TY - JOUR
T1 - A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center
T2 - Comprehensive alert characteristic analysis
AU - Chien, Shuo Chen
AU - Chin, Yen Po Harvey
AU - Yoon, Chang Ho
AU - Islam, Md Mohaimenul
AU - Jian, Wen Shan
AU - Hsu, Chun Kung
AU - Chen, Chun You
AU - Chien, Po Han
AU - Li, Yu Chuan Jack
N1 - Funding Information:
This research is funded in part by the Ministry of Education (MOE) under grant MOE 109-6604-001-400.
Publisher Copyright:
© 2021 Chien et al.
PY - 2021/2
Y1 - 2021/2
N2 - 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.
AB - 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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U2 - 10.1371/journal.pone.0246597
DO - 10.1371/journal.pone.0246597
M3 - Article
C2 - 33561178
AN - SCOPUS:85101014099
SN - 1932-6203
VL - 16
JO - PLoS ONE
JF - PLoS ONE
IS - 2 February
M1 - e0246597
ER -