Enhancing clinical decision-making: a scenario-based patient simulation study using QR code-based algorithms for the management of acute intoxication-type inborn errors of metabolism

Abstract

Background. Acute intoxication-type inborn errors of metabolism (IEMs) present significant challenges in pediatric care. Prompt recognition and appropriate management are essential to prevent serious complications and reduce mortality. Recent studies increasingly highlight the use of quick response (QR) code-based tools to facilitate rapid intervention, particularly in emergency departments and primary healthcare settings. In this study, the effectiveness of a newly developed QR code-based algorithm, designed to support the accurate recognition and effective management of acute intoxication-type IEMs and, indirectly, to reduce sequelae and mortality, was evaluated for the first time. Methods. This study included 113 pediatric residents from two centers, one with (Group 1, n=77) and one without (Group 2, n=36) a mandatory pediatric metabolism rotation. All participants completed a scenario-based simulation with 10 clinical questions on a standardized patient case of acute intoxication-type inborn errors of metabolism, both before and after using the QR code-based algorithm. The algorithm, developed in accordance with international guidelines, was accessed via mobile devices. Pre-and post-intervention responses were compared using appropriate statistical tests. The effectiveness of the QR code in guiding the management of a simulated patient was analyzed. Results. Of the participants, 73 (64.6%) were female and 40 (35.4%) male; the median age was 28.0 years. Forty-two residents (37.2%) had previous experience in a pediatric metabolism unit. Correct identification of urgent treatment increased from 77.9% to 97.3% (p<0.001). Preliminary diagnosis improved from 79.6% to 88.5% (p=0.050). Only 0.9% initially selected the correct treatment sequence versus 81.4% post-intervention (p<0.001). Hemodialysis decisions improved from 81.4% to 95.6% (p<0.001). Satisfaction was high, with 92.0% assigning an average score of 93.3/100. Conclusion. Considering the limited knowledge of rare diseases among physicians, scenario-based simulation training and the widespread use of QR code-accessed algorithms in emergency departments appear essential to improve outcomes in patients at risk of severe complications.