Faculty of Informatics, University of the Basque Country UPV/EHU, Donostia/San Sebastián, Spain.
Division of Pharmacy and Optometry, University of Manchester, Manchester, United Kingdom; NIHR Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom.
Int J Med Inform. 2019 Sep;129:395-403. doi: 10.1016/j.ijmedinf.2019.07.014. Epub 2019 Jul 23.
To characterise the use of an electronic medication safety dashboard by exploring and contrasting interactions from primary users (i.e. pharmacists) who were leading the intervention and secondary users (i.e. non-pharmacist staff) who used the dashboard to engage in safe prescribing practices.
We conducted a 10-month observational study in which 35 health professionals used an instrumented medication safety dashboard for audit and feedback purposes in clinical practice as part of a wider intervention study. We modelled user interaction by computing features representing exploration and dwell time through user interface events that were logged on a remote database. We applied supervised learning algorithms to classify primary against secondary users.
We observed values for accuracy above 0.8, indicating that 80% of the time we were able to distinguish a primary user from a secondary user. In particular, the Multilayer Perceptron (MLP) yielded the highest values of precision (0.88), recall (0.86) and F-measure (0.86). The behaviour of primary users was distinctive in that they spent less time between mouse clicks (lower dwell time) on the screens showing the overview of the practice and trends. Secondary users exhibited a higher dwell time and more visual search activity (higher exploration) on the screens displaying patients at risk and visualisations.
We were able to distinguish the interactive behaviour of primary and secondary users of a medication safety dashboard in primary care using timestamped mouse events. Primary users were more competent on population health monitoring activities, while secondary users struggled on activities involving a detailed breakdown of the safety of patients. Informed by these findings, we propose workflows that group these activities and adaptive nudges to increase user engagement.
通过探索和对比主要用户（即药剂师）和次要用户（即非药师员工）的交互作用，描述电子用药安全仪表板的使用情况，主要用户引领干预，次要用户使用仪表板参与安全处方实践。
我们进行了一项为期 10 个月的观察性研究，35 名卫生专业人员在临床实践中使用仪器化用药安全仪表板进行审计和反馈，作为更广泛干预研究的一部分。我们通过计算代表探索和停留时间的特征来建模用户交互，这些特征是通过记录在远程数据库中的用户界面事件得出的。我们应用监督学习算法对主要用户和次要用户进行分类。
我们观察到的准确度值高于 0.8，这表明 80%的时间我们能够区分主要用户和次要用户。特别是，多层感知机（MLP）产生了最高的精度（0.88）、召回率（0.86）和 F 度量（0.86）值。主要用户的行为特征是，他们在显示实践概述和趋势的屏幕上鼠标点击之间的时间间隔（停留时间）更短。次要用户在显示有风险的患者和可视化信息的屏幕上表现出更高的停留时间和更多的视觉搜索活动（更高的探索性）。
我们能够使用带时间戳的鼠标事件区分初级保健中用药安全仪表板的主要用户和次要用户的交互行为。主要用户在人群健康监测活动方面更熟练，而次要用户在涉及患者安全详细细分的活动方面遇到困难。根据这些发现，我们提出了将这些活动分组的工作流程和自适应提示，以增加用户参与度。
