Journal articles on the topic 'Alert fatigue'
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Pashtan, Itai Max, Tara Kosak, Kevin Beaudette, Amy Buckman, Abigail Clark, Jill Connolly, Lynne Hicks, et al. "Addressing alert fatigue by reducing radiation oncology software alert volume." Journal of Clinical Oncology 39, no. 28_suppl (October 1, 2021): 261. http://dx.doi.org/10.1200/jco.2020.39.28_suppl.261.
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261 Background: Radiation therapists (RTTs) administer radiation treatments to patients with cancer. Treatments are delivered using linear accelerators (LINACs), operated by vendor specific software. Prior to delivering treatment, RTTs perform a time-out, and read aloud critical electronic communications (alerts) entered by members of the radiation oncology care team. Alerts are effective at communicating critical information, including treatment setup and imaging instructions, but can become a source of error due to alert fatigue when placed indiscriminately. Methods: A multicenter retrospective review of alert use per patient was conducted in 4 radiation oncology centers with a total of 6 LINACs. Alert usage was reviewed pre-intervention for 40 randomly selected patients using manual chart review. Each alert was reviewed for frequency and utilization. In attempt of improving communication and reducing alert fatigue, a multidisciplinary process improvement working group (with Radiation Oncologists, RTTs, nursing, physicists, and administration) was formed to review the utilization of alerts in our department and propose interventions. Three months after intervention, an additional 40 chart review was performed. Our aim was to reduce the volume of alerts by 20% within 3 months. A 2-tail t-test was used for statistical analysis. Results: Process improvements were implemented to reduce the volume of alerts per patient. Interventions included 1) defining an alert for all departmental staff, 2) creating guidelines for appropriate utilization of alerts, 3) routing communications not critical to RTTs at the time of radiation treatment administration through other channels, and 4) training staff as to the above. The pre-intervention review yielded 239 alerts. Post-intervention, there were 173 alerts, a reduction of 27% (p =.008). Conclusions: This practice change reduced average alert volume by 27%. As a result, alerts which are critical to safe treatment delivery by RTTs (i.e. daily setup alerts), became more heavily represented. Other alerts, which could be communicated effectively in other ways (i.e. OTVs [weekly on treatment visit with Radiation Oncologist]), were eliminated. By decreasing alert volume, the risk of RTT alert fatigue is reduced, communication improved, and treatment safety enhanced.[Table: see text]
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Cash, Jared J. "Alert fatigue." American Journal of Health-System Pharmacy 66, no. 23 (December 1, 2009): 2098–101. http://dx.doi.org/10.2146/ajhp090181.
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Schleyer, Titus, and Thankam P. Thyvalikakath. "Alert Fatigue." Journal of the American Dental Association 143, no. 4 (April 2012): 332–33. http://dx.doi.org/10.14219/jada.archive.2012.0166.
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BARTON, AMY J. "Alert Fatigue." Clinical Nurse Specialist 25, no. 5 (September 2011): 218–19. http://dx.doi.org/10.1097/nur.0b013e318229962d.
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Wan, Paul Kengfai, Abylay Satybaldy, Lizhen Huang, Halvor Holtskog, and Mariusz Nowostawski. "Reducing Alert Fatigue by Sharing Low-Level Alerts With Patients and Enhancing Collaborative Decision Making Using Blockchain Technology: Scoping Review and Proposed Framework (MedAlert)." Journal of Medical Internet Research 22, no. 10 (October 28, 2020): e22013. http://dx.doi.org/10.2196/22013.
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Background Clinical decision support (CDS) is a tool that helps clinicians in decision making by generating clinical alerts to supplement their previous knowledge and experience. However, CDS generates a high volume of irrelevant alerts, resulting in alert fatigue among clinicians. Alert fatigue is the mental state of alerts consuming too much time and mental energy, which often results in relevant alerts being overridden unjustifiably, along with clinically irrelevant ones. Consequently, clinicians become less responsive to important alerts, which opens the door to medication errors. Objective This study aims to explore how a blockchain-based solution can reduce alert fatigue through collaborative alert sharing in the health sector, thus improving overall health care quality for both patients and clinicians. Methods We have designed a 4-step approach to answer this research question. First, we identified five potential challenges based on the published literature through a scoping review. Second, a framework is designed to reduce alert fatigue by addressing the identified challenges with different digital components. Third, an evaluation is made by comparing MedAlert with other proposed solutions. Finally, the limitations and future work are also discussed. Results Of the 341 academic papers collected, 8 were selected and analyzed. MedAlert securely distributes low-level (nonlife-threatening) clinical alerts to patients, enabling a collaborative clinical decision. Among the solutions in our framework, Hyperledger (private permissioned blockchain) and BankID (federated digital identity management) have been selected to overcome challenges such as data integrity, user identity, and privacy issues. Conclusions MedAlert can reduce alert fatigue by attracting the attention of patients and clinicians, instead of solely reducing the total number of alerts. MedAlert offers other advantages, such as ensuring a higher degree of patient privacy and faster transaction times compared with other frameworks. This framework may not be suitable for elderly patients who are not technology savvy or in-patients. Future work in validating this framework based on real health care scenarios is needed to provide the performance evaluations of MedAlert and thus gain support for the better development of this idea.
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Raman, Kirbana Jai, Afizan Azman, Siti Zainab Ibrahim, Sumendra Yogarayan, Mohd Fikri Azli Abdullah, Siti Fatimah Abdul Razak, Anang Hudaya Muhamad Amin, and Kalaiarasi Sonai Muthu. "Fatigue Alert System." Indian Journal of Science and Technology 11, no. 20 (May 1, 2018): 1–7. http://dx.doi.org/10.17485/ijst/2018/v11i20/123348.
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Nanji, Karen C., Diane L. Seger, Sarah P. Slight, Mary G. Amato, Patrick E. Beeler, Qoua L. Her, Olivia Dalleur, et al. "Medication-related clinical decision support alert overrides in inpatients." Journal of the American Medical Informatics Association 25, no. 5 (October 27, 2017): 476–81. http://dx.doi.org/10.1093/jamia/ocx115.
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Abstract Objective To define the types and numbers of inpatient clinical decision support alerts, measure the frequency with which they are overridden, and describe providers’ reasons for overriding them and the appropriateness of those reasons. Materials and Methods We conducted a cross-sectional study of medication-related clinical decision support alerts over a 3-year period at a 793-bed tertiary-care teaching institution. We measured the rate of alert overrides, the rate of overrides by alert type, the reasons cited for overrides, and the appropriateness of those reasons. Results Overall, 73.3% of patient allergy, drug-drug interaction, and duplicate drug alerts were overridden, though the rate of overrides varied by alert type (P < .0001). About 60% of overrides were appropriate, and that proportion also varied by alert type (P < .0001). Few overrides of renal- (2.2%) or age-based (26.4%) medication substitutions were appropriate, while most duplicate drug (98%), patient allergy (96.5%), and formulary substitution (82.5%) alerts were appropriate. Discussion Despite warnings of potential significant harm, certain categories of alert overrides were inappropriate >75% of the time. The vast majority of duplicate drug, patient allergy, and formulary substitution alerts were appropriate, suggesting that these categories of alerts might be good targets for refinement to reduce alert fatigue. Conclusion Almost three-quarters of alerts were overridden, and 40% of the overrides were not appropriate. Future research should optimize alert types and frequencies to increase their clinical relevance, reducing alert fatigue so that important alerts are not inappropriately overridden.
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McDaniel, Robert B., Jonathan D. Burlison, Donald K. Baker, Murad Hasan, Jennifer Robertson, Christine Hartford, Scott C. Howard, Andras Sablauer, and James M. Hoffman. "Alert dwell time: introduction of a measure to evaluate interruptive clinical decision support alerts." Journal of the American Medical Informatics Association 23, e1 (October 24, 2015): e138-e141. http://dx.doi.org/10.1093/jamia/ocv144.
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Abstract Metrics for evaluating interruptive prescribing alerts have many limitations. Additional methods are needed to identify opportunities to improve alerting systems and prevent alert fatigue. In this study, the authors determined whether alert dwell time—the time elapsed from when an interruptive alert is generated to when it is dismissed—could be calculated by using historical alert data from log files. Drug–drug interaction (DDI) alerts from 3 years of electronic health record data were queried. Alert dwell time was calculated for 25,965 alerts, including 777 unique DDIs. The median alert dwell time was 8 s (range, 1–4913 s). Resident physicians had longer median alert dwell times than other prescribers ( P < .001). The 10 most frequent DDI alerts ( n = 8759 alerts) had shorter median dwell times than alerts that only occurred once ( P < .001). This metric can be used in future research to evaluate the effectiveness and efficiency of interruptive prescribing alerts.
