlandX. Houston Methodist joined the round after putting Apella in more than 200 operating rooms, and Tampa General Hospital and the Medical University of South Carolina are also customers. The company said it had supported 500,000 surgical cases and that hospitals using it saw an average 5 percent increase in surgical volume.
Compare LeanTaaS when the main job is block scheduling and infusion chair capacity, Qventus when a hospital wants AI help with inpatient flow and discharge, and Artisight when it wants cameras for virtual nursing as well as surgery.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| LeanTaaS | 7.8 | 7.2 |
| Apella | 7.6 | 7.2 |
| Qventus | 7.2 | 6.8 |
| Artisight | 7.1 | 6.6 |
Pricing
| Item | Detail |
|---|---|
| Model | Enterprise subscription quoted by Apella, usually priced by the number of operating and procedure rooms covered. |
| What usually drives cost | Number of rooms, camera hardware and installation, EHR integration, and modules such as Horizon scheduling predictions. |
| What to ask in diligence | Annual price per room, hardware and installation costs, and how a pilot converts to an enterprise contract. |
| Published pricing | No public list price. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Apella to function | |
| Surgeon and staff agreement on cameras | Recording in the operating room needs buy-in and a written policy. |
| Privacy and security review | Video in clinical areas needs clear retention rules. |
| EHR write-back approval | Case times should not be entered twice. |
| Perioperative data owner | Someone has to act on late starts and slow turnovers. |
| Installation schedule by room | Rooms go offline during hardware setup. |
| What will maximize your value | |
| Start with the rooms that run late most often | Delays are easiest to measure there. |
| Share first-case start times with surgeons weekly | Teams change habits when they see the data. |
| Use case-length predictions when booking | Better estimates free up room time. |
| Deal-breakers | |
| No camera policy | |
| No perioperative data owner | |
| Too few rooms | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Security review and pilot rooms | 6-10 weeks |
| 2 | Pilot results | 2-3 months |
| 3 | Enterprise rollout | 3-9 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 actually 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.