Frequent False Alarms Research Articles

An Sand Plug of Fracturing Intelligent Early Warning Model Embedded in Remote Monitoring System

In recent years, data mining technology has been widely used in different fields. For petroleum industry, the application of data mining method not only can analyze the data faster and more accurately but also can reduce the defects such as misjudgment and missed judgment caused by relying on artificial monitoring. Fracturing is one of the key techniques for increasing petroleum production. Due to the contingency of the abnormal variation of the fracturing construction curve, there is also a large lag and contingency by using the construction curve for sand block warning. Frequent false alarms and delayed alarms often occur. Therefore, the paper proposes an early-warning method of the sand plug of fracturing based on data mining. First, an early warning model of double logarithmic curve sand plug of fracturing is established, and the time series analysis algorithm is used to predict the oil pressure and casing pressure in the double logarithmic curve sand plug of fracturing risk warning model, so the early warning accuracy is improved. And then the general regression neural network (GRNN) algorithm is designed to optimize the prediction results of the time domain analysis. The improved affinity propagation (AP) clustering algorithm is used to cluster the monitoring data to help to improve the accuracy of the subsequent slope calculation, so as to improve the coincidence rate of the fracturing and sand plug risk. Finally, the risk warning model is applied and analyzed on site, and the validity and accuracy of the model are verified. The model is embedded in the remote monitoring system to realize online remote intelligent monitoring of risks by urban office workers.

Improved control charts for fraction non-conforming based on hypergeometric distribution

In this paper we introduce the control chart for fraction non-conforming based on the Hypergeometric distribution, and propose the exact Hypergeometric test for a population proportion to detect significant changes in the process quality level. In contrast to the common Binomial p-chart, which is especially suitable for sampling from continuous production processes, the Hypergeometric p-chart can be established also for periodical production processes, where sampled items successively produced during one period are not stochastically independent and the probability of a sampled item being non-conforming depends on the previous sampled item. Further, while the Binomial p-chart does not take the fluctuation of the production process into account, the Hypergeometric p-chart enables consideration of the population size of periodical production appropriately. The Hypergeometric p-chart leads in general to lower probabilities of the type I error, i.e. false rejection of the hypothesis of statistical control, to a considerably higher in-control ARL, and thus to a significantly lower frequency of false alarms. Moreover, it provides monitoring of a manufacturing process with a considerably lower inspection effort than its Binomial counterpart. Finally, the exact Hypergeometric test leads to more accurate test decisions in detecting process shifts compared with the common used approximate Binomial test.

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

BackgroundClinicians in intensive care units experience alarm fatigue related to frequent false and non-actionable alarms produced by physiologic monitors. To reduce non-actionable alarms, alarm settings may need to be customized for individual patients; however, nurses may not customize alarms because of competing demands and alarm fatigue.ObjectiveTo examine the effectiveness and acceptance of physiologic monitor software to support customization of alarms.MethodsThis pre/post intervention study was conducted in a 56-bed medical intensive care unit. IntelliVue® Alarm Advisor customization support software for alarm limit violations was installed on all monitors and education on its use provided. For 2 months before and after implementation of the software, data were collected on patient characteristics from the electronic health record, alarm counts and duration from the monitoring system, and nurses’ experience of alarms from a survey.ResultsMedium-priority heart rate, respiratory rate, and arterial pressure alarms were significantly reduced after software implementation (9.3%, 11.8%, and 15.9% reduction respectively; p<0.001 for all). The duration of these alarms was also significantly shorter (7.8%, 13.3%, and 9.3% reduction respectively; p<0.05 for all). The number and duration of SpO2 alarms did not decrease (p>0.05 for both). Patients post-intervention had worse Glasgow Coma Scale scores (p = 0.014), but otherwise were comparable to those pre-intervention. Nurses reported less time spent on non-actionable alarms post-intervention than pre-intervention (p = 0.026). Also lower post-intervention were the proportions of nurses who reported that alarms disturbed their workflow (p = 0.027) and who encountered a situation where an important alarm was ignored (p = 0.043). The majority (>50%) agreed that the software supported setting appropriate alarm limits and was easy to use.ConclusionAlarm customization software was associated with a reduction in alarms. Use of software to support nurses’ recognition of trends in patients’ alarms and facilitate changes to alarm settings may add value to alarm reduction initiatives.

