The Biggest Mistake Diversion Auditors MakeThe Biggest Mistake Diversion Auditors Make
Drug Diversion Insights with Terri Vidals
Terri Vidals talks about how bias can creep into drug diversion audits at the data stage and how that affects fairness and accuracy. She shares practical steps to keep investigations objective, contextual, and defensible for both patients and staff.
5:57•15 Jul 2026
The Biggest Mistake Diversion Auditors Make: Bias at the Data Stage
Episode Overview
- Audit objectivity must start at the data-pulling stage, not just at management review.
- Always compare a flagged staff member’s activity with peers to see if behaviour is truly unusual.
- Look across multiple months and context before calling a pattern, and question why behaviour changes after coaching.
- Adjust for workload and shift differences so raw counts of pulls, wastes, or overrides aren’t misread.
- Understand how analytics tools generate scores and flags; otherwise you may be trusting numbers you can’t explain.
“Our job is not to confirm suspicion. It’s to find out what’s actually true.”
What can we learn from those who have battled addiction? This short, sharp episode of Drug Diversion Insights with Terri Vidals zooms in on a problem many healthcare auditors don’t realise they have: bias in the data stage. Terri, a seasoned pharmacist and drug diversion expert, talks directly to those in healthcare who are responsible for pulling and interpreting audit data before it ever hits a manager’s desk.
She points out that, “It is very easy to make it say whatever you walked in expecting to find,” reminding auditors that the way they frame data can quietly steer an investigation long before leadership gets involved. You’ll hear clear, practical examples, like a nurse whose medication pattern looks suspicious until you check peer comparisons and see four other nurses doing the same thing.
Terri explains why “comparing peers isn’t optional” and why one alarming month doesn’t automatically mean a long-term problem. At the same time, she warns against assuming behaviour is fine just because it stopped after a coaching conversation. The episode is especially useful for diversion auditors, pharmacy leaders, and managers who rely on automated analytics.
Terri breaks down subtle traps: starting with a report and trying to prove it, cherry-picking date ranges, misreading raw counts without adjusting for workload, and blindly trusting software scores you can’t explain. Her reminder is blunt: “If the story only holds together because of what I already believed walking in… then I haven’t done an audit. I’ve built a case.” Anyone serious about fair, evidence-based diversion monitoring will find this a practical reality check.
It’s a nudge to slow down, step back, and ask: would my conclusion stand if someone neutral reviewed the same data from scratch?
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