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Elias, Pierre, Eric Peterson, Bob Wachter, Cary Ward, Eric Poon, and Ann Marie Navar. "Evaluating the Impact of Interruptive Alerts within a Health System: Use, Response Time, and Cumulative Time Burden." Applied Clinical Informatics 10, no. 05 (October 2019): 909–17. http://dx.doi.org/10.1055/s-0039-1700869.
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Abstract Background Health systems often employ interruptive alerts through the electronic health record to improve patient care. However, concerns of “alert fatigue” have been raised, highlighting the importance of understanding the time burden and impact of these alerts on providers. Objectives Our main objective was to determine the total time providers spent on interruptive alerts in both inpatient and outpatient settings. Our secondary objectives were to analyze dwell time for individual alerts and examine both provider and alert-related factors associated with dwell time variance. Methods We retrospectively evaluated use and response to the 75 most common interruptive (“popup”) alerts between June 1st, 2015 and November 1st, 2016 in a large academic health care system. Alert “dwell time” was calculated as the time between the alert appearing on a provider's screen until it was closed. The total number of alerts and dwell times per provider per month was calculated for inpatient and outpatient alerts and compared across alert type. Results The median number of alerts seen by a provider was 12 per month (IQR 4–34). Overall, 67% of inpatient and 39% of outpatient alerts were closed in under 3 seconds. Alerts related to patient safety and those requiring more than a single click to proceed had significantly longer median dwell times of 5.2 and 6.7 seconds, respectively. The median total monthly time spent by providers viewing alerts was 49 seconds on inpatient alerts and 28 seconds on outpatient alerts. Conclusion Most alerts were closed in under 3 seconds and a provider's total time spent on alerts was less than 1 min/mo. Alert fatigue may lie in their interruptive and noncritical nature rather than time burden. Monitoring alert interaction time can function as a valuable metric to assess the impact of alerts on workflow and potentially identify routinely ignored alerts.
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Zeng, Chao, Wenjun Wang, Chaoyang Chen, Chaofei Zhang, and Bo Cheng. "Sex Differences in Time-Domain and Frequency-Domain Heart Rate Variability Measures of Fatigued Drivers." International Journal of Environmental Research and Public Health 17, no. 22 (November 17, 2020): 8499. http://dx.doi.org/10.3390/ijerph17228499.
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The effects of fatigue on a driver’s autonomic nervous system (ANS) were investigated through heart rate variability (HRV) measures considering the difference of sex. Electrocardiogram (ECG) data from 18 drivers were recorded during a simulator-based driving experiment. Thirteen short-term HRV measures were extracted through time-domain and frequency-domain methods. First, differences in HRV measures related to mental state (alert or fatigued) were analyzed in all subjects. Then, sex-specific changes between alert and fatigued states were investigated. Finally, sex differences between alert and fatigued states were compared. For all subjects, ten measures showed significant differences (Mann-Whitney U test, p < 0.01) between different mental states. In male and female drivers, eight and four measures, respectively, showed significant differences between different mental states. Six measures showed significant differences between males and females in an alert state, while ten measures showed significant sex differences in a fatigued state. In conclusion, fatigue impacts drivers’ ANS activity, and this impact differs by sex; more differences exist between male and female drivers’ ANS activity in a fatigued state than in an alert state.
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Topaz, Maxim, Diane L. Seger, Sarah P. Slight, Foster Goss, Kenneth Lai, Paige G. Wickner, Kimberly Blumenthal, et al. "Rising drug allergy alert overrides in electronic health records: an observational retrospective study of a decade of experience." Journal of the American Medical Informatics Association 23, no. 3 (November 17, 2015): 601–8. http://dx.doi.org/10.1093/jamia/ocv143.
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Objective There have been growing concerns about the impact of drug allergy alerts on patient safety and provider alert fatigue. The authors aimed to explore the common drug allergy alerts over the last 10 years and the reasons why providers tend to override these alerts. Design: Retrospective observational cross-sectional study (2004–2013). Materials and Methods Drug allergy alert data (n = 611,192) were collected from two large academic hospitals in Boston, MA (USA). Results Overall, the authors found an increase in the rate of drug allergy alert overrides, from 83.3% in 2004 to 87.6% in 2013 (P < .001). Alarmingly, alerts for immune mediated and life threatening reactions with definite allergen and prescribed medication matches were overridden 72.8% and 74.1% of the time, respectively. However, providers were less likely to override these alerts compared to possible (cross-sensitivity) or probable (allergen group) matches (P < .001). The most common drug allergy alerts were triggered by allergies to narcotics (48%) and other analgesics (6%), antibiotics (10%), and statins (2%). Only slightly more than one-third of the reactions (34.2%) were potentially immune mediated. Finally, more than half of the overrides reasons pointed to irrelevant alerts (i.e., patient has tolerated the medication before, 50.9%) and providers were significantly more likely to override repeated alerts (89.7%) rather than first time alerts (77.4%, P < .001). Discussion and Conclusions These findings underline the urgent need for more efforts to provide more accurate and relevant drug allergy alerts to help reduce alert override rates and improve alert fatigue.
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Horn, John, and Stephen Ueng. "The Effect of Patient-Specific Drug-Drug Interaction Alerting on the Frequency of Alerts: A Pilot Study." Annals of Pharmacotherapy 53, no. 11 (July 11, 2019): 1087–92. http://dx.doi.org/10.1177/1060028019863419.
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Background: False-positive drug-drug interaction alerts are frequent and result in alert fatigue that can result in prescribers bypassing important alerts. Development of a method to present patient-appropriate alerts is needed to help restore alert relevance. Objective: The purpose of this study was to assess the potential for patient-specific drug-drug interaction (DDI) alerts to reduce alert burden. Methods: This project was conducted at a tertiary care medical center. Seven of the most frequently encountered DDI alerts were chosen for developing patient-specific, algorithm-based DDI alerts. For each of the DDI pairs, 2 algorithms featuring different values for modifying factors were made. DDI alerts from the 7 drug pairs were collected over 30 days. Outcome measures included the number of DDI alerts generated before and after patient-specific algorithm application to the same patients over the same time period. Results: A total of 14 algorithms were generated, and each was evaluated by comparing the number of alerts generated by our existing, customized clinical decision support (CDS) software and the patient-specific algorithms. The CDS DDI alerting software generated an average of 185.3 alerts per drug pair over the 30-day study period. Patient-specific algorithms reduced the number of alerts resulting from the algorithms by 11.3% to 93.5%. Conclusion and Relevance: Patient-specific DDI alerting is an innovative and effective approach to reduce the number of DDI alerts, may potentially increase the appropriateness of alerts, and may decrease the potential for alert fatigue.
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Yang, Gang, Chaojing Tang, and Xingtong Liu. "DualAC2NN: Revisiting and Alleviating Alert Fatigue from the Detection Perspective." Symmetry 14, no. 10 (October 13, 2022): 2138. http://dx.doi.org/10.3390/sym14102138.
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The exponential expansion of Internet interconnectivity has led to a dramatic increase in cyber-attack alerts, which contain a considerable proportion of false positives. The overwhelming number of false positives cause tremendous resource consumption and delay responses to the really severe incidents, namely, alert fatigue. To cope with the challenge from alert fatigue, we focus on enhancing the capability of detectors to reduce the generation of false alerts from the detection perspective. The core idea of our work is to train a machine-learning-based detector to grasp the empirical intelligence of security analysts to estimate the feasibility of an incoming HTTP request to cause substantial threats, and integrate the estimation into the detection stage to reduce false alarms. To this end, we innovatively introduce the concept of attack feasibility to characterize the composition rationality of an inbound HTTP request as a feasible attack under static scrutinization. First, we adopt a fast request-reorganization algorithm to transform an HTTP request into the form of interface:payload pair for further alignment of structural components which can reveal the processing logic of the target program. Then, we build a dual-channel attention-based circulant convolution neural network (DualAC2NN) to integrate the attack feasibility estimation into the alert decision, by comprehensively considering the interface sensitivity, payload maliciousness, and their bipartite compatibility. Experiments on a real-world dataset show that the proposed method significantly reduces invalid alerts by around 86.37% and over 61.64% compared to a rule-based commercial WAF and several state-of-the-art methods, along with retaining a detection rate at 97.89% and a lower time overhead, which indicates that our approach can effectively mitigate alert fatigue from the detection perspective.
