Duration Of Alarms Research Articles

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units (ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU setting led to a higher alarm burden and more staff with alarm fatigue. We aimed to compare alarm situations between provisional COVID-19 ICUs and non-COVID-19 ICUs during the second COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing the level of alarm fatigue experienced by health care staff. Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard non-COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarm load, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patient monitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with a questionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel. COVID-19 ICUs had significantly more alarms per bed per day than non-COVID-19 ICUs (P<.001), and the majority of the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found no COVID-19-specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level of alarm fatigue (P=.04). Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing the importance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm management strategies in different ICU settings.

Modeling of processes of creation and organization of bank employees qualification improvement courses using ARIS Express

The article analyzes the role of bank staff qualifications in the financial component of the banking system. The influence of the human factor on the activity of a banking institution is also analyzed. Crisis phenomena greatly affect every element of the banking sector, which encourages the use of risk management to respond to new realities. Under these circumstances, it is not necessary to forget about the bank's staff, providing them with opportunities to acquire knowledge and improve the level of qualifications in order to adapt to new realities. This approach will help to improve the professional and moral condition of employees even in crisis conditions of operation. In connection with the fact that some of the bank's employees are now in less safe working conditions due to the significant duration of air alarms and the high risk of missile attacks, the question arose, how can it be possible in the current conditions to ensure the opportunity to acquire new knowledge by taking qualification improvement courses. To solve the existing problem, the creation of an online course platform is proposed as an option that will provide the opportunity to take courses in a distance format. The process of creating, organizing and checking the effectiveness of courses was modelled using the ARIS Express program using special software tools. The online platform is an effective alternative for the bank and employees, as it will provide an opportunity to improve qualifications remotely at a convenient time and allow the bank's staff to stay in a safe place. The developed platform will be an indicator for checking the results of courses, will provide an opportunity for top management to monitor employees' grows to develop in this area. The creation of an online platform for training courses for bank employees will contribute to increasing the efficiency of the bank's work, which will become the basis for the development of professional skills of employees. The use of the proposed approach to improving the qualifications of bank employees will, as a result, have a positive effect on the economic indicators of the banking institution.

Prediction of GABA receptor antagonist-induced convulsion in cynomolgus monkeys by combining machine learning and heart rate variability analysis

Drug-induced convulsion is a severe adverse event; however, no useful biomarkers for it have been discovered. We propose a new method for predicting drug-induced convulsions in monkeys based on heart rate variability (HRV) and a machine learning technique. Because autonomic nervous activities are altered around the time of a convulsion and such alterations affect HRV, they may be predicted by monitoring HRV. In the proposed method, anomalous changes in multiple HRV parameters are monitored by means of a convulsion prediction model, and convulsion alarms are issued when abnormal changes in HRV are detected. The convulsion prediction model is constructed based on multivariate statistical process control (MSPC), a well-known anomaly detection algorithm in machine learning. In this study, HRV data were collected from four cynomolgus monkeys administered with multiple doses of pentylenetetrazol (PTZ) and picrotoxin (PTX), which are GABA receptor antagonists, as convulsant agents. In addition, low doses of pilocarpine (PILO) were administered as a negative control. Twelve HRV parameters in three hours after drug administration were monitored by means of the prediction model. The number and duration of convulsion alarms from HRV increased at medium and high doses of PTZ and PTX (1/3 or 1/4 of convulsion dose). On the other hand, the frequency of convulsion alarms did not increase with PILO. Although vomiting was observed in all drugs, convulsion alarms were not associated with the vomiting. Thus, convulsion alarms can be used as a biomarker for convulsions induced by GABA receptor antagonists.

Indexing and Designing Deadbands for Industrial Alarm Signals

Alarm systems play critically important roles for the safe and efficient operation of industrial plants, but they often suffer from too many nuisance alarms. Deadbands are widely used in industrial alarm systems to reduce the number of nuisance alarms. This paper formulates an index to determine whether deadbands are suitable for removing nuisance alarms of an analog signal, and proposes a method to design the deadband width. First, the deadband index is defined from two metrics, namely, normalized alarm durations and normalized alarm deviations. If the index is larger than 45°, as indicated by a hypothesis test, then nuisance alarms are associated with large/small normalized alarm durations/deviations, so that deadbands are suitable. Second, a design method is proposed to find an optimal deadband width, so as to achieve the best weighted balance between the time duration of nuisance alarms and the delay in detecting abnormal conditions. The proposed method is applicable to nonindependent and identically distributed (IID) signals, whereas available design methods are confined to IID signals. Industrial examples are provided to validate the deadband index and the design method.

