AssistIQ was in San Antonio last week for AHRMM26. The same idea kept resurfacing from different angles across a general session on cutting through AI hype and a panel on what actually happens after new technology goes live: most AI disappointments aren't AI problems. They're foundational data problems.
Here are three of our key takeaways:
The advice on cutting through vendor noise was concrete: walk the floor, ask staff at every level where they're actually getting stuck, and build a shortlist from those specific answers rather than a trend report.
That's a useful filter for perioperative and procedural leaders specifically, because the category is full of tools that make data look more organized without changing what gets fed into it. A dashboard that's easier to read isn't the same as data that's more complete. Before evaluating any new tool, it's worth asking whether it's solving your actual capture gap or just presenting the same gap more attractively.
During a panel discussion, leaders from St. Luke's and Ochsner Health walked through what happens post-implementation. Standing up a control tower at St. Luke's exposed unit-of-measure errors and item master misalignment across ERP, WMS, and EDI systems that had been sitting there, unaddressed, for years. Ochsner's team pointed to the everyday workarounds that never make it into a system at all: proxy items, free-text orders, substitute handling that clinical staff manage manually because the formal system doesn't have a place for it.
Panelists were direct about the timeline: cleaning up governance, ownership, and standardization at that scale takes 18 to 24 months for workflow-heavy transformations. The takeaway wasn't to skip that work or look for a shortcut around it. It was to go in with an accurate sense of the timeline.
One panelist put it plainly: a healthy balance sheet reflects a healthy supply chain, especially as systems face increasing reimbursement pressure and rising labor costs.
That reframe matters for revenue integrity and finance leaders specifically. But treating supply chain as a margin lever only works if finance, supply chain, and clinical teams are working from the same usage data. Too often they're not; each pulls its own numbers from a different system, and contract negotiations or cost-per-case reviews turn into arguments about whose spreadsheet is right before anyone gets to the actual decision.
That's a real cost of not having one shared dataset. It also means the upside of fixing it is real: Owensboro Health realized a 12% net revenue increase from more accurately documenting chargeable products, and Allina Health recovered 84% of previously invisible waste value through vendor credits.
Every lesson from the week pointed back to the same starting condition: fix the foundation before chasing the next tool. That's the case AssistIQ has been making to perioperative, procedural, and supply chain leaders for a while now, and it was good to hear it echoed from every direction in San Antonio.
If you stopped by Booth #1338, thank you for the conversation. If you didn't get a chance, reach out to us and we'll find time to connect.