Development of a clinical decision support system for lower-limb prosthetic prescription

Abstract

Lower-limb prosthetic prescription is a complex process, and in Thailand, practice remains inconsistent because of the limited availability of ISPO Category I professionals and the absence of localized guidance. To address this gap, a clinical decision support system (CDSS) was developed to help standardize decision-making and improve the quality of amputee care. This study set out to evaluate whether the CDSS could enhance prescription accuracy, reduce decision time, and provide acceptable usability to its users. A prospective, simulation-based crossover trial was conducted, in which participants completed two standardized case vignettes—once with and once without CDSS support—in randomized order. Completion time was automatically recorded, and outcomes included component-level accuracy, decision time, and usability as measured by the System Usability Scale (SUS). Thirty-seven professionals participated, comprising 12 physiatrists, 14 ISPO Category I prosthetists/orthotists, and 11 ISPO Category III technicians, with a mean age of 39.5 years and nearly 60% having more than five years of professional experience. Overall, the use of CDSS led to improved accuracy, with median scores rising from 40% to 60%, and reduced decision time, which fell from 144 to 120 seconds, although these changes did not reach statistical significance. Subgroup analysis revealed that physiatrists showed greater gains in accuracy (40% to 70%) but required more time when using the system. By contrast, ISPO Category III technicians achieved stable accuracy while significantly reducing their decision time (133 to 99 seconds). ISPO Category I prosthetists/orthotists showed no meaningful differences across outcomes. The overall mean SUS score was 63.5, suggesting marginal usability, though less-experienced users rated the system more positively, with mean scores approaching 72. In this simulation, the CDSS did not yield statistically significant gains in accuracy or decision time. Nonetheless, with usability improvements, targeted training, and local adaptation, its decision-support value may increase. Adequately powered, real-world studies are needed to confirm clinical impact.

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