BACKGROUND:Improving paediatric diagnosis by addressing errors (Safety-I) is insufficient for diagnostic excellence. Resilience engineering's approach to understanding adaptations to achieve success in everyday work may elucidate how individuals and systems manage diagnostic complexity to achieve diagnostic success (Safety-II). Our objective was to characterise resilience-promoting behaviours of patients/families and clinicians and resilient organisational properties in outpatient diagnosis of children with medical complexity. METHODS:This ethnographic study included observations of outpatient visits and semistructured interviews of parents/guardians and providers. Resilience engineering frameworks informed data collection and analysis. We prospectively sampled patients <18 years old with medical complexity presenting with acute problems in clinics at three academic health systems from 25 January 2024 to 26 February 2025. We qualitatively coded field notes and interview transcripts, focusing on individual resilient behaviours and instances of organisational resilience. We identified resilience themes using directed content analysis and described the frequency and types of resilience concepts. RESULTS:We observed 258 unique resilience-promoting behaviours and organisational properties in 31 of 33 (94%) patient visits, with an average of 7.8 resilient behaviours/properties per visit. Of these, 178 (69%) reflected patient/family and clinician activities to promote diagnostic resilience and 80 (31%) reflected organisational resilience relevant to diagnosis. The most frequently identified resilience behaviours/properties were anticipation (prediction of future events and contingency planning), monitoring (vigilant observation for clinical evolution), robustness (comprehensiveness and redundancy in the diagnostic process) and graceful extensibility (stretching resources to meet diagnostic needs). CONCLUSIONS:Resilient behaviours by patients/families and clinicians and resilient organisational properties supporting diagnostic excellence can be prevalent in outpatient paediatrics for children with medical complexity. Future research should characterise diagnostic resilience's impact on diagnostic safety and patient/family outcomes.
BACKGROUND:Knowledge brokers act as intermediaries to bridge evidence-to-practice gaps. Limited evidence describes how the knowledge broker role in health settings evolves over time. This study aimed to (a) examine the evolution of knowledge broker activities over a 12-month implementation trial and (b) identify enablers necessary to strengthen and support the role. METHODS:This longitudinal multi-method study is nested within the 12-month Evidence-based Medication knowledge Brokers in Residential Aged CarE (EMBRACE) trial. The EMBRACE intervention involved knowledge brokers working in 19 residential aged care facilities (RACFs) to support implementation of Australia's Clinical Practice Guidelines for the Appropriate Use of Psychotropic Medications in People Living with Dementia in Residential Aged Care. Seventy-six knowledge broker local action plans, representing 19 RACFs at four time points, were analysed to characterise core knowledge broker functions and the frequency of planned quality improvement activities targeting seven pre-defined sub-indicators of psychotropic appropriateness. The Consolidated Framework for Implementation Research informed thematic analysis of 76 separate quarterly written reflections and 28 semistructured interviews with knowledge brokers. RESULTS:The number and breadth of knowledge broker activities increased over time. At baseline, six RACF local action plans included activities targeted towards at least one sub-indicator, increasing to all 19 local action plans from the 3-month timepoint onwards. Knowledge brokers enacted overlapping functions as knowledge managers, linkage agents and capacity builders, although the timing and extent of role progression varied across RACFs and individuals. Evolution of activities was shaped by individual (clinical leaders, implementation facilitators, peer support) organisational (culture, governance pathways, digital integration) and outer setting factors (COVID-19 disruptions, national policy directives). CONCLUSION:The knowledge broker role fundamentally evolved from assessing local context and establishing foundational rapport to driving systemic change. Effective translation of guidelines into practice requires future implementation programmes to support this role with a strong organisational culture, robust infrastructure and strategic alignment with RACFs and national priorities.
BACKGROUND:Sepsis and infection are distinct yet overlapping conditions in the intensive care unit (ICU), posing diagnostic and management challenges due to non-specific clinical features and delayed microbiological confirmation. This study aimed to develop and evaluate a real-time dual-model clinical decision support system for early identification of infection and sepsis based on the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) framework. METHODS:We conducted a retrospective model development and prospective non-interventional deployment study of adult ICU admissions between 2018 and 2020 at a tertiary-care medical centre. Infection was defined by positive microbiological culture results while sepsis was defined according to Sepsis-3 criteria. Two machine learning models were developed using structured clinical features from an 8-hour feature window to predict infection and sepsis. The framework included an 8-hour lead time and a 1-hour prediction window. Class imbalance was addressed using propensity score matching and Borderline Synthetic Minority Oversampling Technique. External validation was performed using the Medical Information Mart for Intensive Care IV database. Real-time deployment analyses evaluated ICU risk surveillance and cluster-based stratification using combined sepsis and infection probabilities. RESULTS:The dual-model system demonstrated consistent discrimination across internal, reduced-feature and external validation cohorts, with area under the receiver operating characteristic curves ranging from 0.75 to 0.85. Both models prioritised high sensitivity and negative predictive value to minimise missed cases. During real-time ICU implementation, the decision support dashboard provided interpretable risk estimates at the bedside. Exploratory pre-post comparisons showed numerically lower point estimates across most clinical and resource-utilisation outcomes, without adjustment for confounding factors. Cluster-based analysis further identified a high-risk subgroup with greater healthcare resource utilisation. CONCLUSION:This real-time clinical decision support system enables early identification of infection and sepsis, supports timely clinical decision-making and may inform antibiotic and resource use in the ICU.
