This paper is in the following e-collection/theme issue:

Artificial Intelligence (1400) Alarm Fatigue (20) Safety and Error Prevention in Health (206)

Published on in Vol 21, No 11 (2019): November

Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation

Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation

Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation

Authors of this article:

Chrystinne Oliveira Fernandes1 Author Orcid Image ;   Simon Miles2 Author Orcid Image ;   Carlos José Pereira De Lucena1 Author Orcid Image ;   Donald Cowan3 Author Orcid Image
Article Authors Cited by (26) Tweetations (30) Metrics

Journals

  1. Duclos C, Bouaud J. Pragmatic Considerations on Clinical Decision Support from the 2019 Literature. Yearbook of Medical Informatics 2020;29(01):155 View
  2. Fernandes C, Miles S, Lucena C. Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation. JMIR Medical Informatics 2020;8(5):e15407 View
  3. Sbaffi L, Walton J, Blenkinsopp J, Walton G. Information Overload in Emergency Medicine Physicians: A Multisite Case Study Exploring the Causes, Impact, and Solutions in Four North England National Health Service Trusts. Journal of Medical Internet Research 2020;22(7):e19126 View
  4. Au-Yeung W, Sevakula R, Sahani A, Kassab M, Boyer R, Isselbacher E, Armoundas A. Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm. European Heart Journal - Digital Health 2021;2(3):437 View
  5. Kosa G, Morozov O, Lehmann A, Pargger H, Marsch S, Hunziker P. Robots and Intelligent Medical Devices in the Intensive Care Unit: Vision, State of the Art, and Economic Analysis. IEEE Transactions on Medical Robotics and Bionics 2023;5(1):2 View
  6. Sowan A, Staggers N, Reed C, Austin T, Chen Q, Xu S, Lopez E. State of Science in Alarm System Safety: Implications for Researchers, Vendors, and Clinical Leaders. Biomedical Instrumentation & Technology 2022;56(1):19 View
  7. Alsuyayfi S, Alanazi A. Impact of clinical alarms on patient safety from nurses’ perspective. Informatics in Medicine Unlocked 2022;32:101047 View
  8. Bollepalli S, Sevakula R, Au‐Yeung W, Kassab M, Merchant F, Bazoukis G, Boyer R, Isselbacher E, Armoundas A. Real‐Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks. Journal of the American Heart Association 2021;10(23) View
  9. Kim D, Jin B. Development and Comparative Performance of Physiologic Monitoring Strategies in the Emergency Department. JAMA Network Open 2022;5(9):e2233712 View
  10. Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. Journal of Medical Internet Research 2021;23(11):e26522 View
  11. Flaherty A. The Chemistry Teaching Laboratory: A Sensory Overload Vortex for Students and Instructors?. Journal of Chemical Education 2022;99(4):1775 View
  12. Loeb R. Incremental advances will improve medical device alarm sounds. British Journal of Anaesthesia 2023;130(4):401 View
  13. Ding S, Huang X, Sun R, Yang L, Yang X, Li X, Liu J, Yang H, Zhou H, Huang X, Su F, Shu L, Zheng X, Wang X. The relationship between alarm fatigue and burnout among critical care nurses: A cross‐sectional study. Nursing in Critical Care 2023;28(6):940 View
  14. Charan G. Alarm fatigue: Can Indian intensive care unit nurses keep up with the noise and maintain their edge?. Muller Journal of Medical Sciences and Research 2023;14(1):122 View
  15. Cánovas-Segura B, Morales A, Juarez J, Campos M. Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework. Journal of Biomedical Informatics 2023;143:104397 View
  16. Gheysen F, Rex S. Artificial intelligence in anesthesiology. Acta Anaesthesiologica Belgica 2023;74(3):185 View
  17. Priporas C, Hussain S, Khaneja S, Rahman H. Technology distraction in Generation Z: The effects on consumer responses, sensory overload, and discomfort. International Journal of Information Management 2024;75:102751 View
  18. Sudarshan V, Seider W, Patel A, Oktem U, Arbogast J. Alarm rationalization and dynamic risk analyses for rare abnormal events. Computers & Chemical Engineering 2024;184:108633 View
  19. Corti K. Understanding the Micro, Meso, and Macro Worlds of User Experience. Research-Technology Management 2024;67(2):24 View
  20. Ranjbar A, Mork E, Ravn J, Brøgger H, Myrseth P, Østrem H, Hallock H. Managing Risk and Quality of AI in Healthcare: Are Hospitals Ready for Implementation?. Risk Management and Healthcare Policy 2024;Volume 17:877 View
  21. Kissler M, Porter S, Knees M, Kissler K, Keniston A, Burden M. Attention Among Health Care Professionals. Annals of Internal Medicine 2024;177(7):941 View
  22. Thakrar S, Kim C, Suarez-Winowiski O, Navarrete S, Potter K, Prasanna P, Graham J, Diallo M, Lahaye L, Coombs A. A Process-Based Standardization of an Intraoperative Escalation Protocol in Anesthesiology. Anesthesia & Analgesia 2024;139(4):851 View
  23. van Voorst R. Challenges and Limitations of Human Oversight in Ethical Artificial Intelligence Implementation in Health Care: Balancing Digital Literacy and Professional Strain. Mayo Clinic Proceedings: Digital Health 2024;2(4):559 View
  24. Arini M, Prakoso D, Suryanto F, Dewi A, Basha M. NAVIGATING BARRIERS: THE NEED FOR INTEGRATED HEALTH INFORMATION SYSTEM FOR TUBERCULOSIS-DIABETES CO-SCREENING. Jurnal Administrasi Kesehatan Indonesia 2024;12(1):162 View

Books/Policy Documents

  1. Ganesh L. Data Analytics and AI for Quantitative Risk Assessment and Financial Computation. View