Integrating retrospective quality assessment with real-time guideline application to support the episodic application of clinical guidelines over significant time periods

Background Evidence-based clinical guidelines (GLs) are essential for standardizing care, yet often difficult to apply. Most clinical decision support systems (CDSSs) assume continuous application, which misaligns with the episodic nature of real-world workflows. Objectives To design, implement, and evaluate e-Picard, a CDSS that provides GL-based recommendations through episodic, intermittent, on-demand consultations. The system supports retrospective assessment of past care and prospective identification of required actions. The evaluation focused on system validity and on its potential, in a retrospective simulation on real-world data, to enhance staff adherence to the GLs and to assess the potential effect of varying the frequency of the consultations. Methods The system development involved three preprocessing steps: (1) acquisition of free-text GLs with domain experts; (2) modeling procedural logic as workflows; and (3) flattening these into declarative temporal patterns for retrospective quality assessment and prospective recommendations. At runtime, e-Picard analyzes offline patient data to identify missed actions, computes compliance using fuzzy logic, and generates context-specific recommendations. e-Picard was applied to pressure-ulcer (PU) and diabetes management (DM) GLs, adapted for episodic use. Technical validation was performed on records from 43 PU and 82 DM patients. A retrospective simulation using 1,000 patients per domain estimated potential increases in adherence under varying consultation frequencies. Results Technical manual validation showed high correctness (≥99 %) and completeness (up to 98 %), based on 3,110 PU and 12,538 DM data instances (i.e., clinical measurements or actions), across various clinical scenarios over two-week observation periods. Retrospective simulation covered 57,860 PU and 100,940 DM data instances with estimated adherence potentially increasing from 68 %–69 % to 89 %–97 % for PU and from 14 %–15 % to 60 %–87 % for DM, in the real-world data retrospective simulation, assuming full adherence of the staff to the system’s recommendations, depending on the scenario. Higher consultation frequency yielded greater gains, and adherence variability across hospital units and patient subgroups was reduced. Conclusions Episodic CDSSs can deliver accurate, context-aware recommendations in environments with intermittent use and incomplete data, with the potential, assuming that the real-world data retrospective simulation results hold, to enhance adherence and consistency in care.