What’s inside:

Read our case study to see how AI computer vision outperformed the standard EHR, and uncovered missed orthopedic surgical charges in a major U.S. hospital.

Orthopedic surgeries are some of the highest-value, highest-risk procedures for lost revenue, and hospitals are often unaware of how much disappears in every case. In a complex, implant-dense elbow surgery at a major U.S. hospital, IDENTI’s Snap&Go patented AI computer vision technology was benchmarked against a standard EHR documentation tool.

The question was simple but urgent:
Can AI computer vision capture the full financial reality inside the OR, where manual documentation routinely fails?

The answer was undeniable.

Snap&Go didn’t just enhance documentation; it captured 127% more chargeable value than the EHR, revealing revenue that would have been permanently lost under manual workflows.

This case demonstrates the unprecedented value of AI computer vision in orthopedic surgery: recovering real dollars, closing documentation blind spots, and delivering the accuracy surgical service lines require to protect their margins.

What Are the Benefits of AI Computer Vision in Orthopedic Surgery?

1. $1,800 in missed revenue from bill-only implants

Two high-value Smith & Nephew plates, representing approximately 60% of the case value, were used but not documented in the EHR because they weren’t onboarded in the item master. Snap&Go captured them instantly with full UDI details.

2. Wrong items selected in the EHR

Manual SKU errors led to incorrect reimbursement values.
Snap&Go provided image-backed evidence and accurate size and UDI data.

3. 18–21% of case consumables were missing from the record

Critical med-surg items like staplers, Ioban, and irrigation sets were not captured in the EHR.
Snap&Go recorded every consumable all at the point of use.

4. UDI completeness: Snap&Go ~100% vs. EHR ~10%

AI computer vision captured nearly all lot/batch + expiry identifiers, providing a defensible audit trail that the EHR could not.

workflow for data collected by AI computer vision, transmitted through the IDENTIPlatform, and into hospital's IT systems

Why Does AI Computer Vision Matter for Hospital Revenue Integrity?

IDENTI’s patented computer vision technology:

  • Prevents missed charges and revenue leakage
  • Strengthens claims accuracy and audit defensibility
  • Improves recall readiness and patient safety
  • Delivers real, verified utilization for true case costing

Orthopedic surgery margins depend on capturing what actually happens in the OR, and only AI computer vision can see it all in real time. In this single procedure, Snap&Go proved that computer vision is not just a documentation upgrade; it is a revenue-protection engine, exposing hidden financial risk and setting a new standard for surgical data integrity.

Hospitals relying solely on manual or EHR workflows are losing money. Hospitals using AI computer vision are recovering it.

Read the Full Case Study

FAQ: Orthopedic Surgery Case Study: AI Computer Vision for Accurate Charge Capture

AI computer vision uses advanced image recognition to automatically identify medical devices, implants, and consumables at the point of use. Technologies like IDENTI’s Snap&Go capture item details, UDI data, and chargeable use instantly, eliminating manual data-entry errors.

AI computer vision improves revenue capture by documenting every single item used: high-value implants, plates, screws, med-surg consumables, and bill-only items. With complete image and product data stored on the IDENTIPlatform and seamlessly integrated with hospital IT platforms, hospitals can confidently bill accurately, avoid missed charges, and strengthen claim defensibility.

Computer vision is significantly more accurate because it does not rely on staff scanning or picking the correct SKU. It captures multiple items instantly, handles crumpled labels, and eliminates manual errors that lead to missed charges in orthopedic cases.

Yes, this is one of the biggest advantages. Even if a bill-only implant isn’t in the EHR item master, computer vision still recognizes it, captures the UDI, and records the label image, ensuring the hospital bills correctly and doesn’t lose revenue.

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About the author

Natalie is a Marketing and Content Manager at IDENTI, writing about IDENTI’s AI innovations and exploring how technology can streamline hospital efficiency and improve patient care. With a background in public health, she has a deep understanding of the interconnectivity between healthcare operations, data, and patient outcomes, allowing her to translate complex technological solutions into meaningful impact for hospitals and clinicians.
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