Information comes at us so fast and furious these days that look.docx

Information comes at us so fast and furious these days that
looking at new books on Amazon.com is actually a good
practice for learning about new, hot topics such as Agile PM.
The single best book, arguably, to come out to date on how
PMBOK-schooled adn practiced PM's can best make the
transition to Agile PM and/or learn some new tricks from Agile
PM is The Software Project Manager's Bridge to Agility. Click
on the link below to see the Amazon.com page o this book:
http://www.amazon.com/Software-Project-Managers-Bridge-
Agility/dp/0321502752/ref=sr_1_2?ie=UTF8&qid=1359604494
&sr=8-2&keywords=pmbok+agile
Note: This book is amoong the required readings for candidates
studying for the PMI-ACP certification from PMI. The PMI-
ACP is the leading industry certification in Agile PM, along
with the ScrumMaster certification.
To complete this conference, you need to do two things:
1) Read over the information at the above link and "look inside"
the book's table of contents by clicking on the book image, and
tell me why or why not this books appeals to you. Then find the
single piece of information on this page that potentially helps
you most in preparing for doing Scenario 2, in which you read
two short articles and write an email of your analysis relative to
which projects are best completed using PMBOK and which
projects are best with Agile PM techniques. Describe in this
first response, after you discuss the book's appeal to you, which
information may be of most value to you in
successfully executing Scenario 2.
2) Search the web for this book and its authors
(Sliger/Broderick). Write a literature review entry consisting of

the source you referenced and 50+ words summarizing what the
source is saying about this book. Then describe in 50+ words
what this tells you about how PMBOK and Agile PM techniques
compare with each other OR what was said about one of the
these two PM techniques.
Scenario 2 for your reference:
Scenario #2 (Analysis Email - individual)
Scenario 2: Article and Project Fit Analysis for the PMO
Director
UMUC IFSM 441
Introduction
Before you begin this assignment, be sure you have read the
“XYZ Corporation Case”.
Purpose of the Assignment
This assignment gives students the opportunity to apply IFSM
441 course concepts and specifically addresses the following
course outcomes :
1. Evaluate the values, principles, strategies and practice of
Agile tools in order to mitigate uncertainty and risk with project
delivery.
2. Compare Agile versus traditional project methodologies in
order to provide best fit for the organization.
3. Apply Agile framework to meet the specific operational
needs.
Your Job Title, Role, and Background at XYZ Corporation
You are a Senior Project Analyst. You report to the PMO
Director. Your main role is as an analyst and advisor to the
PMO Director. Your background includes a BS in IFSM from
UMUC and PMP and PMI-ACP certifications. You have 4 years
work experience at XYZ since joining the company after
graduation. In your short career, you have shown an ability to

perform good analysis and to assist management and senior
management in decision support. Your job responsibilities
routinely call for you to write analyses of published information
and project governance decision papers at the request of the
PMO Director.
Overview of Scenario #1 Task Assignment
The PMO Director has two related task requests for you that she
wants you to address in one email:
1) Send her your brief reactions in email to two articles that she
has requested that you read and comment on; 2) Send her your
recommendations/justifications in an email for which project
methodology projects should use. You should create only one
email deliverable for these two requests. The two web-available
articles to read are authored by notable Agile visionary Michele
Sliger:
1. Relating PMBOK Practices to Agile Practices - Part 1 of 4
(Sliger)
http://www.stickyminds.com/sitewide.asp?ObjectId=10365&Fun
ction=DETAILBROWSE&ObjectType=COL&sqry=%2AZ%28S
M%29%2AJ%28MIXED%29%2AR%28relevance%29%2AK%28
simplesite%29%2AF%28sliger%29%2A&sidx=2&sopp=10&site
wide.asp?sid=1&sqry=%2AZ%28SM%29%2AJ%28MIXED%29
%2AR%28relevance%29%2AK%28simplesite%29%2AF%28slig
er%29%2A&sidx=2&sopp=10
2. Relating PMBOK Practices to Agile Practices - Part 2 of 4
(Sliger)
http://www.stickyminds.com/sitewide.asp?ObjectId=10631&Fun
ction=DETAILBROWSE&ObjectType=COL&sqry=%2AZ%28S
M%29%2AJ%28MIXED%29%2AR%28relevance%29%2AK%28
simplesite%29%2AF%28sliger%29%2A&sidx=1&sopp=10&site
wide.asp?sid=1&sqry=%2AZ%28SM%29%2AJ%28MIXED%29
%2AR%28relevance%29%2AK%28simplesite%29%2AF%28slig
er%29%2A&sidx=1&sopp=10
The PMO Director has assigned the following 3 projects to
briefly review to help him prep for his upcoming PMO Project
Review Meeting. For each of these 3 projects you are to advise

the PMO Director which XYZ Project Management
Methodology is most suitable for use with each proposed
project, along with a 6-8 sentence analysis of your main
justifications. The list of projects and related key information
follows:
1. Internal/External: Internal project for the Administration
Division. Project Title: XYZ Headquarters Construction Review
Project. Brief Description: XYZ has contracted with ABC
Construction for the building of a new $40M corporate
headquarters building in Rockville, MD and a XYZ project team
is needed to oversee the construction process and ensure all
requirements are met per building plans and architectural
drawings.
2. Other Key Information: The project team will serve as the
main interface with the ABC Construction company and will be
responsible for providing all needed internal support for the
building project. The Administration Division has not requested
any project management methodology. Past construction
monitoring projects have used the PMBOK Methodology. The
timeline of the project is 1.5 years.
3. Internal/External: External project for the Health Crisis
Prevention and Response Management Service/Product Delivery
Division. Project Title: Health Crisis Response Portal
Project. Brief Description: XYZ has just won a contract with the
federal government to put in place a state-of-the art health crisis
response portal for collaboration between key governmental and
private sector entities and health information exchange. While
governmental requirements in the RFP were not detailed, XYZ’s
proposal emphasized an iterative approach to defining
requirements ahead of a crisis and the ability to adapt quickly to
refine requirements based on the type and extent of crisis. Other
Key Information: The project team will be responsible for
refining the specifications proposed in the RFP response by
XYZ according to the approvals of the client. The government’s
RFP indicated that Agile methodology approaches involving
Scrum and XL were welcomed, and XYZ went with that option

