Critical Alarms Research Articles

Re-imagining the future state of the ventricular assist device controller interface through human-centered design.

Ventricular assist devices (VADs) are effective therapy for patients with end-stage heart failure. Current VAD controllers offer improved interactivity, yet limitations of the visual, tactile, and auditory interface persist that impact patient experience and quality of life (QoL). This study explores how VAD controllers can be redesigned using a human-centered design approach to enhance the emotional and functional experience of the device for patients. VAD patients (n = 21), caregivers (n = 4) and healthcare practitioners (n = 24) were interviewed to uncover design opportunities. From this, a series of realistic scenarios to design for emerged. A "design by analogy" method took inspiration from existing consumer products to ideate new functionality for the VAD wearable system, creating concepts for a controller interface and paired wearable device. An additional 15 patients and 2 caregivers were engaged to explore current VAD controller experiences and evaluate the future-state concepts. This research validated the need for increased automation and emergency functionality in VAD controllers, including remote monitoring of data, accurate communication of battery status, and automated medical alerts for critical device alarms. "Manage my health," "Feeling normal," "Social belonging," "Feeling safe," and "Sense of control" emerged as key patient concerns to be met by future VAD controller designs. The study demonstrated an innovative and relevant approach to improve usability of future VAD peripherals. By considering both emotional and functional perspectives in the design of lifesaving medical devices such as VADs, device manufacturers can uncover new opportunities to improve patient QoL through improved user experiences.

Rationalizing Operation Alarms

An operational alarm system rationalization process helps operators identify and prioritize critical alarms, reduce nuisance alarms in the system, and shorten response times.

Sistem Pemantauan dan Pengendali Suhu Ruangan Server Berbasis Internet Of Things Dengan Notifikasi Alarm dan Telegram

Server room is a room that is usually used to store network equipment such as routers, personal computer servers, and other important items. These spaces require special attention and proper maintenance, and must be monitored effectively to protect the equipment located there. The impact of not paying special attention to the server room is that the temperature rise of network activity above normal can cause less than optimal hardware performance due to device overheating.In this research, a server room control and monitoring system based on the Internet of Things will be designed with alarms and telegram notifications to measure temperature, humidity, vibration and electrical energy conditions in the server room. The methodology used in this study is the Research and Development system. The system is designed to achieve results, namely monitoring the server room, controlling the temperature of components in the server room, and the system can issue warnings when room conditions are abnormal.The system can send sensor data from slave to master via Modbus RTU connected to RS485. The system can then send data on the 16x2 LCD to the device and issue alarms such as low, high and critical alarms as well as alarms when abnormal room conditions are detected. When the system detects abnormal room conditions, fans cool hot components in the server room.

Observing sources of system resilience using in situ alarm simulations.

Alarm fatigue (and resultant alarm nonresponse) threatens the safety of hospitalized patients. Historically threats to patient safety, including alarm fatigue, have been evaluated using a Safety I perspective analyzing rare events such as failure to respond to patients'critical alarms. Safety II approaches call for learning from the everyday adaptations clinicians make to keep patients safe. To identify such sources of resilience in alarm systems, we conducted 59 in situ simulations of a critical hypoxemic-event alarm in medical/surgical and intensive care units at a tertiary care pediatric hospital between December 2019 and May 2022. Response timing, observations of the environment, and postsimulation debrief interviews were captured. Four primary means of successful alarm responses were mapped to domains of Systems Engineering Initiative for Patient Safetyframework to inform alarm system design and improvement.

A priority-aware sequential pattern mining method for detection of compact patterns from alarm floods

Alarm floods are commonly present in modern complex industrial process, and usually degrade the performance of the alarm systems severely. In an alarm flood, the massive alarm messages may overwhelm plant operators and submerge critical alarms, which would further worsen the situation and lead to severe consequences. One effective way to deal with alarm flood situations is to mine meaningful alarm patterns that can be used to configure dynamic alarm suppression modules that suppress irrelevant alarms in alarm floods based on certain conditions or to help with online alarm prediction. Using the existing methods in literature, alarms of higher priorities could be easily overlooked in the pattern mining as they usually appear less frequent; thus, it makes the results become unreliable. Motivated by this issue, this paper proposes a new pattern mining method to extract priority-aware compact alarm patterns from historical alarm flood sequences. The main contributions of this study are twofold: (1) a priority-aware sequential pattern mining algorithm is designed to mine alarm sequential patterns using a closed sequential pattern mining algorithm with alarm priority information incorporated; (2) a pattern compression strategy is proposed to merge similar sequences into compact patterns, making it easier to search for alarm patterns. The effectiveness of the proposed pattern mining method is demonstrated by alarm data generated using a vinyl acetate monomer chemical plant model.

