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Medication Errors Observed in 36 Health Care Facilities
Kenneth N. Barker, PhD;
Elizabeth A. Flynn, PhD;
Ginette A. Pepper, PhD;
David W. Bates, MD, MSc;
Robert L. Mikeal, PhD
Arch Intern Med. 2002;162:1897-1903.
ABSTRACT
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Background Medication errors are a national concern.
Objective To identify the prevalence of medication errors (doses administered
differently than ordered).
Design A prospective cohort study.
Setting Hospitals accredited by the Joint Commission on Accreditation of Healthcare
Organizations, nonaccredited hospitals, and skilled nursing facilities in
Georgia and Colorado.
Participants A stratified random sample of 36 institutions. Twenty-six declined,
with random replacement. Medication doses given (or omitted) during at least
1 medication pass during a 1- to 4-day period by nurses on high medication–volume
nursing units. The target sample was 50 day-shift doses per nursing unit or
until all doses for that medication pass were administered.
Methods Medication errors were witnessed by observation, and verified by a research
pharmacist (E.A.F.). Clinical significance was judged by an expert panel of
physicians.
Main Outcome Measure Medication errors reaching patients.
Results In the 36 institutions, 19% of the doses (605/3216) were in error. The
most frequent errors by category were wrong time (43%), omission (30%), wrong
dose (17%), and unauthorized drug (4%). Seven percent of the errors were judged
potential adverse drug events. There was no significant difference between
error rates in the 3 settings (P = .82) or by size
(P = .39). Error rates were higher in Colorado than
in Georgia (P = .04)
Conclusions Medication errors were common (nearly 1 of every 5 doses in the typical
hospital and skilled nursing facility). The percentage of errors rated potentially
harmful was 7%, or more than 40 per day in a typical 300-patient facility.
The problem of defective medication administration systems, although varied,
is widespread.
INTRODUCTION
THE 1999 Institute of Medicine report1
on the quality of care, entitled To Err Is Human: Building
a Safer Health System, has drawn national attention to the occurrence,
clinical consequences, and cost of adverse drug events (ADEs) in hospitals.
The report calls for more systematic approaches to the prevention of injuries
due to medical care. Many of these ADEs are viewed as originating from systems
problems (ie, problems with the processes of the medication use system). We
divide those processes into (1) prescribing and (2) delivery and administration.
The focus of this article is on the latter.
Leape and associates2 studied ADEs involving
medications using methods that included solicited self-report and daily medical
record review by clinical nurse researchers. They found that 56% of the events
they detected were due to prescribing errors and 44% involved administration.
Obviously, drug therapy cannot be successful unless prescribing and delivery
and administration are performed correctly.
A key variable in assessing the medication system in health care facilities
is whether the patient receives the prescribed medication. A medication error
was defined for this study as a discrepancy between the dose ordered and the
dose received. This definition takes a systems view of medication error, because
the focus is on the system outcome rather than on the actions of individual
health care workers. Medication error is operationalized as an easily understood
rate that is simply calculated: (doses in error/total doses given or omitted)
x 100. This measure of medication error rate has been extensively used
to test hypotheses about system improvements. For example, it was used to
evaluate the impact of the unit dose system that was ultimately adopted by
90% of US hospitals.3-6
This report is part of a study to seek the best method for detecting
and counting the frequency of medication errors in US hospitals and skilled
nursing facilities, comparing validity with cost-effectiveness. This article
reports the errors verified by a research pharmacist (E.A.F.) using the observation
method data as a gold standard in a study comparing different methods and
data collectors. Observation was superior to medical record review, and to
the examination of incident reports.7
Observation uses as the primary outcome measure the percentage of doses
ordered that are in error when administered to the patient (or omitted). Uses
of this measure have included benchmarking to help hospitals test and evaluate
new systems (eg, unit dose) in 40 studies, comparing with "best practice"
hospitals, evaluating expensive interventions (eg, automated pharmacy systems)
before and after installation, and enforcing governmental standards and regulations.8-9
Because this measure may vary by accreditation status, whether the site
is an acute-care or skilled nursing facility, or geographic location, we performed
a study to assess the medication error rate in various hospitals and skilled
nursing facilities in 2 states.
