Interdisciplinary differences in needlestick injuries among healthcare professionals in training: Improving situational awareness to prevent high-risk injuries

Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models' feasibility in clinical decision-making

BACKGROUND

Recent enhancements in Large Language Models (LLMs) such as ChatGPT have exponentially increased user adoption. These models are accessible on mobile devices and support multimodal interactions, including conversations, code generation, and patient image uploads, broadening their utility in providing healthcare professionals with real-time support for clinical decision-making. Nevertheless, many authors have highlighted serious risks that may arise from the adoption of LLMs, principally related to safety and alignment with ethical guidelines.

OBJECTIVE

To address these challenges, we introduce a novel methodological approach designed to assess the specific feasibility of adopting LLMs within a healthcare area, with a focus on clinical nursing, evaluating their performance and thereby directing their choice. Emphasizing LLMs' adherence to scientific advancements, this approach prioritizes safety and care personalization, according to the "Organization for Economic Co-operation and Development" frameworks for responsible AI. Moreover, its dynamic nature is designed to adapt to future evolutions of LLMs.

METHOD

Through integrating advanced multidisciplinary knowledge, including Nursing Informatics, and aided by a prospective literature review, seven key domains and specific evaluation items were identified as follows:A Peer Review by experts in Nursing and AI was performed, ensuring scientific rigor and breadth of insights for an essential, reproducible, and coherent methodological approach. By means of a 7-point Likert scale, thresholds are defined in order to classify LLMs as "unusable", "usable with high caution", and "recommended" categories. Nine state of the art LLMs were evaluated using this methodology in clinical oncology nursing decision-making, producing preliminary results. Gemini Advanced, Anthropic Claude 3 and ChatGPT 4 achieved the minimum score of the State of the Art Alignment & Safety domain for classification as "recommended", being also endorsed across all domains. LLAMA 3 70B and ChatGPT 3.5 were classified as "usable with high caution." Others were classified as unusable in this domain.

CONCLUSION

The identification of a recommended LLM for a specific healthcare area, combined with its critical, prudent, and integrative use, can support healthcare professionals in decision-making processes.

The global incidence and associated factors of surgical gloves perforation: A systematic review and meta-analysis

BACKGROUND

Surgeon's hands are exposed to a range of chemical and physical stressors that influence the integrity of gloves. Thus, glove perforation facilitates pathogen transmission through the holes. Numerous studies have shown that glove perforation is of great importance, but there is still no complete consensus.

OBJECTIVE

This study aimed to investigate the safety conditions of surgical gloves based on the incidence of glove perforation among surgical teams.

METHODS

A literature review was conducted by searching PubMed, Embase, Scopus, ISI, and ProQuest databases. The incidence of glove perforation was reviewed in eight categories. A total of 580 articles were found in the initial search.

RESULTS

109 articles containing 130 datasets were analyzed. The overall incidence of glove perforation was 14.44 %. Surgeons had the highest incidence of glove perforation (23.09 %). An increase in the duration of surgery increased the odds of perforation. It was found that 68.58% of the glove perforations were not identified by practitioners.

CONCLUSION

Despite major advances in the design of surgical gloves, the issue of safety and integrity of gloves has not improved. Due to the high incidence of surgical glove perforations, most of which had not been identified, it is imperative to consider strategies to minimize the incidence of perforation. The results of the present study can be used to integrate information related to the rate of perforation of surgical gloves in different surgeries.