A grounded theory study of alarm fatigue among nurses in intensive care units

ObjectivesThe aim of this study was to explore the process of how nurses experienced and dealt with alarm fatigue in intensive care units based on Iranian nurses' perceptions and experiences. BackgroundAlarm fatigue is the overstimulation of senses due to the constant ringing of alarms in intensive care units. It is associated with nurses’ desensitization to critical alarms that can directly influence patient safety and quality of care. MethodsA qualitative exploratory study using the grounded theory approach by Strauss and Corbin was carried out. Participants were 20 nurses working in intensive care units. The sampling process was started purposively and continued theoretically. Data were collected using semi-structured, in-depth, and individual interviews and continued to data saturation. The constant comparative analysis approach was used consisting of the following steps: open coding, developing concepts, analysing the context, entering the process into data analysis, integrating categories. FindingsThe participants' main concern in the exposure to alarm fatigue was ‘threat to personal balance’. The core category in this research was ‘trying to create a holistic balance’, which reflected a set of strategies that the nurses consistently and continuously used to deal with alarm fatigue and consisted of four main categories as follows: ‘smart care’, ‘deliberate balancing’, ‘conditional prioritisation’, and ‘negligent performance’. Threat to personal balance was strengthened by ‘inappropriate circuit of individual roles’, ‘distortion of the organisational structure’, and ‘insecurity of the infrastructure’. The consequences of this process was harm to the patient, burnout among nurse, and damage to the healthcare organisation. ConclusionsThe research findings have practical implications for healthcare management, policymaking, nursing education, research, and clinical practice. Mitigating staff shortages, improving staff competencies, enhancing nurses’ authority for responding to alarms, modifying care routines, improving the physical environment, and removing problems related to alarm equipment can prevent alarm fatigue and its unappropriated consequences.

Alarm Pattern Recognition in Continuous Process Control Systems using Data Mining

An alarm management system with the Human Machine Interface in a process control system is used to alert an operator of any abnormal situation, so that corrective action can be taken to ensure safety and productivity of the plant and quality of the product. An alarm system reporting many alarms even during the normal state of the plant is due to chattering alarms, duplicated alarms, intermittent equipment problems and certain alarms configured in the system which may not have any importance. In such a situation the operator may miss certain critical alarms leading to undesirable outcomes. So, to have an optimum alarm system, the unwanted alarms have to be identified and eliminated. In this paper, we propose an offline method to identify repetitive, frequent sequences or patterns using PrefixSpan and Bi-Directional Extension algorithms. With the identified sequences or patterns, plant operation experts can improve the effectiveness of the alarm system through alarm rationalization so that this will help the operator in making the plant more safe, reliable and productive. The main objectives of this work are the following: (i) to use a definitive method to represent alarm data in an alarm log which is Temporal data as Itemsets without a need for complex mathematical, statistical or visual methods; (ii) to use data mining algorithms for identifying Frequent sequences which can be implemented on a normal computing resource such as Personal computer; (iii) to apply the method to the complete alarm data available no matter how big they are; (iv) to study and establish that the chosen method is possible to be applied to larger sized datasets.

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review.

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

Improving Home Ventilator Alarm Use Among Children Requiring Chronic Mechanical Ventilation.

Children requiring long-term mechanical ventilation are at high risk of mortality. Setting ventilator alarms may improve safety, but best practices for setting ventilator alarms have not been established. Our objective was to increase the mean proportion of critical ventilator alarms set for those children requiring chronic mechanical ventilation followed in our pulmonary clinic from 63% to >90%. Using the Institute for Healthcare Improvement Model for Improvement, we developed, tested, and implemented a series of interventions using Plan-Do-Study-Act cycles. We followed our progress using statistical process control methods. Our primary interventions were: (1) standardization of the clinic workflow, (2) development of an algorithm to guide physicians in selecting and setting ventilator alarms, (3) updating that algorithm based on review of failures and inpatient testing, and (4) enhancing staff engagement to change the culture surrounding ventilator alarms. We collected baseline data from May 1 to July 13, 2017 on 130 consecutive patients seen in the pulmonary medicine clinic. We found that 63% of critical ventilator alarms were set. Observation of the process, standardization of workflow, and adaptation of an alarm algorithm led to an increase to 85.7% of critical alarms set. Through revising our algorithm to include an apnea alarm, and maximizing provider engagement, more than 95% of critical ventilator alarms were set, exceeding our goal. We sustained this improvement through January 2021. Our stepwise approach, including process standardization, staff engagement, and integration of an alarm algorithm, improved the use of ventilator alarms in chronically ventilated pediatric patients.

