# 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"

## TL;DR

This paper introduces a new text mining method to expand the scope of healthcare circular economy research beyond traditional review methods.

## Contribution

A novel abstract text mining approach is introduced to complement PRISMA in identifying new scope areas for healthcare circular economy.

## Key findings

- The ATM approach identified three new scope areas: alternative treatment pathways.
- Pharmaceutical footprint reduction was revealed as an additional focus area.
- Emerging technologies were highlighted as a new scope area through topic modeling.

## 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.