gms | German Medical Science

Aims: Case-based learning is central to medical education. However, the cases used by educators should be adapted to learners’ abilities and determining the difficulty of a case can be challenging [1]. Case complexity, i.e. the weighted informational density of a medical case, is an emerging concept in this regard [2]. The aim of the present study was

1.

to develop a universal scoring system of case complexity that is applicable to a broad spectrum of cases and

2.

to validate this scoring system by predicting observed case difficulty with complexity scores.

Methods: Case information was classified on three different levels: dimensions, categories, and classes. Medical history and examination results represent the two dimensions of the first level. The second level features several categories (e.g. history of present illness, past medical history for the history dimension; e.g. imaging, laboratory results for the examination results dimension). Finally, on the third level, general information on individual organ system classes was scored per category with increasing score points in each class depending on pathological cues. In total, we scored 338 cases of different formats (e.g. key-feature case vignettes, serial-cue cases, whole cases) and chief complaints. Interrater reliabilty was determined on all levels. A linear logistic test model (LLTM) was used to evaluate the scoring system’s validity in a dataset of 12 virtual patients that were diagnosed by 88 students. In this linearization of the Rasch model, item difficulty is estimated from a matrix of item features and the weighted scoring values. A high correlation between Rasch model difficulty and the LLTM represents a valid representation of item difficulties through the theoretically assumed item features.

Results: Cohen’s kappa values for all three levels were consistently above 0.7. Use of the scoring system yielded a complexity score range from 2 (for a short case vignette) to 249 (for a very elaborated case). The LLTM analyses showed a good fit for the Rasch model based on a Martin-Löf Test (Χ²(35)=25.162, p=.890). A strong correlation was found between difficulties estimated based on the Rasch model and the LLTM of r=.74 (p<.001). Complexity based on laboratory and physical examination increased wheras complexity based on the history of present illness and imaging decreased the case difficulty.

Discussion: We propose a novel scoring system for cases based on the level of case information that does not require profound clinical reasoning skills of the rater. Based on the observed correlations between case complexity and case difficulty, case complexity scores can be applied to adapt case-based education to the learners’ abilities. Interestingly, complexity from different domains and categories affects case difficulty in opposing ways. Further research is needed to clarify these findings along with other factors that influence difficulty and complexity of a case.