Jamie J Coleman1, James Hodson2, Sarah K Thomas1, Hannah L Brooks2, Robin E Ferner3. 1. College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK. 2. University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK. 3. College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Abstract
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.
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.
Authors: Basit Chaudhry; Jerome Wang; Shinyi Wu; Margaret Maglione; Walter Mojica; Elizabeth Roth; Sally C Morton; Paul G Shekelle Journal: Ann Intern Med Date: 2006-04-11 Impact factor: 25.391
Authors: Shobha Phansalkar; Heleen van der Sijs; Alisha D Tucker; Amrita A Desai; Douglas S Bell; Jonathan M Teich; Blackford Middleton; David W Bates Journal: J Am Med Inform Assoc Date: 2012-09-25 Impact factor: 4.497
Authors: Margaret H Reckmann; Johanna I Westbrook; Yvonne Koh; Connie Lo; Richard O Day Journal: J Am Med Inform Assoc Date: 2009-06-30 Impact factor: 4.497
Authors: Sarah Ross; Cristín Ryan; Eilidh M Duncan; Jillian J Francis; Marie Johnston; Jean S Ker; Amanda Jane Lee; Mary Joan MacLeod; Simon Maxwell; Gerard McKay; James McLay; David J Webb; Christine Bond Journal: BMJ Qual Saf Date: 2012-10-30 Impact factor: 7.035
Authors: Blake J Lesselroth; Kathleen Adams; Victoria L Church; Stephanie Tallett; Yelizaveta Russ; Jack Wiedrick; Christopher Forsberg; David A Dorr Journal: Appl Clin Inform Date: 2018-05-02 Impact factor: 2.342