Surgical supplies account for roughly 15% of total operating expenses on average, and up to 30-40% in hospitals with a high case-mix index. In implant-heavy service lines like orthopedics and spine, joint replacement materials alone can consume 30% of a hospital's entire supply budget.
Nobody disputes the stakes or that variation exists. It shows up everywhere: in budget variances, in contract negotiations that quietly fail to deliver the savings everyone promised, in preference cards that don't reflect what surgeons actually open and use. The hard part isn't seeing the variation. It's knowing what's driving it, and where the real opportunity to fix it lives.
Case cost variation comes from several sources: Physician product preferences, pricing that swings by vendor, supplies opened and never used, and trunk stock. These factors and more tangle together across service lines and individual surgeons, so isolating any single root cause takes item-level data captured at the point of use.
Most organizations have that data but often lack the know-how to mine the information buried in EHRs.
Plenty have invested in barcode scanners, RFID, and other tracking tools. But those tools don't catch every item that enters the room; and many organizations, despite the technology, still have manual processes with stickers or paper. For OR workflows that rely on manual documentation, 50–60% of items never get documented once the case gets busy. Documentation happens after the case, when memory is thin and the next patient is waiting. Omissions and errors creep in. The result is a utilization picture that nobody fully trusts at the case level, which is exactly the level where the decisions get made.
Without trustworthy data, every team builds its own version of the truth. Finance pulls from purchasing logs. Supply chain works off EHR exports. Clinical operations lean on preference cards and manual reconciliations. The numbers never line up. So the meeting that was supposed to be about decisions becomes a meeting about whose spreadsheet is right.
Give teams an accurate and verifiable picture of product usage, and the analysis gets simple.
Leaders using AssistIQ point to the same shift: when all teams trust the data, the conversation changes.
Tom Lubotsky, former Supply Chain Leader at Allina Health, described the difference as having “every product” captured consistently, creating the foundation to understand product cost, waste, and utilization across the supply chain. Ryan Ott, Perioperative Finance and Operations leader at Northwell Health, put it in workflow terms: “Recognition happens in under a second. Our staff was genuinely blown away.” And Beth Steele, Chief Operations Officer at Owensboro Health tied the accuracy directly to supply chain impact: “We have a 99% accurate supply and implant capture rate... [and] we've had a 90% reduction in our inventory depletion error, so our supply chain team loves us.”
That is what complete, accurate data makes possible. Clinical teams can see cost per case, how usage compares across peers, and where outliers sit. Supply chain can understand what was actually used. Finance can trust the numbers behind the analysis. Teams stop debating whether the data is real and start acting on the opportunities that were sitting in plain sight all along.
At one US health system, a case cost analysis of total knee and total hip replacements turned up implant pricing inconsistencies that most stakeholders didn't know existed. For total knee, the same product was priced differently by laterality and by size: a spread of nearly $2,000 and nearly $4,000 respectively, worth $192,000 in annualized savings on that procedure alone. For total hip, size-based pricing variation across two products came to an estimated $94,000 a year.
Implants aren't the only lever. At another health system, six different custom packs were running across CABG procedures, every one of them priced differently – an obvious candidate for consolidation and standardization.
At a third, cross-referencing AssistIQ data against existing pick lists exposed items that went consistently unused across multiple procedures: $232,000 in annualized savings inside a single service line. A separate product analysis found near-identical supplies used in the same procedure at different price points. Consolidating to the lower-cost option carried a potential $502,000 in annualized savings.
These figures come from a handful of procedure types across a small number of service lines – targeted changes that already add up to millions.
Clinical operations, supply chain, and finance all understand the stakes. Across North American health systems, AssistIQ has found a real appetite to take on case cost variability. What's been missing is a layer of data everyone actually trusts. One operational truth that turns circular debate into focused, evidence-driven decisions.
With precise, item-level utilization data in place, the AI keeps mining for insights: price gaps by implant size, products opened but rarely used, consolidation opportunities hiding across procedures. Live dashboards push those findings straight into surgeon workgroups. So when a physician asks what's driving cost in a category, the answer is already in the room.
That's the shift. Teams stop reconciling numbers and start making calls: standardizing sizes, consolidating clinically equivalent products, stripping unused items off preference cards. Data is the blocker. Clear the blocker, and the path to action is far shorter than most organizations expect.
–Beth Steele, MSN, RN, FACHE, COO of Owensboro Health