What’s inside:
In a complex, high-cost orthopedic elbow surgery at a large hospital network in the US, Snap&Go AI computer vision was benchmarked against standard perioperative documentation in a leading EHR to evaluate documentation accuracy and financial fidelity. Snap&Go captured 127% more billable value than standard EHR workflows, despite identical clinical behavior. The gap stemmed from bill-only implants, consignment delays, and manual documentation limits.
The findings demonstrate why manual reconciliation and barcode scans are fundamentally unable to close orthopedic surgery charge capture gaps for consignment and bill-only items, and how point-of-use computer vision offers a structurally different solution.
Background: Challenges in Orthopedic Surgery Charge Capture
Hospitals have attempted to reduce implant revenue leakage through item-master cleanup, barcode adoption, preference-card optimization, and post-case reconciliation. Despite these efforts, implant-dense procedures remain highly exposed due to bill-only items, consignment workflows, and manual documentation at the point of use.
In a complex, high-cost orthopedic elbow surgery at a large hospital network in the US, Snap&Go, an AI-powered computer-vision camera, was benchmarked against standard perioperative documentation in a leading EHR.
The case, drawn from a regional academic network, was designed to evaluate AI computer vision for perioperative documentation in an expensive, implant-dense elbow procedure performed under routine conditions with manual implant count sheets. Inventory included both consignment and hospital-owned stock.
The objective was to compare documentation completeness, accuracy, and financial fidelity between the EHR workflow and Snap&Go in the same procedure, and to assess whether camera-based capture delivers materially better recording of implant and med-surg utilization, superior lot/expiry completeness, and clearer financial traceability.
Methodology: Evaluating Charge Capture Accuracy in Orthopedic Procedures
The study mirrors real-world conditions by using standard EHR workflows, manual count sheets, and routine OR behavior, ensuring that results reflect operational reality.
During routine documentation, the OR team used the EHR’s standard charge-capture tools (pick lists/barcode scans), implant logs, and charge rules.
In parallel, a designated RN team recorded all chargeable implants and consumables with the Snap&Go device positioned at the point of use.
Post-case analyses included: line-item reconciliation, catalog-level matching, charge valuation, data integrity, and completeness checks (lot and expiration capture).
Key Findings: Where Orthopedic Surgery Charge Capture Breaks Down
1. Bill‑Only high‑value implants and item‑master gap
Snap&Go captured 127.08% more chargeable value than the EHR, indicating material revenue exposure. The dominant driver was two Smith & Nephew EVOS plates – $900 each – recorded only by Snap&Go and corroborated by a vendor count-sheet completed by the OR team and imaged by the Snap&Go camera.
These implants, approximately 60% of the case value, were bill-only items. With no manufacturer catalog number onboarded in the EHR item master, they were not selectable and therefore not billed. The missed charges total $1,800.
Notably, several days post-op, the items still had not been added to the EHR record, a significant reconciliation/governance red flag.
2. EHR Misdocumentation and item value gap
The EHR contained two misdocumented items; the assumption is that human error was involved (wrong selection/SKU errors), which undermines claim accuracy and may impact the ability to obtain full reimbursement.
The size captured in the Snap&Go reflects a 15% higher item value. Snap&Go provided corroborating evidence – a handwritten vendor count-sheet image and a time-stamped device record – which indicates that this mismatch in the EHR documentation could potentially undermine claim accuracy and the hospital’s claim credibility.
3. Med-surg under-documentation
Med-surg consumables, such as skin staplers, Ioban, NaCl irrigation, and an irrigation set, representing approximately 18-21% of this case’s value, were missing from the EHR record.
Root causes include reliance on manual pick lists/barcode scans and implant-centric preference cards, while these items are typically stocked via SPD/distributor channels rather than vendor implant trays.
In contrast, Snap&Go captured these lines consistently at the point of use, with complete lot/expiry where applicable, closing the transparency gap and enabling accurate, actual-use case costing, improved costing fidelity, and reduced charge variability.
4. Data completeness gap
To audit the integrity of item data, we defined completeness as having both lot/batch and expiration fields populated for each item. Snap&Go recorded these identifiers on nearly 100% of captured items, with image-backed label evidence at the point of use. In contrast, the EHR export included lot/expiry on only 10.5% of items, leaving most entries blank, consistent with manual pick/scan workflows and implant-centric preference cards.
This completeness gap directly affects UDI compliance and traceability, recall readiness, audit defensibility, shelf-life management, and cost–charge reconciliation, with Snap&Go providing the materially stronger data trail.
EHR vs AI Computer Vision Charge Capture Outcomes
| Finding Category | EHR Performance | Snap&Go Performance | Quantified Impact |
|---|---|---|---|
| Bill-Only High-Value Implants (Item-Master Gap) | Missed two plates ($900 each); still absent days post-op | Captured both implants at the point of use | Snap&Go captured 127.08% more chargeable value, including $1,800 in charges missed by EHR (~60% of total case value). |
| Item Misdocumentation / SKU Error | Two items misdocumented (wrong selection/SKU), resulting in lower recorded value | Correct size captured with image-backed documentation | 15% higher item value recorded by Snap&Go compared to EHR documentation |
| Med-Surg Under-Documentation | Missing consumables (skin staplers, Ioban, NaCl irrigation, irrigation set); not recorded in EHR | Consistently captured consumables at the point of use with lot/expiry where applicable | Med-surg items represented approximately 18–21% of the total case value absent from the EHR record |
| Data Completeness (Lot/Expiry Capture) | Lot/expiry populated on only 10.5% of items | Nearly 100% lot/batch and expiration capture with image-backed evidence | ~90% completeness gap between workflows |
Conclusion: Strengthening Orthopedic Surgery Charge Capture and Revenue Integrity
In this complex elbow case, AI computer vision documented more of what matters than the EHR, including high-value implants that were bill-only and not yet onboarded in the item master, complete lot/expiry identifiers, and med-surg consumables that manual workflows often miss.
The result is stronger orthopedic surgery charge capture, cleaner and more defensible claims, better UDI/recall traceability, and truer procedure-level costing.
The study mirrors real-world conditions by using standard EHR workflows, manual count sheets, and routine OR behavior, ensuring that results reflect operational reality rather than idealized process compliance.
The gaps trace to item-master setup deficiencies, the inherent difficulty of bill-only capture (non-onboarded SKUs aren’t selectable), manual pick/scan friction, and consignment onboarding delays. The evidence speaks for itself.
Evaluate Whether Computer Vision Can Close Your Orthopedic Charge Capture Gaps
Download the full case study to see how point-of-use computer vision compares to EHR-based workflows in implant-dense orthopedic procedures and assess whether this approach aligns with your revenue integrity and documentation goals.
