*This is the short version of a full article that’s published on our substack here.
Every healthcare leader has lived some version of this story. You evaluate a new tool for months, build the business case, get budget approval, announce it at a staff meeting, and six months later a third of your team is using it well, a third is using it halfheartedly, and a third has quietly gone back to doing things the old way. Nobody rebelled. Nobody complained. The tool just didn’t take.
The largest real-world study of AI scribes ever conducted just put hard numbers on exactly this pattern. Clinicians using ambient AI documentation saved an unremarkable 16 minutes a day, on average, across five academic health systems. But clinicians who used the tool on more than half their visits saved two to three times that — and only about a third of adopters ever used it that heavily. Separate research found up to 15% of assigned clinicians never engaged with the tool at all.
That gap — between the average outcome and what the committed third achieved — isn’t a technology gap. It’s a leadership gap. A 2024 review of AI adoption in health care found roughly 70% of health IT projects fail to reach sustained use, and that trust and governance, not the underlying technology, were the strongest predictors of which ones survived.
Why good clinicians revert to old habits
This isn’t a willpower problem. A clinician deciding, mid-visit, whether to use a new workflow or fall back to the one they’ve used for a decade is making that call dozens of times a day under time pressure. The old habit requires zero deliberation; the new one requires some. Across enough repetitions, the path of least resistance wins unless something actively counteracts it — which is exactly why “we trained everyone at launch” so reliably fails. Training addresses knowledge. It doesn’t address the weeks of micro-decisions afterward where the old habit keeps quietly winning by default.
Your staff go through the same cycle your patients do
If you’ve thought about how patients change behavior, this will look familiar — precontemplation, contemplation, planning, action, maintenance, and then either the change sticks or it relapses. A staff member who doesn’t yet see their documentation habits as a problem needs a different conversation than one who’s already watching a peer use the new tool successfully. Most rollouts broadcast one message to everyone — usually an “action stage” message, a login and a training date — and lose everyone who hasn’t gotten there yet.
The rollout framework
The data points to a specific operating discipline: set an adoption threshold as the actual goal instead of a go-live date, identify champions before launch instead of after adoption stalls, budget real coaching time for the awkward middle instead of front-loading support into week one, and watch usage data weekly instead of at the next QBR — because relapse happens quietly, in maintenance, long before anyone official notices.
This pattern isn’t unique to ambient scribes, either — it shows up across rehab and outpatient technology broadly, from AI-enhanced rehab tools to basic digital documentation, where interest in the technology consistently outpaces actual clinical use.
Read the full article here.
The Full Article Goes Further
Substack subscribers got the complete breakdown this week — the full five-part rollout framework with specific numbers and timelines, the stages-of-change model applied step by step to staff adoption, and the rehab-specific research showing this same pattern playing out across PT and OT technology adoption more broadly.
If you run a healthcare organization, a practice, or a clinical team, that playbook is the part worth having.
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