PARTICIPANTS AND METHODS
SAMPLE
The areas from which the samples of each type of facility were drawn
were the Atlanta, Ga, metropolitan statistical area and the Denver-Boulder-Greeley,
Colo, consolidated metropolitan statistical area, using lists provided by
the Health Care Financing Administration. Data provided for each facility
included address, telephone number, accreditation status, and bed size. From
these lists, 18 facilities were randomly selected for each of 3 facility types
in each state: 6 accredited hospitals, 6 nonaccredited hospitals, and 6 skilled
nursing facilities, for a total of 36 sites. Facilities were invited to participate
via letter and telephone. When a facility declined, the hospital or skilled
nursing facility in the same positional order in the next random sample was
contacted in turn until enough facilities of that category agreed to participate.
Facilities were required to have an incident report system in place (and all
approached did). A minimum bed size requirement of 24 was established after
it was found that nonaccredited hospitals with fewer beds often had too few
patients; 6 such hospitals had to be excluded.
Based on previous experience, a sample size of 50 doses per nursing
unit was chosen as large enough to obtain an adequate measure of an observation-based
error rate for each of the 36 facilities. The doses were those occurring during
a medication pass on a nursing unit identified as high volume by an official
of the facility. Up to 4 different nursing units were included if available
at each site, so that 200 doses per facility could have been observed.
The nonaccredited hospitals presented special sampling problems. Five
achieved accreditation status during the study period (7 months). The judgment
was made that the data from these hospitals should be analyzed as nonaccredited
and then as accredited.
Another problem was the small number of doses per day in some of the
nonaccredited facilities. Nonaccredited hospitals accounted for 21% of all
acute-care hospitals in the United States in 1998, with a mean bed size of
67 (median, 44).10 In the sample, the mean
bed size was 48, compared with 268 for the accredited hospitals. A consequence
was that the research team (E.A.F. and G.A.P.) arriving on the previously
negotiated day sometimes encountered fewer than 50 doses for study, and sometimes
none for several days, due to unanticipated changes in the census. In contrast,
in the larger accredited hospitals and skilled nursing facilities, the workload
of doses offered many more than 50 doses per day for study, at minimum incremental
cost. These additional data were collected to achieve a better description
of the error rate in that facility type.
RECRUITMENT AND TRAINING OF DATA COLLECTORS
Two registered nurses, 2 licensed practical nurses, and 2 pharmacy technicians
per state were sought from the general population by placing advertisements
in the newspapers and on the Internet in Denver and Atlanta. Only 1 pharmacy
technician was hired in Colorado because of a lack of qualified applicants.
Applicants took a qualifying test to determine their base knowledge of medication
and administration techniques.
Training in the observation technique required 20 hours and included
classroom lectures, an interactive videotape program, practice observations
on a nursing unit, and 2 examinations. Additional practice observations were
performed after training. One registered nurse in Georgia withdrew after training
for personal reasons.
The final examination included a paper test of the observer's ability
to detect errors when provided with a typed list of drugs administered and
a typed set of drug orders for the patients involved. A test set of 49 doses,
which included 27 errors and 22 nonerrors, was constructed. The frequency
of each error type was proportional to the occurrence of error categories
in 12 previous observation-based studies (wrong time, 16; omissions, 8; wrong
dose, 2; and unauthorized drug, 1). The scores on the examination served as
the basis for interrater and intrarater reliability assessments. The percentage
agreement on each question was used to calculate the interrater reliability
score. A repeated-measures analysis of variance was performed on the split-halves
test scores to determine intrarater reliability.
DATA COLLECTION PROCEDURES
Direct observation was used to detect medication errors, based on the
method of Barker and McConnell.11 An orientation
to each site was provided by facility personnel before observations started.
