Digital Medical Simulation Platform Improves Neurologists’ Ability to Assess for and Diagnose Muscular Dystrophies
Christine Considine1, Thomas Finnegan1, Meg Monday1, John Brandsema2
1Medscape Education, 2Children's Hospital of Philadelphia
Objective:
This study utilized an online medical simulation platform to improve the ability of neurologists’ to assess, differentiate between, and diagnose either Duchenne muscular dystrophy (DMD) or limb-girdle muscular dystrophy (LGMD).
Background:
DMD is the most common form of muscular dystrophy. Despite what is known about the causes and symptoms of DMD, initial symptoms may be subtle and or overlap with other muscular dystrophies resulting in delays in diagnosis.
Design/Methods:
The simulation consisted of two cases presented in a platform that allowed learners to choose from lab tests and assessment scales to arrive at the correct diagnosis. The clinical decisions made by the participants were analyzed using an artificial intelligence engine, and instantaneous clinical guidance was provided employing current evidence-based and expert faculty recommendations. Statistical significance was determined by a McNemar’s test, comparing decisions made after guidance with those made before guidance. Data were collected from June 2022 through September 2022.
Results:
The assessment sample consisted of 116 neurologists. A significant (P<0.05) pre-vs post guidance improvement was seen in selecting a Gowers’s sign assessment in both cases. Approximately 80% of learners correctly ordered creatinine kinase levels for both cases, a number that improved to approximately 89% after receiving guidance. >50% learners knew the correct diagnosis (either DMD or LGMD) on the first try. After receiving guidance, there was an approximate 20% improvement in making the correct diagnosis. Among learners who never made a correct diagnosis, commonly chosen rationales included: symptoms indicated another diagnosis, neuromuscular testing indicated another diagnosis, and lack of familiarity with how to assess the patient.
Conclusions:
This study demonstrated the success of simulation-based educational interventions on improving the assessment and diagnosis of patients with muscular dystrophies. Future education should focus on how early symptoms of a muscular dystrophy should inform the selection of appropriate assessment modalities and how to interpret diagnostic testing.