BACKGROUND: Asynchronous electronic health record (EHR)-based alerts used to notify practitioners via an inbox-like format rather than through synchronous computer "pop-up" messages are understudied. Our objective was to create an asynchronous alert taxonomy and measure the impact of different alert types on practitioner workload. METHODS: We quantified and categorized asynchronous alerts according to the information they conveyed and conducted a time-motion analysis to assess practitioner workload. We reviewed alert information transmitted to all 47 primary care practitioners (PCPs) at a large, tertiary care Veterans Affairs facility over 4 evenly spaced 28-day periods. An interdisciplinary team used content analysis to categorize alerts according to their conveyed information. We then created an alert taxonomy and used it to calculate the mean number of alerts of each type PCPs received each day. We conducted a time-motion study of 26 PCPs while they processed their alerts. We used these data to estimate the uninterrupted time practitioners spend processing alerts each day. RESULTS: We extracted 295,792 asynchronously generated alerts and created a taxonomy of 33 alert types categorized under 6 major categories: Test Results, Referrals, Note-Based Communication, Order Status, Patient Status Changes, and Incomplete Task Reminders. PCPs received a mean of 56.4 alerts/day containing new information. Based on 749 observed alert processing episodes, practitioners spent an estimated average of 49 minutes/day processing their alerts. CONCLUSIONS: PCPs receive a large number of EHR-based asynchronous alerts daily and spend significant time processing them. The utility of transmitting large quantities and varieties of alerts to PCPs warrants further investigation. Published by Elsevier Inc.
BACKGROUND: Asynchronous electronic health record (EHR)-based alerts used to notify practitioners via an inbox-like format rather than through synchronous computer "pop-up" messages are understudied. Our objective was to create an asynchronous alert taxonomy and measure the impact of different alert types on practitioner workload. METHODS: We quantified and categorized asynchronous alerts according to the information they conveyed and conducted a time-motion analysis to assess practitioner workload. We reviewed alert information transmitted to all 47 primary care practitioners (PCPs) at a large, tertiary care Veterans Affairs facility over 4 evenly spaced 28-day periods. An interdisciplinary team used content analysis to categorize alerts according to their conveyed information. We then created an alert taxonomy and used it to calculate the mean number of alerts of each type PCPs received each day. We conducted a time-motion study of 26 PCPs while they processed their alerts. We used these data to estimate the uninterrupted time practitioners spend processing alerts each day. RESULTS: We extracted 295,792 asynchronously generated alerts and created a taxonomy of 33 alert types categorized under 6 major categories: Test Results, Referrals, Note-Based Communication, Order Status, Patient Status Changes, and Incomplete Task Reminders. PCPs received a mean of 56.4 alerts/day containing new information. Based on 749 observed alert processing episodes, practitioners spent an estimated average of 49 minutes/day processing their alerts. CONCLUSIONS: PCPs receive a large number of EHR-based asynchronous alerts daily and spend significant time processing them. The utility of transmitting large quantities and varieties of alerts to PCPs warrants further investigation. Published by Elsevier Inc.
Authors: Shailaja Menon; Daniel R Murphy; Hardeep Singh; Ashley N D Meyer; Dean F Sittig Journal: Appl Clin Inform Date: 2016-06-22 Impact factor: 2.342
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Authors: Sarah L Cutrona; Hassan Fouayzi; Laura Burns; Rajani S Sadasivam; Kathleen M Mazor; Jerry H Gurwitz; Lawrence Garber; Devi Sundaresan; Thomas K Houston; Terry S Field Journal: J Gen Intern Med Date: 2017-08-14 Impact factor: 5.128
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