False alarm moderation for performance monitoring in industrial water distribution systems

While considerable attention has been given to data driven methods that analyse and control energy systems in buildings, the same cannot be said for building water systems. As a result, approaches which support enhanced efficiency in building water consumption are somewhat underdeveloped, particularly in industrial settings. Water consumption in industrial systems features non-stationarity (i.e., variations in statistical properties over time), making it challenging to distinguish between routine and non-routine water uses. In such scenarios, fault detection and diagnosis methods that leverage multivariate statistical process control with, for example, principal component analysis and detection indices (Hotelling T2-statistics and Q-statistics), can be successfully used to identify system alarms. However, even with these approaches there can be a high prevalence of false alarms leading to low industry uptake of fault detection and diagnosis systems, or where in place, alarms can be ignored. To efficiently detect and diagnose water distribution system faults, false alarms should be controlled through false alarm moderation approaches so that building managers/operators only need to focus on critical system alarms or system alarms with high risk levels. This paper utilises two statistical non-parametric false alarm moderation approaches (window-based, and trial-based) that generate a second control limit for T2-statistics and Q-statistics. The implementation of these false alarm moderation approaches was combined with principal component analysis to detect faults with real water time series data from two case-study sites. Using both approaches false alarms were reduced, and the overall performance and reliability of the fault detection and diagnosis approach was improved. The principal component analysis model with the window-based approach was shown to be particularly effective.

Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference hospital complex. An observational retrospective cohort study. The Machine Learning (ML) tool, Robot Laura®, scores changes in vital parameters and lab tests, classifying them by severity. Inpatients and patients over 18 years of age were included. A total of 122,703 alarms were extracted from the platform, classified as 2 to 9. The pre-selection of critical alarms (6 to 9) indicated 263 urgent alerts (0.2%), from which, after filtering exclusion criteria, 254 alerts were delimited for 61 inpatients. Patient mortality from sepsis was 75%, of which 52% was due to sepsis related to the new coronavirus. After the alarms were answered, 82% of the patients remained in the sectors. Far beyond technology, ML models can speed up assertive clinical decisions by nurses, optimizing time and specialized human resources.

WATER LEVEL INDICATOR IN THE DIESEL FUEL FILTER

The article presents the conductometric level sensor and the electrical circuit of the control system for the water level in the fuel filter, as well as the technology of their manufacture and installation on the car. (Research purpose) The research purpose is increasing the reliability of the diesel fuel system by using built-in critical water level alarms in the diesel fuel filter on the example of KAMAZ-5320. (Materials and methods) As a sensitive element of the water level sensor was used an element from the Cummins 5297619 fuel filter-water separator common in Russia for cars of the Gazelle family. (Results and discussion) A sensitive element was installed in the drain plug hole of the fuel filter for rough cleaning and filled with an epoxy compound. The sensor plug was wrapped into the hole of the coarse fuel filter cup and was connected to the mating part of the cable laid to the board with the electrical circuit of the car dashboard. The housing of the signal LED placed on the board was installed from the inside into the opening of the panel, after which the board was attached with hot glue to the inner plane of the dashboard. As a result of experimental studies, the dependence of the current consumed by the system on the water level in the glass was determined. The article describes developed technologies for installing the sensor and the proposed system on the car. (Conclusions) The implementation of the developed system for monitoring the water level in the fuel filter of the coarse fuel of a KamAZ-5320 type car will allow draining the sludge not only during scheduled maintenance, but also if necessary, and thereby eliminate abnormal operating modes of fuel equipment and ensure the resource of its elements with increased water content of the fuel used.

Applying Human Factors Engineering to Address the Telemetry Alarm Problem in a Large Medical Center.

Address the alarm problem by redesigning, reorganizing, and reprioritizing to better discriminate alarm sounds and displays in a hospital. Alarms in hospitals are frequently misunderstood, disregarded, and overridden. Discovery-oriented, intervention, and translational studies were conducted. Study objectives and measures varied, but had the shared goals of increasing positive predictive value (PPV) of critical alarms by reducing low-PPV alarms in the background, prioritizing alarms, redesigning alarm sounds to increase information content, and transparently conveying who initiated alarms. An alarm ontology was iteratively generated and refined until consensus was achieved. The ontology distinguishes five levels of urgency that incorporate likely PPV, three categories for who initiates the alarm (hospital staff, patient, or machine), whether it is clinical or technical, and clinical functions. This unique collaboration allowed us to make progress on the alarm problem by making unintuitive leaps, avoiding common missteps, and refuting conventional healthcare approaches. Hospitals can consistently redesign, reorganize, reprioritize, and better discriminate alarms by priority, PPV, and content to reduce nurse response times.

