Clinical Data Intelligence and Advanced Analytics to Reduce Drug Diversion across the Care Delivery Cycle and Drug Supply Chain in Health Systems

This SBIR project will research mechanisms to detect when Health Care Workers HCWs in hospitals steal or divert legal drugs either to abuse themselves or to illegally sell to others We focus on HCWs in hospitals because of the alarming rates of substance abuse and diversion in hospitals with multiple studies finding roughly of our nation s nurses anesthesiologists and pharmacists are currently diverting drugs in their workplaces HCWs are becoming addicted destroying their careers jeopardizing their patients safety and increasingly dying from drug diversion overdoses Diversion continues even though most hospitals already lock p addictive drugs in Automated Dispensing Machines ADMs and run monthly anomalous usage computer reports to try to detect diversion Hospitals broadly agree these current methods have two main weaknesses Data in the ADM only show part of the equation the dispensing of the drug from the locked cabinet ignoring drug administration data in the Electronic Medical Record EMR as well as other data available in other existing hospital computer systems Motivated diverters can game the system with falsified data entries to avoid detection This SBIR project will conduct research to address these two problems by building a computer system with a automated data feeds from multiple existing hospital computer systems and b advanced analytics to flag potential diversion for investigation We will test the following four hypotheses Data Consolidation hypotheses and experimentation plan Phase If we consolidate data from two systems EMR andamp ADM then we can detect diversion that would have been undetected using data only from the ADM Hypothesis Phase If we consolidate data from five systems EMR ADM Purchasing Systems Internal Inventory System s and Employee Time Clocks then we can detect diversion that would have been undetected using only EMR andamp ADM data Hypothesis Data Analytics hypotheses and experimentation plan Phase If we create and test algorithms on blinded consolidated historical data from EMR ADM then we can detect known cases of drug diversion that that current methods do not detect with fewer Type II errors false negatives Hypothesis Phase If we refine and test additional algorithms using near real time consolidated data from the five computer systems above then we can detect drug diversion that current methods do not detect faster with fewer Type I errors false positives and fewer Type II errors Hypothesis This SBIR Phase project will research mechanisms to detect when Health Care Workers HCWs in hospitals steal or divert legal drugs either to abuse themselves or to illegally sell to others Drug diversion is considered a very important and visible public health issue because of its impact on patient safety In addition this research has the potential to reduce injuries to HCWs who are becoming addicted destroying their careers jeopardizing their patients safety and increasingly dying from drug diversion overdoses