Research Outcomes of Implementing CEASE: An Innovative, Nurse-Driven, Evidence-Based, Patient-Customized Monitoring Bundle to Decrease Alarm Fatigue in the Intensive Care Unit/Step-down Unit.

The research literature is replete with evidence that alarm fatigue is a real phenomenon in the clinical practice environment and can lead to desensitization of the need to respond among nursing staff. A few studies attest to the effectiveness of incorporating parts of the American Association of Critical-Care Nurses recommended nursing practices for alarm management. No studies could be found measuring the effectiveness of the American Association of Critical-Care Nurses recommendations in their entirety or the effectiveness of a nursing-driven, evidence-based, patient-customized monitoring bundle. The purpose of this study was to describe the effect of implementing CEASE, a nurse-driven, evidence-based, patient-customized monitoring bundle on alarm fatigue. CEASE is an acronym for Communication, Electrodes (daily changes), Appropriateness (evaluation), Setup alarm parameters (patient customization), and Education (ongoing). (1) In a 36-bed intensive care unit/step-down unit (ICU/SDU) with continuous hemodynamic and respiratory monitoring, does application of an evidence-based, patient-customized monitoring bundle compared with existing monitoring practice lead to less alarm fatigue as measured by the number of hemodynamic and respiratory monitoring alarms? (2) In a 36-bed ICU/SDU with continuous hemodynamic and respiratory monitoring, does application of an evidence-based, patient-customized monitoring bundle compared with existing monitoring practice lead to less alarm fatigue as measured by duration of alarms? and (3) In a 36-bed ICU/SDU with continuous hemodynamic and respiratory monitoring, does application of an evidence-based, patient-customized monitoring bundle compared with existing monitoring practice lead to less alarm fatigue as measured by nurse perception? This was an institutional review board approved exploratory, nonrandomized, pretest and posttest, 1-group, quasi-experimental study, without-comparators design describing difference in pretest and posttest measures following CEASE Bundle implementation. The study was conducted over a 6-month period. Convenience sample of 74 registered nurses staffing a 36-bed ICU/SDU using the CEASE Bundle participated. Preimplementation/postimplementation number of alarms and alarm duration time for a 30-day period were downloaded from the monitoring system and compared. Nurses completed an electronic 36-item Clinical Alarms Survey provided by the Healthcare Technology Foundation: 35 before implementation and 18 after implementation. Researchers measured CEASE alarm bundle adherence. χ and t-tests determined statistical significance. Total number of monitoring alarms decreased 31% from 52 880 to 36 780 after CEASE Bundle implementation. Low-priority Level 1 alarms duration time significantly decreased 23 seconds (t = 1.994, P = .045). Level 2 duration time did not change. High-priority Level 3 alarms duration time significantly increased to 246 seconds (t = 4.432, P < .0001). CEASE alarm bundle adherence significantly improved to 22.4% (χ = 5.068, P = .0244). Nurses perceived a significant decrease in nuisance alarm occurrence (68% to 44%) postimplementation (χ = 3.243, P = .0417). No adverse patient events occurred. Decreased total number of monitoring alarms improved nurse perception of alarm fatigue. Continued monitoring of CEASE Bundle adherence by nursing staff is required. Longer high-priority Level 3 alarms duration suggests need for further research.

ACUsmart Continuous Renal Replacement Therapy Platform: Multicenter Pilot Study for Technical and Clinical Assessment (A.M.P. Study)

Background: ACUsmart (Medica S.P.A., Italy) is a new-generation, continuous renal replacement therapy (CRRT) machine for critically ill patients with acute kidney injury. We designed a multicenter international pilot study to provide a description of outlines of the ACUsmart system, evaluation aspects of functionality, usability, and feasibility, discriminating reasons of possible treatment’s withdrawals or discontinuations and highlighting strong and weak points of the machine. Methods: Data of 23 CRRT (and 11 plasma exchange) treatments were collected from 4 intensive care units. Parameters such as treatment duration, downtime, delivered dose, and number and type of alarms were recorded. The general perception of the machine was quantified through the administration of a survey to each component of the evaluating staff. Results: A total treatment time of 447 h was carried with ACUsmart. Eleven continuous veno-venous hemofiltration, 4 continuous veno-venous hemodialysis , and 8 continuous veno-venous hemodiafiltration were performed. The average percentage of net treatment duration with respect to total treatment duration was 92.37%. The mean prescribed dose and delivered dose were 26.33 and 24.10 mL/kg/h, respectively. In general, the machine was rated by users involved as practical and easy to use, although few components need to be slightly improved. Conclusion: ACUsmart is a new multifunctional machine that meets most of the features required in a fourth-generation CRRT equipment.

