Using Machine Learning and Blockchain Technology to Reduce Drug Diversion in Hospitals

使用机器学习和区块链技术减少医院的药物转移

• 批准号: 10761130
• 负责人: Behnood Gholami
• 金额: $ 157.72万
• 依托单位: AUTONOMOUS HEALTHCARE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10761130
• 关键词: Address; Adherence; Administrator; Adopted; Adoption; Algorithmic Analysis; Algorithms; Ambulatory Surgical Procedures; Architecture; Cellular Phone; Clinic; Clinical; Collaborations; Computer software; Computerized Medical Record; Consumption; Coupled; Data; Data Analyses; Data Set; Detection; Development; Documentation; Electronics; Ensure; Environment; Event; Feedback; Goals; Health care facility; Health system; Hospitals; Information Systems; Investigation; Learning; Location; Machine Learning; Measures; Medication Management; Medication Systems; Modeling; Monitor; Movement; Online Systems; Paper; Pharmaceutical Preparations; Phase; Policies; Process; Protocols documentation; Provider; Records; Sensitivity and Specificity; System; Technology; Testing; Time; Training; Transportation; Update; Work; blockchain; cloud based; dashboard; digital; dosage; drug testing; improved; in silico; multiple data sources; pilot test; prevent; programs; simulation; smartphone application; software systems; substance use

Based on an analysis, the volume of dosage lost due to diversion increased from 21 million in 2017 to 47 million in 2018, a 126% increase. Addressing the drug diversion problem is a multi-faceted problem involving many components ranging from provider training to implementation of hardware and software systems to manage access to controlled substances. However, despite recent improvements in controlling and monitoring access to controlled substances, the process of identifying drug diversion and ensuring compliance is complicated and time consuming. Our overall goal is to further develop a technology based on blockchains to track and document transportation and administration of controlled substances in a hospital environment and detect drug diversion. The feasibility of the technology and the associated machine learning-based data analysis engine was established in the Phase I project. Our specific aims are: 1. Further Development of a cloud-based software platform to leverage smartphones to capture drug transactions in clinic. We will further develop the cloud-based software platform and its associated smartphone app and web-based dashboard developed in the Phase I project. The software platform, which uses blockchains to create an immutable audit trail, will be further developed to capture the “cradle-to-grave” documentation of controlled substance use and location within an ambulatory surgical center. Prior to deployment, the platform will be tested by simulation testing using the in silico model developed in our Phase I project, which is capable of generating realistic transaction data by using a multi-agent simulation framework. 2. Deploying the software platform at the collaborating health system. We will perform a two-stage rollout of our software platform, where in the first stage (the focus of this Specific Aim), we plan to perform a pilot test of our smartphone app and the associated administrator web-based dashboard to ultimately replace the paper logs. The goal of this stage of the rollout is to understand and address challenges of deploying a new system and potential impacts on the workflow and its adoption. Data related to adoption, adherence to protocols, and impact on clinical workflow will be measured and any challenges will be addressed by fine-tuning of the software platform. 3. Further development and deployment of an analysis engine to detect drug diversion. The goal of this specific aim is to further develop and fine-tune the analysis engine that uses data (recorded on the blockchain) to detect drug diversion. This effort involves two main tasks. Real data (collected as part of Specific Aim 2) will be analyzed offline by the framework developed in Phase I. Through collaboration with our collaborating health system, we will investigate the generated red flags and use the results of such investigation to fine-tune the parameters of our model. Next, we will integrate the updated analysis engine and rollout the drug diversion detection capability to the pilot in collaboration with the partner health system in the second stage of the rollout.

基于分析，由于转移的转移量从2017年的2100万增加到47 2018年百万美元增长了126％。解决药物转移问题是一个多方面的问题，涉及 许多组件从提供商培训到实施硬件和软件系统到 管理对受控物质的访问。但是，任务最近在控制和监视方面进行了改进 获得受控物质，识别药物转移和确保合规性的过程是 复杂而耗时。我们的总体目标是进一步开发基于区块链的技术 跟踪和记录在医院环境中受控物质的运输和管理 并检测药物转移。技术和基于机器学习的数据的可行性 分析引擎是在第一阶段项目中建立的。我们的具体目的是：1。进一步发展 基于云的软件平台利用智能手机捕获诊所中的药物交易。我们将 进一步开发基于云的软件平台及其关联的智能手机应用程序和基于Web的智能手机应用程序 仪表板在第一阶段项目中开发。该软件平台，该平台使用区块链创建一个 不变的审计步道将进一步开发，以捕获受控的“摇篮到垃圾”文档 物质使用和位于门诊手术中心的位置。在部署之前，该平台将是 通过模拟测试使用在我们阶段I项目中开发的In In Silico模型进行测试，该模型能够 通过使用多代理仿真框架生成现实的交易数据。 2。部署软件 合作卫生系统的平台。我们将执行我们的软件平台的两个阶段推出， 在第一阶段（这个特定目标的重点）的位置，我们计划对我们的智能手机应用进行试点测试 和关联的管理员基于Web的仪表板最终替换纸质日志。目标的目标 推出的阶段是理解和解决部署新系统和潜在影响的挑战 关于工作流程及其采用。与采用，遵守协议以及对临床的影响有关的数据 将测量工作流程，并通过微调软件平台来解决任何挑战。 3。 进一步开发和部署分析引擎以检测药物转移。目标的目标 具体目的是进一步开发和调整使用数据的分析引擎（记录在区块链上） 检测药物转移。这项工作涉及两个主要任务。实际数据（作为特定目标的一部分收集）将 通过在第一阶段开发的框架来离线分析。通过与我们的合作健康合作 系统，我们将调查产生的危险信号，并使用此类投资的结果进行微调 我们模型的参数。接下来，我们将整合更新的分析引擎并推出药物转移 在推出的第二阶段，与合作伙伴卫生系统合作检测了飞行员的能力。