Stakeholder Perspectives on Digital Twin Implementation Challenges in Healthcare: Insights from a Provider Digital Twin Case Study

• URL: http://arxiv.org/abs/2508.00936v1
• Date: Thu, 31 Jul 2025 07:57:48 GMT
• Title: Stakeholder Perspectives on Digital Twin Implementation Challenges in Healthcare: Insights from a Provider Digital Twin Case Study
• Authors: Md Doulotuzzaman Xames, Taylan G. Topcu,
• Abstract summary: This research investigates DT implementation challenges in healthcare by capturing the perspectives of four distinct stakeholders.<n>We conducted semi-structured interviews guided by the updated Consolidated Framework for Implementation Research (CFIR 2.0)<n>We then mapped each stakeholder group's preferences and concerns, revealing a nuanced landscape of converging and diverging perspectives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital twin (DT) technology holds immense potential for transforming healthcare systems through real-time monitoring, predictive analysis, and agile interventions to support various decision-making needs. However, its successful implementation depends on addressing a range of complex sociotechnical challenges. Using a case study of provider workload DT, this research investigates DT implementation challenges in healthcare by capturing the perspectives of four distinct stakeholders: family medicine specialists (FMS), organizational psychologists (OP), engineers (EE), and implementation scientists (IS). We conducted semi-structured interviews guided by the updated Consolidated Framework for Implementation Research (CFIR 2.0), a widely used implementation science framework for understanding factors that influence implementation outcomes. We then mapped each stakeholder group's preferences and concerns, revealing a nuanced landscape of converging and diverging perspectives that highlight both shared and group-specific implementation barriers. Through thematic coding, the 66 identified challenges were categorized into seven domains: data-related, financial and economic, operational, organizational, personnel, regulatory and ethical, and technological. Our findings reveal shared concerns such as data privacy and security, interoperability, and regulatory compliance. However, divergences also emerged, reflecting each group's functional focus. These findings emphasize the need for a multidisciplinary, stakeholder-sensitive approach that addresses both functional and practical concerns, highlighting the importance of tailored implementation strategies to support successful DT adoption in healthcare.

Related papers

• Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Healthcare: A Qualitative Study [1.2144656790395498]
  Digital phenotyping, affective computing and related passive sensing approaches offer unprecedented opportunities to personalize healthcare.<n>These tools provoke concerns about privacy, bias and the erosion of empathic, relationship-centered practice.<n>This study provides the first evidence-based account of key stakeholder perspectives on the integration of CP technologies into patient care.
  arXiv  Detail & Related papers  (2025-08-04T16:01:56Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration [51.452664740963066]
  Collaborative Gym is a framework enabling asynchronous, tripartite interaction among agents, humans, and task environments.<n>We instantiate Co-Gym with three representative tasks in both simulated and real-world conditions.<n>Our findings reveal that collaborative agents consistently outperform their fully autonomous counterparts in task performance.
  arXiv  Detail & Related papers  (2024-12-20T09:21:15Z)
• Exploring the Requirements of Clinicians for Explainable AI Decision Support Systems in Intensive Care [1.950650243134358]
  Thematic analysis revealed three core themes: (T1) ICU decision-making relies on a wide range of factors, (T2) the complexity of patient state is challenging for shared decision-making, and (T3) requirements and capabilities of AI decision support systems. We include design recommendations from clinical input, providing insights to inform future AI systems for intensive care.
  arXiv  Detail & Related papers  (2024-11-18T17:53:07Z)
• Navigating Distribution Shifts in Medical Image Analysis: A Survey [23.012651270865707]
  This paper systematically reviews approaches that apply deep learning techniques to MedIA systems affected by distribution shifts.<n>We categorize the existing body of work into Joint Training, Federated Learning, Fine-tuning, and Domain Generalization.<n>By delving deeper into these topics, we highlight potential pathways for future research.
  arXiv  Detail & Related papers  (2024-11-05T08:01:16Z)
• Implementing a Nordic-Baltic Federated Health Data Network: a case report [56.96209893909196]
  We formed an interdisciplinary consortium to develop a feder-ated health data network, comprised of six institutions across five countries. The objective of this report is to offer early insights into our experiences developing this network.
  arXiv  Detail & Related papers  (2024-09-26T14:15:54Z)
• Making Software Development More Diverse and Inclusive: Key Themes, Challenges, and Future Directions [50.545824691484796]
  We identify six themes around the theme challenges and opportunities to improve Software Developer Diversity and Inclusion (SDDI)<n>We identify benefits, harms, and future research directions for the four main themes.<n>We discuss the remaining two themes, Artificial Intelligence & SDDI and AI & Computer Science education, which have a cross-cutting effect on the other themes.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP) [3.8657431480664717]
  The study involved semi-structured interviews with 28 professionals from medical device companies. The participants highlighted the need for strategies to streamline regulatory compliance. The study concludes that these elements are vital for establishing coherent and effective regulatory procedures.
  arXiv  Detail & Related papers  (2023-12-30T03:39:57Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• Moral Decision-Making in Medical Hybrid Intelligent Systems: A Team Design Patterns Approach to the Bias Mitigation and Data Sharing Design Problems [0.0]
  Team Design Patterns (TDPs) describe successful and reusable configurations of design problems in which decisions have a moral component. This thesis describes a set of solutions for two design problems in a medical HI system. A survey was created to assess the usability of the patterns on their understandability, effectiveness, and generalizability.
  arXiv  Detail & Related papers  (2021-02-16T17:09:43Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Nine Recommendations for Decision Aid Implementation from the Clinician Perspective [0.0]
  Time pressure and patient characteristics were cited as major barriers by 55% of the clinicians we interviewed. Structural factors such as external quotas for certain treatment procedures were also considered as barriers by 44% of the clinicians. Our findings suggest a role for external stakeholders such as healthcare insurers in creating economic incentives to facilitate implementation.
  arXiv  Detail & Related papers  (2020-07-21T13:40:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.