Frequency and Duration of Infusion Pump Alarms: Establishing National Benchmarks.

Reduction of clinical alarms is a priority due to alarm fatigue and the high incidence of nonactionable alarms, especially those generated from physiological monitors. However, research on infusion pump alarm types and frequencies is limited. The purpose of this study was to establish a baseline for infusion pump alarm frequencies and duration in the hospital setting. Frequency and duration of alarms across 29 hospitals using 11,410 infusion pumps revealed 987,240 alarms associated with 568,164 infusions during a consecutive 60-day period. Pump alarms accounted for only 0.8% of infusion time, with an average of 1.74 alarms per delivery and 0.18 alarms per hour. Average alarm duration was 0:02:38 (h:min:s), with 60% of alarms being addressed within 0:01:08. The most frequent alarms were keep vein open (33.77%), hold expired (27.18%), and downstream occlusion (22.94%). The medical/surgical and intensive care unit (ICU) care areas had the highest number of alarms (41.66% and 39.70% of total alarms, respectively), but pediatrics/neonatal ICU had the highest frequency of alarms per delivery (4.91). Intravenous fluids accounted for 47.16% of total alarms, with an average of 3.03 alarms per delivery, whereas parenteral nutrition and propofol had 6.77 and 6.74 average alarms per delivery, respectively. A higher average number of alarms per delivery occurred on Saturdays (1.74) and Sundays (1.73) compared with weekdays. Infusion pump alarm data collected and analyzed were sufficient to establish a reasonable baseline of infusion pump alarm types and relative frequencies for the device.

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.

A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study

Using 10-minute wind turbine supervisory control and data acquisition (SCADA) system data to predict faults can be an attractive way of working toward a predictive maintenance strategy without needing to invest in extra hardware. Classification methods have been shown to be effective in this regard, but there have been some common issues in their application within the literature. To use these data-driven methods effectively, historical SCADA data must be accurately labelled with the periods when turbines were down due to faults, as well as with the reason for the fault. This can be manually achieved using maintenance logs, but can be highly tedious and time-consuming due to the often unstructured format in which this information is stored. Alarm systems can also help, but the sheer volume of alarms and false positives generated complicate efforts. Furthermore, a way to implement and evaluate the field deployed system beyond simple classification metrics is needed. In this work, we present a prescribed and reproducible framework for: (i) automatically identifying periods of faulty operation using rules applied to the turbine alarm system; (ii) using this information to perform classification which avoids some of the common pitfalls observed in literature; and (iii) generating alerts based on a sliding window metric to evaluate the performance of the system in a real-world scenario. The framework was applied to a dataset from an operating wind farm and the results show that the system can automatically and accurately label historical stoppages from the alarms data. For fault prediction, classification scores are quite low, with precision of 0.16 and recall of 0.49, but it is envisaged that this can be greatly improved with more training data. Nonetheless, the sliding window metric compensates for the low raw classification scores and shows that 71% of faults can be predicted with an average of 30 h notice, with false alarms being active for 122 h of the year. By adjusting some of the parameters of the fault prediction alerts, the duration of false alarms can be drastically reduced to 2 h, but this also reduces the number of predicted faults to 8%.

Does the architectural layout of a NICU affect alarm pressure? A comparative clinical audit of a single-family room and an open bay area NICU using a retrospective study design

ObjectivesTo determine differences in alarm pressure between two otherwise comparable neonatal intensive care units (NICUs) differing in architectural layout—one of a single-family room (SFR) design and the other of an...

Frequency, duration and cause of ventilator alarms on a neonatal intensive care unit

ObjectiveTo investigate the frequency and cause of neonatal ventilator alarms. Neonatal ventilators frequently alarm and also disturb babies, parents and nurses. If frequent they may cause alarm fatigue and be...

The heuristics of nurse responsiveness to critical patient monitor and ventilator alarms in a private room neonatal intensive care unit.

