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.