Testing physiologic monitor alarm customization software to reduce alarm rates and improve nurses’ experience of alarms in a medical intensive care unit

Ruppel, Halley; Vaux, Laura De; Cooper, Dawn; Kunz, Steffen; Duller, Bernd; Funk, Marjorie

doi:10.1371/journal.pone.0205901

Cited by 38 publications

(41 citation statements)

References 20 publications

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“…A common approach to identify patients at risk of organ system deterioration is the use of medical device alarms for individual physiological measurements based on critical thresholds. Since such alarm systems lack access to comprehensive information their alarms are often nonspecific 7,8 and lead to alarm fatigue, which was rated seventh on the list of top 10 technology hazards from the ECRI Institute 9,10 .…”

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confidence: 99%

Early prediction of circulatory failure in the intensive care unit using machine learning

Hyland

Faltys

Hüser

et al. 2020

Nat Med

260

207

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Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patientyears of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort.

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mentioning

confidence: 99%

Early prediction of circulatory failure in the intensive care unit using machine learning

Hyland

Faltys

Hüser

et al. 2020

Nat Med

260

207

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“…Customisation of alarm logic is increasingly recognised as an effective solution to reduce alarms 18 24 25. Graduated algorithms mean that for time-sensitive changes, the correct bedside providers are quickly notified and are able to respond appropriately, and for less time-sensitive changes, are not interrupted too early or too often.…”

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confidence: 99%

Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital

et al. 2020

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Background10The Joint Commission identified inpatient alarm reduction as an opportunity to improve patient safety; enhance patient, family and nursing satisfaction; and optimise workflow. We used quality improvement (QI) methods to safely decrease non-actionable alarm notifications to bedside providers.MethodsIn a paediatric tertiary care centre, we convened a multidisciplinary team to address alarm notifications in our acute care cardiology unit. Alarm notification was defined as any alert to bedside providers for each patient-triggered monitor alarm. Our aim was to decrease alarm notifications per monitored bed per day by 60%. Plan-Do-Study-Act testing cycles included updating notification technology, establishing alarm logic and modifying bedside workflow processes, including silencing the volume on all bedside monitors. Our secondary outcome measure was nursing satisfaction. Balancing safety measures included floor to intensive care unit transfers and patient acuity level.ResultsAt baseline, there was an average of 71 initial alarm notifications per monitored bed per day. Over a 3.5-year improvement period (2014–2017), the rate decreased by 68% to 22 initial alarm notifications per monitored bed per day. The proportion of initial to total alarm notifications remained stable, decreasing slightly from 51% to 40%. There was a significant improvement in subjective nursing satisfaction. At baseline, 32% of nurses agreed they were able to respond to alarms appropriately and quickly. Following interventions, agreement increased to 76% (p<0.001). We sustained these improvements over a year without a change in monitored balancing measures.ConclusionWe successfully reduced alarm notifications while preserving patient safety over a 4-year period in a complex paediatric patient population using technological advances and QI methodology. Continued efforts are needed to further optimise monitor use across paediatric hospital units.

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“…Although standardization is the foundation for promoting physician compliance, streamlining clinic flow, and improving system cost-effectiveness, [25][26][27] customization to user requirements including more refined firing criteria by subspecialty may benefit user engagement and sustainability. [28][29][30] Lack of such consideration may result in alert overlook or underutilization of referral.…”

Section: Alert Standardization and Customizationmentioning

confidence: 99%

Developing an Ophthalmology Clinical Decision Support System to Identify Patients for Low Vision Rehabilitation

Guo

Swenor

Smith

et al. 2021

Trans. Vis. Sci. Tech.

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The purpose of this study was to develop and evaluate an electronic health record (EHR) clinical decision support system to identify patients meeting criteria for low vision rehabilitation (LVR) referral. Methods: In this quality improvement project, we applied a user-centered design approach to develop an interactive electronic alert for LVR referral within the Johns Hopkins Wilmer Eye Institute. We invited 15 ophthalmology physicians from 8 subspecialties to participate in the design and implementation, and to provide user experience feedback. The three project phases incorporated development evaluation, feedback analysis, and system refinement. We report on the final alert design, firing accuracy, and user experiences. Results: The alert was designed as physician-centered and patient-specific. Alert firing relied on visual acuity and International Classification of Diseases (ICD)-10 diagnosis (hemianopia/quadrantanopia) criteria. The alert suppression considerations included age < 5 years, recent surgeries, prior LVR visit, and related alert actions. False positive rate (firing when alert should have been suppressed or when firing criteria not met) was 0.2%. The overall false negative rate (alert not firing when visual acuity or encounter diagnosis criteria met) was 5.6%. Of the 13 physicians who completed the survey, 8 agreed that the alert is easy to use, and 12 would consider ongoing usage. Conclusions: This EHR-based clinical decision support system shows reliable firing metrics in identifying patients with vision impairment and promising acceptance by ophthalmologist users to facilitate care and LVR referral. Translational Relevance: The use of real-time data offers an opportunity to translate ophthalmic guidelines and best practices into systematic action for clinical care and research purposes across subspecialties.

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Testing physiologic monitor alarm customization software to reduce alarm rates and improve nurses’ experience of alarms in a medical intensive care unit

Cited by 38 publications

References 20 publications

Early prediction of circulatory failure in the intensive care unit using machine learning

Early prediction of circulatory failure in the intensive care unit using machine learning

Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital

Developing an Ophthalmology Clinical Decision Support System to Identify Patients for Low Vision Rehabilitation

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