Optical-digital complex for detection of remote mines and mapping of minefields

The article is dedicated to a new method of detecting masked ground and subsurface objects for mapping of minefields and engineering reconnaissance. We proposed to use stereoscopic hyperspectral IR remote sensing in order to increase the reliability of detecting and reducing the frequency of false alarms. Stereoscopic surveying allows exclude skipping of objects masked in vegetation and laid at an angle, and separate the disturbed surface (friable signs) from ordinary soil and embankments. The results of processing hyperspectral data from two channels are combined into a thematic thermal stereomodel of the terrain, on which the anomalies found by the algorithm are highlighted. The concept of an optical-electronic complex of remote sensing of minefields, structural and optical schemes, applied calculations and design solutions are presented in the first part of article. We described basic algorithms for processing hyperspectral data, the method of combining them into a thermal stereomodel of the terrain with the possibility of classifying objects and cross-linking with a three-dimensional map of the area in the second part of the article.

THE IMPROVEMENT OF PROFESSIONAL TRAINING ORGANIZATION OF THE X-RAY SCREENING SYSTEMS OPERATORS BY USING THE EYE MOVEMENTS REGISTRATION SYSTEM AND METHODS OF CLUSTER AND DISCRIMINANT ANALYSIS

The X-ray screening systems operators’ professional training is based on the CBT (computer-based training) principle, which has algorithms of adaptive training. These algorithms in existing computer simulators include feedback mechanisms on the basis of trainability exponents – such as the frequency of detecting dangerous objects, the frequency of false alarms and detection time. Further enhancement of the operators’ simulator training effectiveness is associated with the integration of psychophysiological mechanisms providing monitoring of their functional state. Based on the analysis of the particularities of x-ray screening systems operators’ professional training associated with the formation of competences in dangerous objects visual search, the most perspective method is the Eye tracking technology. Domestic and foreign studies of the eye movements characteristics while solving professional tasks in training process are actively developed in various areas. There are no studies of visual search peculiarities in domestic practice in contrast to exterior studies. This research is aimed at considering the usage of Eye tracking technology in the training of x-ray screening systems operators. As the result of the experimental research with the use of mobile eye-tracker Sensomotoric Instruments Eye Tracking Glasses 2.0 the statistical data of eye movement parameters of two groups of subjects with different levels of training have been received. The application of cluster and discriminant analyses methods allowed to identify General classes of these parameters, as well as to obtain the discriminants functions for each group under examination. The theoretical significance of the peculiarities of the operators’ eye movement studies is to identify the patterns of prohibited items visual search. The practical importance of implementation of Eye tracking technology and statistical analysis methods is to increase the reliability of assessment the level of formed competence of x-ray screening systems’ operators in visual search, as well as to develop the potential system of operators’ state monitoring and assessing their visual fatigue.

Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study

ObjectivesTo determine critical care nurses’ knowledge of alarm fatigue and practices toward alarms in critical care settings. Research methodology/designA cross-sectional survey using an adaptation of The Health Technology Foundation Clinical Alarms Survey. SettingA sample of critical care nurses (n = 250) from 10 departments across six hospitals in Ireland. ResultsA response rate of 66% (n = 166) was achieved. All hospital sites reported patient adverse events related to clinical alarms. The majority of nurses (52%, n = 86) did not know or were unsure, how to prevent alarm fatigue. Most nurses (90%, n = 148) agreed that non-actionable alarms occurred frequently, disrupted patient care (91%, n = 145) and reduced trust in alarms prompting nurses to sometimes disable alarms (81%, n = 132). Nurses claiming to know how to prevent alarm fatigue stated they customised patient alarm parameters frequently (p = 0.037). Frequent false alarms causing reduced attention or response to alarms ranked the number one obstacle to effective alarm management; this was followed by inadequate staff to respond to alarms. Only 31% (n = 50) believed that alarm management policies and procedures were used effectively. ConclusionAlarm fatigue has the potential for serious consequences for patient safety and answering numerous alarms drains nursing resources.

Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules

Given advantages of artificial intelligent technology, the genetic algorithm (GA) and back propagation (BP) neural network is used to construct time series model for recognizing temperature change waveform of single thermocouple in mold breakout process. Based on breakout mechanism, the logic rules are used to construct spatial model of multi-thermocouples for identifying two-dimensional (2D) propagation behavior of the sticker. Time series model based on GA-BP neural network and spatial model based on logic rules form a new breakout prediction method. And the simulation and field test of this method are carried out for validating its performance. Simulation results of time series model show that the GA-BP neural network has better recognition precision than BP neural network for sticking temperature pattern of single thermocouple. And simulation results of spatial model show that it can predict all stickers accurately and timely, with no missed alarm and false alarm. Furthermore, field test results show that this breakout prediction method has detection ratio of 100% and a lower false alarm frequency (0.1365% times/heat), which is better than actual breakout prediction system used in continuous casting production. So the combined method of GA-BP neural network and logic rules is feasible and effective in breakout prediction and can be used in more intelligently industrial process.

