Related Experiment Video
Updated: Jan 5, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
The diagnostic expertise acceleration module (DEAM): promoting the formation of organized knowledge
Brian Rissmiller1, Danny Castro1, Charles G Minard2
1Department of Pediatric Critical Care Medicine, Baylor College of Medicine , TX , USA.
Abstract:
Background: Ensuring that learners acquire diagnostic competence in a timely fashion is critical to providing high quality and safe patient care. Resident trainees typically gain experience by undertaking repetitive clinical encounters and receiving feedback from supervising faculty. By critically engaging with the diagnostic process, learners encapsulate medical knowledge into discrete memories that are able to be recollected and refined in subsequent clinical encounters. In the setting of exponentially increasing medical complexity and current duty hour limitations, the opportunities for successful practice in the clinical arena have become limited. Novel educational methods are needed to more efficiently bridge the gap from novice to expert diagnostician. Objective: Using a conceptual framework which incorporates deliberate practice, script theory, and learning curves, we developed an educational module prototype to coach novice learners to formulate organized knowledge (i.e. a repertoire of illness scripts) in an accelerated fashion thereby simulating the ideal experiential learning in a clinical rotation. Design: We developed the Diagnostic Expertise Acceleration Module (DEAM), a web-based module for learning illness scripts of diseases causing pediatric respiratory distress. For each case, the learner selects a diagnosis, receives structured feedback, and then creates an illness script with a subsequent expert script for comparison. Results: We validated the DEAM with seven experts, seven experienced learners and five novice learners. The module data generated meaningful learning curves of diagnostic accuracy. Case performance analysis and self-reported feedback demonstrated that the module improved a learner's ability to diagnose respiratory distress and create high-quality illness scripts. Conclusions: The DEAM allowed novice learners to engage in deliberate practice to diagnose clinical problems without a clinical encounter. The module generated learning curves to visually assess progress towards expertise. Learners acquired organized knowledge through formulation of a comprehensive list of illness scripts.
Related Concept Videos
Formulating and Validating Nursing Diagnosis II
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
Formulating and Validating Nursing Diagnosis I
There are thirteen domains...
Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
Methods of Documentation IV: Focus Charting
It typically involves three columns for recording information:
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
Methods of Documentation III: PIE

