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.

To study the effect of chronic excess growth hormone on adipose tissue, we performed RNA sequencing in adipose tissue biopsies from patients with acromegaly (n = 7) or nonfunctioning pituitary adenomas (n = 11). The patients underwent clinical and metabolic profiling including assessment of HOMA-IR. Explants of adipose tissue were assayed ex vivo for lipolysis and ceramide levels. Patients with acromegaly had higher glucose, higher insulin levels and higher HOMA-IR score. We observed several previously reported transcriptional changes (IGF1, IGFBP3, CISH, SOCS2) that are known to be induced by GH/IGF-1 in liver but are also induced in adipose tissue. We also identified several novel transcriptional changes, some of which may be important for GH/IGF responses (PTPN3 and PTPN4) and the effects of acromegaly on growth and proliferation. Several differentially expressed transcripts may be important in GH/IGF-1-induced metabolic changes. Specifically, induction of LPL, ABHD5, and NRIP1 can contribute to enhanced lipolysis and may explain the elevated adipose tissue lipolysis in acromegalic patients. Higher expression of TCF7L2 and the fatty acid desaturases FADS1, FADS2 and SCD could contribute to insulin resistance. Ceramides were not different between the two groups. In summary, we have identified the acromegaly gene expression signature in human adipose tissue. The significance of altered expression of specific transcripts will enhance our understanding of the metabolic and proliferative changes associated with acromegaly.