in its proposal. The Health Crisis Prevention and Response
Management Service/Product Delivery Division has requested
the Agile Methodology. The timeline of the project is 1.5 years
but some initial portal capabilities are due within 4 months of
contract begin with add-on refinements as needed to be
regularly added until completion.
4. Internal/External: External project for the Emergency
Management Service/Product Delivery Division. Project
Title: Hurricane Emergency Management Control Center. Brief
Description: XYZ has contracted with the Federal Emergency
Management Agency (FEMA) to provide expedited delivery of
emergency management control services to south Florida in the
immediate aftermath of the widespread destruction of Miami
during Hurricane Beth in September 2012. Services include
putting in place a web portal, intranets, information exchange,
and facilitation of collaboration among key stakeholders. Other
Key Information: While initial plans call for an immediate
project start and expedited service delivery, project plans call
for an extended planning phase starting after three months,
followed by further requirements elicitation and scope increases
according to the results of initial deployment and the follow-on
planning phase of 6 months. The Emergency Management
Service/Product Delivery Division has requested that the PMO
determine the best suited project management methodology.
JONA
Volume 42, Number 12, pp 562-566
Copyright B 2012 Wolters Kluwer Health | Lippincott Williams
& Wilkins
T H E J O U R N A L O F N U R S I N G A D M I N I S T R A

T I O N
Bar-code Verification: Reducing but not
Eliminating Medication Errors
Philip L. Henneman, MD
Jenna L. Marquard, PhD
Donald L. Fisher, PhD
Justin Bleil, BS
Brendan Walsh, BS
Justin P. Henneman, MS
Fidela S. Blank, MS, RN
Ann Marie Higgins, RN
Brian H. Nathanson, PhD
Elizabeth A. Henneman, PhD, RN
Using observation, eye tracking, and clinical simu-
lation with embedded errors, we studied the impact
of bar-code verification on error identification and
recovery during medication administration. Data sup-
ported that bar-code verification may reduce but does
not eliminate patient identification (ID) and medica-
tion errors during clinical simulation of medication
administration.
Serious medication errors, many related to medica-
tion administration, are common in hospitals.1,2 The

medication administration process is complex and
is subject to numerous potential errors. Two critical
subprocesses of the medication administration pro-
cess are necessary for safe medication administration.
They are verifying that the patient’s identity (VPtID)
matches the identity (identify) on the medication and
orders and then verifying that the medication name
and dose match the medication name and dose on
the patient’s medication orders (VMed).
Nurses play the central role in medication admin-
istration. Studies suggest that nurses recover (ie, iden-
tify, interrupt, and correct) the majority of medication
errors.3-7 It is crucial that efficient and effective tech-
nologies be implemented to assist the nurse in per-
forming the recovery process.
The VPtID process is among the most common
safety process performed by healthcare workers. It
should be completed prior to performing most patient-
specific tasks. The 1st Joint Commission’s National
Patient Safety Goal calls for identifying patients with
at least 2 patient identifiers when providing care, treat-
ment, and services.8 The VPtID process requires match-
ing at least 2 unique patient identifiers, such as name,
date of birth (DOB), or medical record number (MRN)
on the task artifact (eg, patient identity label on a phar-
macy prepared medication) directly to the patient or
indirectly to another artifact (eg, patient’s ID band).
The VMed process involves matching the med-
ication name, dose, route, and scheduled time to the
patient’s orders. It includes confirming that the pa-
tient is not allergic to the ordered medication.

Bar-code verification technology in conjunction
with computer provider order entry and an electronic
medication administration system (eMAS) has been
introduced to reduce certain medication administra-
tion errors, in particular those related to incorrect pa-
tient ID and/or medication. In the absence of bar-code
eMAS, the nurse must (1) visually or verbally match the
patient’s orders to the patient or their ID band (VPtID),
562 JONA � Vol. 42, No. 12 � December 2012
Author Affiliations: Professor of Emergency Medicine (Dr P. L.
Henneman) and Assistant Professor of Emergency Medicine (Ms
Blank), Tufts University School of Medicine, Baystate Medical
Cen-
ter, Springfield; Assistant Professor of Engineering (Dr
Marquard),
Professor of Engineering (Dr Fisher), and Undergraduate
Students
(Mr Bleil and Ms Walsh), University of Massachusetts,
Amherst;
Research Assistant (Mr Henneman) and Registered Nurse (Ms
Higgins), Baystate Medical Center, Springfield; and Chief Ex-
ecutive Officer (Dr Nathanson), OptiStatim, LLC, Longmeadow,
Massachusetts; and Associate Professor of Nursing (Dr E. A.
Henneman), University of Massachusetts, Amherst.
This study was funded in part by National Science Foundation
(awards 0829901 [to P.L.H.], 1032574 [to J.L.M.], and 0820198
[to E.A.H.]).
The authors declare no conflicts of interest.
Correspondence: Dr P. L. Henneman, 109 Lake Ave, Sunapee,
NH 03782 ([email protected]).
Supplemental digital content is available for this article. Direct

URL citations appear in the printed text and are provided in the
HTML and PDF versions of this article on the journal’s Web
site
(www.jonajournal.com).
DOI: 10.1097/NNA.0b013e318274b545
Copyright © 2012 Lippincott Williams & Wilkins. Unauthorized
reproduction of this article is prohibited.
http://www.jonajournal.com
(2) match the patient ID on the medication to the
patient or their ID band, and (3) match the informa-
tion on the medication itself to the medication infor-
mation listed on the patient’s order sheet (VMed).
To start the process of bar-code eMAS, the nurse
scans the bar code on the patient’s ID band (VPtID).
The bar code in the patient’s ID band contains a unique
identifier signifying patient identity and date of the
present visit. The nurse’s action results in a patient-
specific, documentation-ready medication administration
record appearing on the computer screen (ie, matches
the patient to the patient’s computer record/order).
Next, the nurse scans the bar code on the medication
container itself (package, intravenous bag, etc). The
bar code on the medication contains the drug’s na-
tional drug code, medication name, and dose. Other
information may be contained in the bar code (eg lot
number, expiration date, etc) but usually does not con-
tain the patient-specific ID. Scanning the medication
bar-code results in computer verification of the name
and dose of the medication compared with the name

