Introduction: Accurate and timely documentation of pediatric early warning scores (PEWS) by the bedside nurse into the electronic health record (EHR) is important to promote early identification of patients in stages of deterioration. Through the implementation of a PEWS calculator embedded in the EHR, we hope to improve the accuracy of the recorded score and reduce the time between vital sign collection and final documentation in the EHR. Methods: Identification of the highest PEWS value in the 24 hours before all unplanned transfers or rapid response activations without a transfer occurred between the period November 1, 2013, through December 31, 2016. The accuracy of the calculated cardiac or respiratory subscore based on heart rate or the respiratory rate at the time of PEWS calculation was determined. We tracked the calculation of the time to chart via the difference between nursing documentation of PEWS compared to vital sign collection. Before September 3, 2015, PEWS was calculated mentally by the bedside nurse; afterward, the nurse entered the unique PEWS features into the EHR, and the EHR automatically calculated PEWS. Results: This study evaluated 2,409 PEWS scores, 1,411 before and 998 after initiation of the PEWS calculator. Accuracy before the EHR calculator was 71%, and the median time to document was 55 minutes. Following the implementation of the calculator, no scores were incorrectly calculated too low, and the median time to document was 20 minutes. Conclusions: Transition to an EHR-based PEWS calculator eliminated inaccurately low PEWS values and reduced time to document.
Background: Patient transitions create vulnerability for care teams. Failures in the handoff process result in communication errors and knowledge gaps, mainly when the handoff occurs between resident and expert-level subspecialty clinicians. The authors set out to develop a standardized handoff using resident comfort as a proxy for implementation. The primary measurable aim of this study was to increase the percentage of pediatric residents who self-reported comfort in assuming care of patients transitioned from the cardiac intensive care unit to the cardiology acute care unit. Methods: Investigators surveyed residents at a 323-bed pediatric hospital on their handoff experiences. The study team performed a Failure Mode Effect Analysis and created a key driver diagram. Interventions included a transfer checklist and algorithm, a huddle between care teams, and education surrounding the transfer process. Results: Residents completed a survey before (n = 74) or after (n = 23) intervention. The percentage of residents who reported feeling “always” or “very often” prepared to care for patients at the time of transfer increased from 15% to 83%. The percentage of residents who reported that they “always” or “very often” had concerns about floor appropriateness decreased from 23% to 4%. Conclusions: The authors designed a transfer process to improve communication, resident-level education, and psychological safety among team members to ensure safe, thorough handoffs between providers with different levels of training. Although we cannot definitively conclude that resident comfort improved due to a small “n” postintervention, we offer a description outlining process changes, barriers to implementation, and lessons learned.
Objective: To reduce the frequency of non-ICU arrests through the implementation of an intramural collaborative focused on patient deterioration. Design: Prospective quality improvement project. Setting: Single-center, free-standing, tertiary children’s hospital. Patients: All patients admitted to acute care units. Interventions: The Late Rescue Collaborative was formed in 2014 to monitor compliance with hospital escalation protocols and evaluate episodes of patient deterioration. The collaborative is a multidisciplinary team of physicians, nurses, and respiratory care providers. Three monthly meetings occur: 1) individual acute care unit–based meetings to evaluate trends and performance; 2) hospital-wide multidisciplinary whole group meetings to review hospital trends in deterioration and share lessons learned; and 3) steering committee to determine areas of focus. Based on these three meetings, unit- and hospital-based interventions have been put in place to improve recognition of deterioration and promote early rescue. Measurements and Main Results: Rates of rapid response team activations, unplanned transfers, and non-ICU arrest are reported. Non-ICU arrest rates fell from a baseline of 0.31 per 1,000 non-ICU patient days to a new centerline of 0.11 and sustained for 36 months. Days between non-ICU arrests increased from a baseline of 15.5 days in year 2014 to a new centerline of 61.5 days and sustained for 37 months. Mortality following non-ICU arrests fell from four in 2014 and 2015 to zero in years 2016–2018. Conclusion: The Late Rescue Collaborative is an effective tool to improve patient safety by reducing non-ICU arrests.
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