These pressures aren't new for perioperative and supply chain leaders, but they're intensifying, and the systems hospitals have relied on to manage them are showing their age.
That is why AI-powered capture is becoming harder to ignore. When product documentation is incomplete, the impact does not stay inside the OR. It shows up in revenue, inventory, staff burden, cost reporting, value analysis, and the decisions leaders are expected to make every day.
In a recent OR Manager webinar, three leaders shared what changed when they moved away from patchwork supply documentation workflows — barcodes, RFID, manual entry, paper — to AI-powered capture with AssistIQ.
The panel featured Ryan Ott, Perioperative Finance and Operations Manager at North Shore University Hospital (Northwell Health); Megan Harris, Director of Materials Management at Owensboro Health; and Matt Pavlovec, Patient Care Manager at Abbott Northwestern Hospital. Here are a few of the key takeaways.
At Owensboro Health, Harris described her materials management team manually counting products on shelves multiple times a day to maintain an inventory picture that still wasn't reliable. When supplies weren't documented correctly in a case, they didn't deplete from inventory, which meant they appeared available when they weren't. In some situations, supplies had to be couriered from a sister hospital in the middle of a case.
At Abbott Northwestern, Pavlovec pointed to a parallel problem on the financial side:
At North Shore, Ott's team had been running a dual process: EMR documentation plus a parallel paper form meant to catch what the EMR missed. The cost in time was real, "A number of our stakeholders mentioned spending up to 15 minutes at the end of the case just making sure that they were writing down each individual implant." And the burden didn't stop there, "You can imagine the charge capture team kind of leaning over the computer trying to figure out exactly what the nurse wrote on that piece of paper, versus just seeing it objectively on the screen — that has been huge for us, and just a huge staff satisfier."
Pavlovec's team had been managing two separate handheld scanners — one for the charting system, one for inventory — with unreliable results either way. Any item entered incorrectly could go unnoticed for months.
The common thread: clinical staff were absorbing administrative work that simply does not need to exist with what AI is capable of today. Every minute spent reconciling implants, scanning supplies, or decoding paper forms is time pulled away from higher-priority work in the room: patient care, sterility, room turnover, and team coordination.
Transforming OR and procedural area workflows touches every role, including supply chain, clinicians, and finance. All three panelists were deliberate about who shaped the rollout of Assist IQ, and their approaches converged on the same principle: involve end users before decisions are made, not after.
Ott's OR nurse manager was brought in from the start and became the team's most vocal advocate before the broader rollout began. Harris included OR coordinators — the people managing day-to-day flow in each area — in planning from the beginning. Pavlovec intentionally minimized his input, pulling in lead techs, billing coordinators, and end users to shape how the tool would be implemented.
All three recommended a staged rollout: start in one hospital or area, learn, then expand. The pattern held across systems of different sizes and configurations — what changed was the starting point, chosen specifically to reduce complexity and build confidence before scaling.
AI-powered capture creates immediate value by improving documentation accuracy, but the bigger opportunity is what happens once leaders can trust the data. As much as 30% of charge-related revenue can slip through the cracks each month when using suboptimal capture methods. ORs have seen monthly billing revenue lifts of 12%, with procedural areas seeing multiples of that.
Real-time accurate data prevents costly overpurchasing and underuse. Since going live, Owensboro has seen a 48% reduction in expired products and a 90% drop in inventory depletion errors.
At North Shore, working groups focused on cost spending used to spend hours creating and processing manual data. This workflow is now run off of accurate capture data and insights powered by live AssistIQ dashboards and related insights.
At Owensboro, Harris brought that same data to value analysis committee meetings to compare physician spend patterns, surface pricing inconsistencies, and make the case for standardization with evidence. At Abbott Northwestern, Pavlovec's team used it to evaluate forced product substitutions — driven by supply chain disruptions — on actual cost and clinical performance rather than instinct.
The throughline: visibility changes what is possible. Not just in recovering what was lost, but in making decisions that were never possible before. That is why AI-powered capture is moving from a “future innovation” conversation to an operational priority for OR and supply chain leaders right now.