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Automated drug dispensing system reduces medication errors in an intensive care setting


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We aimed to assess the impact of an automated dispensing system on the incidence of medication errors related to picking, preparation, and administration of drugs in a medical intensive care unit. We also evaluated the clinical significance of such errors and user satisfaction. Preintervention and postintervention study involving a control and an intervention medical intensive care unit. Two medical intensive care units in the same department of a 2,000-bed university hospital. Adult medical intensive care patients. After a 2-month observation period, we implemented an automated dispensing system in one of the units (study unit) chosen randomly, with the other unit being the control. The overall error rate was expressed as a percentage of total opportunities for error. The severity of errors was classified according to National Coordinating Council for Medication Error Reporting and Prevention categories by an expert committee. User satisfaction was assessed through self-administered questionnaires completed by nurses. A total of 1,476 medications for 115 patients were observed. After automated dispensing system implementation, we observed a reduced percentage of total opportunities for error in the study compared to the control unit (13.5% and 18.6%, respectively; p<.05); however, no significant difference was observed before automated dispensing system implementation (20.4% and 19.3%, respectively; not significant). Before-and-after comparisons in the study unit also showed a significantly reduced percentage of total opportunities for error (20.4% and 13.5%; p<.01). An analysis of detailed opportunities for error showed a significant impact of the automated dispensing system in reducing preparation errors (p<.05). Most errors caused no harm (National Coordinating Council for Medication Error Reporting and Prevention category C). The automated dispensing system did not reduce errors causing harm. Finally, the mean for working conditions improved from 1.0±0.8 to 2.5±0.8 on the four-point Likert scale. The implementation of an automated dispensing system reduced overall medication errors related to picking, preparation, and administration of drugs in the intensive care unit. Furthermore, most nurses favored the new drug dispensation organization.
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Feature Articles
Automated drug dispensing system reduces medication errors in
an intensive care setting
Claire Chapuis, PharmD, MSc; Matthieu Roustit, PharmD, MSc; Gae¨lle Bal, MSc; Carole Schwebel, MD, PhD;
Pascal Pansu, PhD; Sandra David-Tchouda, MD, PhD; Luc Foroni, PharmD; Jean Calop, PharmD, PhD;
Jean-Franc¸ois Timsit, MD, PhD; Benoît Allenet, PharmD, PhD; Jean-Luc Bosson, MD, PhD;
Pierrick Bedouch, PharmD, PhD
Drug therapy safety relies on
many parameters, making
drug complications a major
cause of medical injury. Ad-
verse drug events, defined as injuries
caused by medical treatment, represent a
major health issue (1). Certain adverse
drug events are considered as medication
errors (MEs), defined as any preventable
event that may lead to inappropriate med-
ication use or patient harm (2). According
to available data, MEs harm more than 1.5
million people and cause 7,000 deaths an-
nually in the United States (3).
In the past 15 years, many studies
have described MEs in drug preparation
and administration in medical and surgi-
cal units (4 –7). Others have specifically
assessed the incidence of MEs in medical
intensive care units (MICUs) (8 –14).
Highly unstable critically ill patients
are more vulnerable to MEs, and the
risk of errors is increased in these pa-
tients because of the number of drugs
they receive and the way they are ad-
ministered (i.e., intravenous infusions)
(1). In most cases, MEs reached the
patient (8, 9, 12, 13).
Therefore, reducing errors is crucial to
improving patients’ outcomes. Information
technology and automated systems have
been introduced to improve the medication
use process: computerized physician order
entry systems (15), unit dose drug distribu-
tion (5), bar-coded medication administration
(16), and automated dispensing systems
(ADSs), which are computer-controlled dis-
pensing units providing secure storage and
drug distribution in care units. ADSs have
improved medication use in surgical and
medical units, with an impact on administra-
tion time errors, omissions, and work activi-
ties (17–20).
A recent report showed that ward-based
ADSs can reduce costs while reducing error
rates (21). However, their clinical impact in
intensive care units (ICUs) remains to be
From the Pharmacy Department (CC, LF, JC, BA, PB),
Grenoble University Hospital, France; Clinical Research Cen-
ter—Inserm CIC03 (MR, GB, SDT, JLB), Grenoble University
Hospital, Grenoble, France; Medical Intensive Care Depart-
ment (CS, JFT), Grenoble University Hospital, Grenoble,
France; Educational Science Laboratory (PP), Pierre Mende`s-
France University, Grenoble, France; ThEMAS TIMC UMR
CNRS 5525 (SDT, JC, BA, JLB, PB), Joseph Fourier Univer-
sity, Grenoble, France.
Financial support provided by De´le´gation Re´ gion-
ale a` la Recherche Clinique, Grenoble University Hos-
pital, Grenoble, France.
The authors have not disclosed any potential con-
flicts of interest.
For information regarding this article, E-mail:
Copyright © 2010 by the Society of Critical Care
Medicine and Lippincott Williams & Wilkins
DOI: 10.1097/CCM.0b013e3181f8569b
Objectives: We aimed to assess the impact of an automated
dispensing system on the incidence of medication errors related
to picking, preparation, and administration of drugs in a medical
intensive care unit. We also evaluated the clinical significance of
such errors and user satisfaction.
Design: Preintervention and postintervention study involving a
control and an intervention medical intensive care unit.
Setting: Two medical intensive care units in the same depart-
ment of a 2,000-bed university hospital.
Patients: Adult medical intensive care patients.
Interventions: After a 2-month observation period, we imple-
mented an automated dispensing system in one of the units (study
unit) chosen randomly, with the other unit being the control.
Measurements and Main Results: The overall error rate was
expressed as a percentage of total opportunities for error. The
severity of errors was classified according to National Coordinat-
ing Council for Medication Error Reporting and Prevention cate-
gories by an expert committee. User satisfaction was assessed
through self-administered questionnaires completed by nurses. A
total of 1,476 medications for 115 patients were observed. After
automated dispensing system implementation, we observed a
reduced percentage of total opportunities for error in the study
compared to the control unit (13.5% and 18.6%, respectively; p<
.05); however, no significant difference was observed before
automated dispensing system implementation (20.4% and 19.3%,
respectively; not significant). Before-and-after comparisons in the
study unit also showed a significantly reduced percentage of total
opportunities for error (20.4% and 13.5%; p<.01). An analysis of
detailed opportunities for error showed a significant impact of the
automated dispensing system in reducing preparation errors (p<
.05). Most errors caused no harm (National Coordinating Council
for Medication Error Reporting and Prevention category C). The
automated dispensing system did not reduce errors causing
harm. Finally, the mean for working conditions improved from
1.0 0.8 to 2.5 0.8 on the four-point Likert scale.