and dose on the medication order (VMed). The lack of
a match between medication and order (name or dose)
will result in an error message being displayed on the
computer screen. If the medication name and dose
match, the nurse verifies such, and the system will doc-
ument the medication as administered.
Bar-code eMAS will successfully complete VPtID
and VMed when stock medications are administered.
When a medication is mixed by the pharmacy for a
specific patient (eg, unique dose or medication), pa-
tient ID information is placed on the medication bag
often as a label but not in the bar code. The nurse is
expected to match the patient’s ID information on the
medication to the patient or ID band.
In a previous study, using observation, eye track-
ing, and clinical simulation of medication administra-
tion without bar-code eMAS, we found that nurses
gave a medication to the wrong patient 39% of the
time when presented with an unexpected patient iden-
tity error similar to that used in this study.9 The pres-
ent study evaluated the impact of bar-code eMAS on
the incidence of a subset of medication errors com-
mitted by nurses during the medication administra-
tion process in a clinical simulation setting.
The research questions were as follows:
1. Does bar-code eMAS reduce patient ID (VPtID)
and medication (VMed) errors?
2. Is there a difference in the numbers of patient
ID (VPtID) and medication (VMed) errors with
and without bar-code eMAS?

3. What specific patient identifiers are viewed
by nurses during the administration of the
medication?
Methods
This was a prospective, observational study of emer-
gency department (ED) nurses administering a med-
ication to a patient (actor) in a simulated setting. The
study was approved by the institutional review board
at Baystate Medical Center, and all subjects gave their
signed, informed consent before participation. Nurses
volunteered to participate during one of their day or
evening shifts. Student volunteers were trained as pa-
tient actors and were given instructions on what to
say during the simulation scenario.
All nurses worked in a busy, urban ED with more
than 100 000 annual visits. The nurses were told that
the purpose of the study was to evaluate how experts
used visual cues during medication administration
using bar-code eMAS. Nurses were not told that there
were embedded patient ID and medication errors in
the simulation scenarios. All nurses were trained and
experienced in using the Cerner millennium bar-code
eMAS that was used in the simulation. All nurses
wore an eye-tracking device (see Figure, Supplemental
Digital Content 1, http://links.lww.com/JONA/A139)
that would video the field in front of them and place
crosshairs on the recorded video where the nurse was
looking at each moment during the simulation. This
was used to determine which specific patient identi-
fiers the nurses examined during the process of medi-
cation administration.
After placing and calibrating the eye-tracking de-

vice, the subject was given 2 labeled intravenous med-
ication bags to be administered to 2 separate patients.
Subjects were asked the complete this task using the
same bar-code eMAS process used in their clinical
practice. The embedded error in the simulation was
that the medication bag intended for patient 2 had
the same patient name but different DOB than the
DOB reported by the patient. In addition, the MRN
on the medication label did not match the MRN on
the patient’s ID band or on the patient’s computer
screen. The medication bag also listed the correct med-
ication name but a different dose than had been or-
dered for the patient (ie, gentamicin 400 mg instead of
60 mg).
An observer followed the nurse to each simulated
patient and completed a standardized data collection
sheet. If the nurse did not give the medication, the
observer asked the reason why. Identifying the medi-
cation error was defined as the nurse not administer-
ing the medication and voicing that there was a dose
discrepancy. Identifying the patient ID error was de-
fined as not giving the medication and voicing that the
medication was intended for a different patient.
The ASL (Applied Science Laboratory) mobile
eye is a tetherless eye-tracking system, which can
JONA � Vol. 42, No. 12 � December 2012 563
http://links.lww.com/JONA/A139

be worn by an active person in a free-moving envi-
ronment (see Figure, Supplemental Digital Content,
http://links.lww.com/JONA/A139). The eye tracker in-
cludes a scene camera, optics, and reflecting mirror
all mounted on safety glasses. Pupil corneal reflec-
tions are used to measure the position of the eye. The
eye-tracking device is 1st calibrated to each user. The
calibration process has the subject look at multiple
specific reference points in both the area where the
patient’s ID band would be and the area where the
computer screen would be. A mark will appear on
the video near each reference point. The marks are
adjusted to each of the specific reference points. The
output is stored on tape. The tape is analyzed with
the mobile eye software program, which after cali-
bration is able to overlay crosshairs at the approx-
imate 1-cm2 location in a scene where the individual
was looking.
Following the experiment, all videos were re-
viewed by 2 independent observers who recorded
whether the nurse looked or did not look at each of
the patient identifiers on the medication label, the
patient’s ID band, or the computer screen. Disagree-
ments between the 2 observers were resolved by a 3rd
observer. A nurse was assumed to have looked at a
specific patient identifier if 2 observers agreed that the
specific identifier was in the imaginary 1-cm2 box out-
lined by the crosshairs during a 0.4-second interval.
Eye-tracking or video failures were documented.
Eye-tracking failures occurred when cross hairs were
absent or only intermittently observed on the video.
Video failures occurred when patient ID information
could not be discerned because the image was washed
out from excessive glare.

Verifying patient ID on the medication bag to the
patient was defined as looking at 2 specific identifiers
on the medication label and matching them to the 2
same identifiers with the patient (verbal report), their
ID band (looking), or the ID information on the com-
puter screen (looking). Verifying patient to their ID
band required looking at 2 identifiers on the ID band
and verbally matching them to the patient’s self-report.
Comparison of study results were made to histori-
cal controls in a similar study previously reported.9
In this previous study, again using observation, eye
tracking, and clinical simulation, 28 nurses gave in-
travenous medications to 3 simulated patients with-
out using bar-code eMAS. The medication bag for the
3rd patients had the same name but different DOB
and MRN than reported by the patient, present on
their ID band, or present on the patient’s order sheet.
For statistical analysis, we used Stata/SE 11.1
(StataCorp, College Station, Texas). We calculated
95% binomial exact confidence intervals (CIs) for all
percentages. Chi square tests were used for all cat-
egorical inferences, except when the cell counts were
less than 4, in which case Fisher exact test was used.
P G 0.05 (ie, " = .05) was considered significant.
Percentage agreement and 0 statistics were calcu-
lated for interrater agreement.
Results
Twenty-five nurses participated in 50 patient scenarios
(2 per nurse). Eighty-four percent of nurses (21/25;

