Outlook Magazine, Spring 2015

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Exploring experiences of alarm response and alarm fatigue among nurses working in the ICU of a tertiary care hospital in Karachi, Pakistan: A qualitative study

Background: Alarm fatigue is a significant concern within the healthcare sector, marked by the excessive exposure of healthcare practitioners to clinical alarms. This prolonged exposure can result in desensitization and delay nurse’s responsiveness to vital warnings. The occurrence of inaccurate alarms intensifies desensitization, undermining confidence in alarm systems and potentially leading nurses to ignore alarms. Alarm fatigue is a prevalent issue of worldwide significance, which calls for more investigation to effectively tackle these issues and enhance understanding of alarm systems to get the best possible treatment results. However, there is a lack of study undertaken in Pakistan about the investigation regarding response of intensive care unit (ICU) nurses to clinical alarms and their understanding of alarm fatigue.Purpose: The objectives of this study are to describe the type and criticality of alarms produced by machines at the patient’s bedside and explore the experiences of alarm response and alarm fatigue among nurses working in the ICU of a tertiary care hospital in Karachi, Pakistan.Methodology: This study used an observational descriptive exploratory methodology to investigate the experiences of alarm response and alarm fatigue among nurses working in the ICU. The research was conducted in an 11-bed intensive care unit, with nurses selected through purposive sampling. Data was collected through observations (camera and documentation) and interviews, and the findings were analyzed through content analysis. Moreover, synergy model was used as a theoretical framework for this study.Finding: The study investigated how nurses respond to clinical alerts and their perception of alarm fatigue. The observational findings highlighted a diverse range of alarms and demonstrated variations in nurse responses to these alerts. Additionally, the observations identified discrepancies between the alarms set on monitors and those documented on the flowsheet. The content analysis study identified five key themes: Alarm setting guarantees patient Safety, Alarms response follows standards and patient priority, alarm driven nurse’s actions, alarm fatigue exists and is dangerous, alarm fatigue can be combat. Moreover, the study illuminates the consequences of alarm desensitization and explores methods to alleviate alarm fatigue.Conclusion: The findings of the study offer crucial suggestions for nursing leaders and administrators to enact policies aimed at reducing alarm fatigue among nurses. The recommendations encompass the implementation of procedures aimed at prioritizing alerts according to their level of criticality, the provision of education and training programs focused on the appropriate handling of alarms, and the frequent evaluation of the efficacy of existing alarm systems

Effectiveness of Physiological Alarm Management Strategies to Prevent Alarm Fatigue

There is limited clinical research on the effectiveness of alarm management strategies and nursing behaviors related to alarms in clinical settings. As many as 76% of physiological monitor alarms are overlooked as clinically insignificant by nursing staff. Excessive alarms may impact patient outcomes and cause cognitive overload for nurses that can result in medical errors and missed patient resuscitations. The purpose of this systematic review was to rate alarm management studies on level of evidence for interventions, nursing responses to alarms, and impact on alarm fatigue behavior. The nursing role effectiveness model guided this project. Twenty-seven studies were reviewed to analyze outcome effectiveness by addressing structure, process, and outcomes related to how the roles of the nurse affect nurse-sensitive patient outcomes. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) and the Cochrane guidelines guided study selection and analysis. A second reviewer collaborated on the search strategy and provided an independent review of the identified literature. The effectiveness of alarm management was difficult to determine because most studies were descriptive, cohort, or nonrandomized trials. Review findings did not support a relationship between the amount of alarms and increased alarm fatigue behaviors. Findings indicated that nurses\u27 attitudes and alarm fatigue behaviors are present globally and have not significantly altered since reduction strategies were implemented. The findings may impact social change by decreasing nurses\u27 stress levels related to cognitive workloads, improving patient outcomes, and supporting increased levels of nurses\u27 workforce satisfaction

The effect of transitions intervention to ensure patient safety and satisfaction when transferred from hospital to home health care-A systematic review

Aim The aim of this systematic review is to describe and evaluate the effectiveness of transition interventions to safeguard patient safety and satisfaction during patients' transition from hospital to home health care. Design Systematic review. Data Sources MEDLINE, Ovid Nursing Database, PsycINFO, EMBASE, CINAHL, Clinical Trials and SveMed+ was systematic searched in January 2019 and September 2020 to identify peer-reviewed papers. No language, geographical or publication date restrictions. Review Methods Cochrane Handbook for Systematic Reviews of Interventions was used. Data analysis focused on aggregated data and a descriptive synthesis. Risk of bias was rated using Cochrane risk-of-bias tool. Results In total, 10,524 references were identified in the literature search, twenty-six articles were included. The interventions were divided into three main groups: (i). systematic patient education pre-discharge; (ii). establishment of contact with the local healthcare services pre-discharge and (iii). follow-up initiated by nurses from the hospital at home post-discharge. The studies either used one intervention or combined two or three interventions. We considered the intervention to improve patient safety or satisfaction when they reported statistically significant results. Only four interventions increased both patient safety and satisfaction, seven interventions increased patient safety and thirteen increased patient satisfaction. Interventions that appear to be quite similar, with the same duration, measured different effects on patients' satisfaction and safety. Interventions that ensured patient safety did not necessarily facilitate patient satisfaction and vice versa. Conclusion Interventions can improve patient safety and satisfaction during transfer. However, interventions that improve patient safety or satisfaction do not always match. Impact This review suggests that transition interventions can improve patients' safety and satisfaction. However, to compare the impact of future interventions is it important to use standardized measurement tools of satisfaction. There is a need to try out tailored interventions, where interventions are customized to the needs of each patient.publishedVersio