Conclusions: The implementation of an automated dispensing
system reduced overall medication errors related to picking,
preparation, and administration of drugs in the intensive care
unit. Furthermore, most nurses favored the new drug dispensation
organization. (Crit Care Med 2010; 38:2275–2281)
KEY WORDS: hospital drug distribution systems; intensive care
units; medication errors; drug safety; clinical pharmacy informa-
tion systems; job satisfaction
2275Crit Care Med 2010 Vol. 38, No. 12
Besides technological advances, the
quality of medication use relies on inter-
actions between care providers, patients,
information, and technology. The major-
ity of MEs are a direct consequence of the
intrinsic complexity of these interactions
(22). To be successful, users must be in-
volved in the implementation of new
technologies and be able to express per-
ceived benefits, or disadvantages, of the
new system.
Consequently, we aimed to assess the
impact of ADS implementation on the
incidence of MEs related to picking, prep-
aration, and administration of drugs in
the MICU. We also evaluated the clinical
impact of errors and user satisfaction as
secondary objectives.
Population and Setting
This study was conducted over the course
of 4 months in two MICUs of the same depart-
ment in a 2,000-bed university hospital. Both
units (8 and 10 beds) had comparable activi-
ties and shared the same staff.
Medications were delivered daily with a
floor stock drug distribution system. Nurses
picked drugs directly from the classic medi-
cine cabinet. There was no onsite pharmaceu-
tical review of medication orders. Study ethics
approval was obtained on November 26, 2007
(Institutional Review Board 5891).
An OmniRx ADS (Omnicell, Mountain
View, CA) was implemented in one unit (study
unit), chosen randomly, while the former dis-
tribution system was maintained in the other
unit (control unit). The computer-controlled
ADS stores most of the medications directly in
the nursing unit and records medication pick-
ing. The compartments of the ADS are re-
loaded daily by a pharmacy technician, except
on weekends, when drugs are dispensed con-
ventionally by an on-call pharmacist.
Before implementing the ADS, nurses at-
tended a training program. A 2-wk run-in pe-
riod was allowed before data collection to per-
mit nurses to become familiar with the
Study Design and Data
Errors were collected by direct observation
(23), which is previously shown to be reliable
for identifying MEs (24). A pharmacist ob-
served picking, preparation, and administra-
tion of drugs by nurses. Two-month observa-
tion periods were performed: before (phase I)
and after (phase II) implementation of the
ADS in the study unit, each preceded by a
15-day run-in period.
Observation sessions were 3– 4 hrs long
and occurred 4 days per week, including
nights and weekends. They were unannounced
and concerned all the nurses present in the
unit (two or three patients per nurse), and the
unit and nurse were randomly assigned by a
methodologist. Preparations for several pa-
tients could be observed simultaneously (they
took place in the same workstation), whereas
administration was observed for only one pa-
tient at a time.
The data collection procedure had been
tested during the run-in period to lessen the
so-called Hawthorne effect (people may be-
have differently when they know they are be-
ing observed) (25). For ethical reasons, the
observer intervened whenever an error was
considered as possibly harmful (the error was
then included in the analysis).
ME Assessment
Any discrepancy between the medication
use process (picking, preparation, and admin-
istration) and prescriptions or recommenda-
tions was considered as an error.
Our primary outcome was the overall error
rate during picking, preparation, and admin-
istration. Overall error rate was defined as the
percentage of total opportunities for error
(%TOE), calculated by dividing the number of
drugs associated with one or more errors by
the number of drugs ordered (whether
picked), as previously described (11, 25).
As secondary outcomes, we expressed MEs
as the percentage of detailed opportunities for
error (%DOE) to calculate distinct error rates
for picking, preparation, and administration
(13). We defined as DOE any gesture/action by
a nurse that could result in the following types
of error: name, dosage, and pharmaceutical
form for picking; dose, solvent type/volume for
reconstitution, and mixtures for preparation;
technique (13), route, rate, time more than 1
hour before or 1 hour after expected time, and
physicochemical incompatibility for adminis-
tration. Thus, each prescribed dose was asso-
ciated with up to 12 DOE. For each type of
error we calculated %DOE by dividing all ob-
served errors by the number of DOE. Omis-
sions and extra doses were analyzed separately.
Another secondary objective was to com-
pare errors in the conditions of drug storage
(storage errors) in both units before and after
ADS introduction. Errors concerned inappro-
priate storage temperature and/or protection
from light and humidity. They were expressed
as the percentage of storage errors, calculated
by dividing the number of storage errors by
the number of drugs. To ensure the quality of
data collection, 10% of the case report forms
were monitored by a research assistant.
ME Severity
An independent multidisciplinary commit-
tee (intensive care specialist, methodologist,
pharmacologist, clinical pharmacist), not in-
volved in the observation process, retrospec-
tively reviewed all errors. The committee had
access to the whole description of the error
but was blinded to the unit and the phase (i.e.,
ADS or conventional system) and to the nurse
and the patient.
Each error was classified according to the
following criteria: stage in the process (pick-
ing, preparation, and administration), type of
error, and severity using the National Coordi-
nating Council for Medication Error Report-
ing and Prevention (NCC MERP) method. We
distinguished among errors reaching the pa-
tient but causing no harm (NCC MERP cate-
gories C and D), those causing harm (NCC
MERP categories E to H), and those causing
death (NCC MERP category I) (2).
User Satisfaction
Perceived usefulness is related to the in-
tention of use and usage behavior (26). Self-
administered questionnaires were used to
evaluate nurses’ perceptions of the new sys-
tem. A four-point Likert scale was used to
avoid inconclusive neutral answers (27). Sur-
veys were organized 2 wks before, 6 wks after,
and 8 months after ADS implementation to
highlight any “resistance-to-change” phenom-
ena (28).
Sample Size Calculation and
Statistical Analysis
According to literature, the ME rate was
estimated at 7% DOE in the MICU (13, 29). A
39% decrease in errors after implementing the
ADS was described, including organizational
errors (19). Because our methodology did not
allow their detection, we hypothesized a 33%
decrease to calculate the number of observa-
tions needed. Assuming an error rate of 7%
DOE in the control group, 1,785 observations
would be needed in each group to detect sta-
tistical significance (chi-square test or an ex-
act Fisher test if required) with a risk of 0.05
and a power of 0.85 (nQuery Advisor 6.01 for
Windows). Anticipating 10% missing data,
2,000 observations per unit would be needed
before and after ADS introduction.
Categorical data were reported as fre-
quency and percentage and continuous data as
mean and standard deviation or median and
interquartile range (i.e., 25th and 75th per-
centiles) when appropriate. Exact 95% confi-
dence intervals were computed from the bino-
mial distribution for binary outcomes.
Characteristics were compared between study
groups using the Kruskal-Wallis test for quan-
titative data and the chi-square test, or Fish-
2276 Crit Care Med 2010 Vol. 38, No. 12
er’s exact test when appropriate, for categori-
cal data.