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Chaparro, Juan D., Cory Hussain, Jennifer A. Lee, Jessica Hehmeyer, Manjusri Nguyen, and Jeffrey Hoffman. "Reducing Interruptive Alert Burden Using Quality Improvement Methodology." Applied Clinical Informatics 11, no. 01 (January 2020): 046–58. http://dx.doi.org/10.1055/s-0039-3402757.
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Abstract Background Increased adoption of electronic health records (EHR) with integrated clinical decision support (CDS) systems has reduced some sources of error but has led to unintended consequences including alert fatigue. The “pop-up” or interruptive alert is often employed as it requires providers to acknowledge receipt of an alert by taking an action despite the potential negative effects of workflow interruption. We noted a persistent upward trend of interruptive alerts at our institution and increasing requests for new interruptive alerts. Objectives Using Institute for Healthcare Improvement (IHI) quality improvement (QI) methodology, the primary objective was to reduce the total volume of interruptive alerts received by providers. Methods We created an interactive dashboard for baseline alert data and to monitor frequency and outcomes of alerts as well as to prioritize interventions. A key driver diagram was developed with a specific aim to decrease the number of interruptive alerts from a baseline of 7,250 to 4,700 per week (35%) over 6 months. Interventions focused on the following key drivers: appropriate alert display within workflow, clear alert content, alert governance and standardization, user feedback regarding overrides, and respect for user knowledge. Results A total of 25 unique alerts accounted for 90% of the total interruptive alert volume. By focusing on these 25 alerts, we reduced interruptive alerts from 7,250 to 4,400 per week. Conclusion Systematic and structured improvements to interruptive alerts can lead to overall reduced interruptive alert burden. Using QI methods to prioritize our interventions allowed us to maximize our impact. Further evaluation should be done on the effects of reduced interruptive alerts on patient care outcomes, usability heuristics on cognitive burden, and direct feedback mechanisms on alert utility.
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Harrison, Andrew M., Charat Thongprayoon, Christopher A. Aakre, Jack Y. Jeng, Mikhail A. Dziadzko, Ognjen Gajic, Brian W. Pickering, and Vitaly Herasevich. "Comparison of methods of alert acknowledgement by critical care clinicians in the ICU setting." PeerJ 5 (March 14, 2017): e3083. http://dx.doi.org/10.7717/peerj.3083.
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Background Electronic Health Record (EHR)-based sepsis alert systems have failed to demonstrate improvements in clinically meaningful endpoints. However, the effect of implementation barriers on the success of new sepsis alert systems is rarely explored. Objective To test the hypothesis time to severe sepsis alert acknowledgement by critical care clinicians in the ICU setting would be reduced using an EHR-based alert acknowledgement system compared to a text paging-based system. Study Design In one arm of this simulation study, real alerts for patients in the medical ICU were delivered to critical care clinicians through the EHR. In the other arm, simulated alerts were delivered through text paging. The primary outcome was time to alert acknowledgement. The secondary outcomes were a structured, mixed quantitative/qualitative survey and informal group interview. Results The alert acknowledgement rate from the severe sepsis alert system was 3% (N = 148) and 51% (N = 156) from simulated severe sepsis alerts through traditional text paging. Time to alert acknowledgement from the severe sepsis alert system was median 274 min (N = 5) and median 2 min (N = 80) from text paging. The response rate from the EHR-based alert system was insufficient to compare primary measures. However, secondary measures revealed important barriers. Conclusion Alert fatigue, interruption, human error, and information overload are barriers to alert and simulation studies in the ICU setting.
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McGreevey, John D., Colleen P. Mallozzi, Randa M. Perkins, Eric Shelov, and Richard Schreiber. "Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems." Applied Clinical Informatics 11, no. 01 (January 2020): 001–12. http://dx.doi.org/10.1055/s-0039-3402715.
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Abstract Background Electronic health record (EHR) alert fatigue, while widely recognized as a concern nationally, lacks a corresponding comprehensive mitigation plan. Objectives The goal of this manuscript is to provide practical guidance to clinical informaticists and other health care leaders who are considering creating a program to manage EHR alerts. Methods This manuscript synthesizes several approaches and recommendations for better alert management derived from four U.S. health care institutions that presented their experiences and recommendations at the American Medical Informatics Association 2019 Clinical Informatics Conference in Atlanta, Georgia, United States. The assembled health care institution leaders represent academic, pediatric, community, and specialized care domains. We describe governance and management, structural concepts and components, and human–computer interactions with alerts, and make recommendations regarding these domains based on our experience supplemented with literature review. This paper focuses on alerts that impact bedside clinicians. Results The manuscript addresses the range of considerations relevant to alert management including a summary of the background literature about alerts, alert governance, alert metrics, starting an alert management program, approaches to evaluating alerts prior to deployment, and optimization of existing alerts. The manuscript includes examples of alert optimization successes at two of the represented institutions. In addition, we review limitations on the ability to evaluate alerts in the current state and identify opportunities for further scholarship. Conclusion Ultimately, alert management programs must strive to meet common goals of improving patient care, while at the same time decreasing the alert burden on clinicians. In so doing, organizations have an opportunity to promote the wellness of patients, clinicians, and EHRs themselves.
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Chaparro, Juan D., Jonathan M. Beus, Adam C. Dziorny, Philip A. Hagedorn, Sean Hernandez, Swaminathan Kandaswamy, Eric S. Kirkendall, Allison B. McCoy, Naveen Muthu, and Evan W. Orenstein. "Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts." Applied Clinical Informatics 13, no. 03 (May 2022): 560–68. http://dx.doi.org/10.1055/s-0042-1748856.
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AbstractInterruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.
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Tsai, Ming-Kuan. "Enhancing nuclear power plant safety via on-site mental fatigue management." Nuclear Technology and Radiation Protection 32, no. 1 (2017): 109–14. http://dx.doi.org/10.2298/ntrp1701109t.
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Nuclear incidents and accidents have occurred at various nuclear power plants. Since some of these incidents and accidents caused by human errors might be preventable, numerous researchers argue that fatigue management for on-site workers is the key, especially for mental fatigue. Thus, this study proposes an approach consisting of two mechanisms. A fatigue monitor could identify the mentally fatigued workers by detecting their brain wave rhythms through a brain-computer interface. For such workers, a fatigue alert would awaken them. If the status of the mentally fatigued workers becomes worse, based on a positioning technique (i.e., wireless networks), this mechanism would alert the nearby workers and managers to deal with this condition. The test results indicate that the proposed approach enhanced the capacity to examine the mentally fatigued workers, ensured the accuracy in locating these workers, and avoided possible nuclear incidents. This study is a useful reference for similar applications in the nuclear industry.
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Gouveia, William A. "Alert fatigue: A lesson relearned." American Journal of Health-System Pharmacy 67, no. 8 (April 15, 2010): 603–4. http://dx.doi.org/10.2146/ajhp100010.
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Cash, Jared J., and Stuart Levine. "Alert fatigue: A lesson relearned." American Journal of Health-System Pharmacy 67, no. 8 (April 15, 2010): 604. http://dx.doi.org/10.2146/ajhp100033.
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Simpao, Allan F., Luis M. Ahumada, Bimal R. Desai, Christopher P. Bonafide, Jorge A. Gálvez, Mohamed A. Rehman, Abbas F. Jawad, Krisha L. Palma, and Eric D. Shelov. "Optimization of drug–drug interaction alert rules in a pediatric hospital's electronic health record system using a visual analytics dashboard." Journal of the American Medical Informatics Association 22, no. 2 (October 15, 2014): 361–69. http://dx.doi.org/10.1136/amiajnl-2013-002538.