95% CI, 64%-96%) determined that the medication
dose was incorrect for patient 2 before starting the
medication and did not give the medication. Nineteen
percent of the nurses (4/21; 95% CI, 5%-42%) who
identified the medication error also identified the pa-
tient ID error.
Sixteen percent of nurses (4/25; 95% CI, 5%-36%)
failed to identify the medication or ID error and
administered the medication to the wrong patient.
Two of the 4 nurses who started to administer the
medication to the wrong patient promptly recovered
the medication portion of the error when they noted
the error message on the computer and stopped the
medication.
Eye-tracking data could not be used in 8% (4/50)
of the patient scenarios because of intermittent or
absent crosshairs on the videos. Glare from the com-
puter screen prevented eye-tracking data from being
recovered when participants were looking at the com-
puter screen for an additional 28% (14/50) of the
patient scenarios. There was an 85% agreement be-
tween the initial 2 independent observers (0 = 0.71).
Twenty percent of nurses (9/46; 95% CI, 9%-34%)
verified the patient’s ID band to the patient, using 2
identifiers, yet all used the ID band for bar coding.
Forty-six percent (21/46; 95% CI, 31%-61%) verified
the patient’s name (asked name or looked at name
on ID band) to the patient name on the medication
bag; 4 of these nurses also checked the DOB or MRN
on patient 2 and noted the discrepancy between the
medication bag and the patient, their ID band, or the
computer screen (ie, detected patient ID error).

Table 1 outlines the comparison of medication
administration with bar-code eMAS in this study with
medication administration without bar-code eMAS in
historical controls. Eighty percent or more of nurses in
either experiment did not verify the patient to the ID
band (they assumed that they matched). The percent-
age of RNs verifying patient ID on the medication bag
to the patient, their ID band, or the patient’s verified
computer screen was 16% (5/32; 95% CI, 5%-33%)
with bar-code eMAS and 67% (45/67; 95% CI,
55%-78%) without bar-code eMAS.
Verification using bar-code eMAS in clinical
simulation reduced the percentage of nurses giving
http://links.lww.com/JONA/A139
a medication to the wrong patient from 39% (11/28)
without bar-code eMAS to 16% (4/25; 95% CI,
5%-36%) with bar-code eMAS (P = .06). If we in-
clude nurses who promptly recovered the error by
noting the error message on the computer screen and
stopping the medication, only 8% of nurses (2/25;
95% CI, 1%-26%) using bar-code eMAS did not
identify and recover the error and give the complete
medication to the wrong patient (P = .01).
Discussion
Verification using bar-code eMAS reduced the inci-

dence of patient ID and medication errors in clinical
simulation. This has also been observed in a before-and-
after clinical study where implementation of bar-code
eMAS reduced the incidence of errors in medication
administration.1 Our study demonstrates ways that
errors continue to occur.
Few nurses verified that the information provided
by patient matched the information on their ID band;
most assumed that the ID band was accurate. In a
review of 2.4 million patient ID bands at 712 hospi-
tals, 8.6% had erroneous information, and 0.5% of
patients were wearing an ID band with another pa-
tient’s information.10 In clinical simulation, we found
that clerks, when presented with an unexpected pa-
tient ID error similar to that used in this study, would
place an incorrect wrist band on as many as 71% of
patients.9 Perhaps hospitals need to develop a verifi-
cation process of the patient’s ID band by someone
other than the individual who applies it.
Few nurses identified the patient ID error in our
simulation experiments; only 4 of the 25 noted the
different DOB or MRN. Only half of the nurses in
our trial verified the patient’s name, but we have
found that verifying the name alone is insufficient in
that 11% of patients will have another patient in the
ED with the same last name at the same time.9 Most
nurses did not give the medication in our simulation
because they noted the dosing error; however, few
nurses noted the patient ID error. Adding patient ID
information (eg, account number) to the medication
bar code would allow the software to add an error
message for the ID error and therefore might reduce
or prevent prepared medications being given to the

wrong patient even with bar-code eMAS system.
The bar-code eMAS should routinely pick up a
medication name and dose error and create an error
message. In our study, a small percentage of nurses
either did not look at the computer prior to starting
the medication or ignored the error message. Multi-
ple different types and causes for workarounds by
nurses with bar-code eMAS have been identified.
One study found that nurses overrode bar-code
eMAS alerts for 4% of patients and 10% of medica-
tions charted.11 Perhaps adding audible alarms that
cannot be silenced for specific patient safety alerts
might reduce delays in error recognition. Improving
the process to alleviate the need for workarounds
and raising awareness of the impact of workarounds
during patient safety processes might help reduce
errors.
We observed inefficient and interrupted visual
scanning patterns by nurses during medication
administration in the historical control study with-
out bar-code eMAS. Many nurses looked at multi-
ple identifiers in between key VPtID steps, such as
looking at the patient’s name and MRN in between
looking at the DOB on 2 artifacts.12 Although this
could be considered an appropriate match between
the 2 artifacts, the nurse likely could not keep the
DOB in working memory so could not remember
the DOB correctly. We also observed random visual
scanning patterns by nurses who did not identify
patient ID errors. These findings suggest that train-
ing nurses to use specific visual surveillance techni-
ques might improve the effectiveness and shorten
the time required to perform VPtID and VMed dur-
ing medication administration. We are continuing to

Table 1. Comparison of Simulated Medication Administration
With and Without Bar-code eMAS
With Bar-code eMAS
Without Bar-code eMAS
(Historical Controls)9 P
Verify patient to ID band 20% (9/46) (95% CI, 9%-34%) 13%
(9/67) (95% CI, 6%-24%) .38
Verify patient ID on medication to
patient/ID band/computer
16% (5/32) (95% CI, 5%-33%) 67% (45/67) (95% CI, 55%-
78%) G.001
Did not recover errorVgave
medication to wrong patient
8% (2/25) (95% CI, (0%-26%) 39% (11/28) (95% CI, 22%-59%)
.01
Recovered errorVdid not give
entire medicationa
92% (23/25) 61% (17/28) .01
aIncludes 2 nurses who started the medication but promptly
stopped the infusion when they noted the error message on the
computer screen
(ie recovered the error).
JONA � Vol. 42, No. 12 � December 2012 565