How Much Alarm Can the Human Being Tolerate?

Due to growing technisation of intensive care the number of devices with integrated alarm systems is steadily increasing. However, most of the sounding alarms are false alarms causing high levels of frustration, aggression and inappropriate behaviour amongst the medical personnel. All this jeopardises patient care. The high number of alarms also disturb the patients interrupting their sleep and provokes anxiety, and also increases the already high noise level in intensive care units and the operating theatre alike. In the interest of the medical staff and our patients, we should reduce the high frequency of false alarms by using modern alarm algorithms techniques, lower both noise exposure and stress load with the help of modern individualized alarm systems and by increasing awareness on the dangers of alarm fatigue through training and by using individualized patient-related alarm limits. Despite economic challenges hospitals and intensive care units should optimize staffing, thereby lowering the risk to patients and improving employee satisfaction.

Optimization design of the CUSUM and EWMA charts for autocorrelated processes

AbstractThe traditional control charts produce frequent false alarm signals in the presence of autocorrelation. The implementation of the modified chart scheme is a way of handling the problem of autocorrelation in control charts. In modified charts, the standard control limits of the traditional charts are adjusted to offset the influence because of the autocorrelation. The exponentially weighted moving average– and cumulative sum–modified charts are 2 widely used charts for monitoring autocorrelated data. These charts have design parameters in their formulation, and the choice of these parameters play significant roles in the detection of out‐of‐control situations. In reality, the magnitude of the mean shift is uncertain, and this leads to a difficulty in the choice of design parameters by the practitioner. The use of optimal parameters can enhance process performance in such situations. In this paper, we determine optimal design parameters for the charts using an exhaustive search procedure. In the optimization process, we determine the parameters that produce the smallest extra quadratic loss (EQL) value for each autocorrelation coefficient. This criterion measures the anticipated loss attributed to poor quality in the process. The loss in quality is lowered by minimizing the EQL and the combination of parameters in the chart that yields the smallest EQL has a better detection ability over the entire shift range. For the purpose of this work, we concentrate on autocorrelation that can be specifically modelled with autoregressive models. This article provides the practitioner with optimal parameters that can be used to enhance the overall effectiveness of the charts over an entire shift range.

Patient characteristics associated with false arrhythmia alarms in intensive care.

IntroductionA high rate of false arrhythmia alarms in the intensive care unit (ICU) leads to alarm fatigue, the condition of desensitization and potentially inappropriate silencing of alarms due to frequent invalid and nonactionable alarms, often referred to as false alarms.ObjectiveThe aim of this study was to identify patient characteristics, such as gender, age, body mass index, and diagnosis associated with frequent false arrhythmia alarms in the ICU.MethodsThis descriptive, observational study prospectively enrolled patients who were consecutively admitted to one of five adult ICUs (77 beds) at an urban medical center over a period of 31 days in 2013. All monitor alarms and continuous waveforms were stored on a secure server. Nurse scientists with expertise in cardiac monitoring used a standardized protocol to annotate six clinically important types of arrhythmia alarms (asystole, pause, ventricular fibrillation, ventricular tachycardia, accelerated ventricular rhythm, and ventricular bradycardia) as true or false. Total monitoring time for each patient was measured, and the number of false alarms per hour was calculated for these six alarm types. Medical records were examined to acquire data on patient characteristics.ResultsA total of 461 unique patients (mean age =60±17 years) were enrolled, generating a total of 2,558,760 alarms, including all levels of arrhythmia, parameter, and technical alarms. There were 48,404 hours of patient monitoring time, and an average overall alarm rate of 52 alarms/hour. Investigators annotated 12,671 arrhythmia alarms; 11,345 (89.5%) were determined to be false. Two hundred and fifty patients (54%) generated at least one of the six annotated alarm types. Two patients generated 6,940 arrhythmia alarms (55%). The number of false alarms per monitored hour for patients’ annotated arrhythmia alarms ranged from 0.0 to 7.7, and the duration of these false alarms per hour ranged from 0.0 to 158.8 seconds. Patient characteristics were compared in relation to 1) the number and 2) the duration of false arrhythmia alarms per 24-hour period, using nonparametric statistics to minimize the influence of outliers. Among the significant associations were the following: age ≥60 years (P=0.013; P=0.034), confused mental status (P<0.001 for both comparisons), cardiovascular diagnoses (P<0.001 for both comparisons), electrocardiographic (ECG) features, such as wide ECG waveforms that correspond to ventricular depolarization known as QRS complex due to bundle branch block (BBB) (P=0.003; P=0.004) or ventricular paced rhythm (P=0.002 for both comparisons), respiratory diagnoses (P=0.004 for both comparisons), and support with mechanical ventilation, including those with primary diagnoses other than respiratory ones (P<0.001 for both comparisons).ConclusionPatients likely to trigger a higher number of false arrhythmia alarms may be those with older age, confusion, cardiovascular diagnoses, and ECG features that indicate BBB or ventricular pacing, respiratory diagnoses, and mechanical ventilatory support. Algorithm improvements could focus on better noise reduction (eg, motion artifact with confused state) and distinguishing BBB and paced rhythms from ventricular arrhythmias. Increasing awareness of patient conditions that apparently trigger a higher rate of false arrhythmia alarms may be useful for reducing unnecessary noise and improving alarm management.

