Suchergebnisse

Objective: This study samples interruption frequency in intensive care unit (ICU) settings to assess the relationship between interruptions and common patient hazards. Background: Task interruptions are accident contributors in numerous industries. Recently, studies on health care interruptions and their impact on patient hazards have received attention. Method: Seven ICUs in four hospitals participated in a 24-month study. Experienced ICU nurses directly observed nursing tasks, interruptions, and patient hazards (delays in care, breaks in device task protocols, and patient safety hazards). Results: During 1,148 hours of observation, 175 nurses performed 74,733 nursing tasks. Interruptions occurred at a rate of 4.95 per hour, and 8.4% of tasks were interrupted. Interruptions originated mostly from humans (65.9%), alarms (24.1%), and others (10%). A total of 774 patient hazards were observed, with a hazard occurring on average every 89 minutes. Relative to noninterrupted tasks, device alarm interrupted nonstructured tasks were associated with increased rates of delays in care and safety hazards (rate ratio [RR] = 3.19). In contrast, rate of delays in care and safety hazards did not increase during human interrupted tasks (RR = 1.13). Rates of protocol nonadherence varied by device type and were highest during artificial airway, medication administration, chest tube, and supplemental oxygen management. Conclusion: Interruptions in the ICU are frequent and contribute to patient hazards, especially when caused by device alarms during nonstructured tasks. Nonadherence to protocols is common and contributed to patient hazards. Application: The findings suggest a need for improvement in task and device design to reduce patient hazards.

The coronavirus disease pandemic has highlighted the key role epidemiologic models play in supporting public health decision-making. In particular, these models provide estimates of outbreak potential when data are scarce and decision-making is critical and urgent. We document the integrated modeling response used in the US state of Utah early in the coronavirus disease pandemic, which brought together a diverse set of technical experts and public health and healthcare officials and led to an evidence-based response to the pandemic. We describe how we adapted a standard epidemiologic model; harmonized the outputs across modeling groups; and maintained a constant dialogue with policymakers at multiple levels of government to produce timely, evidence-based, and coordinated public health recommendations and interventions during the first wave of the pandemic. This framework continues to support the state's response to ongoing outbreaks and can be applied in other settings to address unique public health challenges.