Shuo-Chen Chien1,2, Yen-Po Harvey Chin1,3,4, Chang Ho Yoon3, Md Mohaimenul Islam1,2, Wen-Shan Jian5, Chun-Kung Hsu6, Chun-You Chen1,2,6,7, Po-Han Chien8, Yu-Chuan Jack Li1,2,9. 1. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan. 2. International Center for Health Information and Technology, College of Medical science and Technology, Taipei Medical University, Taipei, Taiwan. 3. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America. 4. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America. 5. School of Health Care Administration, Taipei Medical University, Taipei, Taiwan. 6. Information Technology Office, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan. 7. Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan. 8. Department of Business Administration, National Taiwan University, Taipei, Taiwan. 9. Department of Dermatology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
Abstract
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.
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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