We then modeled binary outcomes using
logistic regression models that comprised the
study group, the study period, and a first-order
interaction between the study group and pe-
riod. We estimated the absolute difference in
change between study groups from the pre-
dicted probabilities derived from the logistic
regression models. This is the same strategy as
used previously (30).
We considered p.05 as significant. Sta-
tistical analyses were performed using Stata
version 10.2 (Stata Corporation, College Sta-
tion, TX).
Descriptive Data
Sixty-eight nurses were involved in
this 4-month study. They picked, pre-
pared, and administered 1,476 medica-
tions (involving 8,753 DOE) to 115 pa-
tients. Patient characteristics are given in
Table 1. The median (interquartile range)
length of stay was 10 days (4 –23 days).
The most frequent principal diagnoses
were respiratory failure and septic shock.
During the whole study, we identified
295 errors related to picking, prepara-
tion, or administration. The observer in-
tercepted seven errors (four before and
three after ADS implementation). The
type of electrolytes (e.g., potassium, so-
dium, calcium, magnesium) was involved
in the highest number of errors (26%).
The second most common error con-
cerned insulin (6%). Most medications
(88%) were intravenous drugs.
Impact of the ADS on MEs
After ADS implementation, we ob-
served a difference in the %TOE between
control and study units (18.6% and
13.5% TOE, respectively; p.05),
whereas no difference was observed be-
fore ADS implementation (19.3% and
20.4% TOE, respectively; not significant).
The %TOE also significantly decreased in
the study unit between phase I and phase
II (p.01) (Fig. 1). The %TOE decrease
in the intervention unit was 6.9% and
0.7% in the control unit, with a 6.2%
absolute difference in change (95% con-
fidence interval, 1.8% to 14.2%; p
.13). Table 2 shows errors expressed as
%DOE in both units for both phases. In
the study unit, the incidence of prepara-
tion dose errors significantly decreased
from 3.8% to 0.5% DOE (p.017). How-
ever, the ADS did not decrease picking
and administration errors. The latter
mostly concerned the rate of infusion
(47%) and administration time (34.2%).
We also observed 16 omission errors and
two extra doses during the whole obser-
vation period, with no difference between
the two phases.
Besides the 295 errors observed dur-
ing picking, preparation, and administra-
tion, we identified 145 storage errors. The
ADS had a major impact on this type of
error with 51 (27.7%) and 65 (34.9%)
errors (study and control units, respec-
tively) in phase I compared to 2 (0.7%)
and 27 (14.4%; p.01) errors after ADS
implementation (phase II, study and con-
trol units, respectively). Storage errors
were drastically decreased in the study
unit (96% reduction; p.01). It is of
note that they were also significantly de-
creased in the control unit (58% de-
crease; p.01). Table 3 provides exam-
ples of observed errors and propositions
to avoid them.
Severity of Errors
The severity of observed errors, with
and without ADS, is summarized in Table
4. Most errors (84%) were classified as
errors causing no harm (NCC MERP cat-
egories C and D). The ADS decreased
NCC MERP category C errors by 35%
(2.6% [2.2%–3%] before and 1.7%
[1.3%–2.3%] after implementation).
However, errors in categories D to H
were not affected. No error resulted in
patient death. We observed 10 picking
errors causing patient harm, all in the
control unit.
User Satisfaction
The median age of the 68 nurses in-
volved in this study was 27 (4.2) years;
76.2% were female and they had worked
in the MICU for a median of 1.1 (1.8)
years. A total of 64 questionnaires were
returned by the nurses. Response rates
were 36% (n 18) before ADS imple-
Figure 1. Overall medication error rate expressed as a percentage of total opportunities for error
(%TOE) in the study and the control units before (phase I) and after (phase II) implementation of the
automated dispensing system. *p.05 (chi-squared test). §p.01 (chi-squared test). #p.10
(interaction test).
Table 1. Characteristics of the studied population in the control and the study units before (phase I)
and after (phase II) implementation of the automated dispensing system
Control Unit Study Unit
(n 115)
Phase I
(n 31)
Phase II
(n 25)
Phase I
(n 32)
Phase II
(n 27)
Age (yrs) 63 (54–73) 61 (53–74) 63 (56–71) 62 (49–76) 62 (53–73)
Male 17 (63) 21 (67.7) 20 (62.5) 18 (72) 76 (66.1)
Length of stay (days) 11 (5–25) 6 (3–12) 11.5 (6–25) 12 (4–34) 10 (4–23)
Simplified Acute Physiology
Score II
41 (37–53) 44 (30–57) 44 (36–61) 48 (37–68) 44 (34–60)
Deaths 8 (29.6) 5 (16.1) 5 (15.6) 6 (24) 24 (20.9)
Quantitative data are expressed as median (interquartile range). Qualitative data are expressed as
number (percentage).
2277Crit Care Med 2010 Vol. 38, No. 12
mentation, 31% (n 14) at 6 wks, and
57% (n 32) at 8 months. Only 10
nurses answered all three surveys, prob-
ably because of high staff turnover. De-
scriptive data about nurse satisfaction re-
vealed a tendency to greater satisfaction
with time, especially regarding time
saved and working conditions (Fig. 2).
Finally, the majority of nurses wished to
continue using ADS (96.7%).
Using a direct observation approach,
we showed a decrease in the incidence of
errors after implementing ADS. In addi-
tion, storage errors were drastically de-
creased by ADS. The most frequent errors
concerned preparation (especially the use
of wrong diluents) and administration
(especially rate and time) processes. Dose
errors were significantly reduced (p
.05) by ADS. Furthermore, ADS was well
perceived by nurses.
To date, several studies have assessed
MEs in ICUs, showing highly variable
rates from one study to another (3.3%–
44.6%) (8, 10, 11, 13, 14). These discrep-
ancies may be explained by heteroge-
neous drug distribution systems and ways
of data expression (%TOE or %DOE).
