Introduction: Critical care quality review processes often rely on ad hoc referrals or retrospective mortality reviews, which may overlook systemic contributors to poor outcomes. We developed a standardized, trigger-based case identification framework to support consistent, high-yield monthly review by ICU dyads across a large academic medical center. This approach emphasizes objective, reproducible criteria that align with SCCM-endorsed quality metrics to proactively identify improvement opportunities and promote cross-unit learning. Methods: A monthly “triggered cases” report was implemented and curated for each ICU dyad by both physical ICU location and critical care service line, accounting for shared units and off-unit admissions. Cases were flagged if they met one or more of five pre-defined criteria: (1) ICU mortality; (2) early mortality (≤24 hours of admission); (3) delayed ICU admission (>4 hours from order to arrival); (4) ICU bounceback (readmission within 48 hours); and (5) extended ICU length-of-stay (>2 standard deviations from the unit or service average). These triggers were selected for clinical relevance, alignment with national metrics, and feasibility of electronic extraction. Results: The framework enabled systematic identification of clinically significant cases and improved review consistency across dyads. Compared to referral-based processes, it generated a more diverse and representative case mix. Dyads reported increased engagement and perceived value of monthly discussions. Several cross-cutting QI initiatives were developed in response to recurring patterns (e.g., consult delays, resource outliers). Cases meeting multiple trigger criteria (e.g., mortality + prolonged length-of-stay) were especially instructive. A recurring “lessons learned” forum disseminated insights across all ICUs. Conclusions: A trigger-based ICU case identification framework is a feasible, scalable tool to enhance critical care quality oversight. When tailored to institutional context and aligned with SCCM-endorsed metrics, it facilitates more proactive, reproducible, and collaborative case review processes.
Moore et al. (Sun,) studied this question.