Development and user evaluation of a rare disease gene prioritization workflow based on cognitive ergonomics.

<bold>Objective: </bold>The clinical diagnosis of genetic disorders is undergoing transformation, driven by whole exome sequencing and whole genome sequencing (WES/WGS). However, such nucleotide-level resolution technologies create an interpretive challenge. Prior literature suggests that clinicians may employ characteristic cognitive processes during WES/WGS investigations to identify disruptions in genes causal for the observed disease. Based on cognitive ergonomics, we designed and evaluated a gene prioritization workflow that supported these cognitive processes.<bold>Materials and Methods: </bold>We designed a novel workflow in which clinicians recalled known genetic diseases with similarity to patient phenotypes to inform WES/WGS data interpretation. This prototype-based workflow was evaluated against the common computational approach based on physician-specified sets of individual patient phenotypes. The evaluation was conducted as a web-based user study, in which 18 clinicians analyzed 2 simulated patient scenarios using a randomly assigned workflow. Data analysis compared the 2 workflows with respect to accuracy and efficiency in diagnostic interpretation, efficacy in collecting detailed phenotypic information, and user satisfaction.<bold>Results: </bold>Participants interpreted genetic diagnoses faster using prototype-based workflows. The 2 workflows did not differ in other evaluated aspects.<bold>Discussion: </bold>The user study findings indicate that prototype-based approaches, which are designed to model experts' cognitive processes, can expedite gene prioritization and provide utility in synergy with common phenotype-driven variant/gene prioritization approaches. However, further research of the extent of this effect across diverse genetic diseases is required.<bold>Conclusion: </bold>The findings demonstrate potential for prototype-based phenotype description to accelerate computer-assisted variant/gene prioritization through complementation of skills and knowledge of clinical experts via human-computer interaction. [ABSTRACT FROM AUTHOR]