Arcus Experience: Bridging the Data Science Gap for Nurse Researchers.

BACKGROUND: For years, nurse researchers have been called upon to engage with "big data" in the electronic health record (EHR) by leading studies focusing on nurse-centric patient outcomes and providing clinical analysis of potential outcome indicators. However, the current gap in nurses' data science education and training pose a significant barrier. OBJECTIVES: We aimed to evaluate the viability of conducting nurse-led, big-data research projects within a custom-designed computational lab and examine the support required by a team of researchers with little to no big-data experience. METHODS: Four nurse-led research teams developed a research question reliant on existing EHR data. Each team was given its own virtual computational lab populated with raw data. A data science education team provided instruction in coding languages-primarily structured query language and R-and data science techniques to organize and analyze the data. RESULTS: Three research teams have completed studies, resulting in one manuscript currently undergoing peer-review and two manuscripts in progress. The final team is performing data analysis. Four barriers and four facilitators to big-data projects were identified. DISCUSSION: As the data-science learning curve is steep, organizations need to help bridge the gap between what is currently taught in doctoral nursing programs and what is required of clinical nurse researchers to successfully engage in big-data methods. Additionally, clinical nurse researchers require protected research time and a data science infrastructure that supports novice efforts with education, mentorship, and computational lab resources.

Face and content validity of variables associated with the difficult-to-sedate child in the paediatric intensive care unit: A survey of paediatric critical care clinicians.

BACKGROUND: Clinicians recognise that some critically ill children are difficult-to-sedate. It may be possible to identify this clinical phenotype for sedation response using statistical modelling techniques adopted from machine learning. This requires identification of a finite number of variables to include in the statistical model. OBJECTIVE: To establish face and content validity for 17 candidate variables identified in the international literature as characteristic of the difficult-to-sedate child phenotype. METHODS: Paediatric critical care clinicians rated the relevance of 17 variables characterising the difficult-to-sedate child using a four-point scale ranging from not (1) to highly relevant (4). Face and content validity of these variables were assessed by calculating a mean score for each item and computing an item-level content validity index. Items with a mean score >1 were rated as having adequate face validity. An item-level content validity index ≥0.70 indicated good to excellent content validity. SETTING AND PARTICIPANTS: Web-based survey emailed to members of the Pediatric Acute Lung Injury and Sepsis Investigators Network or the Society of Critical Care Medicine Pediatric Sedation Study Group. RESULTS: Of 411 possible respondents, 121 useable surveys were returned for a response rate of 29%. All items had a mean score >1, indicating adequate face validity. Ten of 17 items scored an item-level content validity index ≥0.70. The highest scoring items were requiring three or more sedation classes simultaneously, daily modal sedation score indicating agitation, sedation score indicating agitation for 2 consecutive hours, receiving sedatives at a dose >90th percentile of the usual starting dose, and receiving intermittent paralytic doses for sedation. CONCLUSIONS: Computation of an item-level content validity index validated variables to include in statistical modelling of the difficult-to-sedate phenotype. The results indicate consensus among paediatric critical care clinicians that the majority of candidate variables identified through literature review are characteristic of the difficult-to-sedate child.

Nurses' Perceptions of Workload Burden in Pediatric Critical Care.

BACKGROUND: Quantifying nurses' perceptions of workload burden when managing critically ill patients is essential for designing interventions to ease nurses' workday. OBJECTIVES: To explore pediatric intensive care unit (PICU) nurses' perceptions of their workload when caring for critically ill patients and managing protocolized therapies. METHODS: This study was embedded in a multicenter randomized clinical trial where participants were assigned to receive either lower-target or higher-target glucose control. Nurses from 35 participating PICUs completed a baseline survey containing questions about their perceptions of PICU workload in general. They completed an intervention survey after caring for a study patient. Two workload measurement instruments, the Subjective Workload Assessment Technique (SWAT) and the National Aeronautics and Space Administration-Task Load Index (NASA-TLX), were embedded in these surveys. RESULTS: Baseline surveys were completed by 1476 PICU nurses, predominantly female with a bachelor's degree and a median (interquartile range) of 6 (3-11) years of nursing experience and 4 (2-9) years of PICU experience. Most nurses (65%) rated time burden as the most important component of their workload, followed by cognitive (22%) or psychological stress (13%) burden. Work performance was selected most often as contributing to workload, followed by cognitive demand, time pressure, effort, and physical demand. Intervention surveys were completed by 73% of enrolled participants (505 of 693). Nurses managing the lower glucose target group reported higher levels of workload burden as measured by the SWAT (P = .002) and NASA-TLX (P < .001). CONCLUSIONS: This study describes the workload burden perceived by PICU nurses when managing critically ill patients in general and when managing protocolized therapies.