Furthermore, the ratio of intravenous
compared to oral doses prescribed may
influence the error rate. Our results ex-
pressed as %TOE are consistent with the
findings from Kopp et al (14) and Barker
et al (7), showing an incidence of approx-
imately one error for every five doses of
medication administered before ADS was
implemented. Besides, Tissot et al (13)
and Fahimi et al (10) found slightly
higher error rates with data expressed as
%DOE, probably because of variations in
Table 2. Distribution of medication errors related to picking, preparation, and administration, expressed as a percentage of detailed opportunities for error
before (phase I) and after (phase II) implementation of the automated dispensing system
Control Unit Study Unit
Phase I (n 300) Phase II (n 333) Phase I (n 368) Phase II (n 475)
Stage Type of Error %DOE Errors DOE %DOE Errors DOE %DOE Errors DOE %DOE Errors DOE
Picking Total 1.9 9 465 3.3 15 460 1.8 8 435 1.5 9 595
Brand name 0.8 2 241 1.7 4 240 1.3 3 236 0.6 2 331
Dosage 4.0 4 100 5.7 7 123 4.5 4 88 3.2 5 156
Form 2.4 3 124 4.1 4 97 2.4 1 111 1.9 2 108
Preparation Total 3.8 26 678 4.1 25 604 6 42 695 3.4 26 764
Dose 1.6 3 191 1.9 3 157 3.8 7 185 0.5 1 200
Diluent type 0.7 1 148 2.3 3 131 0.6 1 159 1.1 2 178
Diluent volume 11.0 18 163 10.5 16 153 15.7 26 166 11.6 22 189
Incompatibility 2.3 4 176 1.8 3 163 4.3 8 185 0.5 1 197
Administration Total 3.1 28 898 3.1 25 799 2.8 31 1120 2.7 33 1240
Technique 0 0 186 0.6 1 164 0.4 1 232 0.4 1 251
Route 0.5 1 187 0.6 1 165 0 0 232 0 0 249
Rate 10.6 18 170 7.3 11 150 4.3 9 211 6.8 17 250
Time 4.2 8 191 3.8 7 182 7.4 18 243 2.6 7 271
Incompatibility 0.6 1 164 3.6 5 138 1.5 3 202 3.7 8 219
Total DOE
3.1 (2.4–4.0) 63 2041 3.9 (3.1–4.9) 65 1863 3.8 (3.1–4.7) 81 2250 2.7 (2.1–3.4) 68 2599
DOE, detailed opportunities for error; N, number of medications observed.
Data are expressed as numbers (errors and DOE) or percentages (%DOE), with 95% confidence interval in parenthesis for total DOE.
Table 3. Examples of errors and how automated dispensing system can avoid them
Examples of Error Stage Type of Error Solution ADS
Magnesium chloride instead of calcium chloride Picking Brand name Write calcium on the screen of ADS Yes
Cefotaxime instead of ceftriaxone (rocephine) Picking Brand name Write Rocephine, the ADS recognizes it as
Oral piracetam administered by intravenous route Picking Form Write piracetam and the ADS will propose both
intravenous and oral
Mycophenolate mofetil 250 instead of 500 mg
(capsule opened)
Picking Dosage ADS proposes both dosages Yes
L-Dopa immediate release instead of prolonged
Picking Form ADS proposes both forms Yes
L-Thyroxin, wrong dosage Picking Dosage ADS proposes several dosages Yes
Insulin administered at wrong rate Administration Rate Follow protocol and adjust rate to glycemia No
Potassium chloride, wrong dose Preparation Dose Check quantity prescribed twice No
Dilution of liposomal amphotericin B in NaCl 0.9% Preparation Diluent type Check type of diluent in reference book before
Intravenous ofloxacin administered within 5
Administration Rate Check recommendation for infusion rate in reference
ADS, automated dispensing system.
2278 Crit Care Med 2010 Vol. 38, No. 12
the nature and number of observed DOE.
Because there is no consensus about data
expression, we used both methods. The
%TOE as defined by Barker et al (31) has
been widely used, making it easier to com-
pare our results with other works. The ap-
proach of Tissot et al (29) is more realistic,
calculating separate error rates for each
stage of the preparation and administration
process, which is the reason why we also
expressed our results as %DOE. Interest-
ingly, some recent works have expressed
MEs as the number of events per patient
ICU days. Valentin et al (12) showed an
incidence of 74.5 errors per 100 patient ICU
days. Previous data revealed a highly vari-
able rate of MEs, ranging from 1.2 to 947
errors per 1000 patient ICU days, with a
median of 106 errors per 1000 patient ICU
days (32). Because of this variability, and
because our study was not designed to in-
clude all patients admitted to the partici-
pating units, we did not express our results
as MEs per patient days.
We performed multiple pairwise com-
parisons between study groups (i.e., con-
trol vs. intervention) and periods (prein-
tervention vs. postintervention) as
described by Bates et al (15), which is a
common method, to compare our results
with those of other similar studies. Al-
though less common, a more appropriate
approach is to compute the absolute
change in TOE for each study group sep-
arately and then derive the difference in
changes from logistic regression models,
including a first-order interaction term
between study group and period (30). Us-
ing such a method, however, we did not
reach statistical significance, probably
because of a lack of power. Sample size
calculation was not based on this test.
Most studies have assessed MEs using
direct observation. Although this is con-
sidered as more efficient and accurate
than reviewing charts and incident re-
ports (24), it may underestimate MEs.
Our study design did not provide infor-
mation linking the MEs to a nurse. Thus,
we could not account for ME clustering
by nurse, with the potential for optimistic
95% confidence intervals.
The picking of incorrect medications
and the confusion of similar drug names
are problems that occur with open-shelf
storage of pharmaceuticals (22). It was
therefore a reasonable assumption that the
ADS would reduce picking errors. How-
ever, although we observed a significant
reduction in preparation dose errors, we
found no significant decrease in picking
errors with the ADS. This may be explained
by the scarcity of such events and a poten-
tial lack of power and should be tested in a
larger-scale study. Nonetheless, on close in-
spection, we realized that ADS could not
prevent all picking errors. For example,
when potassium chloride 2 g/L is pre-
scribed in a 500-mL volume, there is a risk
that 2 grams are added instead of 1 gram.
Another example we observed was picking
of fluconazole 400 mg instead of flucon-
azole 200 mg, because they appeared next
to one another on the screen. Our ADS
allowed users to select any drug available,
thus overriding the prescription, creating a
potential source of error. Optimal integra-
tion of a computerized physician order en-
try interface could help to achieve greater
error reduction, making medications avail-
able to nurses only after physician orders
are entered into the system (18 –20).
The lack of effect on administration
errors (time, omissions, rate) was ex-
pected because most of these are not di-
rectly preventable by ADS. For example,
Figure 2. User satisfaction assessed through self-administered questionnaires filled in by the nurses
2 wks before automated dispensing system implementation (n 18), 6 wks after (n 14), and 8
months after (n 32). Satisfaction is expressed as mean score of a Likert scale ranging from 0 (not
satisfied) to 3 (very satisfied).
Table 4. Potential clinical significance of errors in the study unit before the automated dispensing
system implementation and in the control unit (without automated dispensing system) compared to
the study unit after automated dispensing system implementation (with automated dispensing system)
No Automated Dispensing System
(6,154 Detailed Opportunities for
Error) % (n)
Automated Dispensing
System (2,599
Detailed Opportunities
for Error) % (n)
Errors not causing harm 3.1 (190) 2.1 (54)
C (reached the patient) 2.6 (158) 1.7 (44)
D (reached the patient and resulted
in increased patient monitoring
and/or intervention)
0.5 (32) 0.4 (10)
Errors causing harm 0.6 (35) 0.7 (16)
E (resulted in need for therapy or
intervention, caused temporary
0.3 (17) 0.3 (7)
F (resulted in initial or prolonged
hospitalization and temporary
harm to patient)
0.2 (13) 0.3 (6)
H (resulted in near-death event) 0.1 (5) 0.1 (3)
Data from the control unit (phases I and II) and from the study unit (phase I) were pooled and
compared with data in the study unit after automated dispensing system implementation (phase II).
