Adaption of medical information system's e-learning extension to a simple suggestion tool

P. Rajković, D. Jankovic, A. Milenković
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引用次数: 1

Abstract

Developing suggestion tools in the scope of health information systems can be a complex task, followed by a risk of not being accepted by the end users. Thus, we decide to start the implementation around the existing functionality. In this paper we present a case study showing the adaptation of e-learning medical information system extension to a set of simple suggestion tools. While some features of initial system had to be modified, the domain specific knowledge collected for the e-learning extension is used to suppress potential errors. Presented suggestion tool is based on highly configurable lists of pre-defined entities that can be easily selected, and after the verification from the medical practitioner, copied into an active visit. After four years of active use, and several iteration of update, described suggestion tools are mostly accepted among the general practitioners, especially within certain scenarios where faster medication prescription is a must.
将医疗信息系统的电子学习扩展到一个简单的建议工具
在卫生信息系统范围内开发建议工具可能是一项复杂的任务,随之而来的是不被最终用户接受的风险。因此,我们决定围绕现有功能开始实现。在本文中,我们提出了一个案例研究,展示了电子学习医疗信息系统扩展到一套简单的建议工具的适应。在修改初始系统的某些特征时,利用为电子学习扩展而收集的领域特定知识来抑制潜在的错误。提出的建议工具基于高度可配置的预定义实体列表,这些列表可以很容易地选择,并在医生验证后复制到主动访问中。经过四年的积极使用和几次迭代更新,描述的建议工具在全科医生中大部分被接受,特别是在必须快速用药的某些情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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