look at the visual scanning patterns of nurses during
bar-code eMAS.
Limitations
The main limitation in this study was that it was
performed in a simulated setting without any of the
usual stressors found in clinical practice. However,
only in simulations could specific errors be embedded
to observe nurse behavior. Our study was performed
using nurses from a single institution with 1 kind of
bar-code eMAS. We do not know if the behavior we
observed is similar to that found in other settings. The
eye-tracking device failed in 8%, and glare on the
computer screen prevented complete data collection
during another 28% of patient scenarios. Improving
the lighting from the computer screen will be nec-
essary in future work. The comparison of medica-
tion administration with and without bar-code eMAS
used historical controls instead of a direct comparison,
but the historical control was derived from the same
hospital. Finally, looking at 2 patient identifiers for
0.4 seconds does not necessarily mean that patient
identity was verified; we know that inattention
occurs.9
Conclusion
Bar-code eMAS reduces but does not eliminate pa-
tient ID and medication verification errors during
medication administration in a simulated setting. This

study demonstrates that further human and techno-
logical improvements are needed to ensure that the
right patient receives the right medication.
References
1. Poon EG, Keohane CA, Yoon CS, et al. Effect of bar-code
technology on the safety of medication administration.
N Engl J Med. 2010;362:1698-1707.
2. Henneman PL, Blank FS, Smithline HA, et al. Voluntarily
reported emergency department errors. J Patient Saf. 2005;1:
126-132.
3. Henneman EA, Gawlinski A. A ‘‘near-miss’’ model for
describing the nurse’s role in the recovery of medical errors.
J Prof Nurs. 2004;20:196-201.
4. Henneman EA, Blank FS, Gawlinski A, et al. Strategies used
by nurses to recover medical errors in an academic emergency
department setting. Appl Nurs Res. 2006;19:70-77.
5. Henneman EA, Roche JP, Fisher DL, Cunningham H, et al.
Error identification and recovery by student nurses using hu-
man patient simulation: opportunity for improving patient
safety. Appl Nurs Res. 2010;23:11-21.
6. Rothschild JM, Hurley AC, Landrigan CP, et al. Recovery
from medical errors: the critical care nursing safety net.

J Qual Patient Saf. 2005;32:63-72.
7. Dykes PC, Rothchild JM, Hurley AC. Medical errors
recovered by critical care nurses. J Nurs Adm. 2010;40:
241-246.
8. JCAHO National Safety Goals. http://www.jointcommission
.org/assets/1/6/NPSG_EPs_Scoring_HAP_20110706.pdf.
Accessed October 25, 2011.
9. Henneman PL, Fisher DL, Henneman EA, et al. Patient
identification errors are common in a simulated setting. Ann
Emerg Med. 2010;55:503-509.
10. Renner SW, Howanitz PJ, Bachner P. Wristband identifica-
tion error reporting in 712 hospitals. Arch Pathol Lab Med.
1993;117:573-577.
11. Koppel R, Wetterneck T, Telles JL, et al. Workarounds to
barcode administration systems: their occurrences, causes,
and threats to patient safety. J Am Med Inform Assoc. 2008;
15:408-423.
12. Marquard J, Henneman P, Jo J, et al. Nurses’ behaviors and
visual scanning patterns may reduce patient identification
errors. J Exp Psychol Appl. In press.

http://www.jointcommission.org/assets/1/6/NPSG_EPs_Scoring
_HAP_20110706.pdf
http://www.jointcommission.org/assets/1/6/NPSG_EPs_Scoring
_HAP_20110706.pdf
Practice rePorts Direct refill program
1659Am J Health-Syst Pharm—Vol 69 Oct 1, 2012
Effects of a direct refill program for automated
dispensing cabinets on medication-refill errors
Pieter J. Helmons, AsHley J. DAlton, AnD CHArles e. DAniels
Purpose. The effects of a direct refill pro-
gram for automated dispensing cabinets
(ADCs) on medication-refill errors were
studied.
Methods. This study was conducted in
designated acute care areas of a 386-bed
academic medical center. A wholesaler-
to-ADC direct refill program, consisting
of prepackaged delivery of medications
and bar-code-assisted ADC refilling, was
implemented in the inpatient pharmacy
of the medical center in September 2009.
Medication-refill errors in 26 ADCs from the
general medicine units, the infant special
care unit, the surgical and burn intensive

care units, and intermediate units were
assessed before and after the implemen-
tation of this program. Medication-refill
errors were defined as an ADC pocket con-
taining the wrong drug, wrong strength, or
wrong dosage form.
Results. ADC refill errors decreased by
77%, from 62 errors per 6829 refilled
Pieter J. Helmons, PHarm.D., M.A.S., is Hospital Pharmacist,
Department of Pharmacy, St. Jansdal Hospital, Harderwijk, The
Netherlands; at the time of writing he was Pharmacist
Specialist—
Pharmacoeconomics, University of California San Diego
(UCSD)
Health System, San Diego. asHley J. Dalton, PHarm.D., is Inpa-
tient Pharmacy Manager, UCSD Health System La Jolla, San
Diego.
CHarles e. Daniels, PH.D., B.s.PHarm., is Professor of Clinical
Pharmacy and Associate Dean for Clinical Affairs, Skaggs
School of
Pharmacy and Pharmaceutical Sciences, University of
California,
La Jolla.
Address correspondence to Dr. Helmons at the Department of
Pharmacy, St. Jansdal Hospital, Postbus 138, 3840 AC
Harderwijk,
The Netherlands ([email protected]).
Supported by an unrestricted Cardinal Health Patient Safety
Grant.
The authors have declared no potential conflicts of interest.
Copyright © 2012, American Society of Health-System Pharma-

cists, Inc. All rights reserved. 1079-2082/12/1001-1659$06.00.
DOI 10.2146/ajhp110503
pockets (0.91%) to 8 errors per 3855 re-
filled pockets (0.21%) (p < 0.0001). The
predominant error type detected before
the intervention was the incorrect medi-
cation (wrong drug, wrong strength, or
wrong dosage form) in the ADC pocket. Of
the 54 incorrect medications found before
the intervention, 38 (70%) were loaded in
a multiple-drug drawer. After the imple-
mentation of the new refill process, 3 of
the 5 incorrect medications were loaded
in a multiple-drug drawer. There were 3
instances of expired medications before
and only 1 expired medication after imple-
mentation of the program.
Conclusion. A redesign of the ADC refill
process using a wholesaler-to-ADC direct
refill program that included delivery of
prepackaged medication and bar-code-
assisted refill significantly decreased the
occurrence of ADC refill errors.
Am J Health-Syst Pharm. 2012; 69:1659-
64
M
ost acute care hospitals in the
United States use automated
dispensing cabinets (ADCs)
as the core of their medication dis-
tribution system. In 2008, an ASHP
survey of 527 hospitals found that

