Developing A Decision Support System for Healthcare Practices: A Design Science Research Approach

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Data & Knowledge Engineering Pub Date : 2024-07-17 DOI:10.1016/j.datak.2024.102344
Arun Sen , Atish P. Sinha , Cong Zhang
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Abstract

We propose a new approach for designing a decision support system (DSS) for the transformation of healthcare practices. Practice transformation helps practices transition from their current state to patient-centered medical home (PCMH) model of care. Our approach employs activity theory to derive the elements of practice transformation by designing and integrating two ontologies: a domain ontology and a task ontology. By incorporating both goal-oriented and task-oriented aspects of the practice transformation process and specifying how they interact, our integrated design model for the DSS provides prescriptive knowledge on assessing the current status of a practice with respect to PCMH recognition and navigating efficiently through a complex solution space. This knowledge, which is at a moderate level of abstraction and expressed in a language that practitioners understand, contributes to the literature by providing a formulation for a nascent design theory. We implement the integrated design model as a DSS prototype; results of validation tests conducted on the prototype indicate that it is superior to the existing PCMH readiness tracking tool with respect to effectiveness, usability, efficiency, and sustainability.

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为医疗实践开发决策支持系统:设计科学研究方法
我们提出了一种设计决策支持系统(DSS)的新方法,用于医疗实践的转型。实践转型有助于医疗实践从当前状态过渡到以患者为中心的医疗之家(PCMH)护理模式。我们的方法采用活动理论,通过设计和整合两个本体:领域本体和任务本体,推导出实践转型的要素。通过整合实践转型过程中的目标导向和任务导向两个方面,并明确它们之间的互动方式,我们的 DSS 集成设计模型提供了有关评估实践在 PCMH 识别方面的现状以及在复杂的解决方案空间中有效导航的规范性知识。这些知识的抽象程度适中,并以从业人员能够理解的语言表达,为新生的设计理论提供了一种表述方式,从而为文献做出了贡献。我们将综合设计模型作为一个 DSS 原型来实施;对该原型进行的验证测试结果表明,它在有效性、可用性、效率和可持续性方面都优于现有的 PCMH 准备情况跟踪工具。
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来源期刊
Data & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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