Assessing the Value of Prescription Information From Outside Pharmacy Sources

评估外部药房来源的处方信息的价值

• 批准号: 7969234
• 负责人: Clement McDonald
• 金额: $ 49.27万
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7969234
• 关键词: Accident and Emergency department; Agreement; Blood Pressure; Caring; Characteristics; Collection; Communication; Computing Methodologies; Confidentiality; Data; Data Set; Databases; Disasters; Emergency Care; Family; Goals; Hand; Hospitals; Human Resources; Individual; Insurance; Intervention; Link; Manuals; Maps; Modeling; Names; Nature; Patient Care; Patients; Pharmaceutical Preparations; Pharmacy facility; Policies; Population; Process; Provider; Readiness; Recording of previous events; Records; Reporting; Research; Running; Security; Source; System; Text; Time; Water; Work; base; demographics; drug standard; interest; pharmacy benefit; pill; routine care; suburb

The target hospitals for this study are the three Bethesda hospitals. In order to accomplish this work, we will first develop a system to securely manage communication between the target hospitals and a consortium of Pharmacy Benefit Managers (PBMs) who carry the prescription dispensing records of interest. Under appropriate confidentiality agreements and security protection, this system will request the medication profile for patients entering care at the target hospitals from this consortium to assist patient care. The need for information about medication is especially pressing during disasters, when patients are cared for by unfamiliar providers who do not have adequate time to spend with each patient. This study was motivated by the need to obtain a medication history during disasters, without needing much personnel time. Our hypothesis is that when a patient has medication information in the PBM consortiums databases, that information will provide a better medication history obtained from that source more complete and more precise than the corresponding information collected manually from the patient. The value of such data for a population will depend upon the proportion of patients in the population who have medication information within the consortium database. The PBM consortium will have no information about patients without insurance and those with insurance that is not processed by the PBM consortium. We will set a binary variable to identify patients who had no data in the PBM consortium and will collect demographic, administrative variables (arrival mode), and insurance class on all patients. Then we will model the existence of PBM consortium data on these attributes. We will use this model to predict the proportion of patients with data in the PBM based on the above mentioned PBMs and to assess policies that might eliminate this gap. The drug history collected automatically by the PBM, the one collected manually by the hospitals, the patient demographics and other patient characteristics will be linked and then de-identified. The research will be performed only on the de-identified databases. The research will only involve data that is collected for routine care purposes, and there will be no intervention. During FY2009, we used HL7 messages from the Suburban Emergency Department registrations to obtain the patients prescriptions-filled history from SureScripts/RxHub. All of the data was de-identified at Suburban Hospital before delivery to the research team at NLM. We made most of the comparisons between the two sources of medication history (PBM consortium versus direct collection from the patient) via computer methods. To prepare for the comparison, we have mapped the information from the PBMs to a standard list of drug names derived from Rx.Norm. The drug information taken directly from the patient (or their family) came from the hospitals as free text. The free text drug names were mapped to the same list of standard drug names using a combination of text matching and manual inspection. In the hospital history encountered classes, e.g. sulfa med, water pill, or blood pressure med, instead of drug names in some cases, and classified them as such. We are in the process of comparing the two information sources with regard to the completeness, degree of overlap, and nature of discrepancies. Among patients with medications data from both SureScripts and the Suburban ED medication history, we counted individual medication matches by patient. After excluding all SureScripts medications which may have run out (based on dispensing data), more than 30% of the individual medications from SureScripts were not captured in the Suburban EDs medications history. On the other hand, 62% of the medications in the ED medications history were not included in the SureScripts data set. We also analyzed overlaps and gaps if we extended the time window to include medications prescribed within 3 months of the ED encounter: 50% of the SureScripts medications were not reported by the patients and 50% of the patient-reported medications were not in the SureScripts database. Neither source is complete, but SureScripts adds a lot of information to the medication histories collected by Suburban Hospital ED personnel. We have further work and analyses to do regarding the differences identified. Emergency Department providers are now getting the hardy copy summary of the SureScripts data, as needed for routine care and disaster preparedness.

这项研究的目标医院是三家贝塞斯达医院。 为了完成这项工作，我们将首先开发一个系统，以安全地管理目标医院和药房福利经理联盟（PBMS）之间的通信，他们携带了处方分配感兴趣的记录。 在适当的机密协议和安全保护下，该系统将要求从该财团的目标医院进入护理的患者提供药物概况，以帮助患者护理。 当患者受到没有足够时间与每个患者共度的陌生提供者照顾时，尤其是在灾难期间对药物的信息的需求。 这项研究是出于需要在灾难期间获得药物病史而无需大量人员时间的动机。 我们的假设是，当患者在PBM财团数据库中拥有药物信息时，该信息将提供比从患者手动收集的相应信息更完整，更精确地获得的更好的药物病史。 此类数据对人群的价值将取决于在财团数据库中拥有药物信息的人群中患者的比例。 PBM财团将没有关于没有保险的患者的信息，而拥有PBM财团未处理的保险的人将没有任何信息。 我们将设置一个二进制变量，以识别PBM财团中没有数据的患者，并将收集所有患者的人口统计学，管理变量（到达模式）和保险类。然后，我们将对这些属性的PBM联盟数据的存在进行建模。我们将使用此模型根据上述PBM来预测PBM中数据的比例，并评估可能消除此差距的策略。 PBM自动收集的毒品病史，由医院手动收集的毒品病史，患者人口统计和其他患者特征将被联系起来，然后被取消识别。该研究将仅在被识别的数据库上进行。该研究仅涉及用于常规护理目的的数据，并且不会进行干预。 在2009财年期间，我们使用了来自郊区急诊室注册的HL7消息，从Surescripts/rxhub获得了充满处方的病史。 所有数据都在郊区医院取消了识别，然后将其交付给NLM的研究小组。 我们通过计算机方法进行了两种药物史（PBM联盟与从患者的直接收集）之间进行的大部分比较。 为了准备比较，我们将信息从PBMS映射到了rx.norm派生的药物名称的标准列表。 直接从患者（或其家人）获得的药物信息作为自由文字来自医院。免费文本药物名称使用文本匹配和手动检查的组合映射到同一标准药物名称列表。 在医院历史上遇到了课程，例如Sulfa Med，水药或血压药物在某些情况下而不是药物名称，并将其归类为。 我们正在将两个信息源进行比较有关的完整性，重叠程度和差异性质。 在均来自表面和郊区ED药物史的药物数据的患者中，我们计算了患者的单个药物匹配。在排除所有可能用完的监测药物（基于分配数据）之后，在郊区EDS药物史上未捕获超过30％的个人药物。另一方面，ED药物病史中有62％的药物未包括在Surescripts数据集中。 我们还分析了重叠和差距，如果我们将时间窗口延长到ED遇到的3个月内规定的药物：患者未报告50％的监测药物，而患者报告的药物的50％尚未在Surescripts数据库中。 这两个消息来源都不完整，但是审查却为郊区医院ED人员收集的药物历史增加了很多信息。 关于所确定的差异，我们有进一步的工作和分析要做。 急诊室提供商现在根据需要进行常规护理和灾难准备的监视数据的Hardy副本摘要。