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

这一SBIR项目将研究各种机制，以便发现医院的卫生保健工作者何时窃取或“转移”合法药物用于自己滥用或非法出售给他人。我们关注医院的卫生保健工作者，是因为医院滥用药物和转移药物的比率令人震惊，多项研究发现，我国大约10%的护士、麻醉师和药剂师目前在他们的工作场所转移药物。医护人员正在上瘾，这破坏了他们的职业生涯，危及病人的安全，并且越来越多地死于药物转移过量。尽管大多数医院已经将成瘾性药物锁在自动配药机（adm）中，并每月运行“异常使用”计算机报告，以试图发现转移，但转移仍在继续。医院普遍认为，目前的这些方法有两个主要弱点：

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 10% 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 lock­p 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: 1. 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. 2. 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 1: If we consolidate data from two systems (EMR & ADM), then we can detect diversion that would have been undetected using data only from the ADM (Hypothesis 1) Phase 2: 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 & ADM data (Hypothesis 3) ● Data Analytics hypotheses and experimentation plan: Phase 1: 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 2) Phase 2: If we refine and test additional algorithms using near­real­time, 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 4)