Mapping Patient Complexity to Educational Needs: Proof‐of‐Concept for a Data‐Driven Framework

PURPOSE: This study aimed to determine whether routinely collected clinical data from a university dental clinic could be translated into a coherent framework for organizing competency-based clinical training. By examining patterns of patient complexity, the study sought to generate an evidence-informed set of educational care lines to guide case allocation, support competency development, and strengthen assessment practices. METHODS: A total of 331 anonymized patient records from 2023 to 2025 were extracted from the institutional database. Variables included demographic information, reasons for appointments, treatment duration, number of visits, procedure codes, and completion status. After data cleaning, relevant variables were selected based on their educational relevance. A multivariate K-means clustering model (K = 2-7) was applied using standardized numerical variables and one-hot-encoded categorical variables. The optimal solution (K = 2) was identified through silhouette analysis. Cluster profiles were examined and interpreted pedagogically to generate educational care lines. RESULTS: Two distinct macro-clusters emerged, reflecting low and high clinical complexity. Low-complexity patients (n = 282) typically underwent short, straightforward treatments with high completion rates. High-complexity patients (n = 49) demonstrated longer treatment trajectories, multiple procedures, and a higher risk of interruption. These patterns informed the derivation of five educational care lines: preventive care, simple restorative care, complex chronic care, prosthodontic care, and critical adherence care. For each line, corresponding competencies, learning objectives, assessment criteria, and autonomy expectations were defined. CONCLUSIONS: This proof-of-concept study demonstrates that clinical data can be transformed into a structured educational framework capable of informing competency-based curriculum design. The resulting care-line model offers a practical method for aligning case complexity with student readiness, improving consistency in clinical exposure, and supporting more reliable assessment practices. Further validation in larger or multicenter cohorts is warranted.