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Abstract Objective To develop and evaluate an electronic dashboard of hospital-wide electronic health record medication alerts for an alert fatigue reduction quality improvement project. Methods We used visual analytics software to develop the dashboard. We collaborated with the hospital-wide Clinical Decision Support committee to perform three interventions successively deactivating clinically irrelevant drug–drug interaction (DDI) alert rules. We analyzed the impact of the interventions on care providers’ and pharmacists’ alert and override rates using an interrupted time series framework with piecewise regression. Results We evaluated 2 391 880 medication alerts between January 31, 2011 and January 26, 2014. For pharmacists, the median alert rate prior to the first DDI deactivation was 58.74 alerts/100 orders (IQR 54.98–60.48) and 25.11 alerts/100 orders (IQR 23.45–26.57) following the three interventions (p<0.001). For providers, baseline median alert rate prior to the first round of DDI deactivation was 19.73 alerts/100 orders (IQR 18.66–20.24) and 15.11 alerts/100 orders (IQR 14.44–15.49) following the three interventions (p<0.001). In a subgroup analysis, we observed a decrease in pharmacists’ override rates for DDI alerts that were not modified in the system from a median of 93.06 overrides/100 alerts (IQR 91.96–94.33) to 85.68 overrides/100 alerts (IQR 84.29–87.15, p<0.001). The medication serious safety event rate decreased during the study period, and there were no serious safety events reported in association with the deactivated alert rules. Conclusions An alert dashboard facilitated safe rapid-cycle reductions in alert burden that were temporally associated with lower pharmacist override rates in a subgroup of DDIs not directly affected by the interventions; meanwhile, the pharmacists’ frequency of selecting the ‘cancel’ option increased. We hypothesize that reducing the alert burden enabled pharmacists to devote more attention to clinically relevant alerts.
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Chien, Shuo-Chen, Yen-Po (Harvey) Chin, Chang Ho Yoon, Md Mohaimenul Islam, Wen-Shan Jian, Chun-Kung Hsu, Chun-You Chen, Po-Han Chien, and Yu-Chuan (Jack) Li. "A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center: Comprehensive alert characteristic analysis." PLOS ONE 16, no. 2 (February 9, 2021): e0246597. http://dx.doi.org/10.1371/journal.pone.0246597.
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Background The collection and analysis of alert logs are necessary for hospital administrators to understand the types and distribution of alert categories within the organization and reduce alert fatigue. However, this is not readily available in most homegrown Computerized Physician Order Entry (CPOE) systems. Objective To present a novel method that can collect alert information from a homegrown CPOE system (at an academic medical center in Taiwan) and conduct a comprehensive analysis of the number of alerts triggered and alert characteristics. Methods An alert log collector was developed using the Golang programming language and was implemented to collect all triggered interruptive alerts from a homegrown CPOE system of a 726-bed academic medical center from November 2017 to June 2018. Two physicians categorized the alerts from the log collector as either clinical or non-clinical (administrative). Results Overall, 1,625,341 interruptive alerts were collected and classified into 1,474 different categories based on message content. The sum of the top 20, 50, and 100 categories of most frequently triggered alerts accounted for approximately 80, 90 and 97 percent of the total triggered alerts, respectively. Among alerts from the 100 most frequently triggered categories, 1,266,818 (80.2%) were administrative and 312,593 (19.8%) were clinical alerts. Conclusion We have successfully developed an alert log collector that can serve as an extended function to retrieve alerts from a homegrown CPOE system. The insight generated from the present study could also potentially bring value to hospital system designers and hospital administrators when redesigning their CPOE system.
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Dexheimer, Judith, Eric Kirkendall, Michal Kouril, Philip Hagedorn, Thomas Minich, Leo Duan, Monifa Mahdi, Rhonda Szczesniak, and S. Andrew Spooner. "The Effects of Medication Alerts on Prescriber Response in a Pediatric Hospital." Applied Clinical Informatics 08, no. 02 (April 2017): 491–501. http://dx.doi.org/10.4338/aci-2016-10-ra-0168.
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Summary Objective: More than 70% of hospitals in the United States have electronic health records (EHRs). Clinical decision support (CDS) presents clinicians with electronic alerts during the course of patient care; however, alert fatigue can influence a provider’s response to any EHR alert. The primary goal was to evaluate the effects of alert burden on user response to the alerts. Methods: We performed a retrospective study of medication alerts over a 24-month period (1/2013–12/2014) in a large pediatric academic medical center. The institutional review board approved this study. The primary outcome measure was alert salience, a measure of whether or not the prescriber took any corrective action on the order that generated an alert. We estimated the ideal number of alerts to maximize salience. Salience rates were examined for providers at each training level, by day of week, and time of day through logistic regressions. Results: While salience never exceeded 38%, 49 alerts/day were associated with maximal salience in our dataset. The time of day an order was placed was associated with alert salience (maximal salience 2am). The day of the week was also associated with alert salience (maximal salience on Wednesday). Provider role did not have an impact on salience. Conclusion: Alert burden plays a role in influencing provider response to medication alerts. An increased number of alerts a provider saw during a one-day period did not directly lead to decreased response to alerts. Given the multiple factors influencing the response to alerts, efforts focused solely on burden are not likely to be effective. Citation: Dexheimer JW, Kirkendall ES, Kouril M, Hagedorn PA, Minich T, Duan LL, Mahdi M, Szczesniak R, Spooner SA. The effects of medication alerts on prescriber response in a pediatric hospital. Appl Clin Inform 2017; 8: 491–501 https://doi.org/10.4338/ACI-2016-10-RA-0168
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Joglekar, Natasha N., Yatindra Patel, and Michelle S. Keller. "Evaluation of Clinical Decision Support to Reduce Sedative-Hypnotic Prescribing in Older Adults." Applied Clinical Informatics 12, no. 03 (May 2021): 436–44. http://dx.doi.org/10.1055/s-0041-1730030.
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Abstract Objective We sought to characterize the performance of inpatient and outpatient computerized clinical decision support (CDS) alerts aimed at reducing inappropriate benzodiazepine and nonbenzodiazepine sedative medication prescribing in older adults 18 months after implementation. Methods We reviewed the performance of two CDS alerts in the outpatient and inpatient settings in 2019. To examine the alerts' effectiveness, we analyzed metrics including overall alert adherence, provider-level adherence, and reasons for alert trigger and override. Results In 2019, we identified a total of 14,534 and 4,834 alerts triggered in the outpatient and inpatient settings, respectively. Providers followed only 1% of outpatient and 3% of inpatient alerts. Most alerts were ignored (68% outpatient and 60% inpatient), while providers selected to override the remaining alerts. In each setting, the top 2% of clinicians were responsible for approximately 25% of all ignored or overridden alerts. However, a small proportion of clinicians (2% outpatient and 4% inpatient) followed the alert at least half of the time and accounted for a disproportionally large fraction of the total followed alerts. Our analysis of the free-text comments revealed that many alerts were to continue outpatient prescriptions or for situational anxiety. Conclusion Our findings highlight the importance of evaluation of CDS performance after implementation. We found large variation in response to the inpatient and outpatient alerts, both with respect to follow and ignore rates. Reevaluating the alert design by providing decision support by indication may be more helpful and may reduce alert fatigue.
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Hussain, Mustafa I., Tera L. Reynolds, and Kai Zheng. "Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review." Journal of the American Medical Informatics Association 26, no. 10 (June 17, 2019): 1141–49. http://dx.doi.org/10.1093/jamia/ocz095.
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Abstract Objective Alert fatigue limits the effectiveness of medication safety alerts, a type of computerized clinical decision support (CDS). Researchers have suggested alternative interactive designs, as well as tailoring alerts to clinical roles. As examples, alerts may be tiered to convey risk, and certain alerts may be sent to pharmacists. We aimed to evaluate which variants elicit less alert fatigue. Materials and Methods We searched for articles published between 2007 and 2017 using the PubMed, Embase, CINAHL, and Cochrane databases. We included articles documenting peer-reviewed empirical research that described the interactive design of a CDS system, to which clinical role it was presented, and how often prescribers accepted the resultant advice. Next, we compared the acceptance rates of conventional CDS—presenting prescribers with interruptive modal dialogs (ie, “pop-ups”)—with alternative designs, such as role-tailored alerts. Results Of 1011 articles returned by the search, we included 39. We found different methods for measuring acceptance rates; these produced incomparable results. The most common type of CDS—in which modals interrupted prescribers—was accepted the least often. Tiering by risk, providing shortcuts for common corrections, requiring a reason to override, and tailoring CDS to match the roles of pharmacists and prescribers were the most common alternatives. Only 1 alternative appeared to increase prescriber acceptance: role tailoring. Possible reasons include the importance of etiquette in delivering advice, the cognitive benefits of delegation, and the difficulties of computing “relevance.” Conclusions Alert fatigue may be mitigated by redesigning the interactive behavior of CDS and tailoring CDS to clinical roles. Further research is needed to develop alternative designs, and to standardize measurement methods to enable meta-analyses.
