Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: A computerized physician order entry (CPOE) system with embedded clinical decision support can reduce medication errors in hospitals, but might increase the time taken to generate orders.
Aims: We aimed to quantify the effects of temporal (month, day of week, hour of shift) and other factors (grade of doctor, prior experience with the system, alert characteristics, and shift type) on the time taken to generate a prescription order.
Setting: A large university teaching hospital using a locally developed CPOE system with an extensive audit database.
Design: We retrospectively analyzed prescription orders from the audit database between August 2011 and July 2012.
Results: The geometric mean time taken to generate a prescription order within the CPOE system was 11.75 s (95% CI 11.72 to 11.78). Time to prescribe was most affected by the display of high-level (24.59 s (24.43 to 24.76); p<0.001) or previously unseen (18.87 s (18.78 to 18.96); p<0.001) alerts. Prescribers took significantly less time at weekends (11.29 s (11.23 to 11.35)) than on weekdays (11.88 s (11.84 to 11.91); p<0.001), in the first (11.25 s (11.16 to 11.34); p<0.001) and final (11.56 s (11.47 to 11.66); p<0.001) hour of their shifts, and after the first month of using the system.
Conclusions: The display of alerts, prescribing experience, system familiarity, and environment all affect the time taken to generate a prescription order. Our study reinforces the need for appropriate alerts to be presented to individuals at an appropriate place in the workflow, in order to improve prescribing efficiency.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433371 | PMC |
http://dx.doi.org/10.1136/amiajnl-2014-002822 | DOI Listing |
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