Data are expressed as a percentage of detailed opportunities for errors according to their severity
using the National Coordinating Council for Medication Error Reporting and Prevention method. We
distinguished errors reaching the patient causing no harm (National Coordinating Council for
Medication Error Reporting and Prevention categories C and D) and those causing harm (National
Coordinating Council for Medication Error Reporting and Prevention categories E to H).
2279Crit Care Med 2010 Vol. 38, No. 12
we frequently observed serious errors in
the glycemia control protocol, indepen-
dent of ADS use.
Another finding was the sharp reduction
in storage errors after ADS implementa-
tion, not only in the study unit but also in
the control unit. This may be explained by
the positive effect of ADS on drug storage
information and by a methodologic limita-
tion of our study: the same nurses worked
in both units. This limitation may have
improved the quality of medication use in
the control unit, thus underestimating the
impact of the ADS and explaining the non-
significant absolute difference in change.
However, we favored a comparison between
two units with similar activities and orga-
nization despite sharing the same staff,
rather than choosing a control MICU in
another institution or a surgical ICU in our
The analysis of error severity revealed
that almost 85% of errors caused no
harm (NCC MERP category C). These er-
rors were reduced by the ADS. However,
we observed no impact on errors associ-
ated with harm, mostly concerning per-
fusion rates (e.g., insulin, propofol) and
omissions. Such errors cannot be pre-
vented by ADS.
New technologies may lead to misuse
or incorrect behaviors when not per-
ceived as appropriate (33, 34). In the
present study, evaluation of users’ satis-
faction showed an intermediate (short-
term) stage corresponding to a period of
reorganization and “resistance to
change” phenomena (28). The implemen-
tation of ADS induced a substantial reor-
ganization in the drug distribution sys-
tem. However, overall satisfaction was
clearly improved after 8 months when
nurses estimated that they were spending
less time receiving medication-related ac-
tivities. However, our quantitative data
about user satisfaction should be consid-
ered with caution because response rates
to questionnaires were low (from 31%–
57%) and only ten nurses answered to all
three surveys, thus lessening the validity
of such a tool. Nonetheless, overall feed-
back for ADS was positive, as described
previously (35). Fatigue, stress, heavy
workload, new staff, and personal neglect
are risk factors for MEs (33); thus, efforts
should focus on improving nurses’ work-
ing conditions and knowledge about
medications (34).
Finally, profound changes in medica-
tion use procedures could further im-
prove safety. Particularly, preparation of
doses by the pharmacy department could
decrease preparation errors (36). Fur-
thermore, remarkably low ME rates are
reported after combined ADS introduc-
tion and healthcare team reorganization
(8) involving physicians, nurses, pharma-
cists, unit directors, and managers (37).
The implementation of ADS reduced
overall MEs related to picking, prepara-
tion, and administration of drugs in the
MICU. Most nurses favored the new drug
dispensation organization. However, one
should bear in mind that any changes
may generate new error risks, justifying
continuous quality monitoring. A larger-
scale multicenter study is in preparation
to further assess the impact of ADS on
the detailed medication use process and
to determine its cost efficiency.
We thank Marion Proust, research as-
sistant, for her valuable assistance; Jean-
Louis Quesada for help on statistical
methods; Dr. Ce´ line Villier, pharmacolo-
gist, Pharmacovigilance Centre Grenoble
University Hospital, for her expertise; and
Dr. Alison Foote, for editing the text. We
also thank all the nurses who took part in
the study.
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... Les études identifiées (n=33) ont été conduites dans differents pays répartis sur les cinq continents : l'Europe (n=17), l'Amérique (n=7), l'Asie (n=5), l'Afrique (n=3) et l'Océanie (n=1). Les principaux pays concernés étaient les États-Unis avec six études (34,122,(135)(136)(137)(138), la France avec cinq études (139)(140)(141)(142)(143), le Royaume-Uni aussi avec cinq études (124,(144)(145)(146)(147), les Pays-Bas avec deux études (148,149) et le Danemark aussi avec deux études (150,151). ...
... La plupart des études (n=23) a eu lieu dans des hôpitaux universitaires (34,63,114,122,123,135,(138)(139)(140)(141)143,146,147,(150)(151)(152)(153)(154)(155)(156)(157)(158)(159) ...
... ). Concernant les services, les études ont141,147,149,150,153,154,157,159,160), des services gériatriques(114,142,146), des services de santé mentale(144,145,151), des services de soins intensifs(34,122,135,143,148,152,158), desservices d'urgence (155) ou des services mixtes (123,136-138,156,161). La moyenne d'âge des participants variait entre 46 ans (151) et 84 ans (142). ...
Les erreurs médicamenteuses représentent un défi mondial de santé publique lancé par l’Organisation Mondiale de la Santé en 2017. Ces erreurs peuvent survenir tout au long du processus médicamenteux mais le plus souvent au stade de l’administration, tant en milieu hospitalier que communautaire. Les erreurs médicamenteuses impliquent différents acteurs tels que les patients et les professionnels de santé, notamment le personnel infirmier. L’impact de ces erreurs est plus important pour les populations fragiles comme les enfants et les personnes âgées. En réponse à ce défi, il est important de quantifier les erreurs médicamenteuses et d’investiguer les sources d’hétérogénéité dans leurs taux, ainsi que d’identifier les déterminants de ces erreurs dans les populations à risque. En réalisant une revue systématique de la littérature et une méta-analyse, nous avons estimé la prévalence des erreurs d’administration chez les adultes hospitalisés, tout en explorant et maitrisant l’hétérogénéité. La prévalence moyenne d’au moins une erreur d’administration atteignait 26% avec maitrise d’hétérogénéité. La principale source d’hétérogénéité des taux poolés de cette méta-analyse était liée aux méthodes de calcul, spécifiquement le dénominateur. La standardisation de ces méthodes est donc une nécessité. La deuxième et la troisième études rétrospectives se basaient sur une analyse des déclarations des erreurs médicamenteuses au Guichet Français entre 2013 et 2017. Tout âge et dans milieux de soins confondus, les erreurs les plus fréquemment déclarées concernaient le stade d’administration. Au travers des analyses multivariées, nous avons identifié les déterminants des erreurs dans les populations pédiatrique et gériatrique par comparaison aux adultes. Dans les deux milieux hospitalier et communautaire, les antibactériens à usage systémique (Odds Ratio ajusté, ORa=2.54; Intervalle de Confiance 95%, IC95% : 1.57-4.11 et ORa=2.08 ; IC95% : 1.46-2.96, respectivement), et le type d’erreur de dose (ORa=2.51 ; IC95% : 1.71-3.68 et ORa=1.33 ; IC95% : 1.04-1.70, respectivement) étaient plus susceptibles d’être associés aux erreurs déclarées chez la population pédiatrique par comparaison aux adultes. D’autre part, dans ces deux milieux, les agents antithrombotiques (ORa=2.63 ; IC95% : 1.66-4.16 et ORa=4.46 ; IC95% : 2.70-7.37, respectivement) étaient plus susceptibles d’être associés aux erreurs médicamenteuses, ainsi qu’aux erreurs avec effet indésirable grave chez la population gériatrique par comparaison aux adultes. Les erreurs médicamenteuses représentent un risque majeur pour les patients et particulièrement les populations pédiatrique et gériatrique. Des stratégies de prévention ciblée sont nécessaires.