차량설비의 알람 유형 분류를 위한 오픈셋 인식 심층 신경망

It is important to predict critical alarms in the manufacturing process that can reduce the utilization rate of the production facility. In recent years, deep neural networks have been widely used for prediction of alarm types in the manufacturing processes. However, the existing deep neural network classifiers follow a closed-set assumption that all predictable categories should be learned in the training stage. For this reason, when an unknown alarm type comes into the classifier, it should be classified as one of the predefined alarm types. In the actual manufacturing processes, it is extremely difficult to collect all possible types of alarm data. Therefore, a model with open set recognition is required to identify unknown alarm types. In addition, because the alarm type data collected from production facilities occurs simultaneously, a multi-label classification model is necessary. In this study, we propose a multi-label open set recognition model combined with background data that can improve the ability to identify unknown alarm types. We demonstrated the usefulness and applicability of the proposed method by comparing it with existing open set classification methods using real process data obtained from an automobile industry.

A decision loop for situation risk assessment under uncertainty: A case study of a gas facility

Abstract This paper presents a decision-making support system for situation risk assessment associated with critical alarms conditions in a gas facility. The system provides a human operator with advice on the confirmation and classification of occurred alarm. The input of the system comprises uncertain and incomplete information. In the light of uncertain and incomplete information, different uncertainties laws have been associated with the probabilistic assessment of the system loops which combine data of several sources to reach the ultimate classification. The implemented model used Observe-Orient-Decide-Act loop (OODA) combined with Bayesian networks. Results show that the system can classify the alarms system.

Estimation of Heart Rate Directly from ECG Spectrogram in Neonate Intensive Care Units.

This paper presents a simple yet novel method to estimate the heart frequency (HF) of neonates directly from the ECG signal, instead of using the RR-interval signals as generally done in clinical practices. From this, the heart rate (HR) can be derived. Thus, we avoid the use of peak detectors and the inherent errors that come with them.Our method leverages the highest Power Spectral Densities (PSD) of the ECG, for the bins around the frequencies related to heart rates for neonates, as they change in time (spectrograms).We tested our approach with the monitoring data of 6 days for 52 patients in a Neonate Intensive Care Unit (NICU) and compared against the HR from a commercial monitor, which produced a sample every second. The comparison showed that 92.4% of the samples have a difference lower than 5bpm. Moreover, we obtained a median MAE (Mean Absolute Error) between subjects equal to 2.28 bpm and a median RMSE (Root Mean Square Error) equal to 5.82 bpm. Although tested for neonates, we hypothesize that this method can also be customized for other populations.Finally, we analyze the failure cases of our method and found a direct co-allocation of errors due to moments with higher PSD in the lower frequencies with the presence of critical alarms related to other physiological systems (e.g. desaturation).

Assessing causes of alarm fatigue in long-term acute care and its impact on identifying clinical changes in patient conditions

Abstract Physiologic alarms are an important modality in the care of critically ill patients. Yet the many electronic devices used in patient care and the combination of alarms can cause sensory overload in caregivers. This sensory overload can lead to monitor fatigue, and caregivers may miss critical alarms, which can be fatal for patients. Many factors not related to a change in patients' condition can be directly linked to desensitization and alarm fatigue, leading to a failure to recognize or attend to true instability in spite of the alarm. Research demonstrates that the majority of alarms are non-actionable, and staff can develop alarm fatigue trying to determine which alarms are valid and which are not. We postulate that more experience detecting false alarms among professionals in a long-term acute care unit will lead to improved clinical changes and better survival rates among patients. Our proportional hazards model relates missing clinical changes in patients' condition as time passes, after reduced attention to false alarms, to professional experience. In our proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. Therefore, reduced attention to false alarms by experienced professionals decreases the hazard rate for missing a clinical change. We use survival analyses, the hazard function, the receiver-operating characteristic curve, and the Hosmer-Lemeshow test to support our conclusions. Our results show that monitoring equipment is instrumental in alerting staff in a long-term care unit to serious changes in patients’ condition and in preventing false positives and false negatives.