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Chien, Shuo-Chen, Ya-Lin Chen, Chia-Hui Chien, Yen-Po Chin, Chang Ho Yoon, Chun-You Chen, Hsuan-Chia Yang, and Yu-Chuan (Jack) Li. "Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis." Healthcare 10, no. 4 (March 23, 2022): 601. http://dx.doi.org/10.3390/healthcare10040601.
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A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.
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Bryant, A. D., G. S. Fletcher, and T. H. Payne. "Drug interaction alert override rates in the Meaningful Use era." Applied Clinical Informatics 05, no. 03 (2014): 802–13. http://dx.doi.org/10.4338/aci-2013-12-ra-0103.
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SummaryBackground: Interruptive drug interaction alerts may reduce adverse drug events and are required for Stage I Meaningful Use attestation. For the last decade override rates have been very high. Despite their widespread use in commercial EHR systems, previously described interventions to improve alert frequency and acceptance have not been well studied.Objectives: (1) To measure override rates of inpatient medication alerts within a commercial clinical decision support system, and assess the impact of local customization efforts. (2) To compare override rates between drug-drug interaction and drug-allergy interaction alerts, between attending and resident physicians, and between public and academic hospitals. (3) To measure the correlation between physicians’ individual alert quantities and override rates as an indicator of potential alert fatigue.Methods: We retrospectively analyzed physician responses to drug-drug and drug-allergy interaction alerts, as generated by a common decision support product in a large teaching hospital system.Results: (1) Over four days, 461 different physicians entered 18,354 medication orders, resulting in 2,455 visible alerts; 2,280 alerts (93%) were overridden. (2) The drug-drug alert override rate was 95.1%, statistically higher than the rate for drug-allergy alerts (90.9%) (p < 0.001). There was no significant difference in override rates between attendings and residents, or between hospitals. (3) Physicians saw a mean of 1.3 alerts per day, and the number of alerts per physician was not significantly correlated with override rate (R2 = 0.03, p = 0.41).Conclusions: Despite intensive efforts to improve a commercial drug interaction alert system and to reduce alerting, override rates remain as high as reported over a decade ago. Alert fatigue does not seem to contribute. The results suggest the need to fundamentally question the premises of drug interaction alert systems.Citation: Bryant AD, Fletcher GS, Payne TH. Drug interaction alert override rates in the Meaningful Use era: No evidence of progress. Appl Clin Inf 2014; 5: 802–813http://dx.doi.org/10.4338/ACI-2013-12-RA-0103
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McRee, Griffith Russell. "Improved Detection and Response via Optimized Alerts: Usability Study." Journal of Cybersecurity and Privacy 2, no. 2 (May 31, 2022): 379–401. http://dx.doi.org/10.3390/jcp2020020.
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Security analysts working in the modern threat landscape face excessive events and alerts, a high volume of false-positive alerts, significant time constraints, innovative adversaries, and a staggering volume of unstructured data. Organizations thus risk data breach, loss of valuable human resources, reputational damage, and impact to revenue when excessive security alert volume and a lack of fidelity degrade detection services. This study examined tactics to reduce security data fatigue, increase detection accuracy, and enhance security analysts’ experience using security alert output generated via data science and machine learning models. The research determined if security analysts utilizing this security alert data perceive a statistically significant difference in usability between security alert output that is visualized versus that which is text-based. Security analysts benefit two-fold: the efficiency of results derived at scale via ML models, with the additional benefit of quality alert results derived from these same models. This quantitative, quasi-experimental, explanatory study conveys survey research performed to understand security analysts’ perceptions via the Technology Acceptance Model. The population studied was security analysts working in a defender capacity, analyzing security monitoring data and alerts. The more specific sample was security analysts and managers in Security Operation Center (SOC), Digital Forensic and Incident Response (DFIR), Detection and Response Team (DART), and Threat Intelligence (TI) roles. Data analysis indicated a significant difference in security analysts’ perception of usability in favor of visualized alert output over text alert output. The study’s results showed how organizations can more effectively combat external threats by emphasizing visual rather than textual alerts.
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Anggreini, Yunita Dwi, and Nurul Hidayah. "Beban Kerja dan Kelelahan Kerja dalam Pemberian Obat-Obatan High Alert: Implementasi Prosedur Double Check." Malahayati Nursing Journal 4, no. 10 (October 13, 2022): 2842–50. http://dx.doi.org/10.33024/mnj.v4i10.7873.
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ABSTRACT Introduction: The safety of drug administration, especially high alert drugs, is an indicator of patient safety. Several conditions such as workload and work fatigue can cause errors in medication administration.Purpose: This study aims to identify the relationship between workload and work fatigue with the application of double checking in the administration of high alert drugs in the intensive care unit at RSUD dr. Soedarso Pontianak.Method: This study uses an analiytic observational design with a cross-sectional approach. The research sample was 49 intensive nurses at RSUD dr. Soedarso Pontianak who was selected by accidental sampling technique. The instruments used in data collection consisted of a NASA TLX questionnaire to measure workload, a Fatigue Assessment Scale (FAS) questionnaire to measure work fatigue and a nurse implementation questionnaire in the implementation of double checking in the administration of high alert drugs.Result: The results showed that work fatigue was related to the application of double checking in the administration of high alert drugs in the intensive care unit, while workload was not related. Conclusion: There is a correlation between work fatigue and the implementation of double checking in the administration of high alert drugs in the intensive care unit. Keywords : double checking, high alert drug, work fatigue, workload ABSTRAK Pendahuluan: Keamanan pemberian obat terutama obat high alert merupakan indikator keselamatan pasien. Beberapa kondisi seperti beban kerja dan kelelahan kerja dapat menyebabkan terjadinya kesalahan dalam pemberian obat-obatan.Tujuan: Penelitian ini bertujuan untuk mengidentifikasi hubungan beban kerja dan kelelahan kerja dengan penerapan double checking dalam pemberian obat high alert di unit perawatan intensif di RSUD dr. Soedarso Pontianak.Metode Penelitian: Penelitian ini menggunakan desain observasional analitik dengan pendekatan crossectional. Sampel penelitian adalah 49 perawat intensif RSUD dr. Soedarso Pontianak yang dipilih secara accidental. Instrumen yang digunakan dalam pengumpulan data terdiri dari kuesioner NASA TLX untuk mengukur beban kerja, kuesioner Fatigue Assessment Scale (FAS) untuk mengukur kelelahan kerja dan kuesioner implementasi perawat dalam pelaksanaan double checking dalam pemberian obat-obatan high alert.Hasil: Hasil penelitian diketahui kelelahan kerja berhubungan dengan penerapan double checking dalam pemberian obat high alert di unit perawatan intensif sedangkan beban kerja tidak berhubungan.Kesimpulan: Ada korelasi antara kelelahan kerja dengan implementasi double checking dalam pemberian obat-obatan high alert di unit perawatan intensif. Kata kunci: Beban kerja, double checking, kelelahan kerja, obat high alert
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Meslin, S., W. Zheng, R. Day, E. Tay, and M. Baysari. "Evaluation of Clinical Relevance of Drug–Drug Interaction Alerts Prior to Implementation." Applied Clinical Informatics 09, no. 04 (October 2018): 849–55. http://dx.doi.org/10.1055/s-0038-1676039.
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Introduction Drug–drug interaction (DDI) alerts are often implemented in the hospital computerized provider order entry (CPOE) systems with limited evaluation. This increases the risk of prescribers experiencing too many irrelevant alerts, resulting in alert fatigue. In this study, we aimed to evaluate clinical relevance of alerts prior to implementation in CPOE using two common approaches: compendia and expert panel review. Methods After generating a list of hypothetical DDI alerts, that is, alerts that would have been triggered if DDI alerts were operational in the CPOE, we calculated the agreement between multiple drug interaction compendia with regards to the severity of these alerts. A subset of DDI alerts (n = 13), with associated patient information, were presented to an expert panel to reach a consensus on whether each alert should be included in the CPOE. Results There was poor agreement between compendia in their classifications of DDI severity (Krippendorff's α: 0.03; 95% confidence interval: –0.07 to 0.14). Only 10% of DDI alerts were classed as severe by all compendia. On the other hand, the panel reached consensus on 12 of the 13 alerts that were presented to them regarding whether they should be included in the CPOE. Conclusion Using an expert panel and allowing them to discuss their views openly likely resulted in high agreement on what alerts should be included in a CPOE system. Presenting alerts in the context of patient cases allowed panelists to identify the conditions under which alerts were clinically relevant. The poor agreement between compendia suggests that this methodology may not be ideal for the evaluation of DDI alerts. Performing preimplementation review of DDI alerts before they are enabled provides an opportunity to minimize the risk of alert fatigue before prescribers are exposed to false-positive alerts.
