Digital Transformation in Healthcare: From Digital Tools to Digital Actors (DigiAct)

医疗保健数字化转型：从数字工具到数字参与者 (DigiAct)

• 批准号: 442171588
• 负责人: Professor Dr. Christoph Rosenkranz
• 金额: --
• 依托单位: Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/442171588?language=en
• 关键词: Digital; Transformation; Healthcare; Tools; Actors

We live in a world of increasingly digitized workforces. Technological developments in software, hardware, and artificial intelligence (AI) have enabled the infusion of digital technologies into a wide range of objects that are present in everyday life and at work. These emerging AI-enabled digital artifacts fundamentally reshape how organizations organize and carry out their work, as they are no longer simple tools used by humans to do tasks, but perform organizational work as autonomous, and somewhat intelligent, actors in their own right. As such they become digital actors. Digital actors are central to digital transformation. They can, once made available and permeating their context, serve and act in a self-perpetuating manner. Regardless of whether actions are carried out by human or non-human (i.e., digital) actors, patterns of action are the building blocks of organizations, constituting various organizational capabilities. In organization studies, the concept of organizational routines has been used for almost two decades to describe repetitive, recognizable patterns of interdependent actions, carried out by multiple actors. However, we know next to nothing about the enfolding interactions between human and non-human (digital) actors in organization routines. Digital actors within a routine performance rewrite the “rules of engagement” for work professionals and may lead to self-perpetuation and fundamental changes in their processes and routines. One important domain that has received attention by both organizational routines scholars and researchers of digital transformation and digital actors is healthcare. Digital actors, as one of the more extreme examples of the disruptive character of digital transformation, are now beginning to impact all facets of the current care model. As recommendations, guidelines, and actual implementation and use change, the impact of these novel, digital actors on medical and healthcare work practices and organizational routines in healthcare is considerable. However, empirical and theoretical accounts of these digital challenges largely are missing. With this research project we aim to address these gaps in literature by employing a mixed-methods research approach that builds on a combination of routines mining and a qualitative case study approach in a large clinic that currently undergoes significant digital transformation. Specifically, we aim to: (1) develop a method for computationally-intensive theory building for routines based on digital trace data; (2) apply this method to empirically study the evolution of digital actors in an empirical healthcare setting and build theory based on this analysis; (3) integrate and challenge or support this theory by applying qualitative methods in an embedded single-case study to corroborate the initial theory or extend if necessary.

我们生活在一个劳动力日益数字化的世界。软件、硬件和人工智能（AI）的技术发展使数字技术能够融入日常生活和工作中的各种物品中。这些新兴的人工智能数字产品从根本上重塑了组织组织和开展工作的方式，因为它们不再是人类用来完成任务的简单工具，而是作为自主的、有点智能的行动者来执行组织工作。因此，他们成为了数字演员。数字演员是数字化转型的核心。它们一旦具备并渗透到它们的环境中，就可以以一种自我延续的方式服务和行动。无论行动是由人类还是非人类（即数字）参与者执行的，行动模式都是组织的构建块，构成了各种组织能力。在组织研究中，组织惯例的概念已经被使用了近二十年，用来描述由多个参与者执行的相互依存的重复的、可识别的行为模式。然而，我们对组织例程中人类和非人类（数字）参与者之间的相互作用几乎一无所知。常规表演中的数字演员重写了工作专业人员的“参与规则”，并可能导致其流程和惯例的自我延续和根本变化。组织惯例学者和数字化转型研究人员以及数字化参与者都关注的一个重要领域是医疗保健。数字行为者，作为数字转型破坏性特征的一个更极端的例子，现在开始影响当前护理模式的各个方面。随着建议、指南以及实际实施和使用的变化，这些新颖的数字参与者对医疗保健工作实践和医疗保健组织惯例的影响是相当大的。然而，对这些数字挑战的实证和理论解释在很大程度上是缺失的。在这个研究项目中，我们的目标是通过采用混合方法的研究方法来解决这些文献上的空白，该方法建立在常规挖掘和定性案例研究方法的结合上，该方法目前正在经历重大的数字化转型。具体而言，我们的目标是：(1)开发一种基于数字轨迹数据的计算密集型例程理论构建方法；(2)将该方法应用于实证医疗环境中数字行为体演化的实证研究，并在此基础上构建理论；(3)整合和挑战或支持这一理论，通过在嵌入式单案例研究中应用定性方法来证实初始理论或在必要时进行扩展。