A continuous opioid infusion runs on a med-surg floor for nine hours. Somewhere in that window, a bolus gets factored in, the dispense rate changes from 50 to 75 micrograms per hour, and the bag eventually comes down. To most diversion monitoring tools, that entire nine-hour stretch has been summed up as one unit dispensed and one unit administered. Nothing in between was visible, which meant a rate change that was never verified – or medication that should have been wasted but wasn’t – could pass through completely unexamined because the tool was never built to look inside the infusion’s timeline. What actually happened at the bedside over those nine hours, and whether it matched what should have happened, was never something a diversion team could verify.
A patient’s chart shows a pain score of 8, despite a scheduled pain medication dose being administered on time. A diversion investigator is completely unaware of that inconsistency, because their monitoring tool only shows medication administrations — not what the patient reported afterward. Seeing the two side by side means leaving the tool, opening the EMR, and cross-referencing the chart by hand. Without having the time and foresight to take that extra step, a patient’s pain medication could be diverted and no one would know, because the one piece of evidence that might reveal it — the patient still recording high pain — isn’t visible anywhere in the system meant to be watching. There was no way to validate, right at the point where the medication was actually given, whether what was documented as administered matched what the patient experienced.
Diversion monitoring has always come down to matching what should have happened with what actually happened, and infusions and pain scores are two of the biggest factors in making that comparison at the point of care. With these elements woven into the diversion monitoring workflow, ControlCheck is bringing this important context to light.
Why These Gaps Have Existed
Continuous infusions don’t behave like an oral tablet or a single injection pulled from an ADC. They’re metered out at a rate, over hours, sometimes with a bolus and a rate change in the middle, and whatever medication is left in the tubing when it’s done doesn’t cleanly zero out. Most diversion tools have handled that complexity by treating the whole infusion as one dispense-to-administration event instead of breaking it into its real timeline. Anything that happened inside that window, like a missed waste event or an unverified rate change, never surfaced as its own auditable moment. It’s one of the most requested — and most complex — areas to get right in diversion monitoring, for exactly this reason. And getting it right means being able to confirm, at any point along that timeline, that what was administered at the bedside lines up with what was dispensed.
Pain scores are a different kind of gap. The data itself isn’t hard to find — it’s usually sitting in the EMR right next to every administration record. But it lives in a different system, gets reviewed by a different team, and correlating it with dispensing activity has always meant manually pulling up a chart and reading through it alongside what the diversion tool had already flagged. That works fine for one case, but it doesn’t scale across a full caseload. And without it, there’s no way to check whether what was given at the bedside is actually consistent with what the patient was experiencing.
More Context Changes Diversion Investigations
At their core, both of these gaps are about the same thing: confirming that what happened at the point of care — the actual rate an infusion ran, the pain a patient reported before and after receiving medicine — matches what the record says should have happened.
Continuous infusions are a genuine blind spot industry-wide, not just at any one hospital, precisely because they’re long-running and easy to lose a variance inside of. And pain score context turns “this administration pattern looks unusual” into “this administration pattern looks unusual, and here’s whether the patient’s own reported pain supports that or contradicts it” — which is the difference between a flag and a case an investigator can actually stand behind.
Closing both gaps gives diversion teams real visibility into a delivery method that’s been hard to audit for years, and lets them build every case on faster, better-supported evidence. It’s the kind of coverage that backs up why ControlCheck customers already identify two confirmed diverters daily on average — and it’s exactly the direction needed to continue diversion detection at scale.
Inside the New Infusions and Pain Score Views
In ControlCheck, infusions are now measured as a rate and bundled into event summaries that show all of the related infusion actions, like new bags, boluses, and rate changes. The result is a real administered-versus-wasted reconciliation, not just a record that a bag was opened and closed, and a lot less manual work reconstructing that timeline by hand. That reconciliation is what point-of-care validation looks like in practice: confirmation of what was actually delivered to the patient, not just what was logged when the bag went up and came down.
Pain scores are now surfaced in two places in ControlCheck. See how often a pain score is recorded for a patient in the Patient Event History, and check if a shift in medication lines up with an increase in pain scores in the Pain-Related Analysis IRIS metric. That’s a direct way to validate whether medication use at the bedside matches what the patient is reporting, not just what the chart says was given. This saves investigators from leaving ControlCheck to dig through an external chart mid-case. It gives compliance teams a way to check whether providers are documenting pain scores according to policy, and whether a dosing pattern makes clinical sense given what a patient actually reported. And it cuts both ways — helping build a stronger, better-supported case when something looks wrong, and just as easily ruling out a false positive when the pain score explains what the administration data alone couldn’t,
The Impact of More Complete Diversion Data
Both capabilities extend ControlCheck’s core strengths — closed-loop audits, analysis of staff behavior, and collaborative case management — into two new data sources diversion teams have wanted visibility into. Together, they help ControlCheck validate medication use at the point of care, no longer requiring investigators to reconstruct it after the fact. Neither replaces judgment, but both give the people making the call a lot more to work with before they have to make it.
To learn more about these new features and what else ControlCheck’s diversion surveillance can do for your health system, request a demo today.