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 worth using for hospitals that want to run more surgical cases with the operating rooms and staff they have. It suits health systems willing to put cameras in their ORs. Hospitals mainly focused on block scheduling or inpatient flow may do better with LeanTaaS or Qventus.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Apella is a San Francisco company that uses cameras, computer vision, and machine learning in hospital operating rooms.
David Schummers co-founded it and is its chief executive. He started his healthcare career in finance, covering medical technology companies. In 2014 he joined the surgical robotics company Auris Health as its first commercial executive, and Johnson & Johnson bought Auris for $5.7 billion in 2019.
Apella's 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 and plan staffing more accurately.
Hospitals have put that system into wide use. Houston Methodist has Apella in more than 200 operating rooms, and Tampa General Hospital and the Medical University of South Carolina are also customers. Apella says it has supported 500,000 surgical cases and that hospitals using it saw an average 5 percent increase in surgical volume.
In January 2026 Apella raised $80 million in Series B equity and venture debt, led by HighlandX, and Houston Methodist joined the round.
Other products cover nearby needs. LeanTaaS fits when the main job is block scheduling and infusion chair capacity. Qventus suits a hospital that wants AI help with inpatient flow and discharge, and Artisight suits one that wants cameras for virtual nursing as well as surgery.
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 |
| Caregility | 6.8 | 6.6 |
Pricing
| Item | Detail |
|---|---|
| Model | A subscription quoted by Apella, usually priced by the number of operating and procedure rooms covered. |
| What usually drives cost | The number of rooms, camera hardware and installation, connecting to the EHR, and add-ons such as Horizon's scheduling predictions. |
| What to ask before you buy | The yearly price per room, hardware and installation costs, and how a pilot turns into a full contract. |
| Published pricing | Apella does not publish a price list. |
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. |
| Approval to write case times into the EHR | 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 | |
| You have no policy for cameras | |
| No one owns operating room data | |
| You have too few rooms for it to pay off | |
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 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.