82.9% used ADCs.1 If hospitals with
fewer than 100 staffed beds are ex-
cluded, the percentage of hospitals
using ADCs increases to 95–98.7%.1
ADCs offer a variety of benefits to
the organization and the user, such
as secure and timely access to the
most commonly used medications
in a specific patient care area and
more accurate tracking and capture
of charge data for the medications
used.
However, the impact of ADCs on
medication safety is less well defined,
and several reports have indicated
that the incorrect use or poor design
of ADCs results in medication er-
rors.2,3 ADCs have been the source of
almost 15% of all medication-error
reports received by the Pennsylvania
Patient Safety Reporting System since
its inception in 2004.3 In addition,
123 ADC-related medication errors
have been reported to the National
Medication Errors Reporting Pro-
gram, operated by the Institute for
Safe Medication Practices (ISMP),
since 1971.3
In 2008, ISMP identified 12 core
processes to ensure the safe use of
ADCs4:
1. Provide ideal environmental condi-

tions for the use of ADCs,
2. Ensure ADC system security,
3. Use pharmacy-profiled ADCs,
4. Identify information that should
appear on the ADC screen,
1660 Am J Health-Syst Pharm—Vol 69 Oct 1, 2012
5. Select and maintain proper ADC
inventory,
6. Select appropriate ADC
configuration,
7. Define safe ADC restocking
processes,
8. Develop procedures to ensure the
accurate withdrawal of medications
from the ADC,
9. Establish criteria for ADC system
overrides,
10. Standardize processes for transport-
ing medications from the ADC to
the patient’s bedside,
11. Eliminate the process for returning
medications directly to their origi-

nal ADC location, and
12. Provide staff education and compe-
tency validation.
At the beginning of this study, the
processes used at the University of
California San Diego (UCSD) Health
System were aligned with some of
these core processes. At this institu-
tion, almost all ADCs in the acute
care setting required pharmacist
review and approval before dispens-
ing medications from an ADC and
subsequent administration to the pa-
tient (core process #3). UCSD Health
System also predominantly used
single-drug pockets. These pockets
contain only one specific medication
(core process #6), thereby decreasing
the opportunity for fill errors. In ad-
dition, standard safeguards were in
place to ensure appropriate stocking
of the ADC (core process #7), such as
mandatory checks of any drug prod-
uct to be refilled before it leaves the
pharmacy and an additional phar-
macist check after refilling the prod-
uct in the ADC. Despite these efforts,
ADC refill errors continued to occur.
A prospective before-and-after
study was conducted to determine
the impact of a new ADC refill proc-
ess on medication-refill errors.

Methods
Background. This study was con-
ducted in designated acute care areas
of UCSD Health System, a 386-bed
academic medical center. A total of
2 7 A D C s ( P y x i s Me d S t a t i o n ,
CareFusion, San Diego, CA) from
the general medicine units, the in-
fant special care unit, the surgical
and burn intensive care units, and
intermediate units were included
in this study. These areas predomi-
nantly rely on ADCs for medication
distribution, with more than 90%
of medications billed to the patient
originating in these ADCs.
The typical configuration of the
ADC in the acute care areas com-
prises a cabinet containing predomi-
nantly single-drug pockets and some
multiple-drug pocket drawers, a
refrigerator unit, and an ADC tower
containing bins to store large items
such as large-volume i.v. bags.
At the time of the study, orders
were entered into the computerized
prescriber-order-entry system, which
was interfaced with the pharmacy in-
formation system. Medications could
be administered only after the or-
ders were reviewed by a pharmacist.
Because the pharmacy information

system was interfaced with the ADC,
nurses could view only the medica-
tions on the ADC that had been veri-
fied by a pharmacist.
Pharmacy technicians manu-
ally restocked ADCs twice daily
(morning and evening) by manually
retrieving (“picking”) the medica-
tions to be restocked from the phar-
macy inventory. Pharmacists visually
checked the contents of the retrieved
medications before the products left
the pharmacy and again after the
pockets were restocked. However, the
time period between the technician’s
refilling of the ADC and the second
pharmacist check was variable, de-
pending on the availability of the
pharmacist to perform the double
check. As a result, the ADC restock-
ing process was suboptimal. Manual
retrieval of medications from phar-
macy inventory is time-consuming
and allows for human error. In ad-
dition, the lag between ADC refill
and the pharmacist’s check of the
ADC is a potential vulnerability, as
unchecked (and potentially incor-
rect) medications remain available
for retrieval.
Intervention. In September 2009,
the inpatient pharmacy implemented
a wholesaler-to-ADC direct refill

program. Only unit-of-use packaged
medications are available through
this program. Figure 1 illustrates the
ADC refill process before and after
implementation of this program. The
wholesaler-to-ADC refill program is
offered to hospital pharmacies at an
additional charge. In the redesigned
process, pharmacy technicians no
longer have to manually select most
of the ADC refill orders from the
central pharmacy supply, and the
pharmacist no longer has to check
the selected products before refilling
the ADC. In addition, the software
from the wholesaler-to-ADC direct
refill program automatically creates a
recommendation when the inventory
of a pocket containing medication in
the program falls below the prespeci-
fied level. The wholesaler prepack-
ages and delivers medications in an
ADC pocket-specific bag containing
sufficient medication to fit the pocket
and a bar code with the identity
of the contents. When refilling the
ADC, the pharmacy technician scans
the bar code on the ADC pocket-
specific bag, and the corresponding
pocket automatically opens. This
eliminates the error-prone step of
manually browsing for the product
from an alphabetized list in the ADC.
The double check of the identity and
condition of the refilled medication
at the ADC by a pharmacist is still

required.
Statistical analysis. The pharma-
cists performing the ADC refill checks
collected data on medication-refill
errors before and after implementa-
tion of the new program. However,
data collection for this study was
voluntary. Medication-refill errors
were defined as an ADC pocket
containing the wrong medication,
wrong strength, or wrong dosage
Figure 1. Redesign of the automated dispensing cabinet (ADC)
refill process before and
after implementation of a wholesaler-to-ADC direct refill
program.
6. Technician
Manually selects the
drug record of the
item to be refilled and
pocket opens
Before Intervention
1. Technician

