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Impact of Computerized Provider Order Entry Systems on hospital staff pharmacist workflow productivity: A three site comparative analysis based on level of CPOE implementation

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Abstract and Figures

Objective: Computerized Provider Order Entry (CPOE) is a system that enables physicians to send medication orders electronically rather than physically writing out the order. CPOE can reduce handwriting and transcription related medication errors and has been a major implementation goal for health systems. The objective of this study was to quantify and examine differences seen in the workflow of pharmacists at hospitals, with different levels of CPOE implementation.Methods: An observational, prospective time and motion study was conducted among three hospitals within the same health system: one classified as a non-CPOE system, one as short-term CPOE, and one as long-term CPOE. Pharmacists were observed in one-hour blocks, in which a data instrument was used to record 38 different tasks, which were grouped into four activities: clinical, distributive, administrative, and miscellaneous. The distributive category was further divided into three sub-categories. The average time associated with performing activities across the three hospitals was compared by descriptive and comparative analyses using ANOVAs and the post-hoc Tukey’s range test.Results: A total of 252 hours were collected and 235 met the inclusion criteria. The significant differences in time spent on task categories among hospitals were as follows: Non-CPOE vs. short term CPOE vs. long-term CPOE (mean ± SD in min/h) clinical tasks: (6.55 ± 6.40) vs. (4.95 ± 4.15) vs. (3.79 ± 4.91), respectively, (p < .05); order entry tasks: (29.62 ± 11.24) vs. (17.44 ± 10.73) vs. (10.27 ± 8.88) respectively, (p < .05); order verification tasks: (0.88 ± 1.77) vs. (13.93 ± 8.50) vs. (16.60 ± 9.63) respectively, (p < .05); other distributive tasks: (13.60 ± 10.04) vs. (15.86 ± 8.38) vs. (19.66 ± 8.42) respectively, (p < .05); and miscellaneous: (3.78 ± 4.64) vs. (1.54 ± 3.20) vs. (2.23 ± 3.51) respectively, (p < .05).Conclusions: The presence of a CPOE system could affect pharmacists’ workflow and time allotment on different types of pharmacy activities. Further, the time spent on certain activities was associated with the amount of time the CPOE system was implemented.
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jha.sciedupress.com Journal of Hospital Administration 2018, Vol. 7, No. 1
ORIGINAL ARTICLE
Impact of Computerized Provider Order Entry
Systems on hospital staff pharmacist workflow
productivity: A three site comparative analysis based
on level of CPOE implementation
Benjamin D. Lewing, Mark D. Hatfield, Sujit S. Sansgiry
Department of Pharmaceutical Health Outcomes and Policy, University of Houston, Houston, United States
Received: October 6, 2017 Accepted: December 8, 2017 Online Published: December 22, 2017
DOI: 10.5430/jha.v7n1p1 URL: https://doi.org/10.5430/jha.v7n1p1
ABS TR ACT
Objective:
Computerized Provider Order Entry (CPOE) is a system that enables physicians to send medication orders electroni-
cally rather than physically writing out the order. CPOE can reduce handwriting and transcription related medication errors and
has been a major implementation goal for health systems. The objective of this study was to quantify and examine differences
seen in the workflow of pharmacists at hospitals, with different levels of CPOE implementation.
Methods:
An observational, prospective time and motion study was conducted among three hospitals within the same health
system: one classified as a non-CPOE system, one as short-term CPOE, and one as long-term CPOE. Pharmacists were observed
in one-hour blocks, in which a data instrument was used to record 38 different tasks, which were grouped into four activities:
clinical, distributive, administrative, and miscellaneous. The distributive category was further divided into three sub-categories.
The average time associated with performing activities across the three hospitals was compared by descriptive and comparative
analyses using ANOVAs and the post-hoc Tukey’s range test.