ECG Arrhythmias and Technical Alarms during Left Ventricular Assist Device (LVAD) Therapy and its Potential Impact on Alarm Fatigue

AbstractBackground: During alarm fatigue, true alarms can go unnoticed placing patients at risk for untoward outcomes. Patients with a left ventricular assist device (LVAD) may create challenges during electrocardiographic (ECG) monitoring due to technical alarms (i.e., artifact, ECG leads off), noise and vibrations associated with LVADs, and being able to tolerate some arrythmias. Clinical Nurse Specialists play a central role in and developing evidenced based strategies to improve alarm safety with the ultimate goal of improving patient outcomes. Purpose/Aim: In this case series, we analyze three patients being treated with an LVAD device in the cardiac intensive care unit (ICU) and determine: 1) the number and type of audible arrhythmia alarms; 2) the number of true versus false arrhythmias; 3) the number, type and duration of technical alarms; and 4) report alarm burden. Methods: Secondary analysis using data from the University of California, San Francisco (UCSF) Alarm Study. Results: There were a total of 547 arrhythmia alarms and 98% were false. There were 25,232 technical alarms. Of 514 total hours of ECG monitoring, technical alarms occurred for 65.9 (13%) hours. Alarm burden of 50.15 alarms per monitored hour in the ICU. Conclusion: Audible arrhythmia alarms are common in LVAD patients, and the vast majority are false. Importantly, none of the arrhythmia alarms led to an untoward event (i.e., code blue or death). Technical alarms are also very common and occur for hours during routine ECG monitoring. Continuous ECG monitoring creates unique challenges in LVAD patients. Future studies are needed to explore strategies, both clinical and algorithm bases, to improve the accuracy of arrhythmia detection and minimize technical alarms in LVAD patients

A multivariate Bayesian model for assessing morbidity after coronary artery surgery

INTRODUCTION: Although most risk-stratification scores are derived from preoperative patient variables, there are several intraoperative and postoperative variables that can influence prognosis. Higgins and colleagues previously evaluated the contribution of preoperative, intraoperative and postoperative predictors to the outcome. We developed a Bayes linear model to discriminate morbidity risk after coronary artery bypass grafting and compared it with three different score models: the Higgins' original scoring system, derived from the patient's status on admission to the intensive care unit (ICU), and two models designed and customized to our patient population. METHODS: We analyzed 88 operative risk factors; 1,090 consecutive adult patients who underwent coronary artery bypass grafting were studied. Training and testing data sets of 740 patients and 350 patients, respectively, were used. A stepwise approach enabled selection of an optimal subset of predictor variables. Model discrimination was assessed by receiver operating characteristic (ROC) curves, whereas calibration was measured using the Hosmer-Lemeshow goodness-of-fit test. RESULTS: A set of 12 preoperative, intraoperative and postoperative predictor variables was identified for the Bayes linear model. Bayes and locally customized score models fitted according to the Hosmer-Lemeshow test. However, the comparison between the areas under the ROC curve proved that the Bayes linear classifier had a significantly higher discrimination capacity than the score models. Calibration and discrimination were both much worse with Higgins' original scoring system. CONCLUSION: Most prediction rules use sequential numerical risk scoring to quantify prognosis and are an advanced form of audit. Score models are very attractive tools because their application in routine clinical practice is simple. If locally customized, they also predict patient morbidity in an acceptable manner. The Bayesian model seems to be a feasible alternative. It has better discrimination and can be tailored more easily to individual institutions

HealthPartners: Consumer-Focused Mission and Collaborative Approach Support Ambitious Performance Improvement Agenda

Presents a case study of a nonprofit healthcare organization that exhibits the six attributes of an ideal healthcare delivery system as defined by the Fund, including information continuity, care coordination and transitions, and system accountability

A Health eLearning Ontology and Procedural Reasoning Approach for Developing Personalized Courses to Teach Patients about Their Medical Condition and Treatment

We propose a methodological framework to support the development of personalized courses that improve patients’ understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes—condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom’s taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients’ attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients’ understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners

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