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Footracer, Katherine G. "Alert fatigue in electronic health records." Journal of the American Academy of Physician Assistants 28, no. 7 (July 2015): 41–42. http://dx.doi.org/10.1097/01.jaa.0000465221.04234.ca.
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Gadhiya, Kinjal, Edgar Zamora, Salim M. Saiyed, David Friedlander, and David C. Kaelber. "Drug Alert Experience and Salience during Medical Residency at Two Healthcare Institutions." Applied Clinical Informatics 12, no. 02 (March 2021): 355–61. http://dx.doi.org/10.1055/s-0041-1729167.
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Abstract Background Drug alerts are clinical decision support tools intended to prevent medication misadministration. In teaching hospitals, residents encounter the majority of the drug alerts while learning under variable workloads and responsibilities that may have an impact on drug-alert response rates. Objectives This study was aimed to explore drug-alert experience and salience among postgraduate year 1 (PGY-1), postgraduate year 2 (PGY-2), and postgraduate year 3 (PGY-3) internal medicine resident physicians at two different institutions. Methods Drug-alert information was queried from the electronic health record (EHR) for 47 internal medicine residents at the University of Pennsylvania Medical Center (UPMC) Pinnacle in Pennsylvania, and 79 internal medicine residents at the MetroHealth System (MHS) in Ohio from December 2018 through February 2019. Salience was defined as the percentage of drug alerts resulting in removal or modification of the triggering order. Comparisons were made across institutions, residency training year, and alert burden. Results A total of 126 residents were exposed to 52,624 alerts over a 3-month period. UPMC Pinnacle had 15,574 alerts with 47 residents and MHS had 37,050 alerts with 79 residents. At MHS, salience was 8.6% which was lower than UPMC Pinnacle with 15%. The relatively lower salience (42% lower) at MHS corresponded to a greater number of alerts-per-resident (41% higher) compared with UPMC Pinnacle. Overall, salience was 11.6% for PGY-1, 10.5% for PGY-2, and 8.9% for PGY-3 residents. Conclusion Our results are suggestive of long-term drug-alert desensitization during progressive residency training. A higher number of alerts-per-resident correlating with a lower salience suggests alert fatigue; however, other factors should also be considered including differences in workload and culture.
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Coelho, Chason J., Sunil D. Lakhiani, and Delmar R. “Trey” Morrison. "Staying Alert: Incorporating Human Fatigue in Risk Management." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 1819–23. http://dx.doi.org/10.1177/1071181319631012.
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This paper addresses the role of human fatigue in workplace safety and risk management. It is well known that fatigue can increase the likelihood of workplace injuries, but the systematic application of this knowledge in safety and risk management is less well known. This paper presents a risk-based method for addressing fatigue in safety and risk management processes. The method incorporates elements of a data-driven fatigue risk management system (FRMS). Specific issues include potential data sources for the FRMS and practical applications within existing safety management systems. Special attention is paid to the fatigue risk assessment, which mirrors a common safety risk assessment and affords systematic control of fatigue-related human error.
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Wu, Jian-Da, and Chia-Hsin Chang. "Driver Drowsiness Detection and Alert System Development Using Object Detection." Traitement du Signal 39, no. 2 (April 30, 2022): 493–99. http://dx.doi.org/10.18280/ts.390211.
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Fatigue driving is an invisible killer in car accidents and one of the main causes of traffic accidents. In order to reduce traffic accidents caused by driving fatigue, this research has developed a safety assist device to prevent such traffic accidents. In this research, a non-contact driver drowsiness detection and alert system is established in the vehicle cabin. The real-time facial image of the driver is obtained through the camera installed in front of the driver, and then the image is input to the NVIDIA Jetson TX2 embedded module. YOLO (You Only Look Once) object detection algorithm is used to detect the opening and closing of the driver's eyes, and by processing the eye area, to determine whether the driver is currently awake or fatigued while driving. The driver drowsiness detection and alert system established in this research can be applied to the vehicle interior environment to monitor the driving status. When the driver is fatigued, the system will simultaneously emit sound and light signals to promptly warn such dangerous driving behaviors. It can prevent the driver from continuing to drive when fatigued, and ensure driving safety.
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Russo, Elise, Hardeep Singh, and Megan Gregory. "Electronic Health Record Alert-Related Workload as a Predictor of Burnout in Primary Care Providers." Applied Clinical Informatics 08, no. 03 (2017): 686–97. http://dx.doi.org/10.4338/aci-2017-01-ra-0003.
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SummaryBackground: Electronic health records (EHRs) have been shown to increase physician workload. One EHR feature that contributes to increased workload is asynchronous alerts (also known as inbox notifications) related to test results, referral responses, medication refill requests, and messages from physicians and other health care professionals. This alert-related workload results in negative cognitive outcomes, but its effect on affective outcomes, such as burnout, has been under-studied.Objectives: To examine EHR alert-related workload (both objective and subjective) as a predictor of burnout in primary care providers (PCPs), in order to ultimately inform interventions aimed at reducing burnout due to alert workload.Methods: A cross-sectional questionnaire and focus group of 16 PCPs at a large medical center in the southern United States.Results: Subjective, but not objective, alert workload was related to two of the three dimensions of burnout, including physical fatigue (p = 0.02) and cognitive weariness (p = 0.04), when controlling for organizational tenure. To reduce alert workload and subsequent burnout, participants indicated a desire to have protected time for alert management, fewer unnecessary alerts, and improvements to the EHR system.Conclusions: Burnout associated with alert workload may be in part due to subjective differences at an individual level, and not solely a function of the objective work environment. This suggests the need for both individual and organizational-level interventions to improve alert workload and subsequent burnout. Additional research should confirm these findings in larger, more representative samples.Citation: Gregory ME, Russo E, Singh H. Electronic health record alert-related workload as a predictor of burnout in primary care providers. Appl Clin Inform 2017; 8: 686–697 https://doi.org/10.4338/ACI-2017-01-RA-0003
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Baysari, Melissa Therese, Wu Yi Zheng, Bethany Van Dort, Hannah Reid-Anderson, Mihaela Gronski, and Eliza Kenny. "A Late Attempt to Involve End Users in the Design of Medication-Related Alerts: Survey Study." Journal of Medical Internet Research 22, no. 3 (March 13, 2020): e14855. http://dx.doi.org/10.2196/14855.
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Background When users of electronic medical records (EMRs) are presented with large numbers of irrelevant computerized alerts, they experience alert fatigue, begin to ignore alert information, and override alerts without processing or heeding alert recommendations. Anecdotally, doctors at our study site were dissatisfied with the medication-related alerts being generated, both in terms of volume being experienced and clinical relevance. Objective This study aimed to involve end users in the redesign of medication-related alerts in a hospital EMR, 4 years post implementation. Methods This work was undertaken at a private not-for-profit teaching hospital in Sydney, Australia. Since EMR implementation in 2015, the organization elected to implement all medication-related alert types available in the system for prescribers: allergy and intolerance alerts, therapeutic duplication alerts, pregnancy alerts, and drug-drug interaction alerts. The EMR included no medication administration alerts for nurses. To obtain feedback on current alerts and suggestions for redesign, a Web-based survey was distributed to all doctors and nurses at the site via hospital mailing lists. Results Despite a general dissatisfaction with alerts, very few end users completed the survey. In total, only 3.37% (36/1066) of doctors and 14.5% (60/411) of nurses took part. Approximately 90% (30/33) of doctors who responded held the view that too many alerts were triggered in the EMR. Doctors suggested that most alerts be removed and that alerts be more specific and less sensitive. In contrast, 97% (58/60) of the nurse respondents indicated that they would like to receive medication administration alerts in the EMR. Most nurses indicated that they would like to receive all the alert types available at all severity levels. Conclusions Attempting to engage with end users several years post implementation was challenging. Involving users so late in the implementation process may lead to clinicians viewing the provision of feedback to be futile. Seeking user feedback on usefulness, volume, and design of alerts is extremely valuable; however, we suggest this is undertaken early, preferably before system implementation.
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Poly, Tahmina Nasrin, Md Mohaimenul Islam, Hsuan-Chia Yang, and Yu-Chuan (Jack) Li. "Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review." JMIR Medical Informatics 8, no. 7 (July 20, 2020): e15653. http://dx.doi.org/10.2196/15653.
