Abstract:
:Increasing process complexity in the pediatric intensive care unit (PICU) can lead to information overload resulting in missing pertinent information and potential errors during morning rounds. An efficient model using a novel electronic rounding tool was designed as part of a broader critical care decision support system-checklist for early recognition and treatment of acute illness and injury in pediatrics (CERTAINp). We aimed to evaluate its impact on improving the process of care during rounding. Prospective pre- and post-interventional data included: team performance baseline assessment, patient safety discussion, guideline adherence, rounding time, and a survey of Residents' and Nurses' perception using a Likert scale. Attending physicians were blinded to the components of the assessment. A total of 113 pre-intervention and 114 post-intervention roundings were recorded by direct observation. Pre-intervention (108) and post-intervention staff surveys (80) were obtained. Adherence to standard of care guidelines improved to >97 % in all data points, with maximum increase seen in discussions of ulcer prophylaxis, bowel protocol, DVT prophylaxis, skin care, glucose control and head of bed elevation (2-28 % pre-vs. 100 % for all post-intervention, p < 0.01). Significant improvement was noticed in spontaneous breathing trials, sedation breaks and need for devices (45-57 % pre- vs. 100 % for all post-intervention, p < 0.01). Rounding time (mean ± SD) increased by 2 min/patient (8.0 ± 5.8 min pre-intervention vs. 9.9 ± 5.7 min post-intervention, p = 0.002). Staff reported improved perception of all aspects of rounding. Utilization of the CERTAINp rounding tool led to perfect compliance to the discussion of best practice guidelines; had minimal impact on rounding time and improved PICU staff satisfaction.
journal_name
J Clin Monit Computjournal_title
Journal of clinical monitoring and computingauthors
Hulyalkar M,Gleich SJ,Kashyap R,Barwise A,Kaur H,Dong Y,Fan L,Murthy S,Arteaga GM,Tripathi Sdoi
10.1007/s10877-016-9946-1subject
Has Abstractpub_date
2017-12-01 00:00:00pages
1313-1320issue
6eissn
1387-1307issn
1573-2614pii
10.1007/s10877-016-9946-1journal_volume
31pub_type
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journal_title:Journal of clinical monitoring and computing
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journal_title:Journal of clinical monitoring and computing
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pub_type: 临床试验,杂志文章
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journal_title:Journal of clinical monitoring and computing
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journal_title:Journal of clinical monitoring and computing
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