Applying Abstract Text Mining as a Complement to PRISMA in Reviewing the Scope of Healthcare’s Circular Economy; Comment on “A Review of the Applicability of Current Green Practices in Healthcare Facilities”

Document Type : Commentary

Author

Industrial and Systems Engineering Department, Kennesaw State University, Marietta, GA, USA

Abstract

Efforts to reduce the healthcare sector’s carbon footprint and greenhouse gas (GHG) emissions have brought increased attention to the adoption of the circular economy (CE) in recent years. These efforts aim to lower carbon-intensive products while improving efficiency, waste reduction, and healthcare resilience. Soares et al conducted a scoping review examining CE applicability in healthcare and identified strategies to enhance its implementation. In this commentary paper, a novel abstract text mining (ATM) approach is introduced as a complement to the standard Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Using this approach, the search terms employed by Soares et al were expanded, article abstracts were extracted, and scope areas were mapped with the assistance of a well-established machine learning technique—latent Dirichlet allocation (LDA) topic modeling. Comparison of the ATM results with those reported by Soares et al revealed three additional scope areas: alternative treatment pathways, pharmaceutical footprint reduction, and the utilization of emerging technologies.

Keywords

References

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History

  • Received Date: 17 August 2025
  • Revised Date: 21 January 2026
  • Accepted Date: 28 January 2026
  • First Published Date: 02 February 2026
  • Published Date: 01 December 2026

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