Results:
A total of 252 hours were collected and 235 met the inclusion criteria. The significant differences in time spent on task
categories among hospitals were as follows: Non-CPOE vs. short-term CPOE vs. long-term CPOE (mean
±
SD in min/h) clinical
tasks: (6.55
±
6.40) vs. (4.95
±
4.15) vs. (3.79
±
4.91), respectively, (p< .05); order entry tasks: (29.62
±
11.24) vs. (17.44
±
10.73) vs. (10.27
±
8.88) respectively, (p< .05); order verification tasks: (0.88
±
1.77) vs. (13.93
±
8.50) vs. (16.60
±
9.63)
respectively, (p< .05); other distributive tasks: (13.60
±
10.04) vs. (15.86
±
8.38) vs. (19.66
±
8.42) respectively, (p< .05); and
miscellaneous: (3.78 ±4.64) vs. (1.54 ±3.20) vs. (2.23 ±3.51) respectively, (p< .05).
Conclusions:
The presence of a CPOE system could affect pharmacists’ workflow and time allotment on different types of
pharmacy activities. Further, the time spent on certain activities was associated with the amount of time the CPOE system was
implemented.
Key Words:
Computerized Provider Order Entry, Pharmacist productivity, System implementation, Hospital pharmacy
workflow, Time-and-motion
Correspondence:
Sujit S. Sansgiry; Email: ssansgiry@uh.edu; Address: Health and Biomedical Sciences Building 2, Room 4055. 4849 Calhoun,
College of Pharmacy, Houston, United States.
Published by Sciedu Press 1
jha.sciedupress.com Journal of Hospital Administration 2018, Vol. 7, No. 1
1. INTRODUCTION
Computerized Provider Order Entry (CPOE) is a system that
allows physicians to provide medication orders electronically
rather than manually writing the order, thereby helping to
reduce handwriting or transcription related medication er-
rors.
[1]
Implementation of CPOE can significantly reduce
medication errors which can lead to adverse drug events.
[2–4]
In addition, implementation or improvement of CPOE in
hospitals was a major goal for organizations across the US,
including the Leapfrog Group, Centers for Medicare and
Medicaid (CMS) and the Agency for Healthcare Research
and Quality (AHRQ).[5–7]
Adoption of CPOE is part of Stage Two of the Meaningful
Use Program of the incorporation of health information tech-
nology into hospitals, part of the CMS Electronic Health
Record Incentive Programs.
[7]
In 2014, the Office of the
National Coordinator for Health Information Technology
published a report indicating the percentage of US hospitals
considered to have adopted meaningful use of CPOE for
medications, which rose from 69.8% in 2012 to 84.4% in
2013.[8]
While CPOE systems have been recognized for their valuable
attributes including tools to reduce cost, increase efficiency,
and reduce medication errors, they have also been found to
have numerous barriers of use. One particular barrier is a
shift in workflow.
[9]
Published studies regarding pharma-
cist workflow change following implementation of CPOE
have shown varying and conflicting results.
[10–18]
A literature
review of 51 publications regarding the implementation of
CPOE found that two articles indicated a decrease in interrup-
tions due to clarifying ineligible orders after implementing
a CPOE system, and six studies indicated turnaround time
on drug orders was significantly decreased.
[10]
However,
several studies reported workflow interruptions including
CPOE system availability, such as system malfunction or
system overload during peak times, and numerous issues
with human-computer interactions.
[13, 15–18]
Further, while
one study indicated a decrease in the time spent by pharma-
cists on the medication process after CPOE implementation,
another study indicated no difference.[10,11, 16]
With the significance placed on hospital usage of CPOE and
the rapid rate in which CPOE is being adopted, pharmacy
leadership will be forced to examine how the incorporation
of CPOE affects workflow. It has been previously reported
that CPOE would allow pharmacists to expand their role and
spend more time on clinically related activities.
[19]
In an
article published previously, implementation of CPOE was
shown to increase pharmacist time spent on clinical activi-
ties.
[20]
The present study differs from previous studies in
that it compares three levels of CPOE implementation rather
than two, namely the present study compares a non-CPOE
hospital, a short-term CPOE hospital, and a long-term CPOE
hospital.
The objective of this study was to examine the effect of
CPOE implementation on pharmacist workflow and time
allotment on different categories of tasks, and specifically, to
quantify and compare differences observed in the time spent
on different task categories by pharmacists at hospitals with
varying levels of implementation of a CPOE system.
2. METHODS
A prospective time-and-motion study was designed to mea-
sure the time hospital pharmacists spent on different tasks.
Hospital staff pharmacists were observed in the pharma-
cies of three community teaching hospitals within the same
healthcare system in Houston, TX: Hospital A (non-CPOE
system, 252 beds), Hospital B (short-term CPOE system,
274 beds), and Hospital C (long-term CPOE system, 142
beds). The data collection took place in two time periods,
the first in April to May 2012, and the second from August
to September 2012. There were no changes regarding CPOE
implementation between these time periods for these three
hospitals, and for both time periods, data was collected at all
three hospitals. All three hospital pharmacies used the same
information technology application, namely PharmNet
R
(Cerner Corporation, Kansas City, MO), within the Cerner
Millenium R
hospital-wide information system.
Within the Cerner system, the basic difference for pharma-
cists processing non-CPOE and CPOE orders is the transcrip-
tion required for the non-CPOE orders; both types are still
processed using the PharmNet
R
system. The sites were
defined based on their predominant system and the length of
the implementation. The CPOE sites still utilized some non-
CPOE orders even after implementing CPOE, for instance
Total Parenteral Nutrition orders were manually entered by
the pharmacist into PharmNet
R
. At the non-CPOE site,
a small number of CPOE orders were processed because
the Emergency Department was already using CPOE and
prescriptions sent by the Emergency Department were pro-
cessed as CPOE orders. Hospital C had been using CPOE
for over five years prior to the start of the first observation
period and thus was classified as long-term CPOE. Hospital
B had adopted the CPOE system 19 months prior to the first
observation period and was classified as short-term CPOE.
The amount of time the CPOE system was implemented pro-
vided an opportunity to also consider the role it played and
its effect on pharmacists’ workflow.
The sample population consisted of order entry pharmacists
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at these locations, who worked in the central pharmacy for
the hospital; other pharmacists were excluded. The sampling
units were time (in minutes), collected in one-hour blocks.
The pharmacists’ activities were divided into 38 unique tasks
(see Table 1), comprised of four general categories: clin-
ical, distributive, administrative, and miscellaneous. The
general categories were based on a list of four major activi-
ties (clinical, distributive, administrative, and miscellaneous)
performed by hospital pharmacists, from a study published
by Hatfield et al. in 2014.
[20]
The list used by Hatfield et
al. was refined from categories outlined in a study published
by Gupta et al. in 2006 after consultation with pharmacy
management and clinical pharmacists.
[20–22]
The distributive
category was broken down into three sub-categories: order
entry, order verification, and other distributive task. The task
list used by the Hatfield’s study was modified for the present
study to capture additional tasks which were observed during
the pilot phase of the data collection tool.
Table 1. Tasks collected by the instrument, listed by activity
Task
Activity
Task
Activity
1. Clinical Interview
Clinical
20. TPN Mixing Check
Distributive
2. Physician Order Form
Clinical
21. Technician Check - non-IV room
Distributive
3. Rounds
Clinical
22. Technician Check - IV room
Distributive
4. Patient Consult - Warfarin
Clinical
23. Medication Prep/Delivery
Distributive
5. Patient Consult - Discharge
Clinical
24. Chemo Mixing Check
Distributive
6. Direct Patient Care
Clinical
25. Consult Pharmacist - Distributive
Distributive
7. Drug Information
Clinical
26. Consult Technician
Distributive
8. E-Mar/Lab Review
Clinical
27. Other - Distributive
Distributive
9. Consult Pharmacist - Clinical
Clinical
28. Meeting
Admin.
10. Other - Clinical
Clinical
29. Other - Administration
Admin.
11. Order Entry
Distributive
30. Shift Report
Admin.
12. Chemo Order Review
Distributive
31. Weekly personnel meeting
Admin.
13.TPN Order Review
Distributive
32. Documentation
Admin.
14. Order Verification
Distributive
33. Teaching/Mentoring
Admin.
15. IT Support
Distributive
34. Emails
Admin.
16. Order Review and Order Entry for Surgery Patient
Distributive
35. Q & A - Management
Admin.
17. Pyxis Cart Fill Check
Distributive
36. Scheduling
Admin.
18. Clarification - Nurse
Distributive
37. Miscellaneous
Personal
19. Clarification - Physician
Distributive
A summary of the tasks measured can be viewed in Table
1. Tasks 1 through 10 were included in the clinical activity
category and include such tasks as researching drug informa-
tion, patient consultation upon discharge and performing a
clinical interview. Tasks 11 through 27 were considered to be
distributive and included all tasks that would be considered
distributive in nature such as order entry, order verification,
clarifying order, and medication prep. Tasks 28 through 37
were the tasks considered to be administrative in nature, in-
cluding meeting, scheduling, and teaching. Task 38 was mis-
cellaneous and included pharmacist time spent on personal
activities and was not used in the analysis. The distributive
tasks are further broken down into order verification, order
entry, and other distributive tasks. The distributive category
was defined to include all tasks associated with order entry
and order verification, including discussions with personnel
related to the distribution of medicines. The clinical category
includes all tasks which are unambiguously clinical in nature,
relating to the treatment of patients. The administrative cate-
gory was considered to be tasks that were neither distributive
nor clinical, but were still value-filled tasks.
Order entry was defined as manual entry of medication orders
from written or verbal communication and was considered
to be a non-CPOE order. Hand-written orders were entered
manually into PharmNet
R
by the pharmacists. Order verifi-
cation was defined to be actions performed on orders received
through PharmNet
R
systems and considered to be a CPOE
order. CPOE orders were entered directly by the provider
and sent for pharmacist verification through the PharmNet
R
system. The “other distributive tasks” category was defined
to be distributive tasks not specifically associated with CPOE
or non-CPOE orders.
2.1 Data collection
All data was collected by a single individual in order to
reduce bias. The instrument used for data collection was
an Access
R
(Microsoft Corp., Redmond, WA) database,
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originally developed by Partners Healthcare System for the
AHRQ.
[23]
The instrument was designed to capture time and
motion data and was further modified for this specific study.
One randomly selected pharmacist was observed at each data
collection period, and observations were made in one-hour
intervals. For each data block included, both hospital and
pharmacist characteristics were obtained. A pilot study was
conducted in order for pharmacists to become comfortable
with the data collector, and the data collector’s data collection
was validated by a pharmacy manager expert with previous
experience with the data collection tool.
Sample and site characteristics measured include the num-
ber of unique pharmacists observed at each site, pharmacist
gender, pharmacist experience, the number of unique days
data was collected at the site, and the level of CPOE imple-
mentation for the site. The number of unique pharmacists
observed at each site was recorded because the lower number
of unique pharmacists for one site could mean lower varia-
tion in observations for that site. Also, for each observation,
the institutional experience of the pharmacist was recorded
because it was possible that there could have been a signifi-
cant difference in workflow productivity based on pharmacist
experience.
Each hospital site was compared to the other two sites for
differences in pharmacist time spent on activities: clinical,
administrative, and distributive, as well as the subcategories
for distributive, and the number of tasks performed by the
pharmacist in an hour. The frequency of task changes was
also collected and is defined to be the number of separate
tasks recorded for the pharmacists in a given hour.
Data collection took place on weekdays in one-hour peri-
ods from 7:00 a.m. to 5:00 p.m., and the data collection
was predetermined based on three criteria, including that
the observation period would be considered to consist of
non-irregular pharmacy workload, that it was possible to
observe multiple pharmacists, and that the time period may
span a minimum of four hours and a maximum of 10. Each
one-hour block had to include at least 75% of time spent on
non-miscellaneous tasks so that blocks in which the pharma-
cist took a large amount of personal time, such as a lunch
break, would not be included. The data collector was trained
to be as unobtrusive as possible. Also, the data collector
had an opportunity to collect pilot data to reduce the effect
of data collector bias. Further, the familiarity of the data
collector with the pharmacists reduced possible bias due to
the Hawthorne effect, and the pharmacists were not aware
when the data collection was initiated for each observation
period. In the event that multiple tasks were being completed
at the same time, the data collector would make a judgment
call of which task was occupying more of the pharmacist’s
attention.
This project was approved by the individual hospital adminis-
trators and the University of Houston’s Division of Research
Committee for the Protection of Human Subjects. Written
approval was also granted by an authorized Systems Execu-
tive representing the Healthcare System, and consent forms
were given to each pharmacist prior to requesting permission
to include them in the study.
2.2 Statistical analysis
Data analysis was performed using SAS
R
version 9.3 (SAS
Institute Inc., Cary, NC). Chi-square analyses were used to
test for significant differences in the sample and site charac-
teristics. Analysis of variance tests (ANOVAs) were used
to test for significance between the test sites for time spent
by pharmacists on different activities, and Tukey’s range test
was used for direct comparison between the sites. Addition-
ally, comparisons were made between the sites by the total
number of task changes by pharmacists per hour. The average
different individual tasks per hour was calculated for each
site and compared using ANOVA, along with Tukey’s range
test for post-hoc analysis. Apriori statistical significance was
set at a level of 0.05.
3. RES ULTS
Data collection spanned from April 2, 2012, to October 2,
2012, where 252 hours of data were collected, of which, 235
hours met the inclusion criteria for analyses, and the most
common reason an hour was excluded was due to the unan-
ticipated absence of the pharmacist being observed. Sample
and site characteristics are displayed in Table 2. The gender
distribution, which represents the gender of the pharmacist
for each individual data point indicates there was not a signif-
icant variation among the sites in terms of gender. There was
a significant difference in pharmacist experience between the
sites.
The three hospitals were compared by average time (min/hr)
spent by pharmacists on each activity category (Table 3
shows the results of an ANOVA analysis). For every ac-
tivity except for distributive tasks (combined), the ANOVA
analysis indicated a statistically significant difference among
the sites.
For the clinical activity, pharmacists at the non-CPOE site
spent a statistically significant greater amount of time in clin-
ical activities compared to the long-term CPOE site. For
the administrative category, the difference in time spent per
hour was not statistically significant in direct comparison of
the sites. For order entry, there was a significant difference
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between all three sites, and for order verification, there was
a significant difference measured between the non-CPOE
site and the two CPOE sites. For the remaining distributive
category, a significant difference was indicated between the
long-term CPOE site when compared to the non-CPOE site
and the short-term CPOE site.
The pharmacists at the non-CPOE site had a statistically sig-
nificant number of fewer task changes per hour compared to
the CPOE sites. For the non-CPOE site, short-term CPOE
site, and long-term CPOE site, respectively, the average num-
ber of tasks changes per hour were: (mean
±
SD) (23.74
±
7.32), (34.22 ±9.13), and (34.37 ±12.82).
Table 2. Sample characteristics by hospital
Hospital Characteristics
A: Non-CPOE
C: Long-term CPOE
p-value*
Number of Unique Days Data Was Collected
9
9
-
Number of Unique Pharmacists in Study
11
12
-
Total Hours of Data Recorded
78
79
-
Pharmacist Gender φ
.709
Male n (%)
19 (24.36)
16 (20.25)
Female n (%)
59 (75.64)
63 (79.75)
Pharmacist Institutional Experience φ§
< .001
0 to 1 year n (%)
2 (2.56)
0 (0.0)
1 to 10 years n (%)
32 (41.03)
63 (79.75)
10+ years n (%)
44 (56.41)
16 (20.25)
Note. CPOE = computerized provider order entry; * numerical p-values calculated using ANOVA, categorical, chisquare; p-value of < .05 is
considered statistically significant; φ numbers are based on the aggregate results from each 1 hour block of collected data; § chisquare analysis
significant for each comparison: a to b, a to c, and b to c
Table 3. Average time (min/hr) spent by hospital staff pharmacists on activities
Activity
Mean ± SD
p-value*
A: Non-CPOE Hospital
B: Short-term CPOE Hospital
C: Long-term CPOE Hospital
Clinicalφ
6.55 ± 6.40
4.95 ± 4.15
3.79 ± 4.91
.0046
Administrative§
5.55 ± 6.76
5.59 ± 6.04
8.15 ± 8.31
.0337
Order Entry&
29.62 ± 11.24
17.44 ± 10.73
10.27 ± 8.88
< .0001
Order Verification#
0.88 ± 1.77
13.93 ± 8.50
16.60 ± 9.63
< .0001
All Other Distributive Tasksδ
13.60 ± 10.04
15.86 ± 8.38
19.66 ± 8.42
.0002
Distributive Tasks (Combined)
44.11 ± 9.87
47.23 ± 8.43
46.53 ± 9.17
.0850
Miscellaneous
3.78 ± 4.64
1.54 ± 3.20
2.23 ± 3.51
.0011
Note. CPOE = computerized provider order entry; Minutes may not equal to exactly 60 due to rounding to second digit after decimal; *p-value calculated by
ANOVA - a value of <.05 is considered statistically significant; φ Tukeys test performed to test statistical significance between each site; A to C was significant;
§ Tukeys test performed to test statistical significance between each site; no direct comparisons were significant; & Tukeys test performed to test statistical
significance between each site; A to B, A to C, and B to C were all significant; # Tukeys test performed to test statistical significance between each site; A to B and
A to C were significant; δ Tukeys test performed to test statistical significance between each site; A to C and B to C was significant; Tukeys test performed to test
statistical significance between each site; A to B and A to C was significant
4. DISCUSSION
The pharmacists at both the short-term and long-term-CPOE
sites spent more time on distributive tasks and less time on
clinical tasks compared to the non-CPOE site, and a par-
ticular result of note was that pharmacists at the long-term
CPOE site spent a statistically significant less amount of time
on clinical tasks compared to the non-CPOE site. These re-
sults are unexpected and opposite to those of the previously
mentioned study published by Hatfield et al., in which it
was found that CPOE implementation was associated with
a decrease in the amount of time pharmacists spent on dis-
tributive activities and increase the amount of time spent on
clinical activities.
[20]
One possibility to explain the differ-
ence is that the previous study was conducted in the span of
two weeks, with only a total of 48 observations, and included
11 different pharmacists compared to this study which was
conducted during two different time periods of the year, had
238 observations, with 31 different pharmacists, allowing
a greater depth and variety of data collection. In addition,
factors that were not measured, such as number of orders
requested, could have significantly differed among the hospi-
tals. If the pharmacists completed a larger number of order
requests, this could account for the study results. Number of
orders requested was not collected because permission was
not given by the hospital system.
A particular facet of the results is that while time spent on
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clinical activities at the long-term CPOE site did not differ
significantly compared to the short-term CPOE site, there is
a noticeable trend of decreasing time spent on clinical activi-
ties from the non-CPOE site to the short-term CPOE site to
the long-term CPOE site. This trend raises questions if the
amount of time pharmacists spend doing clinical activities
could decrease with the depth of CPOE implementation. A
similar time-and-motion study was published by Westbrook
et al.
[24]
in 2013 that measured how introduction of an elec-
tronic medication management system (eMMS) affected the
amount of time doctors and nurses spent on direct patient
care and medication tasks, and it was concluded that the
introduction of eMMS did not redistribute time spent on
medication tasks or direct patient care.
One possible explanation for the results of the present study
is that utilization of CPOE places new burdens and challenges
on the pharmacists, with the electronic system directly or
indirectly requiring more of the pharmacists’ time. A study
recently published by Sinsky et. al.,
[25]
indicated that physi-
cians are spending an alarming amount of time on electronic
health records (EHR) and deskwork. The study found that
physicians were spending over twice the amount of time on
EHR and desk work than on direct clinical face time. It
is possible a similar burden of electronic systems is being
placed on the pharmacists. Previous studies have shown that
while implementing CPOE can improve the safety and effi-
ciency of order processing, it can create new challenges and
resource demands that place increased burden on pharma-
cists, such as increasing new types of errors and problems
caused by CPOE systems.