Prints ADC refill list
2. Technician
Manually picks items
3. Technician
Sorts items per ADC
for delivery
4. Pharmacist
Checks items before
ADC refill
5. Technician
Goes to the ADC to be
refilled
7. Technician
Refills the pocket with
the picked medication
8. Pharmacist
Checks if the right
drug is filled in the
appropriate ADC
location
After Intervention

1. ADC
Automatically places
order if ADC pocket
falls below par level
4. Technician
Goes to the ADC to be
refilled
5. Technician
Scans the bar code
on the prepackaged
bag containing the
medication to be
refilled. Pocket opens
automatically.
6. Technician
Refills the pocket with
the picked medication
7. Pharmacist
Checks if the right
drug is filled in the
appropriate ADC
location
2. Wholesaler

Picks, checks,
and delivers items
prepackaged per
individual ADC pocket
and containing product
specific bar code
3. Wholesaler
Sorts items per ADC
for delivery
form. A check of the expiration date
of the medications was included, as
the prepackaging step by the whole-
saler could result in the acquisi-
tion of shorter dated medications.
After each ADC refill check, the
pharmacist filled out a data collec-
tion form capturing the date, ADC
location, duration of the ADC refill
check, and details of any fill errors
(Figure 2). Electronic reports from
the ADC were used to capture the
number of pockets checked by each
pharmacist. Lastly, we used elec-
tronic reports to document the type
of pockets associated with an error.
We based our sample-size calcula-
tion on a previous study by Klibanov
and Eckel5 in a similar-sized hospital
that used a similar ADC system and
refill process. Of the 2858 pockets
inspected, this study found a misfill

rate of 2.3%. Based on a baseline
misfill rate of 2.3% and a power of
80%, we calculated that 6600 pock-
ets would need to be inspected to
detect a misfill error reduction of
30%. An interim analysis during the
postimplementation period showed
an error reduction of more than
70%, larger than was expected. It
was then decided that sufficient data
had been collected for the study to
be adequately powered. Data collec-
tion postimplementation was sub-
sequently halted after 3855 refilled
pockets had been checked.
Data were entered into spread-
sheets (Microsoft Excel, Redmond,
WA) for initial analysis and sum-
mary statistics. Stata 10 (StataCorp
LP, College Station, TX) was used for
the power calculation and additional
statistical tests. Chi-square analysis
was used to compare error rates be-
fore and after the intervention. Con-
tinuous data were analyzed using the
unpaired t test. The a priori level of
significance was set at 0.05.
Results
Totals of 6829 pockets in 26 ADCs
and 3855 pockets in 24 ADCs were
inventoried 5 months before and 18
months after implementation of the

new program, respectively. Since we
relied on voluntary data collection by
the pharmacists assigned to the unit
during a fixed data collection period,
refill data during the preimplemen-
tation and postimplementation pe-
riods were not collected from 1 and
3 ADCs, respectively. Data collected
during both periods were mostly
similar (Table 1), except that medica-
tions were more frequently stored in
a single-drug pocket during the post-
implementation period (73% versus
51%, p < 0.0001). ADC refill errors
decreased by 77%, from 62 errors per
6829 refilled pockets (0.91%) to 8 er-
rors per 3855 refilled pockets (0.21%)
(p < 0.0001). The predominant error
type detected before the intervention
was the incorrect medication (wrong
drug, strength, or dosage form) in the
ADC pocket (Table 2). Of the 54 in-
correct medications found before the
intervention, 38 (70%) were loaded
in a multiple-drug drawer.
After the implementation of the
new refill process, 3 of the 5 incor-

rect medications were loaded in a
multiple-drug drawer. There were
Figure 2. Data collection form completed by the pharmacist
(RPh) while checking the refilled medications in the automated
dispensing
cabinet (ADC).
Error details (Medication Involved in Error—Name,
Strength, Form)
A
D
C
E
rr
o
r
D
a
ta
C
o
ll
e
c
ti
o
n

F
o
rm
—
P
le
a
s
e
r
e
tu
rn
t
o
J
o
h
n
D
o
e
o
r
J

a
n
e
D
o
e
w
h
e
n
c
o
m
p
le
te
Date:
Pyxis Machine Name:
Start time:
Stop time:
RPh:
How many
times were
you

interrupted?Error type
W
ro
n
g
d
ru
g
W
ro
n
g
s
tr
e
n
g
th
W
ro
n
g
f
o

rm
E
x
p
ir
e
d
m
e
d
3 instances of expired medications
before and only 1 expired medica-
tion after implementation of the
program.
Discussion
ADC refill errors decreased by
77% after implementation of a
wholesaler-to-ADC direct refill pro-
gram without increasing the fre-
quency of expired medication. How-
ever, these results should be viewed
in light of the study’s limitations.
First, this study required extensive
data collection, because medication-
refill errors are rare. Twenty-nine
pharmacists collected data during
the preimplementation period, com-
pared with 16 pharmacists during the

postimplementation period. Eleven
pharmacists collected data during
both time periods. Data collection
by different pharmacists could have
led to differences in the consistency
of the data collected. However, the
electronic reports used to capture
ADC refill data are identical for every
ADC. This should result in minor
variance only and would not account
for the large decrease in ADC refill
errors. In addition, the baseline ADC
refill error rate in this study (0.91%)
is similar to the rate reported by
Klibanov and Eckel5 (2.3%), further
strengthening the validity of this
study’s results.
Second, a separate effort to de-
crease ADC refill errors was focused
on increasing the use of single-drug
pockets when storing medications
in ADCs. As a result of these efforts,
medications were more frequently
stored in single-drug pockets in the
postimplementation period. Scan-
ning the wholesaler prepackaged
medication bar code at the ADC
automatically opens the correct

single-drug pocket, making it al-
most impossible to refill the incor-
rect pocket. Multiple-drug pocket
drawers, however, are more prone
to errors, as these pockets do not
contain a lid. The new process re-
quires an additional scan of the bar
code in the specified pocket inside
the multiple-drug pocket drawer as
an added safety feature. It is pos-
sible to misplace a medication in the
compartment without performing a
second scan. During the time of the
study, it was not possible to measure
scanning compliance when refilling
the ADC, which would have quanti-
fied this limitation. However, it is
unlikely that this potential “work-
around” influenced the results of
this study: omitting the second scan
requires the user to cancel the entire
bar-code-assisted refill process and
resume the refill using a much more
labor-intensive manual process.
Third, the redesigned ADC refill
process eliminated two error-prone
steps: (1) medications are no longer
manually collected by the pharmacy
technician in the inpatient phar-
macy but are delivered to the ADC
prepackaged per pocket and (2)
pharmacy technicians no longer have
to browse through an alphabetized
list on the screen of an ADC for the
appropriate pocket. Scanning the bar

code on the prepackaged bag auto-
matically opens the appropriate ADC
pocket. Both error-prone steps were
eliminated at the same time; there-
Table 1.
Comparison of Data Collected Before and After Implementation
of the Wholesaler-to-ADC Direct Refill Programa
Variable
Before
Implementation
After
Implementation
No. pockets checked
Type of pocket, no. (%)
Single-drug pocketb
Multiple-drug drawers
No. (%) ADCsc
Median no. (range) pockets per ADC
Median no. (range) pockets per
medication check
Median duration (range) of medication
check, min
6829
3500 (51)
3329 (49)

26 (96)
169 (3–773)
6 (0–47)
3 (0–23)
3855
2821 (73)
1034 (27)
24 (89)
109 (1–537)
6 (0–50)
2 (0–38)
aUnless otherwise stated, differences were not significant. ADC
= automated dispensing cabinet.
bp < 0.0001.
cSince data collection was voluntary, not all 27 ADCs were
represented in the analysis.
Table 2.
Comparison of Error Rates Before and After Implementation of
Wholesaler-to-ADC Direct Refill Programa
Error
Before
Implementation
After
Implementation

Wrong drug 30 (48) 1 (13)
Wrong strength 16 (26) 4 (50)
Wrong dosage form 8 (13) 0 (0)
Expired medication 3 (5) 1 (13)
Otherb 5 (11) 2 (25)
Total 62 (100) 8 (100)
aADC = automatic dispensing cabinet.
bExamples include nonmedication items such as broken glass
found in the drawer, loose dividers in the
matrix drawer, and technical issues.
No. (%) Errors
fore, it cannot be concluded whether
wholesaler-to-ADC prepackaging
or the use of bar-code-assisted ADC
refilling prevented the most errors.
Not all medications are available
through the wholesaler-to-ADC
program. Although the percentage of
incorrect medication (wrong drug,
wrong strength, and wrong dos-
age form) errors decreased, only 28
(47%) of the medications involved
in incorrect medication errors were
obtained through the new pro-
gram. At the time of the study, only
medications obtained through the
wholesaler-to-ADC program were
available for bar-code-assisted ADC
refilling, as only these products con-
tained a bar code scannable at the

ADC. The decrease in the percentage
of errors related to medications not
obtained through the wholesaler-to-
ADC refill program could potentially
be attributed to other changes insti-
tuted during program implementa-
tion. Nevertheless, there are plans
to expand bar-code-assisted ADC
refilling to all medications stocked in
the ADC to reap the full benefit from
the error-reduction potential of bar-
coding technology.
Conclusion
A redesign of the ADC refill proc-
ess using a wholesaler-to-ADC direct
refill program that included delivery
of prepackaged medication and
bar-code-assisted refill significantly
decreased the occurrence of ADC
refill errors.
References
1. Pedersen CA, Schneider PJ, Scheckelhoff
DJ. ASHP national survey of pharmacy
practice in hospital settings: dispensing
and administration—2008. Am J Health-

Syst Pharm. 2009; 66:926-46.
2. Gaunt MJ, Johnston J, Davis MM. Auto-
mated dispensing cabinets. Don’t assume
they’re safe; correct design and use are
crucial. Am J Nurs. 2007; 8:27-8.
3. Paparella S. Automated medication dis-
pensing systems: not error free. J Emerg
Nurs. 2006; 32:71-4.
4. Institute for Safe Medication Practices.
Guidance on the interdisciplinary safe
use of automated dispensing cabinets.
www.ismp.org/Tools/guidelines/ADC_
Guidelines_Final.pdf (accessed 2011 Jun
30).
5. Klibanov OM, Eckel SF. Effects of auto-
mated dispensing on inventory control,
billing, workload, and potential for medi-
cation errors. Am J Health-Syst Pharm.
2003; 60:569-72.
Copyright of American Journal of Health-System Pharmacy is
the property of American Society of Health
System Pharmacists and its content may not be copied or
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individual use.

Feature Article_739 371..379
Identifying the ‘right patient’: Nurse and
consumer perspectives on verifying patient
identity during medication administration
Teresa Kelly,1,2 Cath Roper,2 Stephen Elsom2 and Cadeyrn
Gaskin3
1Northern Area Mental Health Service, Melbourne Health,
2Centre for Psychiatric Nursing, The University of
Melbourne and 3The Centre for Quality and Patient Safety
Research, Deakin University, Melbourne, Victoria,
Australia
ABSTRACT: Accurate verification of patient identity during
medication administration is an impor-
tant component of medication administration practice. In
medical and surgical inpatient settings, the
use of identification aids, such as wristbands, is common. In
many psychiatric inpatient units in
Victoria, Australia, however, standardized identification aids
are not used. The present paper outlines
the findings of a qualitative research project that employed
focus groups to examine mental health
nurse and mental health consumer perspectives on the
identification of patients during routine
medication administration in psychiatric inpatient units. The
study identified a range of different
methods currently employed to verify patient identity, including
technical methods, such as wristband
and photographs, and interpersonal methods, such as patient
recognition. There were marked simi-

larities in the perspectives of mental health nurses and mental
health consumers regarding their
opinions and preferences. Technical aids were seen as
important, but not as a replacement for the
therapeutic nurse–patient encounter.
KEY WORDS: consumer perspective, medication
administration, mental health, patient identifica-
tion, psychiatric inpatient unit.
INTRODUCTION
Improving patient identification in health-care settings is a
priority for international and national patient safety orga-
nizations (Australian Commission on Safety and Quality
in Health Care 2008; 2009; The Joint Commission 2011;
WHO Collaborating Centre for Patient Safety
Solution
s
2007). The Joint Commission (2011) and the WHO
Collaborating Centre for Patient Safety

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