Lane-Change Detection Based on Vehicle-Trajectory Prediction

We propose a new detection method to predict a vehicle's trajectory and use it for detecting lane changes of surrounding vehicles. According to the previous research, more than 90% of the car crashes are caused by human errors, and lane changes are the main factor. Therefore, if a lane change can be detected before a vehicle crosses the centerline, accident rates will decrease. Previously reported detection methods have the problem of frequent false alarms caused by zigzag driving that can result in user distrust in driving safety support systems. Most cases of zigzag driving are caused by the abortion of a lane change due to the presence of adjacent vehicles on the next lane. Our approach reduces false alarms by considering the possibility of a crash with adjacent vehicles by applying trajectory prediction when the target vehicle attempts to change a lane, and it reflects the result of lane-change detection. We used a traffic dataset with more than 500 lane changes and confirmed that the proposed method can considerably improve the detection performance.

Mixed EWMA-CUSUM and mixed CUSUM-EWMA modified control charts for monitoring first order autoregressive processes

In practice most processes are known to produce autocorrelated observations. Autocorrelation degrades the performance of control charts by producing frequent false alarms when the process is stable or makes the charts respond slowly to the detection of out-of-control states. The effects due to autocorrelation can be eliminated by using modified charts. In this procedure, the control limits of the traditional charts are adjusted to account for the autocorrelation. In this paper, we present the Mixed EWMA-CUSUM and Mixed CUSUM-EWMA modified charts for monitoring correlated data. The performance of these charts are compared to existing modified charts such as the Shewhart, CUSUM, EWMA, combined Shewhart-CUSUM and combined Shewhart-EWMA schemes using the average run length, extra quadratic loss and relative average run length measures. Examples are given to illustrate how the charts perform.

Quickest detection in the Wiener disorder problem with post-change uncertainty

We consider the problem of quickest detection of an abrupt change when there is uncertainty about the post-change distribution. In particular, we examine this problem in the continuous-time Wiener model where the drift of observations changes from zero to a random drift with a prescribed discrete distribution. We set up the problem as a stochastic optimization in which the objective is to minimize a measure of detection delay subject to a constraint on frequency of false alarms. We design a novel composite stopping rule and prove that it is asymptotically optimal of third order under a weighted Lorden’s criterion for detection delay. We also develop the strategy to identify the post-change drift and analyze the conditional identification error asymptotically. Our composite rules are based on CUSUM stopping times, as well as their reaction periods, namely the times between the last reset of the CUSUM statistic process and the CUSUM alarm. The established results shed new light on the performance of CUSUM strategies under model uncertainty and offer strong asymptotic optimality results in this framework.

