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

临床数据智能

• 批准号: 9347982
• 负责人: Thomas Knight
• 金额: $ 46.62万
• 依托单位: INVISTICS CORPORATION
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9347982
• 关键词: Address; Algorithms; Behavior; Blinded; Certified registered nurse anesthetist; Clinical Data; Computer Systems; Computerized Medical Record; Computers; Cover-up; Data; Data Analytics; Databases; Detection; Effectiveness; Employee; Equation; Equipment and supply inventories; Failure; Feeds; Health Insurance Portability and Accountability Act; Health Personnel; Health system; Hospitals; Injury; Intelligence; Investigation; Legal; Methods; Overdose; Pain; Patients; Pharmaceutical Preparations; Pharmacists; Pharmacologic Substance; Phase; Privacy; Public Health; Regulation; Reporting; Research; Research Methodology; Running; Sales; Scanning; Small Business Innovation Research Grant; Substance abuse problem; System; Testing; Theft; Time; Update; Urban Hospitals; Workplace; care delivery; career; cloud based; electronic data; experimental study; feeding; innovation; patient safety; prevent; programs; software as a service

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)

SBIR项目将研究检测医院医护人员（HCW）何时盗窃或“转移”法律的药物以滥用或非法出售给他人的机制。我们专注于医院的HCW，因为医院中药物滥用和转移的惊人速度，多项研究发现，我们国家大约10%的护士，麻醉师和药剂师目前正在他们的工作场所转移药物。HCW正在上瘾，摧毁他们的职业生涯，危及病人的安全，越来越多的人死于药物过量。尽管大多数医院已经将可成瘾药物锁定在自动配药机（ADM）中，并每月运行“异常使用”计算机报告以试图检测转移，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缺点： 1. ADM中的数据仅显示了部分等式：从上锁的柜子中分发药物，忽略了电子病历（EMR）中的给药数据以及其他现有医院计算机系统中的其他数据。 2.有动机的转移者可以用伪造的数据条目来欺骗系统，以避免被发现。该SBIR项目将进行研究，以解决这两个问题，方法是建立一个计算机系统，（a）从多个现有的医院计算机系统自动输入数据，（B） 分析以标记潜在的转移以进行调查。我们将检验以下四个假设： ●数据整合假设和实验计划：第1阶段：如果我们整合来自两个系统的数据（EMR和ADM），那么我们就可以检测到仅使用ADM数据无法检测到的转移（假设1）阶段2：如果我们整合五个系统的数据（EMR、ADM、采购系统、内部库存系统、和员工时间时钟），那么我们就可以检测到仅使用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1阶段：如果我们在EMR/ADM的盲法、合并的历史数据上创建和测试算法，那么我们可以检测到当前方法无法检测到的已知药物转移病例，并且II型错误（“假阴性”）更少。（假设2）第2阶段：如果我们使用来自上述五个计算机系统的近100真实的实时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到当前方法无法检测到的药物转移，具有更少的I型错误（“假阳性”）和更少的II型错误。（假设4）