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Background The clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about patient safety and quality of care. Objective The aim of this study was to conduct a systematic review to examine the override rate, the reasons for the alert override at the time of prescribing, and evaluate the appropriateness of overrides. Methods We searched electronic databases, including Google Scholar, PubMed, Embase, Scopus, and Web of Science, without language restrictions between January 1, 2000 and March 31, 2019. Two authors independently extracted data and crosschecked the extraction to avoid errors. The quality of the included studies was examined following Cochrane guidelines. Results We included 23 articles in our systematic review. The range of average override alerts was 46.2%-96.2%. An average of 29.4%-100% of the overrides alerts were classified as appropriate, and the rate of appropriateness varied according to the alert type (drug-allergy interaction 63.4%-100%, drug-drug interaction 0%-95%, dose 43.9%-88.8%, geriatric 14.3%-57%, renal 27%-87.5%). The interrater reliability for the assessment of override alerts appropriateness was excellent (kappa=0.79-0.97). The most common reasons given for the override were “will monitor” and “patients have tolerated before.” Conclusions The findings of our study show that alert override rates are high, and certain categories of overrides such as drug-drug interaction, renal, and geriatric were classified as inappropriate. Nevertheless, large proportions of drug duplication, drug-allergy, and formulary alerts were appropriate, suggesting that these groups of alerts can be primary targets to revise and update the system for reducing alert fatigue. Future efforts should also focus on optimizing alert types, providing clear information, and explaining the rationale of the alert so that essential alerts are not inappropriately overridden.
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Kane, Elizabeth, Alice Lippitt, Octavio Aragon Cuevas, and Andrea Gill. "P17 Dose range checking (DRC) in paediatric electronic prescribing; an effective tool in reducing prescribing errors?" Archives of Disease in Childhood 107, no. 5 (April 20, 2022): e25.18-e25. http://dx.doi.org/10.1136/archdischild-2022-nppg.25.
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AimDRC software aims to reduce dosing errors,1 however, it has been linked to ‘alert fatigue’, a phenomenon which causes prescribers to potentially override alerts despite being clinically relevant.2 3 The project aim is to determine the effectiveness of the DRC software implemented into the electronic prescribing software used in a paediatric tertiary hospital.MethodA retrospective clinical audit was undertaken to investigate the DRC alerts produced during July and August 2020. The DRC alert subtypes and the top 10 drugs that produced alerts were described. Alerts generated due to a ‘missing value’ (height, weight, or dose unit) were counted but not analysed further. Dose-based alerts were clinically screened to ascertain whether they were appropriately or inappropriately overridden. This involved considering whether the DRC recommendations differed from BNFc recommendations, local in-house guidance, or deviations occurred due to individual patient idiosyncrasies or specialist prescribing. Data from dose-based alerts was interrogated to determine whether increasing the acceptable dose range limit from 5% to 10% would have significantly reduced the number of alerts fired. Alerts that were inappropriately overridden and resulted in a medication error were categorised by severity using the EQUIP study scoring tool.3Results1778 alerts generated in July and August 2020 were analysed. 48% (n=846) of those alerts were produced due to a ‘missing value’. The DRC software did not recognise whether the alerted drug was recorded at the same dose in the patients’ ‘home medications’ (verified drug history) or whether in-house dose guidance was used. If recognised, the number of alerts would have reduced by 21.4% (n=200). Conversely, increasing the DRC acceptable dose range from 5 to 10% would have reduced the number of alerts only by 4.5% (n=42).Overall, 741 alerts were clinically screened. 95% (n=704) of these were not actioned by prescribers. Of those alerts 5.2% (n=37) should have been acted upon and this led to medication errors. 35% (n=13) of the errors were significant and 22% (n=8) were serious according to the EQUIP study tool. 62.5% (n=5) of serious medication errors involved a ‘Narrow Therapeutic Index’ drug, such as gentamicin and liposomal amphotericin. 38% (n=5) of significant errors related to no prescribed maximum frequency for intravenous ondansetron.ConclusionDRC systems are effective tools for preventing prescribing errors,1 but it is concerning to see that several significant and serious prescribing errors occurred despite an alert generation. This potentially suggests that alert fatigue may counteract the error preventing effects of DRC alerts. Therefore, further refinement of DRC systems is required to reduce alert fatigue. Unfortunately, increasing the acceptable dose range limits from 5 to 10% does not appear to be a simple way of sorting this problem. Removing ‘missing value’ alerts would significantly reduce the number of alerts generated. Including in-house guidelines alongside BNFc dose recommendations into the DRC software would also reduce unnecessary alerts.ReferencesSahota H, Hughes P, Barrass C, et al. Delivering electronic prescribing and medicines administration in challenging areas such as paediatrics and maternity at King’s College Hospital NHS Foundation Trust. Clinical Medicine Journal [Internet] 2015;15:s5. Available at: https://www.rcpjournals.org/content/clinmedicine/15/Suppl_3/s5Neame M, Moss J, Saez Dominguez J, et al. The impact of paediatric dose range checking software. European Journal of Hospital Pharmacy [Internet] 2020. Available at: https://ejhp.bmj.com/content/early/2020/03/31/ejhpharm-2020-002244Dornan T, Ashcroft D, Heathfield H, et al. An in-depth investigation into causes of prescribing errors by foundation trainees in relation to their medical education. EQUIP study final report. December 2009. Available at: https://www.gmc-uk.org/-/media/documents/FINAL_Report_prevalence_and_causes_of_prescribing_errors.pdf_2893 5150.pdf
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Rush, Jess L., Jamil Ibrahim, Kenneth Saul, and Robert T. Brodell. "Improving Patient Safety by Combating Alert Fatigue." Journal of Graduate Medical Education 8, no. 4 (October 1, 2016): 620–21. http://dx.doi.org/10.4300/jgme-d-16-00186.1.
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Schneider, Mary Ellen. "Joint Commission Issues Alert on ‘Alarm Fatigue’." Caring for the Ages 14, no. 8 (August 2013): 7. http://dx.doi.org/10.1016/j.carage.2013.07.010.
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van der Sijs, I. H. "Alert fatigue – an overdose of drug information?" Clinical Therapeutics 37, no. 8 (August 2015): e169. http://dx.doi.org/10.1016/j.clinthera.2015.05.482.
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Downing, N., John Shepard, Weihan Chu, Julia Tam, Alexander Wessels, Ron Li, Brian Dietrich, et al. "Validation of test performance and clinical time zero for an electronic health record embedded severe sepsis alert." Applied Clinical Informatics 07, no. 02 (April 2016): 560–72. http://dx.doi.org/10.4338/aci-2015-11-ra-0159.
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SummaryIncreasing use of EHRs has generated interest in the potential of computerized clinical decision support to improve treatment of sepsis. Electronic sepsis alerts have had mixed results due to poor test characteristics, the inability to detect sepsis in a timely fashion and the use of outside software limiting widespread adoption. We describe the development, evaluation and validation of an accurate and timely severe sepsis alert with the potential to impact sepsis management.To develop, evaluate, and validate an accurate and timely severe sepsis alert embedded in a commercial EHR.he sepsis alert was developed by identifying the most common severe sepsis criteria among a cohort of patients with ICD 9 codes indicating a diagnosis of sepsis. This alert requires criteria in three categories: indicators of a systemic inflammatory response, evidence of suspected infection from physician orders, and markers of organ dysfunction. Chart review was used to evaluate test performance and the ability to detect clinical time zero, the point in time when a patient develops severe sepsis.Two physicians reviewed 100 positive cases and 75 negative cases. Based on this review, sensitivity was 74.5%, specificity was 86.0%, the positive predictive value was 50.3%, and the negative predictive value was 94.7%. The most common source of end-organ dysfunction was MAP less than 70 mm/Hg (59%). The alert was triggered at clinical time zero in 41% of cases and within three hours in 53.6% of cases. 96% of alerts triggered before a manual nurse screen.We are the first to report the time between a sepsis alert and physician chart-review clinical time zero. Incorporating physician orders in the alert criteria improves specificity while maintaining sensitivity, which is important to reduce alert fatigue. By leveraging standard EHR functionality, this alert could be implemented by other healthcare systems.
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Cai, Qing, Zhong-Ke Gao, Yu-Xuan Yang, Wei-Dong Dang, and Celso Grebogi. "Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection." International Journal of Neural Systems 29, no. 05 (May 29, 2019): 1850057. http://dx.doi.org/10.1142/s0129065718500570.