Editors' Choice—Field Trials Testing of Mixed Potential Electrochemical Hydrogen Safety Sensors at Commercial California Hydrogen Filling Stations

Hydrogen safety sensors must meet specific performance requirements, mandated by the U.S. Department of Energy, for hydrogen fueling station monitoring. Here, we describe the long-term performance of two zirconia-based mixed potential electrochemical hydrogen gas sensors, developed specifically with a high sensitivity to hydrogen, low cross-sensitivity, and fast response time. Over a two-year period, sensors with tin-doped indium oxide and strontium doped lanthanum chromite electrodes were deployed at two stations in four field trials tests conducted in Los Angeles. The sensors documented the existence of hydrogen plumes ranging in concentration from 100 to as high as 2700 ppm in the area surrounding the dispenser, consistent with depressurization from 700 bar following vehicle refueling. As expected, the hydrogen concentration reported by the mixed potential sensors was influenced by wind direction. Baseline stability testing at a Chino, CA station showed no measureable baseline drift throughout 206 days of uninterrupted data acquisition. The high baseline stability, excellent correlation with logged fueling/depressurization events, and absence of false alarms suggest that the zirconia-based mixed potential sensor platform is a good candidate for protecting hydrogen infrastructure where frequent calibrations or sensor replacement to reduce the false alarm frequency have been shown to be cost prohibitive.

Long-term monitoring of cardiorespiratory patterns in drug-resistant epilepsy.

Sudden unexplained death in epilepsy (SUDEP) during inpatient electroencephalography (EEG) monitoring has been a rare but potentially preventable event, with associated cardiopulmonary markers. To date, no systematic evaluation of alarm settings for a continuous pulse oximeter (SpO2 ) has been performed. In addition, evaluation of the interrelationship between the ictal and interictal states for cardiopulmonary measures has not been reported. Patients with epilepsy were monitored using video-EEG, SpO2 , and electrocardiography (ECG). Alarm thresholds were tested systematically, balancing the number of false alarms with true seizure detections. Additional cardiopulmonary patterns were explored using automated ECG analysis software. One hundred ninety-three seizures (32 generalized) were evaluated from 45 patients (7,104 h recorded). Alarm thresholds of 80-86% SpO2 detected 63-73% of all generalized convulsions and 20-28% of all focal seizures (81-94% of generalized and 25-36% of focal seizures when considering only evaluable data). These same thresholds resulted in 25-146 min between false alarms. The sequential probability of ictal SpO2 revealed a potential common seizure termination pathway of desaturation. A statistical model of corrected QT intervals (QTc), heart rate (HR), and SpO2 revealed close cardiopulmonary coupling ictally. Joint probability maps of QTc and SpO2 demonstrated that many patients had baseline dysfunction in either cardiac, pulmonary, or both domains, and that ictally there was dissociation-some patients exhibited further dysfunction in one or both domains. Optimal selection of continuous pulse oximetry thresholds involves a tradeoff between seizure detection accuracy and false alarm frequency. Alarming at 86% for patients that tend to have fewer false alarms and at 80% for those who have more, would likely result in a reasonable tradeoff. The cardiopulmonary findings may lead to SUDEP biomarkers and early seizure termination therapies.

Automated Extraction and Mapping for Desert Wadis from Landsat Imagery in Arid West Asia

Wadis, ephemeral dry rivers in arid desert regions that contain water in the rainy season, are often manifested as braided linear channels and are of vital importance for local hydrological environments and regional hydrological management. Conventional methods for effectively delineating wadis from heterogeneous backgrounds are limited for the following reasons: (1) the occurrence of numerous morphological irregularities which disqualify methods based on physical shape; (2) inconspicuous spectral contrast with backgrounds, resulting in frequent false alarms; and (3) the extreme complexity of wadi systems, with numerous tiny tributaries characterized by spectral anisotropy, resulting in a conflict between global and local accuracy. To overcome these difficulties, an automated method for extracting wadis (AMEW) from Landsat-8 Operational Land Imagery (OLI) was developed in order to take advantage of the complementarity between Water Indices (WIs), which is a technique of mathematically combining different bands to enhance water bodies and suppress backgrounds, and image processing technologies in the morphological field involving multi-scale Gaussian matched filtering and a local adaptive threshold segmentation. Evaluation of the AMEW was carried out in representative areas deliberately selected from Jordan, SW Arabian Peninsula in order to ensure a rigorous assessment. Experimental results indicate that the AMEW achieved considerably higher accuracy than other effective extraction methods in terms of visual inspection and statistical comparison, with an overall accuracy of up to 95.05% for the entire area. In addition, the AMEW (based on the New Water Index (NWI)) achieved higher accuracy than other methods (the maximum likelihood classifier and the support vector machine classifier) used for bulk wadi extraction.

Clinical Alarms in Intensive Care Units: Perceived Obstacles of Alarm Management and Alarm Fatigue in Nurses.

