Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Usability of myfood24 Healthcare and Mathematical Diet Optimisation in Clinical Populations: A Pilot Feasibility Randomised Controlled Trial

Monitoring nutritional intake is of clinical value, but few existing tools offer electronic dietary recording, instant nutritional analysis, and a platform connecting healthcare teams with patients that provides timely, personalised support. This feasibility randomised controlled trial tests the usability of ‘myfood24 Healthcare’, a dietary assessment app and healthcare professional website, in two clinical populations. Patients were recruited from a weight management programme (n21) and from a group of gastroenterology surgery outpatients (n = 27). They were randomised into three groups: standard care, myfood24, or myfood24 + diet optimisation (automated suggestions for dietary improvement). The participants were asked to record their diet at least four times over eight weeks. During the study, healthcare professionals viewed recorded dietary information to facilitate discussions about diet and nutritional targets. The participants provided feedback on usability and acceptability. A total of 48 patients were recruited, and 16 were randomised to each of the three groups. Compliance among app users (n = 32) was reasonable, with 25 (78%) using it at least once and 16 (50%) recording intake for four days or more. Among users, the mean (standard deviation) number of days used was 14.0 (17.5), and the median (interquartile range) was six (2.5–17.0) over 2 months. Feedback questionnaires were completed by only 23 of 46 participants (50%). The mean System Usability Score (n = 16) was 59 (95% confidence interval, 48–70). Patient and healthcare professional feedback indicates a need for more user training and the improvement of some key app features such as the food search function. This feasibility study shows that myfood24 Healthcare is acceptable for patients and healthcare professionals. These data will inform app refinements and its application in a larger clinical effectiveness trial

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings