scholarly journals The impact of electronic prescribing systems on pharmacists’ time and workflow: protocol for a time-and-motion study in English NHS hospitals: Table 1

BMJ Open ◽  
2015 ◽  
Vol 5 (10) ◽  
pp. e008785 ◽  
Author(s):  
Behnaz Schofield ◽  
Kathrin Cresswel ◽  
Johanna Westbrook ◽  
Ann Slee ◽  
Alan Girling ◽  
...  
Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 4704-4704
Author(s):  
David A Hanauer ◽  
Sung W Choi ◽  
Robert W Beasley ◽  
Ronald B Hirschl ◽  
Douglas W Blayney

Abstract No data are available concerning the impact of CPOE on inpatient leukemia and lymphoma care. CPOE may improve patient safety, reduce time between order entry and medication administration, and reduce medication and transcription errors. However, concerns have arisen about potential increased time required to enter electronic orders compared to handwritten orders. Our hypothesis was that CPOE would require more order-related time from caregivers, and reduce the amount of time for direct patient care. We studied the work patterns of three Physician Assistants (PAs) who worked under the supervision of faculty physicians, and were the exclusive inpatient care providers. The PA-staffed hematology service was chosen to minimize the impact of rotating house staff on our results. Faculty, who were not studied, entered the few chemotherapy orders necessary, while PAs entered orders for hydration, antibiotics, supportive care and other medications, and for consultations and diagnostic tests. The UMHS Institutional Review Board reviewed the study protocol and waived the requirement for patient informed consent. We performed a direct observation time and motion study pre- and post-implementation of a commercial CPOE system (Sunrise Clinical Manager™ 4.5, Eclipsys, Boca Raton, Florida) on one inpatient hematology service at the UMHS University Hospital. The same three PAs were shadowed pre- and post-implementation. We also closely matched morning and afternoon observation times in order to reduce variability in activities taking place at different times of the day. Prior to CPOE implementation the PAs had a 4 hour general training session and a 1 hour chemotherapy training session. Pre-built order sets were routinely used by the PAs. A portable tablet computer was used by an independent observer to record data, using a data entry interface containing 63 individual activity categories modified from the Time and Motion database under “IT Tools” at http://www.ahrq.gov. Data were grouped into subcategories for analysis. We grouped 12 activities as ordering-related (e.g. writing orders, writing forms, clarifying orders, etc.) We observed the same three PAs for 85.4 hours (over 2 weeks) pre, and for 75.8 hours (over 4 weeks) starting 3 months post-CPOE. Mean patient census was 11.3 per day pre- and 9.2 per day post implementation observation periods. Overall time for order-related activities was unchanged, requiring 7.7% of total time pre- and 8.1% of total time post-CPOE even though actual order writing took longer with CPOE compared to written (4.9% pre vs. 7.0% post). CPOE had almost no impact on direct patient care time (Figure), with PAs spending 38.2% total time on direct patient care pre-CPOE compared to 38.4% post. A minimal difference was also found with the overall total for indirect patient care activities (37.1% pre vs. 38.7% post). Our results suggest that using CPOE on a busy hematology inpatient service has minimal impact on time spent by trained PAs using standard order sets 3 months after implementation. The decision to adopt CPOE for a busy hematology service should not be based on the hypothesis that there will be a change in workflow or task organization. More study is needed to determine if CPOE for hematology patients results in a change in the quality of patient care or safety. Figure. Percentage of total time spent in 6 analysis categories both before and after implementation of a commercial CPOE system for an inpatient hematology service. These 6 categories represent 63 individual activities categories that were recorded in the time and motion study. Error bars represent 95% confidence intervals. Figure. Percentage of total time spent in 6 analysis categories both before and after implementation of a commercial CPOE system for an inpatient hematology service. These 6 categories represent 63 individual activities categories that were recorded in the time and motion study. Error bars represent 95% confidence intervals.


2020 ◽  
Author(s):  
Chiara Dall’Ora ◽  
Peter Griffiths ◽  
Joanna Hope ◽  
Jim Briggs ◽  
Jeremy Jones ◽  
...  

ABSTRACTIntroductionMonitoring vital signs in hospital is an important part of safe patient care. However, there are no robust estimates of the workload it generates for nursing staff. This makes it difficult to plan adequate staffing to ensure current monitoring protocols can be delivered.ObjectiveTo estimate the time taken to measure and record one set of patient’s vital signs observations; and to identify factors associated with time to measure and record one set of patient’s vital signs observations.MethodsWe undertook a time-and-motion study of 16 acute medical or surgical wards across four hospitals in England. One hospital recorded vital signs on paper, while three recorded measurements on electronic devices. Two trained observers followed a standard operating procedure to record the time taken to measure and record vital sign observations. We used mixed-effects models to estimate the mean time using whole observation rounds, which included preparation time, or time spent taking observations at the bedside. We tested whether our estimates were influenced by nurse, ward and hospital factors.ResultsAfter excluding non-vital signs related interruptions, dividing the length of an observation round by the number of observations in that round yielded an estimated time per observation set of 5 minutes and 1 second (95% Confidence Interval (CI) = 4:39-5:24). If interruptions within the round were included, the estimated time was 6:26 (95% CI = 6:01-6:50). If only time taking each patient’s observations at the bedside was considered, after excluding non-vital signs related interruptions the estimated time was 3:45 (95% CI = 3:32-3:58). We found no substantial differences by hospital, ward or nurse characteristics, despite different systems for recording observations being used across the hospitals.DiscussionThe time taken to observe and record a patient’s vital signs is considerable, so changes to recommended observation frequency could have major workload implications. Variation in estimates derived from previous studies may, in part, arise from a lack of clarity about what was included in the reported times. We found no evidence that nurses save time when using electronic vital signs recording, or that the grade of staff taking the observation influenced the time taken.ConclusionsTaking and recording vital signs observations is time consuming and the impact of interruptions and preparation away from the bedside is considerable. When considering the nursing workload around vital signs observations, no assumption of relative efficiency should be made if different technologies or staff groups are deployed.


1937 ◽  
Vol 16 (11) ◽  
pp. 609
Author(s):  
A. Sykes ◽  
Hall ◽  
George Hepworth ◽  
F. Grover ◽  
E. Drake ◽  
...  

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