Visual Complexity Assessment of Clinical Cases Using Health Issue Network Diagrams: Validation of a Method to Support Progressive Case-Based Learning

Background/Objectives: This study aimed to validate a novel method for assessing the complexity of clinical cases for educational purposes using graphically friendly Health Issue Network (f-HINe) diagrams. Clinical case-based learning requires a progressive calibration of complexity to manage cognitive load and support the development of clinical reasoning, particularly in multimorbidity. Methods: Building on dual coding theory and the visual structure of HIN diagrams, the authors hypothesized that a four-level visual ranking scale could provide a valid and reliable measure of intrinsic case complexity. Sixty-six f-HINe diagrams representing varying structural features (number of health issues, branching evolutions, involved systems, and comorbidity interactions) were evaluated by fourteen physicians unfamiliar with the HIN approach. Using Kane’s validity framework, the study examined scoring reliability, generalization, extrapolation, and implications. Intra-rater and inter-rater reliability were assessed using Spearman’s correlation coefficient and intraclass correlation coefficients (ICC), respectively. Agreement was evaluated using Kendall’s W and weighted Cohen’s kappa, while generalizability analysis was performed to estimate the contribution of diagrams and raters to rating variability. Correlations with Braun’s score and CIRS were also examined. Results: Complexity ratings showed high intra-rater reliability (mean Spearman’s rho = 0.862) and good inter-rater reliability (ICC(2,1) = 0.743), with excellent reliability for averaged ratings (ICC(2,k) = 0.976). Substantial agreement was observed across professional experience and medical specialties, as confirmed by Kendall’s W (0.781) and weighted Cohen’s kappa (0.887). Significant correlations were found with Braun’s score (r = −0.593) and CIRS (r = −0.724), consistent with the opposite coding direction of the scales. Generalizability analysis showed that 74.3% of rating variance was attributable to differences between diagrams, compared with only 2.3% attributable to systematic differences between raters, supporting the reproducibility of the proposed structural complexity assessment. Conclusions: The findings indicate that rapid visual assessment of f-HINe diagrams is a reliable and valid approach for estimating the structural complexity of clinical cases. Compared with existing tools, the HIN method uniquely captures temporal evolution and interactions among multiple conditions, enabling educators to design progressive learning pathways while reducing cognitive load. This graphical strategy may also extend beyond education, offering a concise way to represent complex clinical trajectories in practice. However, further validation in larger, more heterogeneous, and multi-institutional samples is needed to establish the generalizability of these findings and determine whether f-HINe provides additional value over existing complexity measures.