On each day of observation, the observer arrived on the nursing unit in time
to attend the change-of-shift report, to meet the staff and allow nurses to
ask questions about the study. An information sheet approved by the Auburn
University Institutional Review Board was provided to the nurse subjects.
The observer witnessed the preparation and administration of 50 doses by the
first nurse encountered plus a second nurse if necessary. The period for the
observation was 2 hours, or until all doses due were administered. The observer
wrote down exactly what the subject did, including all details about the medication,
and witnessed the administration to the patient. Data recorded included patient
names (which were later coded), drug product, amount of drug, dose form, route
of administration, time of administration, and medication-related procedures
(such as measuring the patient's heart rate or giving with food). After the
medication pass, the observer and research pharmacist made their own independent
copies of the original medication orders for patients involved in the observation.
Each dose observed was compared with what the prescriber ordered. If there
was a difference, the error was described and categorized. After comparing
all doses witnessed, the observer determined if any other drugs should have
been given at the time of the observation based on what the prescriber ordered.
If any were identified, they were recorded as omission errors unless a valid
reason was discovered. Doses given based on orders judged difficult to interpret
were excluded from the study (0.2% of the orders were deemed uninterpretable).
The medication error rate was calculated as follows: [(number of errors, with
no more than 1 error per dose)/(number of doses given + number of omissions)]
x 100.
After the observer finished the error determination, all data were turned
over to a research pharmacist. The researcher made a blinded independent determination
of errors by comparing each dose on the observer's drug pass worksheet with
the pharmacist's copy of the prescriber's orders (correcting 210 false negatives
and 87 false positives). The research pharmacist for the Colorado area (E.A.F.)
reviewed the data collected at the Georgia sites to address inconsistencies.
Only doses confirmed as in error or not in error by the research pharmacist
are reported herein.
DEFINITIONS
A medication error was defined in general as a dose administered differently
than as ordered on the patient's medical record. Such medication errors were
viewed as system defects (ie, outcomes different from those the system was
designed to deliver and administer to the patients). Categories of medication
errors were defined as follows.
1. Unauthorized drug: the administration of a dose of medication that
had never been ordered for that patient.
2. Extra dose: any dose given in excess of the total number of times
ordered by the physician, such as a dose given based on the expired order,
after a drug had been discontinued, or after a drug had been put on hold.
3. Wrong dose: any dose of preformed dosage units (such as tablets)
that contained the wrong strength or number; if an injectable product, then
any dose that was ±10% or more different from the correct dosage; if
any other dosage form, then any dose that was ±17% or more of the correct
dose in the judgment of the observer. In judging dosage, measuring devices
and graduations were those provided for routine use by the institution: graduations
on the syringe for injections, graduations on medicine cups for oral liquids,
and drops for the dropper provided. Wrong dose errors were counted for ointments,
topical solutions, and similar medications only when the dose was specified
quantitatively by the prescriber (eg, in inches of ointment).
4. Omission: failure to give an ordered dose. If no attempt was made
to administer the dose, an omission error was counted. If the patient refused
the medication, an opportunity for error was not counted provided the nurse
responsible for administering the dose tried to give it. Doses withheld according
to policies calling for the withholding of medication doses, such as nothing
by mouth before surgery, were not counted as errors or opportunities for errors.
Omissions were detected by comparing the medications administered at a given
time with doses that should have been given at that time based on the physician's
written order and protocols.
5. Wrong route: medication administered to a patient using a different
route than ordered (eg, oral administration of a drug ordered intramuscularly).
Included in this category were doses given in the wrong site, such as the
right eye instead of the left eye.
6. Wrong form: the administration of a dose in a different form than
ordered by the physician. If enteric-coated aspirin was ordered, but plain
aspirin was administered, a wrong form error was counted.
7. Wrong technique: exclusion, or incorrect performance, of a procedure
ordered by the prescriber immediately before administration of each dose of
medication. Examples include lack of heart rate or blood pressure measurement
before giving a dose.