... Automation is incorporated in pharmacies in many ways including "record keeping, item selection, labeling, and dose packing" (Spinks et al., 2017, p. 394). Both hospital and retail pharmacies may reconfigure the behind-thecounter workspace for machines (Barrett et al., 2012;Chapuis et al., 2010). But machines are also often placed at centralized distant sites to pre-fill bottles for retail locations (Spinks et al., 2017). ...
... Human errors occur at a rate of approximately 5 errors per 100,000 orders (Gorbach et al., 2015). But automated prescription filling has error rates near zero (Fanning et al., 2016; cf., Chapuis et al., 2010) and significantly decreases prescription filling time (Walsh et al., 2011). This bottom-line decision-making focused on operations and productivity likely shapes the organizational reality faced by pharmacy workers, especially those whose roles require less skill (i.e., technicians; Wheeler et al., 2019). ...
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This study uses a sample of pharmacists and pharmacy technicians (N = 240) who differ in skill, education, and income to replicate and extend past findings about socioeconomic disparities in the perceptions of automation. Specifically, this study applies the skills-biased technical change hypothesis, an economic theory that low-skill jobs are the most likely to be affected by increased automation (Acemoglu & Restrepo, 2019), to the mental models of pharmacy workers. We formalize the hypothesis that anxiety about automation leads to perceptions that jobs will change in the future and automation will increase. We also posit anxiety about overpayment related to these outcomes. Results largely support the skillsbiased hypothesis as a mental model shared by pharmacy workers regardless of position, with few effects for overpayment anxiety.
... However, the use of automated systems at the various stages of the medication use process have significantly reduce the occurrence of medication errors and the associated clinical and financial burden [8][9][10][11]. Automated systems such as electronic medical records, computerized physician order entry system, clinical decision support system, automated / robotic dispensing systems and bar code administration system have all contributed to significant reduction in medication errors and patient harms [12][13][14][15][16][17]. A systematic review of the effectiveness of automated systems in outpatients and community settings showed a 37% reduction in medication errors, increased productivity and reduced patient waiting and prescription filling time [18]. ...
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The use of automated systems within the medication use process has significantly reduce the occurrence of medication errors and the associated clinical and financial burden. However, automated systems lull into a false sense of security and increase the risk of medication errors that are often associated with socio-technical interactions, automation bias, workarounds and overrides. The objective of the systematic review is to determine the prevalence, types and severity of medication errors that are associated the use of automated systems in ambulatory and institutionalized care settings. The search strategy will be guided by PRISMA framework. Selected databases and relevant gray literature were searched and screening was done independently by two researchers between 01 April and 29 June 2021. These covered all relevant articles published from the inception of the use of automation in the medication use process (2000) until 2020. De-duplication and screening of all studies were done independently by two researchers with a clear inclusion / exclusion criteria. Data extraction and synthesis are currently on going (started on 06 July 2021) and being conducted independently but the validity and completeness of the processes will be confirmed by the third researcher. The Cochrane Risk of Bias tool and the Hoy et al’s quality assessment checklist will be used for the assessment of methodological bias while the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system will be used for the quality of evidence assessment. Detailed qualitative synthesis of key findings will be done with thematic and descriptive analyses. If the number and types of included studies permit, fixed or random effect model meta-analysis will be conducted based on the degree of homogeneity in the sampling frame used in the included studies. Heterogeneity will be assessed with I ² statistics and I ² > 50% will be considered a high statistical heterogeneity. The systematic review may provide new perspective especially from developing settings about the prevalence, types and severity of medication errors associated with the use of automated systems at all the stages of medication use process, and in all categories of patients. This may add to global knowledge in the research area. Systematic review registration : The systematic review was registered and published by PROSPERO (CRD42020212900).
... The health informatics technique underlying budgeting, personnel, patients, legal disputes, logistics, supplies, and other procedures and medical workflows are often made up of a sequence of conditional steps that can be visualized as a series of repeated patient-care activities (Alotaibi and Federico 2017). Among hospitals and other healthcare service providers, internal controls should be increased; performance, compliance, and consistency should be enhanced; and risk, job time, and overhead should be reduced (Chapuis et al. 2010). This article outlines a healthcare smart contract structure that can handle patient data and simplify complicated medical treatments (Campanella et al. 2016), based on advanced healthcare blockchain analysis and a robust approach to healthcare management. ...
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Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.
... 28 The use of different technology applications may reduce medication incidents during storing 29 , dispensing/preparing phases 30 by using automated drug dispensing cabinets (ADC) which use bar-code technology, especially by integrating a computerized physician order entry interface within the ADC. 29 While fewer incidents were reported within the prescribing and administering phases, such incidents should be prevented from occurring, and incidents should be detected earlier. Replacing handwritten or oral prescribing systems with computerized prescriber order entry and clinical decision support system provides timely alerts, 31 and also introducing and strengthening clinical pharmacy services in ICUs, such as prescription reviews of medicines, including fluids, on a regular basis, and clarification of ambiguities, with the prescriber before dispensing and preparing medicines 32 may improve prescribing phase safety. ...
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Background Fluid therapy is a common intervention in critically ill patients. Fluid therapy errors may cause harm to patients. Thus, understanding of reported fluid therapy incidents is required in order to learn from them and develop protective measures, including utilizing expertise of pharmacists and technology to improve patient safety at the national level. Objectives To describe fluid therapy incidents voluntarily reported in intensive care and high dependency units (ICUs) to a national incident reporting system, by investigating the error types, fluid products, consequences to patients and actions taken to alleviate them, and to identify at which phase of the medication process the incidents had occurred and had been detected. Methods Medication related voluntarily reported incident (n = 7623) reports were obtained from all ICUs in 2007–2017. Incidents concerning fluid therapy (n = 2201) were selected. The retrospective analysis utilized categorized data and narrative descriptions of the incidents. The results were expressed as frequencies and percentages. Results Most voluntarily reported incidents had occurred during the dispensing/preparing phase (n = 1306, 59%) of the medication process: a point of risk. Most incidents (n = 1975, 90%) had reached the patient and passed through many phases in the medication process and nursing shift change checks before detection. One third of the errors (n = 596, 30%) were reported to have caused consequences to patients. One quarter (n = 492, 25%) of the errors were reported to have required an additional procedure to alleviate or monitor the consequences. Conclusions Utilizing national incident report data enabled identifying systemic points of risk in the medication process and learning to improve patient safety. To prevent similar incidents, initial interventions should focus on the dispensing/preparing phase before implementing active medication identification procedures at each phase of the medication process and nursing shift changes. Strengthening clinical pharmacy services, utilizing technology, coordinated by IV Fluid Coordinators and Medication Safety Officers, could improve patient safety in the ICUs.
