Embedding OpenEvidence into Care Workflows

The real moat is not better answers in isolation, it is becoming the system a doctor uses while care is happening. Once OpenEvidence helps write the prior auth letter, suggest the order set, and produce the billing code inside the visit, replacing it means retraining behavior across multiple daily tasks, not just swapping one search box for another. Each task also leaves behind structured signals on diagnoses, workflows, and follow on actions that can sharpen future outputs.

• Moving from search to workflow changes what data OpenEvidence collects. A search query is mostly text. A coding, prior auth, or order set workflow produces cleaner labels around intent, specialty, diagnosis, evidence used, and what the clinician actually needed to do next.
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• This is the same pattern that made ambient scribes sticky. Abridge pushed from note generation into orders and prior authorization inside Epic, because the closer the product gets to the actual click path of care delivery, the harder it is to dislodge and the more enterprise budget it can capture.
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• OpenEvidence started with bottom up physician adoption, reaching roughly 40% to 45% of U.S. physicians before going deeper into EHR linked workflows. That matters because hospitals are more willing to embed a tool clinicians already use every day, which lowers rollout friction and speeds the jump to higher value enterprise contracts.
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The next step is for clinical AI to consolidate scattered point tools into one in workflow layer across search, documentation, coding, and administrative work. If OpenEvidence keeps turning everyday bedside actions into reusable structured data, it can move from being a fast reference product to becoming part of the hospital system of action.