8. Wrong time: administration of a dose more than 60 minutes before
or after the scheduled administration time. A 30-minute window was used for
medications that were ordered before, with, or after a meal. Routine administration
times were obtained from each site, and times assigned on the medication administration
record were used when no other policy was available.
Each dose observed to be given or omitted was operationally defined
to be a dose (ie, opportunity for error), and is the basic unit of data. Any
dose could be only in error or not in error. Doses included only those for
which the preparation and administration of the medication were witnessed
by an observer or that the observer was certain were not administered (ie,
omitted). Doses labeled by the pharmaceutical manufacturers were assumed to
be correct.
STATISTICAL METHODS
The overall medication error rates (with and without wrong time errors)
for each site were compared between states, facility types, accreditation
status, and facility size categories using an analysis of variance. The Tukey
test was used to determine the means between which significant differences
existed in the comparison of facility types. Computer software (SAS statistical
software for Windows, version 6.12; SAS Institute Inc, Cary, NC) was used.
The  level was set at .05.
ASSESSMENT OF POTENTIAL FOR HARM
A potential ADE was defined in general as a medication error that had
the potential to cause a patient discomfort or jeopardize the patient's health
and safety. In this study, the operational definition was the expert judgment
(and majority decision) of a 3-physician advisory panel, each experienced
in making such judgments, who evaluated the same descriptive information for
each medication error detected. Health Care Financing Administration guidelines
(available from the authors) for judging significance were provided to the
panel.
The information sent to the physicians' panel (at Brigham and Women's
Hospital) included a description of each individual error, the drug involved,
and the error category. The information excluded the data collection method
used and the data collector type so as to blind the panel to these factors.
Information sent about each patient's condition included sex, age, allergies,
disease states, selected laboratory data if associated with a medication,
red flag drugs ordered, and physician or nurse progress notes when deemed
noteworthy by the research pharmacist reviewing the patient's medical record.
An institutional review board application was submitted and approved
by Auburn University and the Colorado Multiple Institutional Review Board.
RESULTS
Overall percentage agreement on the Drug Pass Examination was 96%, with
the range of agreement on each individual question between 89% and 100%, indicating
interrater reliability. The result of the repeated-measures analysis of variance
test found that the split-halves test scores were not significantly different
within subjects, indicating intrarater reliability (F1,8 = 2.26, P = .0541).
The mean error rate detected in the 36 sites in Atlanta and Denver was
19% (605 of 3216 doses). Excluding wrong time errors, the error rate was 10%.
The range was 0% to 67%, with a 95% confidence interval of ±4.5%. All
data were collected during 81 observation days from May 4 to November 11,
1999. The error rates by category (Table
1) demonstrate that the most frequent errors were wrong time (8%),
omission (6%), and wrong dose (3%); as a percentage of all errors, the results
included wrong time (43%), omission (30%), wrong dose (17%), and unauthorized
drug (4%).
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Table 1. Error Rates by Error Category and Facility Type*
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The distribution of error rates by error category was similar between
accredited and nonaccredited hospitals and skilled nursing facilities (Table 1). When rate by site was compared
(Table 2), however, substantial
variation between sites was found, with error rates ranging from 0% at one
site to 66.7% at another. To help maintain anonymity of the sites, size was
categorized as large (>100) and small ( 100) based on number of certified
beds.
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Table 2. Error Rates by Site, Facility Type, and Region
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There was no significant difference in error rates by type or size of
facility. The statistics comparing the 3 types of sites were as follows: F2,33 = 0.20, P = .82; and excluding wrong time
errors, F2,33 = 0.39, P = .68. The 17
large sites had a mean ± 95% confidence interval error rate of 16.5%
± 5.8% (excluding wrong time errors, 9.7% ± 2.9%), and the 19
small sites had a mean ± 95% confidence interval error rate of 20.5%
± 6.9% (excluding wrong time errors, 10.2% ± 3.0%), resulting
in F1,34 = 0.75, P = .39 (excluding wrong
time errors: F1,34 = 0.06, P = .80).