[26–29]
While CPOE systems can
increase efficiency in many areas, such systems should be
monitored by management so any new problems that arise
can be addressed.
A similar time-and-motion study was published by Lo et
al. in 2010 that measured time spent on tasks, task frequen-
cies, and other comparisons of tasks among pharmacists in
a hospital ward with eMMS compared to a hospital ward
without eMMS. They found that pharmacists’ work patterns
varied significantly and attributed the changes to increased
access to speedy and easy information retrieval for reviews
and improved clarity regarding orders due to the presence of
eMMS.
[30]
This factor could have played a part in the present
study because in the present study, the CPOE sites had a sig-
nificantly larger number of task changes per hour compared
to the non-CPOE site. Because of this, even though the num-
ber of minutes spent on clinical tasks per hour decreased, the
efficiency of clinical related tasks could have been increased,
more than compensating for time loss.
The frequency of task changes is of interest because it could
indicate potential differences in workflow. Pharmacists at
both of the CPOE sites were recorded to have an average
of approximately ten more task changes per hour compared
to the non-CPOE site. This is a statistically significant re-
sult that could mean a major shift in workflow dynamics
when comparing a non-CPOE system versus a CPOE sys-
tem. This difference could indicate an increased pace in the
workflow or an increased productivity for the pharmacies
that implemented CPOE, regardless of if the CPOE system
has been implemented for 19 months or longer. The signifi-
cant difference in amount of time pharmacists spent on the
miscellaneous category (personal time) in sites with CPOE
implementation compared to the non-CPOE site could again
indicate a fundamental change of workflow or work environ-
ment following CPOE implementation.
Comparing the short-term and long-term CPOE sites, two of
the activity categories compared were significantly different
(Order Entry and all other distributive tasks). This indicates
that time elapsed following CPOE implementation may have
an effect on pharmacists’ task time distribution. Comparing
the non-CPOE site, short-term site, and long-term site as
a continuum, four trends arise: Less time spent on clinical
activities, less time spent on order entry, more time spent on
order verification, and more time spent on all other distribu-
tive tasks. Even though the difference in the non-CPOE site
to the others in terms of order entry and order verification
discrepancy is expected, the differences between the short-
term and long-term CPOE sites indicate that the long-term
CPOE site has an increased depth of CPOE implementation.
The results suggest that after initial CPOE implementation,
the workflow and productivity in the pharmacy continues to
change.
The pharmacist characteristics were comparable across the
three sites, with pharmacist experience having the most vari-
ance. Hospital A had a greater proportion of pharmacists
that had 10 or more years of experience, and there is a pos-
sibility this discrepancy in pharmacy experience could have
had an effect on their time allotment on different tasks. It
was also noted that in many ways the pharmacies differed os-
tensibly in many aspects that were not measured, including,
the unique needs of the hospital, the pharmacy manager’s
managing style, the physical layout of the pharmacies, and
individual pharmacist work ethic.
While previous studies have shown the implementation of a
CPOE system can improve patient safety, there are still pos-
sibilities of unintended consequences, in which the mecha-
nisms are not fully understood. Management should consider
putting in place metrics to assess workflow and productivity
prior to technological implementations and continue moni-
6ISSN 1927-6990 E-ISSN 1927-7008
jha.sciedupress.com Journal of Hospital Administration 2018, Vol. 7, No. 1
toring even after the implementation is complete. A potential
topic of future interest, not addressed by this study is if
the pharmacist time spent in the distributive category cor-
responds to a greater number of orders being completed by
the pharmacists. This would be very important to determin-
ing some of the workflow and productivity changes caused
by implementation of a CPOE system. In addition, future
studies should investigate if CPOE systems do actually cause
less time to be spent on clinical work, such a study could
specifically look at how implementation of CPOE affects
the pharmacists’ time spent on clinical activity, and if in
the presence of a CPOE system, more pressure is placed on
pharmacists to spend more time on filling orders and other
distributive tasks.