ObjectivesThe purpose of this descriptive study was to investigate the current situation of clinical alarms in intensive care unit (ICU), nurses' recognition of and fatigue in relation to clinical alarms, and obstacles in alarm management.MethodsSubjects were ICU nurses and devices from 48 critically ill patient cases. Data were collected through direct observation of alarm occurrence and questionnaires that were completed by the ICU nurses. The observation time unit was one hour block. One bed out of 56 ICU beds was randomly assigned to each observation time unit.ResultsOverall 2,184 clinical alarms were counted for 48 hours of observation, and 45.5 clinical alarms occurred per hour per subject. Of these, 1,394 alarms (63.8%) were categorized as false alarms. The alarm fatigue score was 24.3 ± 4.0 out of 35. The highest scoring item was "always get bothered due to clinical alarms". The highest scoring item in obstacles was "frequent false alarms, which lead to reduced attention or response to alarms".ConclusionsNurses reported that they felt some fatigue due to clinical alarms, and false alarms were also obstacles to proper management. An appropriate hospital policy should be developed to reduce false alarms and nurses' alarm fatigue.

Detection of radioactive sources in urban scenes using Bayesian Aggregation of data from mobile spectrometers

Mobile radiation detector systems aim to help identify dangerous sources of radiation while minimizing frequency of false alarms caused by non-threatening nuisance sources prevalent in cluttered urban scenes. We develop methods for spatially aggregating evidence from multiple spectral observations to simultaneously detect and infer properties of threatening radiation sources.Our Bayesian Aggregation (BA) framework allows sensor fusion across multiple measurements to boost detection capability of a radioactive point source, providing several key innovations previously unexplored in the literature. Our method learns the expected Signal-to-Noise Ratio (SNR) trend as a function of source exposure using Bayesian nonparametrics to enable robust detection. The method scales well in spatial search by leveraging conditional independence and locality in Bayesian updates. The framework also allows modeling of source parameters such as intensity or type to enable property characterization of detected sources. Approaches for incorporating modeling information into BA are compared and benchmarked with respect to other data fusion techniques.

The Effect of Perceived Choice on Performance, Workload, and Stress

Many tasks in both military and industrial settings require monitoring a display or an environment for the appearance of a target stimulus. The capacity to remain vigilant decreases with time on task and, consequently, there has been research devoted to developing methods to improve sustained attention. The goal for this study was to investigate the effects of a perceived choice for task difficulty on the performance, perceived workload, and stress of individuals as well as replicate past vigilance findings related to time on task and event rate using a first-person perspective movement-based vigilance task. The hypotheses were partially supported. There was a marginal reduction in both false alarm frequency and frustration depending on perceived choice condition and a choice by task difficulty interaction for physical demand. The results for time on task and event rate effects were consistent with previous research.

Human factors approach to evaluate the user interface of physiologic monitoring

BackgroundAs technology infiltrates more of our personal and professional lives, user expectations for intuitive design have driven many consumer products, while medical equipment continues to have high training requirements. Not much is known about the usability and user experience associated with hospital monitoring equipment. This pilot project aimed to better understand and describe the user interface interaction and user experience with physiologic monitoring technology. DesignThis was a prospective, descriptive, mixed-methods quality improvement project to analyze perceptions and task analyses of physiologic monitors. MethodsFollowing a survey of practice patterns and perceived abilities to accomplish key tasks, 10 voluntary experienced physician and nurse subjects were asked to perform a series of tasks in 7 domains of monitor operations on GE Monitoring equipment in a single institution. For each task analysis, data were collected on time to complete the task, the number of button pushes or clicks required to accomplish the task, economy of motion, and observed errors. ResultsAlthough 60% of the participants reported incorporating monitoring data into patient care, 80% of participants preferred to receive monitoring data at the point of care (bedside). Average perceived central station usability is 5.3 out of 10 (ten is easiest). ConclusionsHigh variability exists in monitoring station interaction performance among those participating in this project. Alarms were almost universally silenced without cognitive recognition of the alarm state. Education related to monitoring operations appeared largely absent in this sample. Most users perceived the interface to not be intuitive, complaining of multiple layers and steps for data retrieval. These clinicians report real-time monitoring helpful for abrupt changes in condition like arrhythmias; however, reviewing alarms is not prioritized as valuable due to frequent false alarms. Participants requested exporting monitoring data to electronic medical records. Much research is needed to develop best practices for display of real-time information, organization and filtering of meaningful data, and simplified ways to find information.