AimAlarm fatigue is a well-recognized patient safety concern in intensive care settings. Decreased nurse responsiveness and slow response times to alarms are the potentially dangerous consequences of alarm fatigue. The aim of this study was to determine the factors that modulate nurse responsiveness to critical patient monitor and ventilator alarms in the context of a private room neonatal intensive care setting.MethodsThe study design comprised of both a questionnaire and video monitoring of nurse-responsiveness to critical alarms. The Likert scale questionnaire, comprising of 50 questions across thematic clusters (critical alarms, yellow alarms, perception, design, nursing action, and context) was administered to 56 nurses (90% response rate). Nearly 6000 critical alarms were recorded from 10 infants in approximately 2400 hours of video monitoring. Logistic regression was used to identify patient and alarm-level factors that modulate nurse-responsiveness to critical alarms, with a response being defined as a nurse entering the patient’s room within the 90s of the alarm being generated.ResultsBased on the questionnaire, the majority of nurses found critical alarms to be clinically relevant even though the alarms did not always mandate clinical action. Based on video observations, for a median of 34% (IQR, 20–52) of critical alarms, the nurse was already present in the room. For the remaining alarms, the response rate within 90s was 26%. The median response time was 55s (IQR, 37-70s). Desaturation alarms were the most prevalent and accounted for more than 50% of all alarms. The odds of responding to bradycardia alarms, compared to desaturation alarms, were 1.47 (95% CI = 1.21–1.78; <0.001) while that of responding to a ventilator alarm was lower at 0.35 (95% CI = 0.27–0.46; p <0.001). For every 20s increase in the duration of an alarm, the odds of responding to the alarm (within 90s) increased to 1.15 (95% CI = 1.1–1.2; p <0.001). The random effect per infant improved the fit of the model to the data with the response times being slower for infants suffering from chronic illnesses while being faster for infants who were clinically unstable.DiscussionEven though nurses respond to only a fraction of all critical alarms, they consider the vast majority of critical and yellow alarms as useful and relevant. When notified of a critical alarm, they seek waveform information and employ heuristics in determining whether or not to respond to the alarm.ConclusionAmongst other factors, the category and duration of critical alarms along with the clinical status of the patient determine nurse-responsiveness to alarms.

Alarms, oxygen saturations, and SpO2 averaging time in the NICU.

Alarm overload is a significant concern in the Neonatal Intensive Care Unit (NICU). Selecting a longer oxygen saturation (SpO2) averaging time will reduce the number of alarms but may mask fluctuations in oxygenation. Characterize bedside monitor alarms in the NICU and estimate the impact of longer SpO2 averaging time and alarm delay. All bedside monitor alarms were analyzed over a 12-month period in the University of Virginia NICU, using the default averaging time (8 seconds) and SpO2 alarm limits set at 88-95% for infants on supplemental oxygen. In 10 VLBW infants, SpO2 averaging time was lowered to 2 seconds for 24 hours and events of SpO2 out of the target range were estimated with 2-, 8-, or 16-second averaging time, with and without a 15-second alarm delay. There were 3,263,590 alarms in the unit over 12 months. Low or high SpO2 alarms constituted 79% of the total, and 55% of these events lasted <15 seconds. In 10 infants we estimated that increasing SpO2 averaging time from 2 to 16 seconds would have led to 53% fewer SpO2 alarms but the mean duration of alarms would have been 2.15-fold longer. Adding a 15-second alarm delay to 2-second SpO2 averaging in this analysis decreased SpO2 alarms by 67%. Longer SpO2 averaging times mask the number and severity of events of aberrant oxygenation in preterm infants without decreasing total alarm time. Incorporating an alarm delay with shorter SpO2 averaging times can reduce alarm number and duration, and allow more accurate assessment of oxygenation, which may be important for research into consequences of aberrant oxygenation in this vulnerable population.

Whats that noise? Bedside monitoring in the Emergency Department

ObjectiveTo determine the frequency, duration and type of audible monitor alarms in an ED, utilising the standard manufacturer’s classification. MethodsThe audible monitor alarms and the timing of any intervention related to the patient monitoring was observed and recorded. Results110 Patients admitted to the Majors area or Resuscitation Room were observed for a total of 93hours. One monitor was observed at a time. Alarm noise was generated 29% of the observation time. Overall, 429 alarms lasting 21hours 27minutes were judged to be positive and 143 alarms lasting 5hours 47minutes, negative. 74% of Resuscitation Room and 47% of Majors alarms were silenced or paused. Alarm limit parameters were only adjusted after 5% of alarms in Resuscitation Room and 6% of alarms in Majors. ConclusionsWhilst high level monitoring is desired from a patient safety perspective, it contributes to a significant ambient noise level, which is recognised by all who pass through an ED, and can be detrimental to patients, relatives and staff. We have demonstrated that there is a high probability of near-continuous alarm noise from patient monitoring in a 10-bedded Majors area. We make suggestions for methods of noise reduction and intend to implement some of these within our own ED.

