An approach to value-based simulator selection: The creation and evaluation of the simulator value index tool.

BACKGROUND: Currently there is no reliable, standardized mechanism to support health care professionals during the evaluation of and procurement processes for simulators. A tool founded on best practices could facilitate simulator purchase processes. METHODS: In a 3-phase process, we identified top factors considered during the simulator purchase process through expert consensus (n = 127), created the Simulator Value Index (SVI) tool, evaluated targeted validity evidence, and evaluated the practical value of this SVI. A web-based survey was sent to simulation professionals. Participants (n = 79) used the SVI and provided feedback. We evaluated the practical value of 4 tool variations by calculating their sensitivity to predict a preferred simulator. RESULTS: Seventeen top factors were identified and ranked. The top 2 were technical stability/reliability of the simulator and customer service, with no practical differences in rank across institution or stakeholder role. Full SVI variations predicted successfully the preferred simulator with good (87%) sensitivity, whereas the sensitivity of variations in cost and customer service and cost and technical stability decreased (≤54%). The majority (73%) of participants agreed that the SVI was helpful at guiding simulator purchase decisions, and 88% agreed the SVI tool would help facilitate discussion with peers and leadership. CONCLUSION: Our findings indicate the SVI supports the process of simulator purchase using a standardized framework. Sensitivity of the tool improved when factors extend beyond traditionally targeted factors. We propose the tool will facilitate discussion amongst simulation professionals dealing with simulation, provide essential information for finance and procurement professionals, and improve the long-term value of simulation solutions. Limitations and application of the tool are discussed.

Development and validation of a basic arthroscopy skills simulator.

PURPOSE: The purpose of our study was to develop a low-fidelity surgical simulator for basic arthroscopic skills training, with the goal of creating a pretrained novice ready with the basic skills necessary for all joint arthroscopic procedures. METHODS: A panel of education, arthroscopy, and simulation experts designed and evaluated a basic arthroscopic skills training and testing box. Task deconstruction was used to create 2 modules, which incorporate core skills common to all arthroscopic procedures. Core metrics measured were time to completion, number of trials to steady state, and number of errors. Face validity was evaluated using a questionnaire. Construct validity was examined by comparing 8 medical students with 8 expert orthopaedic surgeons. RESULTS: Surgeons were faster than students on both module 1 (P = .0013), simulating triangulation skills, and module 2 (P = .0190) simulating object manipulation skills. Surgeons demonstrated fewer errors (6.9 errors versus 28.1; P = .0073). All surgeons were able to demonstrate steady state (i.e., perform 2 trials that were within 10% of each other for time to completion and errors) on both modules within 3 trials on each module. Only 2 novices were able to demonstrate steady state on either module, and both did so within 3 trials. Furthermore, face validity of the skills trainer was shown by the expert arthroscopists. CONCLUSIONS: We describe a basic arthroscopy skills simulator that has face and construct validity. Our expert panel was able to design a simulator that differentiated between experienced arthroscopists and novices. CLINICAL RELEVANCE: Surgical simulation is an important part of efficient surgical education. This simulator shows good construct and face validity and provides a low-fidelity option for teaching the entry-level arthroscopist.