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

这个SBIR项目将研究机制，以检测医院的卫生保健工作者何时窃取或“转移”合法药物，以滥用自己或非法出售给他人。我们关注医院中的医务工作者，因为医院中药物滥用和转移的比率令人震惊，多项研究发现，我国约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。卫生工作者正在上瘾，摧毁他们的职业生涯，危及他们病人的安全，并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药物锁定在自动配药机(ADMS)中，并运行每月的“异常使用”计算机报告，试图发现转用情况，但分流仍在继续。医院普遍认为，目前的这些方法有两个主要缺陷： 1.ADM中的数据仅显示方程式的一部分：从上锁的柜子中配药，忽略电子病历(EMR)中的给药数据，以及其他现有医院计算机系统中可用的其他数据。 2.有动机的转向者可以用伪造的数据条目来玩弄系统，以避免被发现。这个SBIR项目将进行研究，通过建立一个计算机系统来解决这两个问题：(A)来自多个现有医院计算机系统的自动数据馈送和(B)高级 分析以标记潜在的转移调查。我们将检验以下四个假设： ·数据整合假设和实验计划：第一阶段：如果我们整合来自两个系统(EMR和ADM)的数据，则我们可以检测到仅使用ADM数据无法检测到的转移(假设1)第二阶段：如果我们整合来自五个系统(EMR、ADM、采购系统、内部库存系统(S)和员工时钟)的数据，则我们可以检测到仅使用EMR和ADM数据无法检测到的转移(假设3) ·数据分析假设和实验计划：第一阶段：如果我们在来自EMR/ADM的盲目、合并的历史数据上创建和测试算法，那么我们可以检测出现有方法无法检测到的已知药物转移案例，而第二类错误(“假阴性”)更少。(假设2)第二阶段：如果我们使用来自上述五个计算机系统的近乎实时的合并数据来改进和测试其他算法，那么我们可以更快地检测出当前方法无法检测到的毒品转移，并且类型I错误(“假阳性”)和类型II错误更少。(假设4)

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)