Premonitory spreading of seismicity over the faults' network in southern California: Precursor Accord

We establish a connection between geometry of the faults' network in a region and seismicity preceding the strong earthquakes in the intermediate‐term timescale of years. Previous studies of observed and computer‐simulated seismicity demonstrated that strong earthquakes are preceded in that timescale by the rise of seismic activity in a lower magnitude range. Here, we explore a complementary phenomenon: spreading of activity over the fault network. This phenomenon is depicted by the seismicity pattern Accord, defined as a simultaneous rise of seismicity in a sufficiently large number of fault zones (hence its name). Pattern Accord has been found recently in synthetic seismicity generated by the colliding cascades (CC) model. The present study demonstrates this pattern in observed seismicity of southern California. We applied to observations a prediction algorithm based on the pattern Accord. Scaled to the target magnitude 7.5, the pattern Accord emerges within a few years before each of the three largest earthquakes in southern California (Kern County, 1952; Landers, 1992; and Hector Mine, 1999) and at no other time. Scaled to the target magnitude interval from 6.5 to 7.4, the pattern Accord precedes six out of nine earthquakes. The duration of alarms in both cases is about 30% of the time considered. We repeated prediction with different combinations of adjustable numerical parameters of the algorithm, summing up the success and error scores on an error diagram. This numerical experiment shows that the prediction results are stable under moderate variations of adjustable parameters. The final test would be, as always, an advance prediction. The pattern Accord has a simple physical explanation connected with equilibrium of the crustal blocks' system under the impact of tectonic driving forces. At the same time, Accord is a manifestation of a broader phenomenon: premonitory increase of earthquakes' correlation range. The latter phenomenon was found for the first time in the CC model, although it was hypothesized by one of the authors much earlier.

Colliding cascades model for earthquake prediction

A wide set of premonitory seismicity patterns is reproduced on a numerical model of seismicity, and their performance in prediction of major earthquakes is evaluated. Seismicity is generated by the Colliding Cascades Model, recently developed by the same authors. The model has a hierarchical structure. It describes the interaction of two cascades: a direct cascade of loading, which is applied to the top (largest) element and transfers down the hierarchy, and inverse cascade of failures, which goes up the hierarchy, from smaller to larger elements. These cascades collide and interact: loading leads to failures, while failures release and redistribute the loading. Three basic types of earthquake precursors are considered. They reflect the following integral characteristics of seismicity: (i) the clustering of earthquakes in space and time, (ii) the intensity of earthquake sequences, and (iii) the correlation distance between earthquakes. Patterns of the first two types were established mainly by the analysis of observations and are used in the intermediate term earthquake prediction algorithms with a characteristic duration of the alarm of a few years. These patterns are validated by advance earthquake prediction. Patterns of the third type have been found very recently in the Colliding Cascades Model, though they were hypothesized previously. They have yet to be validated by observations of real seismicity. For each precursor, we explore what is called an “error diagram” showing the total duration of alarms, the rate of failures to predict, and the rate of false alarms

Performance of the earthquake prediction algorithm CN in 22 regions of the world

We evaluate the performance of the intermediate-term earthquake prediction algorithm CN in 22 regions of the world. The choice of regions was defined by accessibility and quality of earthquake catalogs. Prediction is completely reproducible because the parameters of the algorithm were defined and published in advance. Total results of the tests are as follows: 26% of the time of alarms and 11 out of 24 predicted earthquakes (46%) did occur. The total error of prediction may be characterized by the sum R of total duration of alarms and the rate of failures to predict. We obtained R=0.26+(1−0.46)=0.8. This is two times higher than for retrospective prediction in California. Statistical significance of predictions is 91% so far; it will exceed 96% if the quality of prediction would remain the same for the next few years.

Nurses' response to alarms from monitoring systems in NICU. 1027

Objective: To determine the frequencies and duration of alarms in a NICU and the nurses response to these alarms.

Signal Extraction Technology

Signal Extraction Technology

Signal Extraction Technology

Signal Extraction Technology