Digital twins in hospital operations management: a structured review and quantitative content analysis of decision support, workflow engineering and resource allocation

INTRODUCTION: Digital twin (DT) technology is increasingly used to support data-driven hospital operations, but evidence remains fragmented across workflows, technological infrastructures, levels of decision support, implementation stages, and methodological quality. OBJECTIVE: This study aimed to synthesise DT applications in hospital operations management and to develop a structured framework linking data infrastructure, workflow modelling, decision support, resource allocation, implementation, and operational resilience. METHODS: A structured review and quantitative content analysis was conducted across PubMed, Scopus, and Web of Science on 23-24 March 2026. Of 521 records identified, 456 remained after duplicate removal and 20 studies met the eligibility criteria following title/abstract and full-text screening. Studies were independently classified by two reviewers according to application domain, technological infrastructure, decision-support maturity, operational outcomes, and implementation stage, and were formally appraised for methodological and reporting quality using design-appropriate QuADS and JBI tools. RESULTS: Twenty studies were included. Among the 13 studies demonstrating operational decision-support capability, 2 (15.4 %) were descriptive, 3 (23.1 %) predictive, 5 (38.5 %) prescriptive, and 3 (23.1 %) adaptive. Implementation remained limited: 6 studies (30 %) were contextual/conceptual, 8 (40 %) simulation or proof-of-concept, 4 (20 %) hospital-context validation or pilot studies, and only 2 (10 %) demonstrated operational implementation. Among the 17 studies assessed with QuADS, the final consensus median score was 36/39 (IQR 31-38; range 21-38). CONCLUSIONS: DTs are emerging as hospital information-system infrastructures for analytics-enabled, workflow-integrated, and human-supervised decision support. However, technical decision-support sophistication does not necessarily indicate real-world implementation or stronger evidence. Wider adoption requires interoperable data systems, workflow integration, governance, organisational readiness, and rigorous evaluation in operational hospital settings.

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Journal
International Journal of Medical Informatics
Published
2026-10-14
DOI
https://doi.org/10.1016/j.ijmedinf.2026.106768
Primary Topic
Healthcare Operations and Scheduling Optimization
Type
article
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article

Digital twins in hospital operations management: a structured review and quantitative content analysis of decision support, workflow engineering and resource allocation

İbrahim H. Kayral, Seda Taşır Yılmaz
International Journal of Medical Informatics
Healthcare Operations and Scheduling Optimization
article

Digital twins in hospital operations management: a structured review and quantitative content analysis of decision support, workflow engineering and resource allocation

İbrahim H. Kayral, Seda Taşır Yılmaz
article en

Abstract

INTRODUCTION: Digital twin (DT) technology is increasingly used to support data-driven hospital operations, but evidence remains fragmented across workflows, technological infrastructures, levels of decision support, implementation stages, and methodological quality. OBJECTIVE: This study aimed to synthesise DT applications in hospital operations management and to develop a structured framework linking data infrastructure, workflow modelling, decision support, resource allocation, implementation, and operational resilience. METHODS: A structured review and quantitative content analysis was conducted across PubMed, Scopus, and Web of Science on 23-24 March 2026. Of 521 records identified, 456 remained after duplicate removal and 20 studies met the eligibility criteria following title/abstract and full-text screening. Studies were independently classified by two reviewers according to application domain, technological infrastructure, decision-support maturity, operational outcomes, and implementation stage, and were formally appraised for methodological and reporting quality using design-appropriate QuADS and JBI tools. RESULTS: Twenty studies were included. Among the 13 studies demonstrating operational decision-support capability, 2 (15.4 %) were descriptive, 3 (23.1 %) predictive, 5 (38.5 %) prescriptive, and 3 (23.1 %) adaptive. Implementation remained limited: 6 studies (30 %) were contextual/conceptual, 8 (40 %) simulation or proof-of-concept, 4 (20 %) hospital-context validation or pilot studies, and only 2 (10 %) demonstrated operational implementation. Among the 17 studies assessed with QuADS, the final consensus median score was 36/39 (IQR 31-38; range 21-38). CONCLUSIONS: DTs are emerging as hospital information-system infrastructures for analytics-enabled, workflow-integrated, and human-supervised decision support. However, technical decision-support sophistication does not necessarily indicate real-world implementation or stronger evidence. Wider adoption requires interoperable data systems, workflow integration, governance, organisational readiness, and rigorous evaluation in operational hospital settings.

International Journal of Medical InformaticsVol. 223
Izmir University (TR), Ankara University (TR)
Openalex Percentile: Top 8%
Healthcare Operations and Scheduling Optimization
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