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Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we, in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows in not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use the method to construct brain networks from EEG signals recorded from different subjects performing simulated driving tasks under alert and fatigue driving states. We then employ clustering coefficient, global efficiency and characteristic path length to characterize the topological structure of the networks generated from different brain states. In addition, we combine average edge overlap with the network measures to distinguish alert and mental fatigue states. The high-accurate classification results clearly demonstrate and validate the efficacy of our multiplex LPHVG method for the fatigue detection from EEG signals. Furthermore, our findings show a significant increase of the clustering coefficient as the brain evolves from alert state to mental fatigue state, which yields novel insights into the brain behavior associated with fatigue driving.
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Poly, Tahmina Nasrin, Md Mohaimenul Islam, Muhammad Solihuddin Muhtar, Hsuan-Chia Yang, Phung Anh (Alex) Nguyen, and Yu-Chuan (Jack) Li. "Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication–Related Clinical Decision Support System: Model Development and Validation." JMIR Medical Informatics 8, no. 11 (November 19, 2020): e19489. http://dx.doi.org/10.2196/19489.
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Background Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far. Objective Our objective was to develop machine learning prediction models to predict physicians’ responses in order to reduce alert fatigue from disease medication–related CDSSs. Methods We collected data from a disease medication–related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets. Results A total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively. Conclusions In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication–related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.
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Yu, Denny, Marian Obuseh, and Poching DeLaurentis. "Quantifying the Impact of Infusion Alerts and Alarms on Nursing Workflows: A Retrospective Analysis." Applied Clinical Informatics 12, no. 03 (May 2021): 528–38. http://dx.doi.org/10.1055/s-0041-1730031.
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Abstract Background Smart infusion pumps affect workflows as they add alerts and alarms in an information-rich clinical environment where alarm fatigue is already a major concern. An analytic approach is needed to quantify the impact of these alerts and alarms on nursing workflows and patient safety. Objectives To analyze a detailed infusion dataset from a smart infusion pump system and identify contributing factors for infusion programming alerts, operational alarms, and alarm resolution times. Methods We analyzed detailed infusion pump data across four hospitals in a health system for up to 1 year. The prevalence of alerts and alarms was grouped by infusion type and a selected list of 32 high-alert medications (HAMs). Logistic regression was used to explore the relationship between a set of risk factors and the occurrence of alerts and alarms. We used nonparametric tests to explore the relationship between alarm resolution times and a subset of predictor variables. Results The study dataset included 745,641 unique infusions with a total of 3,231,300 infusion events. Overall, 28.7% of all unique infusions had at least one operational alarm, and 2.1% of all unique infusions had at least one programming alert. Alarms averaged two per infusion, whereas at least one alert happened in every 48 unique infusions. Eight percent of alarms took over 4 minutes to resolve. Intravenous fluid infusions had the highest rate of error-state occurrence. HAMs had 1.64 more odds for alerts than the rest of the infusions. On average, HAMs had a higher alert rate than maintenance fluids. Conclusion Infusion pump alerts and alarms impact clinical care, as alerts and alarms by design interrupt clinical workflow. Our study showcases how hospital system leadership teams can leverage infusion pump informatics to prioritize quality improvement and patient safety initiatives pertaining to infusion practices.
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Yao, Ying, Xiaohua Zhao, Hongji Du, Yunlong Zhang, Guohui Zhang, and Jian Rong. "Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study." International Journal of Environmental Research and Public Health 16, no. 11 (May 31, 2019): 1935. http://dx.doi.org/10.3390/ijerph16111935.
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It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of “alert” and “abnormal” driving was 90.9%, and that of “fatigued” and “drunk” driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states.
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Buckley, Mitchell S., Jeffrey R. Rasmussen, Dale S. Bikin, Emily C. Richards, Andrew J. Berry, Mark A. Culver, Ryan M. Rivosecchi, and Sandra L. Kane-Gill. "Trigger alerts associated with laboratory abnormalities on identifying potentially preventable adverse drug events in the intensive care unit and general ward." Therapeutic Advances in Drug Safety 9, no. 4 (March 1, 2018): 207–17. http://dx.doi.org/10.1177/2042098618760995.
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Background Medication safety strategies involving trigger alerts have demonstrated potential in identifying drug-related hazardous conditions (DRHCs) and preventing adverse drug events in hospitalized patients. However, trigger alert effectiveness between intensive care unit (ICU) and general ward patients remains unknown. The objective was to investigate trigger alert performance in accurately identifying DRHCs associated with laboratory abnormalities in ICU and non-ICU settings. Methods This retrospective, observational study was conducted at a university hospital over a 1-year period involving 20 unique trigger alerts aimed at identifying possible drug-induced laboratory abnormalities. The primary outcome was to determine the positive predictive value (PPV) in distinguishing drug-induced abnormal laboratory values using trigger alerts in critically ill and general ward patients. Aberrant lab values attributed to medications without resulting in an actual adverse event ensuing were categorized as a DRHC. Results A total of 634 patients involving 870 trigger alerts were included. The distribution of trigger alerts generated occurred more commonly in general ward patients (59.8%) than those in the ICU (40.2%). The overall PPV in detecting a DRHC in all hospitalized patients was 0.29, while the PPV in non-ICU patients (0.31) was significantly higher than the critically ill (0.25) ( p = 0.03). However, the rate of DRHCs was significantly higher in the ICU than the general ward (7.49 versus 0.87 events per 1000 patient days, respectively, p < 0.0001). Although most DRHCs were considered mild or moderate in severity, more serious and life-threatening DRHCs occurred in the ICU compared with the general ward (39.8% versus 12.4%, respectively, p < 0.001). Conclusions Overall, most trigger alerts performed poorly in detecting DRHCs irrespective of patient care setting. Continuous process improvement practices should be applied to trigger alert performance to improve clinician time efficiency and minimize alert fatigue.
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Carroll, Aaron E. "Averting Alert Fatigue to Prevent Adverse Drug Reactions." JAMA 322, no. 7 (August 20, 2019): 601. http://dx.doi.org/10.1001/jama.2019.11710.
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Kommey, Benjamin, Seth Djanie Kotey, and Andrew Selasi Agbemenu. "Drowsing Driver Alert System for Commercial Vehicles." Computer Engineering and Applications Journal 8, no. 3 (September 24, 2019): 173–79. http://dx.doi.org/10.18495/comengapp.v8i3.308.
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A number of accidents on our roads are caused by driver fatigue or drowsiness. Human fatalities as a result of driver drowsiness has been a major challenge for road safety bodies worldwide. Various road safety campaign messages have been put out to discourage drivers from driving whilst tired, but the problem still persists. Different technologies have been proposed over the years, but most seem to be too expensive to implement on a large scale. We present an inexpensive drowsing driver alert system in this paper. The system, known as Drowsing Driver Alert System (DDAS) is a smart system intended to effectively keep commercial drivers alert when driving. The system is able to detect when a driver is drowsy and alert him/her in real-time to prevent a potential accident. Using a camera, the eyes of the driver are monitored continuously whiles driving and analyzed to determine if they are shut or the blink rate is not normal. Two stages of alerts are given if the driver is determined to be drowsy. Log files of activities performed by the system are also saved to an external storage device to enable further analysis later.
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Saiyed, Salim M., Katherine R. Davis, and David C. Kaelber. "Differences, Opportunities, and Strategies in Drug Alert Optimization—Experiences of Two Different Integrated Health Care Systems." Applied Clinical Informatics 10, no. 05 (October 2019): 777–82. http://dx.doi.org/10.1055/s-0039-1697596.
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Abstract Background Concerns about the number of automated medication alerts issued within the electronic health record (EHR), and the subsequent potential for alarm fatigue, led us to examine strategies and methods to optimize the configuration of our drug alerts. Objectives This article reports on comprehensive drug alerting rates and develops strategies across two different health care systems to reduce the number of drug alerts. Methods Standardized reports compared drug alert rates between the two systems, among 13 categories of drug alerts. Both health care systems made modifications to the out-of-box alerts available from their EHR and drug information vendors, focusing on system-wide approaches, when relevant, while performing more drug-specific changes when necessary. Results Drug alerting rates even after initial optimization were 38 alerts and 51 alerts per 100 drug orders, respectively. Eight principles were identified and developed to reflect the themes in the implementation and optimization of drug alerting. Conclusion A team-based, systematic approach to optimizing drug-alerting strategies can reduce the number of drug alerts, but alert rates still remain high. In addition to strategic principles, additional tactical guidelines and recommendations need to be developed to enhance out-of-the-box clinical decision support for drug alerts.