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5 Examples of Employee Drug Diversion Detected by Advanced Analytics and Machine Learning

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An Invistics Webinar

On January 27th, Rhea Shrivastava attended Invistics’ webinar, “5 Examples of Employee Drug Diversion Detected by Advanced Analytics and Machine Learning”. The Keynote Speaker and CEO of Invistics, Tom Knight, walked the audience through 5 case studies and how Flowlytics, a computer software, detected drug diversion successfully in different circumstances. Below are the details.

This NIH-funded study took place over 5 years in 100 different facilities. The study aimed to reduce drug diversion to ensure patients, communities, and co-workers can remain safe in healthcare settings. They developed a 4-step workflow using Flowlytics to ensure comprehensive Drug Diversion Detection: Detect, Investigate, Adjudicate, and Report. Knight then went on to discuss 5 different example cases where they utilized Flowlytics and the 4 steps to identify drug diversion.

Case #1: Missing Medication Administrations

In this case, the computer software flagged a nurse that was dispensing a larger quantity of medication than the quantity administered/wasted/returned. By doing so, clinics can quickly identify medications that are missing due to diversion or sloppy practice.

Case #2: Falsified Medication Administration

This case also focused on irregular medication administrations but the nurse had hidden their tracks on the computer system. Through the Flowlytics heatmap, the software’s statistics detected that while there was no missing medication, the amount of opioids she was dispensing was way higher than other clinicians in the same area. This demonstrates how the software can compare general statistics to outliers.

Case #3: Multiple High Risks Alerts

This is a case that focuses on the high risk alerts that Flowlytics can provide. Their software’s analytics can detect differences based off employees’ time-clocks. By keeping a log of employee hours and flagging irregularities, they were alerted of a nurse taking small amounts of medications out when they were not clocked-in.

Case #4: Tampering with Waste

In this case, a nurse was dispensing medication and only giving the patient a portion of it. When going to dispense the waste medication, the technician swapped the waste with something that looked similar and kept the actual medication. By using a heat map, the software can detect if bulk or late wasting is occurring by comparing the waste/dosage levels to the levels of other clinicians practicing in the same area.

As another protective measure against this, this study recommends that facilities conduct occasional spectroscopy tests on randomly-sampled clinicians’ waste to identify what the waste is, the concentration of the drug, and potentially volume. By randomly conducting exams, facilities can keep expenses to a minimum and have more security against employees tampering with waste.

Case #5: Missing Restocks and Expired Medications

This last case focused on a pharmacy technician that was diverting medications when medications were being moved from care areas to the pharmacy for almost 4 years. Flowlytic’s software can give alerts when there are inconsistencies between withdrawals from the vault and what is restocked in cabinets. By using this software, facilities can detect diversion when it first occurs and take immediate action to prevent it.

Through these cases, we see that by using an analytic software, companies can detect inconsistencies much earlier than they would by simply looking for behavioral changes in their employees. Flowlytics can provide a comprehensive and efficient overview of statistics in an organization that can in turn help reduce drug diversion significantly.

To learn about our Drug Diversion Prevention Improvement Program, please click here:

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