Strengths and limitations
There are some tasks by the pharmacists which could not be
observed, including decision-making and personal judgment.
Data collection was based on observable actions. In addi-
tion, tasks were considered to be clinical only if they were
unambiguous and clearly clinical in nature, and it is possible
additional tasks could be classified as clinical such as con-
sulting with a nurse about a particular patient’s medicine or
review of a particular chemotherapy order. The generaliz-
ability of the study is limited in that this study included only
a single hospital system and hospital pharmacies and CPOE
systems can vary greatly. The validity of the comparisons of
the hospital sites may be questioned because the dynamic of
pharmacist workflow may be influenced by individual phar-
macy managers, needs of the hospital, and other factors not
measured. Another noted limitation of the study is that the
site considered to be non-CPOE did have a small number of
CPOE orders processed. The results showed that pharmacists
at the non-CPOE site spent on average 0.88 minutes per hour
on CPOE order verification compared to 29.62 average min-
utes spent on non-CPOE order entry, so the bias introduced
by the small number of CPOE orders in the non-CPOE site
should be small. Other biases that could have affected the
study include observer bias, and bias from the Hawthorne
effect. However, the Hawthorne effect was should have been
limited because the sites of this study regularly host multi-
ple 3
rd
and 4
th
-year pharmacy students, so the pharmacists
should have been accustomed to being observed.
A major limitation is the unavailability of the number of
orders the pharmacists completed. This information could
help explain possible workflow and productivity differences
between the short-term CPOE, long-term CPOE, and non-
CPOE systems.
The sample size of 235 observations provides the study with
a large enough sample size to answer the main objective
questions, and observer bias was limited because a single
observer was used for each of the three different sites.
5. CONCLUSIONS
At the CPOE pharmacies, less time was spent on order entry;
however, the amount of time gained by spending less time
on order entries was more than made up for in the amount of
time spent doing order verification tasks. Pharmacists at the
CPOE hospitals spent more time on distributive tasks and less
time on clinical activities. Additionally, while the benefits of
implementing a CPOE system are many, the presence of a
CPOE system as well as time elapsed since CPOE implemen-
tation could have a dramatic effect on pharmacist workflow
dynamics and time spent on certain activities. CPOE sys-
tems should be closely monitored by pharmacy managers to
ensure workflow remains efficient and factors such as time
spent on clinical tasks are not negatively affected.
ACKNOWLEDGEMENTS
The authors are grateful to the directors of pharmacy and
hospital administrators who granted approval for the study
and to the pharmacists at the hospitals for their help in the
successful completion of the study.
CON FLI CT S OF INTEREST DISCLOSURE
The authors declare they have no conflicts of interest.
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8ISSN 1927-6990 E-ISSN 1927-7008
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A computerized provider order entry (CPOE) system can decrease errors and improve quality but also may increase errors and harm. Yet, the current state of the literature on CPOE is particularly impoverished regarding implementation strategies and process measures. VETERANS HEALTH ADMINISTRATION (VHA) CPOE IMPLEMENTATION: The VHA Computerized Patient Record System (CPRS), developed and implemented by the Department of Veterans Affairs (VA) at a national level, was designed to support physician order entry and electronic note entry. Implementation of CPRS was mandated for all clinical sites in the VA in a series of waves from 1997 through 2002. The study site, Salt Lake City Health Care System, is a 110-bed tertiary care university-affiliated facility. Local implementation was conducted in a staggered approach across clinical sites for a total implementation time of 24 months. Information theory is proposed as a basis for process assessment. Early in the process, the implementation team realized the need to create indicators to help monitor the process of change. The percent of orders entered by providers rose to 64% and leveled off. The percent of orders signed within four hours leveled off at 83%. The presence of inappropriate text orders (one-time review presented) was measured, and the percent of orders verified by nursing within two hours leveled off at 42%. Setting up an implementation monitoring process should start when the implementation process begins. Specific recommendations are as follows: (1) Start with the basic process measures, (2) set up reporting structures, and (3) start early with vendor negotiations. In conclusion, information theory provides an effective and efficient framework on which to base indicator development.