An automatic system in equipment can be recognized as a systematic control system to minimize human tasks or automatically complete repetitive tasks without human involvement. This review paper focused on the dispensing machines in the medical field particularly the design development of the automated dispensing machines which can be considered as one of the important technologies in pharmacy. This paper also briefly describes the main criteria for fabricating the dispensing machine. Next, different fabrication methods of the dispensing machine such as simple fabrication and 3D printing as well as the materials used in 3D printing are also thoroughly discussed. The last section consisted of the evaluation methods to justify the developed devices by comparing the performance of fabricated products and also through surveys and feedback from patients and medical staff. Based on the performance evaluation of automated dispensing machines as reported in the literature, it can be concluded that the implementation of an automated dispensing machine is necessary for the high-quality guarantee of community healthcare.
Introducción: Los sistemas intrahospitalarios sobre la distribución de medicamentos comprenden múltiples procesos en cadena, con la participación de diferentes personas por lo que es susceptible a la generación de errores. Objetivo: El propósito fue conocer la generación de posibles errores durante el proceso de registro de datos en el sistema intrahospitalario de distribución de medicamentos del Instituto Gastroenterológico Boliviano Japonés de Cochabamba. Metodología: Estudio observacional, descriptivo y transversal; donde se utilizó una lista de cotejos para la revisión del registro realizado en los formularios: kardex, recetario/recibo y hoja de tratamiento en el servicio de internación, así como también el registro de medicamentos dispensados realizado en el servicio de farmacia. Resultados: En el servicio de internación se identificó errores de omisión en el kardex de tratamiento, específicamente en el registro de la forma farmacéutica: comprimidos y frascos.. En el servicio de farmacia también se encontró errores de omisión en el registro de las mismas formas farmacéuticas. En los servicios de internación y farmacia, se encontró errores de comisión con el registro de dosis diferente en los formularios recibo recetario y hoja de tratamiento y de medicamento dispensado respectivamente superior al 30% en todos los casos. Conclusiones: Los errores identificados en el servicio de internación fueron errores de registro de tipo omisión y comisión. En el servicio de farmacia, los errores encontrados fueron errores de registro de tipo comisión.
Aim This work aimed to evaluate the impact of automated dispensing cabinets on the dispensing error rate, the number of interruptions, and pillbox preparation times. Methods A prospective observational study was conducted across 16 wards in two departments (internal medicine and surgery) of a large teaching hospital. The study compared eight wards using automated dispensing cabinets (ADCs) and eight using a traditional ward stock (TWS) method. A disguised observation technique was used to compare occurrences of dispensing errors and interruptions and pillbox preparation times. The proportion of errors was calculated by dividing the number of doses with one or more errors by the total number of opportunities for error. Wards participating in the ‘More time for patients’ project—a Lean Management approach—were compared with those not participating. The potential severity of intercepted errors was assessed. Results Our observations recorded 2924 opportunities for error in the preparation of 570 pillboxes by 132 nurses. We measured a significantly lower overall error rate (1.0% vs 5.0%, p=0.0001), significantly fewer interruptions per hour (3.2 vs 5.7, p=0.008), and a significantly faster mean preparation time per drug (32 s vs 40 s, p=0.0017) among ADC wards than among TWS wards, respectively. We observed a significantly lower overall error rate (1.4% vs 4.4%, p=0.0268) and a non-significantly lower number of interruptions per hour (3.8 vs 5.1, p=0.0802) among wards participating in the ‘More time for patients’ project. Conclusions A high dispensing-error rate was observed among wards using TWS methods. Wards using ADCs connected to computerised physician order entry and installed in a dedicated room had fewer dispensing errors and interruptions and their nurses prepared pillboxes faster. Wards participating in a Lean Management project had lower error rates than wards not using this approach.
Background: Medication errors in adult intensive care units (ICUs) are both frequent and harmful. For nurses, these errors may be multifactorial and multidisciplinary, extending from prescription stage to monitoring of patient response to medication. Therefore, diverse interventions have been developed to optimise the medication process to prevent such errors. Objectives: The objective of this systematic review was to identify research investigating interventions that may be effective in reducing the rate of nurses' medication errors in adult ICUs. Methods: A systematic search was undertaken of three databases: Cumulative Index of Nursing and Allied Health Literature, Medical Literature Analysis and Retrieval System Online, and EMCARE using a combination of key terms related to "medication errors", "nurses", "interventions", and "intensive care units". The search was limited to studies published in English between 2009 and 2019. Independent screening, quality appraisal, and data extraction were undertaken by two reviewers. Results: A total of 464 records were identified from database searches. Eleven studies met inclusion criteria: ten were quasi-experimental designs and one was a randomised controlled trial. Studies examined six types of interventions: prefilled syringes, barcode-assisted medication administration, an automated dispensing system, nursing education programs, a protocolised program logic form, and a preventive interventions program with protocols and pharmacist-supported supervision and monitoring. Findings revealed that a prefilled syringe, nurses' education programs, and the protocolised program logic form were most effective in reducing medication errors. For the barcode-assisted medication administration, automated dispensing systems, and a preventive interventions program with protocols and pharmacist-supported supervision and monitoring, results showed wide variability in effectiveness. Conclusion: This review found that the evidence for effective interventions to reduce nurses' medication errors in adult ICUs is limited, due largely to inconsistencies in research design and methods. Therefore, further studies such as randomised controlled trials focusing on a single intervention are required to provide robust evidence of the effectiveness of interventions.
Objective: Automated Drug Supplying and management System (ADS) are effective devices that secure drug's circuit and reduce hospital's expenses. The purposes of this study are to estimate the earnings made from ADS through a cost-benefit medical economic study, to highlight its impact on Central Chemotherapy Preparation Unit's (CCPU) global organization, its ergonomy and staff's satisfaction. Method: Measurement of cytotoxic drug's consumption, expiration losses, pharmacy staff's working time, drugs stock-out before and after the implementation of ADS on the one hand, and assess its ergonomy and acceptability by users on the other hand. Results: After the implementation of ADS, cytotoxic drug's consumption decreased by 9 (%), expiration losses by 98.3 (%), and we could see a gain in working time among CCPU'S technicians of 1.32 (h/day) and pharmacist of 0.67 (h/day), in contrast to the stock manager who increased his working time by 0.95 (h/day). Stock-out have decreased by 41.1 (%). The cost-benefit analysis has shown a net benefit of 67,437 between the two six-month phases, which corresponds to an economy of 134,874 (USD) over one year. The ADS was generally appreciated by the CCPU and pharmaceutical staff and 93(%) don't want to return to the old system. Conclusion: ADS implementation within CCPU led to financial savings in the hospital, an optimization of expenses and better pharmaceutical management.
