Background: Quality of metabolic bariatric surgery (MBS) care is often monitored by national registries using quality indicators (QIs), but data collection takes up considerable time and costs. QIs are mostly introduced merely based on expert opinion. Therefore, the study’s aim was to systematically evaluate whether all QIs from the Dutch Audit for Treatment of Obesity (DATO) are still relevant and useful to initiate quality improvement initiatives. Methods: Twenty-four QIs were evaluated using hospital data from 2022 to 2023. To test whether QIs measured the same quality of care aspect (parsimony and relevance), correlations of QI pairs were examined using Pearson correlation coefficients. Usefulness to identify improvement opportunities was considered limited when variance is ≤ 0.001 without any outliers identified, indicating that the QI could be retired. Actionability was assessed through line graphs of hospital performance over the years. Results: Eleven QIs were highly correlated to other QIs, particularly the follow-up and weight loss indicators at 2 and 4 years, and therefore lacked added value. The weight loss QIs showed minimal variance and were adjusted by increasing the threshold to achieving ≥ 25% total weight loss. Multiple QIs showed improving trends and thereby their actionability, most pronounced for postoperative complications. The final QI set measured three constructs with good validity: Cronbach’s alpha values 0.53 (safety), 0.70 (treatment effectiveness), and 0.43 (follow-up process). Conclusion: Through a systematic evaluation of the DATO QI set, a smaller set of 13 QIs was shown to capture the same relevant information to improve MBS care.

The objective of this project is to devise and develop a design tool that lets the user explore different force flows during conceptual design, with the goal to inform different geometries, thereby realizing an integrated design process. Key characteristics that this tool must incorporate to be successful are defined as feedback (the ability to provide rapid and reliable analysis results), guidance (the ability to guide the user towards better designs), design freedom (the ability to allow users to make their own decisions and use their own expertise) and structural overview (the ability to make the general structural behavior clear instantly). Graphic Statics has been identified as a suitable method to model and compute the force flow with, and has been incorporated as the base of the tool. A workflow has been devised where the user first identifies and investigates a design problem manually, creating an initial design that must be used as input for the tool, which includes a definition of joints, members. loads, supports and certain boundary conditions. Due to the nature of Graphic Statics, this initial design must be statically determinate. The tool has been developed for Grasshopper, a parametric and associative modelling plugin of Rhinoceros, which provides a platform that meets the defined development criteria of accessibility, real-time modelling, geometric flexibility, extensibility, and presentation independence. The developed prototype bearing the name of GSDesign consists of custom components scripted in C# containing the main functionality, as well as Grasshopper clusters for specific visualization sequences. GSDesign facilitates the design of 2D truss-like structures, which can be interpreted as force flow designs, whose efficiency is quantified in the total load path. Feedback is generated in real-time, and is presented in a force diagram, form diagram, unified diagram, and/or as numerical values, providing precise and accurate results. Guidance is incorporated intrinsically through Graphic Statics, but also by the incorporation of an optimization process using the total load path value and the genetic optimization component Galapagos native to Grasshopper. Design freedom is ensured through the general setup of the tool, which allows for maximum utilization of the design flexibility that Grasshopper offers. The tool provides a clear structural overview through the visualizations that characterize Graphic Statics, which can be customized to preference by the user. A user experiment has been set up to test the functionality and workflow of the tool in a simple design case carried out by structural engineers to ensure its practical value. Eleven participants were asked to first create an initial design by hand, and subsequently reproduce that design in GSDesign and improve it with optimization. The use of GSDesign visibly led to new insights towards more efficient structural forms, which was supported by an average drop of 31% in structural material volume when comparing the fitness data of the optimized design with the initial design. Also the participants on average rated their own optimized design more than 40% higher on structural efficiency than their initial manual design, importantly without compromising on practical feasibility.