Ling Tristan, Gee Peter, Westbury Juanita, Bindoff Ivan, Peterson Gregory
Unit for Medication Outcomes Research and Education, Division of Pharmacy, School of Medicine, University of Tasmania, Hobart, Australia.
Wicking Dementia Research and Education Centre, Faculty of Health, University of Tasmania, Hobart, Australia.
J Med Internet Res. 2017 Aug 3;19(8):e283. doi: 10.2196/jmir.6938.
Inappropriate use of sedating medication has been reported in nursing homes for several decades. The Reducing Use of Sedatives (RedUSe) project was designed to address this issue through a combination of audit, feedback, staff education, and medication review. The project significantly reduced sedative use in a controlled trial of 25 Tasmanian nursing homes. To expand the project to 150 nursing homes across Australia, an improved and scalable method of data collection was required. This paper describes and evaluates a method for remotely extracting, transforming, and validating electronic resident and medication data from community pharmacies supplying medications to nursing homes.
The aim of this study was to develop and evaluate an electronic method for extracting and enriching data on psychotropic medication use in nursing homes, on a national scale.
An application uploaded resident details and medication data from computerized medication packing systems in the pharmacies supplying participating nursing homes. The server converted medication codes used by the packing systems to Australian Medicines Terminology coding and subsequently to Anatomical Therapeutic Chemical (ATC) codes for grouping. Medications of interest, in this case antipsychotics and benzodiazepines, were automatically identified and quantified during the upload. This data was then validated on the Web by project staff and a "champion nurse" at the participating home.
Of participating nursing homes, 94.6% (142/150) had resident and medication records uploaded. Facilitating an upload for one pharmacy took an average of 15 min. A total of 17,722 resident profiles were extracted, representing 95.6% (17,722/18,537) of the homes' residents. For these, 546,535 medication records were extracted, of which, 28,053 were identified as antipsychotics or benzodiazepines. Of these, 8.17% (2291/28,053) were modified during validation and verification stages, and 4.75% (1398/29,451) were added. The champion nurse required a mean of 33 min website interaction to verify data, compared with 60 min for manual data entry.
The results show that the electronic data collection process is accurate: 95.25% (28,053/29,451) of sedative medications being taken by residents were identified and, of those, 91.83% (25,762/28,053) were correct without any manual intervention. The process worked effectively for nearly all homes. Although the pharmacy packing systems contain some invalid patient records, and data is sometimes incorrectly recorded, validation steps can overcome these problems and provide sufficiently accurate data for the purposes of reporting medication use in individual nursing homes.
几十年来，养老院中镇静药物的不当使用情况一直有报道。减少镇静剂使用（RedUSe）项目旨在通过审计、反馈、员工教育和药物审查相结合的方式来解决这一问题。该项目在塔斯马尼亚州25家养老院的对照试验中显著减少了镇静剂的使用。为了将该项目扩展到澳大利亚各地的150家养老院，需要一种改进的、可扩展的数据收集方法。本文描述并评估了一种从向养老院供应药物的社区药房远程提取、转换和验证电子居民及药物数据的方法。
本研究的目的是开发并评估一种在全国范围内提取和充实养老院中精神药物使用数据的电子方法。
一个应用程序从为参与项目的养老院供应药物的药房的计算机化药物包装系统中上传居民详细信息和药物数据。服务器将包装系统使用的药物代码转换为澳大利亚药品术语编码，随后再转换为解剖治疗化学（ATC）代码进行分组。在此案例中，感兴趣的药物（抗精神病药物和苯二氮䓬类药物）在上传过程中会被自动识别和量化。然后，项目工作人员和参与养老院的“冠军护士”会在网络上对这些数据进行验证。
参与项目的养老院中，94.6%（142/150）上传了居民和药物记录。协助一家药房上传数据平均需要15分钟。总共提取了17722份居民档案，占这些养老院居民的95.6%（17722/18537）。对于这些居民，提取了546535条药物记录，其中28053条被识别为抗精神病药物或苯二氮䓬类药物。在这些记录中，8.17%（2291/28053）在验证和核实阶段被修改，4.75%（1398/29451）被添加。“冠军护士”平均需要33分钟的网站交互时间来验证数据，而手动数据输入则需要60分钟。
结果表明，电子数据收集过程是准确的：居民服用的镇静药物中有95.25%（28053/29451）被识别出来，其中91.83%（25762/28053）在没有任何人工干预的情况下是正确的。该过程对几乎所有养老院都有效。虽然药房包装系统包含一些无效的患者记录，且数据有时记录错误，但验证步骤可以克服这些问题，并为报告各养老院的药物使用情况提供足够准确的数据。
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