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Aim The purpose of this study is to develop an understanding of work environments by analysing the perceptions of a sample of Registered Nurses (RNs). Background Within the context of high staff turnover and a shortage of nurses in the health workforce, it is important that we understand how nurses perceive their work context and view the organisational factors that influence their attitudes towards their workplace. Methods Data was collected using a 160-question survey instrument seeking information from RNs in relation to work and perceptions of the work environment and the organisation. The sample was recruited from a convenience sample of three acute hospitals in Queensland, Australia. A response rate of 41% was achieved (n = 343). Results Respondents across the three survey sites identified a number of variables that had particular impact on their working lives. Team interaction, providing good patient care, communication, and abuse towards RNs elicited strong responses by the study respondents. As well, organisational direction, strategy and management returned strong negative responses. In responding to the questions related to personal and organisational morale it was clear that respondents saw them as two distinct concepts. Conclusions The results of this study have implications for nurse managers in terms of understanding the nursing workforce as well as key organisational factors that have both positive and negative influences on the perceptions of nurses.
Conference Paper
The validity and cost-effectiveness of three methods for detecting medication errors were examined. A stratified random sample of 36 hospitals and skilled-nursing facilities in Colorado and Georgia was selected. Medication administration errors were detected by registered nurses (R.N.s), licensed practical nurses (L.P.N.s), and pharmacy technicians from these facilities using three methods: incident report review, chart review, and direct observation. Each dose evaluated was compared with the prescriber's order. Deviations were considered errors. Efficiency was measured by the time spent evaluating each dose. A pharmacist performed an independent determination of errors to assess the accuracy of each data collector. Clinical significance was judged by a panel of physicians. Observers detected 300 of 457 pharmacist-confirmed errors made on 2556 doses (11.7% error rate) compared with 17 errors detected by chart reviewers (0.7% error rate), and I error detected by incident report review (0.04% error rate). All errors detected involved the same 2556 doses. All chart reviewers and 7 of 10 observers achieved at least good comparability with the pharmacist's results, The mean cost of error detection per dose was $4.82 for direct observation and $0.63 for chart review. The technician was the least expensive observer at $2.87 per dose evaluated. R.N.s were the least expensive chart reviewers at $0.50 per dose. Of 457 errors,35 (8%) were deemed potentially clinically significant; 71% of these were detected by direct observation. Direct observation was more efficient and accurate than reviewing charts and incident reports in detecting medication errors. Pharmacy technicians were more efficient and accurate than R.N.s and L.P.N.s in collecting data about medication errors.
Our CDSS was designed to provide the probability of pulmonary embolism at each stage of a diagnostic work-up by using the Bayes theorem, validated clinical probability scoring, and estimates of test characteristics from a recent meta-analysis (9, 12). It supplied the physician with this information in real time, whereas physicians in the paper guidelines group had to actively search for it. The CDSS thus improved the diagnostic management of suspected pulmonary embolism. Its first and main effect was to prompt physicians to assess the initial pretest probability, and the large difference in such assessments between the computer-based and paper guidelines groups explains some—but not all—of the CDSS's benefit. The system helped physicians to order appropriate testing by offering flexible choices on the basis of clinical situations, locally available tests, and previous diagnostic test results. The CDSS also advised physicians to stop investigations once a diagnosis of pulmonary embolism was excluded or confirmed; physicians in the computer guidelines group were less likely to stop investigations prematurely (such as after a positive d-dimer test result) and used fewer tests to reach a validated diagnostic decision than did physicians in the paper guidelines group. Our study has limitations. More patients were enrolled in the paper guidelines group than in the computer-based guidelines group. Center allocation depended on the frequency of appropriate management during the preintervention period, rather than the number of suspected cases of pulmonary embolism. Imbalances in patient numbers during preintervention and intervention are therefore more likely to be related to the centers than to the intervention, and the number of inclusions per center and patient-related risk factors for inappropriateness were taken into account in the statistical analysis. Use of CDSS was not associated with a significant decrease in the incidence of thromboembolic events during follow-up in patients in whom the diagnosis of pulmonary embolism was excluded and who were left untreated. However, our study was not designed to detect such a difference. The lower thromboembolic event rate than that in our previous observational study (3) may be related to improvements in test performance. Differences in patient characteristics or treatment may explain the imbalance between groups in the number of deaths among patients with confirmed pulmonary embolism. Finally, the frequency of handheld computer use in real time was low, about 40%. This raises questions about whether physicians will use such a decision aid in the long term. However, handheld devices were used much more frequently during the intervention phase in the computer-based guidelines group (80%), which suggests that a diagnostic aid is used by most physicians if it is available at the time of the clinical decision.
Objectives. —To assess incidence and preventability of adverse drug events (ADEs) and potential ADEs. To analyze preventable events to develop prevention strategies.Design. —Prospective cohort study.Participants. —All 4031 adult admissions to a stratified random sample of 11 medical and surgical units in two tertiary care hospitals over a 6-month period. Units included two medical and three surgical intensive care units and four medical and two surgical general care units.Main Outcome Measures. —Adverse drug events and potential ADEs.Methods. —Incidents were detected by stimulated self-report by nurses and pharmacists and by daily review of all charts by nurse investigators. Incidents were subsequently classified by two independent reviewers as to whether they represented ADEs or potential ADEs and as to severity and preventability.Results. —Over 6 months, 247 ADEs and 194 potential ADEs were identified. Extrapolated event rates were 6.5 ADEs and 5.5 potential ADEs per 100 nonobstetrical admissions, for mean numbers per hospital per year of approximately 1900 ADEs and 1600 potential ADEs. Of all ADEs, 1% were fatal (none preventable), 12% life-threatening, 30% serious, and 57% significant. Twenty-eight percent were judged preventable. Of the life-threatening and serious ADEs, 42% were preventable, compared with 18% of significant ADEs. Errors resulting in preventable ADEs occurred most often at the stages of ordering (56%) and administration (34%); transcription (6%) and dispensing errors (4%) were less common. Errors were much more likely to be intercepted if the error occurred earlier in the process: 48% at the ordering stage vs 0% at the administration stage.Conclusion. —Adverse drug events were common and often preventable; serious ADEs were more likely to be preventable. Most resulted from errors at the ordering stage, but many also occurred at the administration stage. Prevention strategies should target both stages of the drug delivery process.(JAMA. 1995;274:29-34)
In summary, we reviewed a random sample of 30,000 medical records from New York State in 1984, analyzing them for the presence of adverse events and substandard care. We believe that our findings indicate that there are certain risk factors, many definable, for the occurrence of adverse events and negligence.