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

ANALYSIS OF ON-DEMAND FORECASTING METHODOLOGY FOR FUTURE CONTRACT ACTIONS AT NAVAL SURFACE WARFARE CENTER, PORT HUENEME DIVISION (NSWC PHD)

For the past decade and continuing today, Naval Surface Warfare Center, Port Hueneme Division (NSWC PHD) has been in a perpetual state of emergency. Inaccurate forecasting for future contract actions contained in our Long Range Acquisition Forecast (LRAF) has left the NSWC PHD acquisition workforce unprepared to face increasing demands. As a result, more pending contract actions are unknown until the need date arrives and an emergency effort must be initiated to complete the action. This emergent trend can be found throughout the Department of the Navy (DON). It is imperative that a more accurate forecasting model be utilized within NSWC PHD to capture the demand signal of the acquisition workforce. This thesis reviewed the policy and procedures of the U.S. federal government including those within the Department of Defense (DOD), DON and Navy Sea Systems Command (NAVSEA), and NSWC PHD to understand the procedure for capturing the demand for future contract actions. This research found that the current methodology in place for the LRAF is heavily dependent on the requirement generator and historical references, which do not cover all contracting data at NSWC PHD. This paper identified a path for the acquisition workforce to incorporate data-driven analytics to its forecasting models to more accurately represent demand for that workforce. This research begins the process of moving toward the data-driven forecasting mentioned and determining the first steps forward.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited