ArticleNPJ digital medicine2025
A health digital twin framework for discrete event simulation based optimised critical care workflows.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- A data-driven digital twin of emergency department flow to quantify congestion dynamics and test operational resilience.Internal and emergency medicine · 2026Article
- Digital Twins for Hospital and Healthcare Operations: A Systematic Review of Resource Allocation, Infection Control, and Workflow Optimization.Bioengineering (Basel, Switzerland) · 2026Review
- From Intelligent Operating Room to Smart ICU: Digital Continuity, AI-Supported Decision-Making and CRRT as a Model of Pharmacokinetic Personalization in Critical Care.Healthcare (Basel, Switzerland) · 2026Review
- Digital Twin-Based Virtual Hospital Platform for IT Outage Disaster Response Training: Implementation and Evaluation Study.JMIR formative research · 2026Article
- Optimized explainable AI and digital twin for patient flow improvement in ICU during respiratory epidemics.BMC medical informatics and decision making · 2026Article
- A simulation framework for evaluating electronic order workflows in integrated health records.npj health systems · 2026Article
- Synergizing advanced materials and artificial intelligence for next-generation carbon capture, utilization, and storage (CCUS): a review.RSC advances · 2026Review
- Mapping the future of medicine through digital twins.Frontiers in molecular medicine · 2026Review
- Analysis of digital twin applications in nursing practice and education: a scoping review.Journal of educational evaluation for health professions · 2026Article
- Time Series Models of the Human Heart in Patients with Heart Failure: Toward a Digital Twin Approach.Sensors (Basel, Switzerland) · 2025Article
- The role of digital twins in P4 medicine: A paradigm for modern healthcare.NPJ digital medicine · 2025Review
- Cardiometabolic risk reduction with digital twinning in patients with type 2 diabetes.Cardiovascular diabetology. Endocrinology reports · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Digital twins have been used in industries and is now gaining traction in healthcare, particularly in precision medicine. Discrete Event Simulation is a modelling methodology for simulating processes and workflows in healthcare. This paper presents a methodology that integrates these technologies to optimise critical care workflows based on real-time state changes, emphasising patient safety, operational efficiency, and sustainability. This study proposes a novel dual-layer architecture to monitor physical and conceptual entities in the Critical Care Unit. In the current scope, this study aims to establish a methodology using Azure cloud to track treatment workflows in real-time. The results indicated that by reviewing observation forms alone successfully tracked 72% of staff-performed tasks in real-time. This study underscores the potential of digital twins to transform precision care in critical care delivery by bridging the gap between actual and ideal clinical practices.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.