The MIDAS touch: Frameworks for procedural model innovation and validation.

Background: Simulation-based procedural practice is crucial to emergency medicine skills training and maintenance. However, many commercial procedural models are either nonexistent or lacking in key elements. Simulationists often create their own novel models with minimal framework for designing, building, and validation. We propose two interlinked frameworks with the goal to systematically build and validate models for the desired educational outcomes.

Methods: Simulation Academy Research Committee and members with novel model development expertise assembled as the MIDAS (Model Innovation, Development and Assessment for Simulation) working group. This working group focused on improving novel model creation and validation beginning with a preconference workshop at 2023 Society for Academic Emergency Medicine Annual Meeting. The MIDAS group sought to (1) assess the current state of novel model validation and (2) develop frameworks for the broader simulation community to create, improve, and validate procedural models.

Findings: Workshop participants completed 17 surveys for a response rate of 100%. Many simulationists have created models but few have validated them. The most common barriers to validation were lack of standardized guidelines and familiarity with the validation process.We have combined principles from education and engineering fields into two interlinked frameworks. The first is centered on steps involved with model creation and refinement. The second is a framework for novel model validation processes.

Implications: These frameworks emphasize development of models through a deliberate, form-follows-function methodology, aimed at ensuring training quality through novel models. Following a blueprint of how to create, test, and improve models can save innovators time and energy, which in turn can yield greater and more plentiful innovation at lower time and financial cost. This guideline allows for more standardized approaches to model creation, thus improving future scholarship on novel models.