Decision making under uncertainty in the alarm systems response

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Characteristic Differences between Actionable and Non-Actionable True Ventricular Tachycardia Alarms

Physiologic alarms are an important modality in the care of critically ill patients. Yet the many electronic devices used in patient care and the combination of alarms can cause sensory overload in caregivers. This sensory overload can lead to monitor fatigue, and caregivers may miss critical alarms, which can be fatal for patients. Many factors not related to a change in patients' condition can be directly linked to desensitization and alarm fatigue, leading to a failure to recognize or attend to true instability in spite of the alarm. Research demonstrates that the majority of alarms are non-actionable, and staff can develop alarm fatigue trying to determine which alarms are valid and which are not. We postulate that more experience detecting false alarms among professionals in a long-term acute care unit will lead to improved clinical changes and better survival rates among patients. Our proportional hazards model relates missing clinical changes in patients' condition as time passes, after reduced attention to false alarms, to professional experience. In our proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. Therefore, reduced attention to false alarms by experienced professionals decreases the hazard rate for missing a clinical change. We use survival analyses, the hazard function, the receiver-operating characteristic curve, and the Hosmer-Lemeshow test to support our conclusions. Our results show that monitoring equipment is instrumental in alerting staff in a long-term care unit to serious changes in patients’ condition and in preventing false positives and false negatives.

Types and Frequency of Infusion Pump Alarms and Infusion-Interruption to Infusion-Recovery Times for Critical Short Half-Life Infusions: Retrospective Data Analysis.

BackgroundAlarm fatigue commonly leads to a reduced response to alarms. Appropriate and timely response to intravenous pump alarms is crucial to infusion continuity. The difficulty of filtering out critical short half-life infusion alarms from nonurgent alarms is a key challenge for risk management for clinicians. Critical care areas provide ample opportunities for intravenous medication error with the frequent administration of high-alert, critical short half-life infusions that require rigorous maintenance for continuity of delivery. Most serious medication errors in critical care occur during the execution of treatment, with performance-level failures outweighing rule-based or knowledge-based mistakes.ObjectiveOne objective of this study was to establish baseline data for the types and frequency of alarms that critical care clinicians are exposed to from a variety of infusion devices, including both large volume pumps and syringe drivers. Another objective was to identify the volume of these alarms that specifically relate to critical short half-life infusions and to evaluate user response times to alarms from infusion devices delivering these particular infusions.MethodsThe event logs of 1183 infusion pumps used in critical care environments and in general care areas within the European region were mined for a range of alarm states. The study then focused on a selection of infusion alarms from devices delivering critical short half-life infusions that would warrant rapid attention from clinicians in order to avoid potentially harmful prolonged infusion interruption. The reaction time of clinicians to infusion-interruption states and alarms for the selected critical short half-life infusions was then calculated.ResultsInitial analysis showed a mean average of 4.50 alarms per infusion in the general critical care pump population as opposed to the whole hospital rate of 1.39. In the pediatric intensive care unit (PICU) group, the alarms per infusion value was significantly above the mean average for all critical care areas, with 8.61 alarms per infusion. Infusion-interruption of critical short half-life infusions was found to be a significant problem in all areas of the general critical care pump population, with a significant number of downstream (ie, vein and access) occlusion events noted. While the mean and median response times to critical short half-life infusion interruptions were generally within the half-lives of the selected medications, there was a high prevalence of outliers in terms of reaction times for all the critical short half-life infusions studied.ConclusionsThis study gives an indication of what might be expected in critical care environments in terms of the volume of general infusion alarms and critical short half-life infusion alarms, as well as for clinician reaction times to critical short half-life infusion-interruption events. This study also identifies potentially problematic areas of the hospital for alarm fatigue and for particular issues of infusion and infusion-line management. Application of the proposed protocols can help create benchmarks for pump alarm management and clinician reaction times. These protocols can be applied to studies on the impact of alarm fatigue and for the evaluation of protocols, infusion-monitoring strategies, and infusion pump-based medication safety software aimed at reducing alarm fatigue and ensuring the maintenance of critical short half-life infusions. Given the frequency of infusion alarms seen in this study, the risk of alarm fatigue due to the white noise of pump alarms present in critical care, to which clinicians are constantly exposed, is very high. Furthermore, the added difficulties of maintaining critical short half-life infusions, and other infusions in specialist areas, are made clear by the high ratio of downstream occlusion to infusion starts in the neonatal intensive care unit (NICU). The ability to quantitatively track the volume of alarms and clinician reaction times contributes to a greater understanding of the issues of alarm fatigue in intensive care units. This can be applied to clinical audit, can allow for targeted training to reduce nuisance alarms, and can aid in planning for improvement in the key area of maintenance of steady-state plasma levels of critical short half-life infusions. One clear conclusion is that the medication administration rights should be extended to include right maintenance and ensured delivery continuity of critical short half-life infusions.