Journal of evaluation in clinical practice
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The WHO Surgical Safety Checklist is a communication tool designed to improve surgical safety processes and enhance teamwork. It has been widely adopted since its introduction over ten years ago. As surgical safety needs evolve, organizations should periodically review and update their checklists. A holistic evaluation of the checklist in the context of an organization is the first step to making informed updates. In this article, we describe a comprehensive but feasible strategy for checklist evaluation which we developed and implemented as part of a surgical safety initiative in a high-performing center. ⋯ We developed and implemented a comprehensive, scalable approach to checklist evaluation which directly informed improvements to the checklist that were tailored to the organization's current context. Organizations can apply this framework to breathe new life into their checklist and transform their safety culture.
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Social determinants of health (SDOH) are being considered more frequently when providing orthopaedic care due to their impact on treatment outcomes. Simultaneously, prognostic machine learning (ML) models that facilitate clinical decision making have become popular tools in the field of orthopaedic surgery. When ML-driven tools are developed, it is important that the perpetuation of potential disparities is minimized. One approach is to consider SDOH during model development. To date, it remains unclear whether and how existing prognostic ML models for orthopaedic outcomes consider SDOH variables. ⋯ The current level of reporting and consideration of SDOH during the development of prognostic ML models for orthopaedic outcomes is limited. Healthcare providers should be critical of the models they consider using and knowledgeable regarding the quality of model development, such as adherence to recognized methodological standards. Future efforts should aim to avoid bias and disparities when developing ML-driven applications for orthopaedics.