Reducing Pulse Oximetry False Alarms Without Missing Life-Threatening Events

Alarm fatigue has been increasingly recognized as one of the most significant problems in the hospital environment. One of the major causes is the excessive number of false physiologic monitor alarms. An underlying problem is the inefficient traditional threshold alarm system for physiologic parameters such as low blood oxygen saturation (SpO2). In this paper, we propose a robust classification procedure based on the AdaBoost algorithm with reject option that can identify and silence false SpO2 alarms, while ensuring zero misclassified clinically significant alarms. Alarms and vital signs related to SpO2 such as heart rate and pulse rate, within monitoring interval are extracted into different numerical features for the classifier. We propose a variant of AdaBoost with reject option by allowing a third decision (i.e., reject) expressing doubt. Weighted outputs of each weak classifier are input to a softmax function optimizing to satisfy a desired false negative rate upper bound while minimizing false positive rate and indecision rate. We evaluate the proposed classifier using a dataset collected from 100 hospitalized children at Children\u27s Hospital of Philadelphia and show that the classifier can silence 23.12% of false SpO2 alarms without missing any clinically significant alarms

Eliminating Monitor Overuse (EMO) Type III Effectiveness-Deimplementation Cluster-Randomized Trial: Statistical Analysis Plan

Background: Deimplementing overused health interventions is essential to maximizing quality and value while minimizing harm, waste, and inefficiencies. Three national guidelines discourage continuous pulse oximetry (SpO2) monitoring in children who are not receiving supplemental oxygen, but the guideline-discordant practice remains prevalent, making it a prime target for deimplementation. This paper details the statistical analysis plan for the Eliminating Monitor Overuse (EMO) SpO2 trial, which compares the effect of two competing deimplementation strategies (unlearning only vs. unlearning plus substitution) on the sustainment of deimplementation of SpO2 monitoring in children with bronchiolitis who are in room air. Methods: The EMO Trial is a hybrid type 3 effectiveness-deimplementation trial with a longitudinal cluster-randomized design, conducted in Pediatric Research in Inpatient Settings Network hospitals. The primary outcome is deimplementation sustainment, analyzed as a longitudinal difference-in-differences comparison between study arms. This analysis will use generalized hierarchical mixed-effects models for longitudinal clustering outcomes. Secondary outcomes include the length of hospital stay and oxygen supplementation duration, modeled using linear mixed-effects regressions. Using the well-established counterfactual approach, we will also perform a mediation analysis of hospital-level mechanistic measures on the association between the deimplementation strategy and the sustainment outcome. Discussion: We anticipate that the EMO Trial will advance the science of deimplementation by providing new insights into the processes, mechanisms, and likelihood of sustained practice change using rigorously designed deimplementation strategies. This pre-specified statistical analysis plan will mitigate reporting bias and support data-driven approaches

Rapid Response Systems 20 Years Later: New Approaches, Old Challenges

In 1990, Schein and colleagues changed the paradigm of in-hospital cardiopulmonary arrest. Their report, “Clinical Antecedents to In-Hospital Cardiopulmonary Arrest,” provided evidence from adults suggesting that many arrests could have been prevented if existing signs of deterioration were identified, interpreted, communicated, and responded to appropriately.1 Five years later, Liverpool Hospital published the first report of a rapid response system.2 This marked the start of a patient safety movement that spread quickly to children’s hospitals.3 Rapid response systems aim to improve the detection and management of deterioration in hospitalized patients. They combine tools to help clinicians identify deterioration with medical emergency teams that can be summoned to the bedsides of ill patients. Rapid response system implementation was associated with reductions in cardiopulmonary arrests (relative risk [RR], 0.62 [95% CI, 0.46-0.84]) and mortality (RR, 0.79 [95%CI,0.63-0.98]) in a recentmeta-analysis,4 and reversed a trend of increasing critical deterioration events (a more proximate outcome) in a quasi-experimental study.5Duein part tomountingevidence,commonsense appeal, and their inclusion in major initiatives like the Institute for Healthcare Improvement’s 5 Million Lives Campaign, rapid response systems are now nearly universally present in hospitals worldwide. Unfortunately, rapid responsesystemshavenotfully solved the problem they targeted 20years ago, and, despite progress, the challenges in pediatrics remain complex. Children still deteriorate on hospital wards, and 40% or more of these events may be preventable.6 There is an opportunity to renew the enthusiasm that surrounded the first generation of rapid response systemwork, which focused primarilyonthe medicalemergency team response, to pursue the more difficultwork of optimizing the identification of deteriorating children. Below we propose a set of recommendations for a research agenda aimed at addressing this challenge. [Introduction

Challenges of customizing electrocardiography alarms in intensive care units: A mixed methods study

Background: Customizing monitor alarm settings to individual patients can reduce alarm fatigue in intensive care units (ICUs), but has not been widely studied. Objectives: To understand ICU nurses' approaches to customization of electrocardiographic (ECG) monitor alarms. Methods: A convergent mixed methods study was conducted in 3 ICUs in 1 hospital. Data on the type and frequency of ECG alarm customization were collected from patient monitors (n=298). Nurses' customization clinical reasoning was explored through semi-structured interviews (n=27). Results: Of the 298 patients, 58.7% had >= 1 alarm(s) customized. Heart rate limits, irregular heart rate, and atrial fibrillation were the most commonly customized alarms. Interviews revealed that customization practices varied widely and were influenced by factors including clinical expertise, lack of customization education, and negative experiences. Conclusion: Alarm customization is nuanced and requires adequate support to develop safe and effective practices. The challenges identified can inform development of strategies to improve alarm customization. (C) 2018 Elsevier Inc. All rights reserved.12 month embargo; published online: 17 August 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]