The one teaching hospital had a low error rate of 4.7% (8.2% including
wrong time errors). The error rates for the accredited and nonaccredited facilities
are shown by error category in Table 1.
There was no significant difference in error rates by accreditation status,
with (F1,22 = 0.30, P = .59) or without
(F1,22 = 0, P = .95) wrong time errors.
This also proved true when those 5 in transition were treated as nonaccredited
(F1,22 = 0.05, P = .82) (excluding wrong
time errors: F1,22 = 0, P = .96).
The mean ± 95% confidence intervals error rates in the Colorado
sites (Table 3), 23.4% ±
7.6% (excluding wrong time errors, 13.0% ± 3.1%) (F1,34
= 4.75, P = .04), were significantly greater than
in the Georgia sites, 13.8% ± 4.0% (excluding wrong time errors, 7.0%
± 1.9%) (F1,34 = 10.25, P = .003).
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Table 3. Error Rates by Error Category, Facility Type, and Region*
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The 3-physician panel rated 7% of the errors detected (48 of 675 errors
assessed) as potential ADEs. When wrong time errors are excluded, 10% of the
errors were considered potential ADEs (45 of 448 errors). Table 4 lists examples of those errors rated as potential ADEs. Table 5 shows the potential clinical significance
for each error type.
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Table 4. Examples of Errors Rated by the Physician Panel as Potential
Adverse Drug Events*
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Table 5. Potential Clinical Significance for Each Error Category
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COMMENT
The results show that medication errors were common, occurring in 19%
or nearly 1 of every 5 doses in the typical site. Assuming 10 doses per patient
day, this would mean the typical patient was subject to about 2 errors every
day. There was substantial variation by site and region, however; therefore,
the results can only be described for the sample observed.
POTENTIAL FOR HARM
A panel of 3 physicians, experienced with such judgments, rated 7% of
these errors as potential ADEs (10% if wrong time errors are excluded). This
is comparable to the 8% of all errors found in the teaching hospital study
by Bates et al.12 (The methods and definitions
used, although not identical, were similar.) For 300 inpatients, assuming
10 doses per patient on 1 day, this would be almost 40 potential ADEs per
day in that facility. Many drugs could have the potential for harm in some
patients, but were judged safe herein because these particular patients were
not susceptible. For example, enteric coated aspirin, 325 mg, was administered
to a patient without an order. This unauthorized drug error was rated as not
significant by the 3-physician panel. However, if the patient were also receiving
warfarin sodium therapy, this could have been a clinically significant error.
When pharmacists in other studies were asked to judge the potential for harm
from drugs involved in errors based on their pharmacological class alone,
they found 67% of the doses threatening harm.13-15
Systems should be designed to eliminate threats to patients for the full range
of clinical conditions that might be encountered.
ACCREDITATION BY THE JOINT COMMISSION ON ACCREDITATION OF HEALTHCARE
ORGANIZATIONS
Statistically, accreditation by the Joint Commission on Accreditation
of Healthcare Organizations was irrelevant for differentiating the hospitals
by error rate. The error rates, excluding wrong time errors, ranged from 0%
(in 2 hospitals gaining accreditation during the study period) to 26.2% (also
an accredited hospital). The Joint Commission on Accreditation of Healthcare
Organizations has identified medication errors as one of the most frequent
sentinel events.16
GEOGRAPHIC LOCATION
It is unclear why the error rates for the 3 types of sites were significantly
higher in Colorado than in Georgia. The possibility that the difference was
in part due to a difference in the skills of the observers was investigated,
but no evidence of this was found. (All observers were checked by the same
pharmacist.)
ERRORS BY TYPE
In general, the prescribers in the typical facility faced the reality
that almost 1 in every 5 doses they ordered (605/3216) would be given in error,
30% of which would be omissions—the most common error type after wrong
time errors. However, the rates across facilities differed widely.
LIMITATIONS
The 36 institutions studied were selected at random (or via random replacement)
from 2 metropolitan statistical areas and were limited to those agreeing to
be studied. Remarks by those 26 institutions declining were to the effect
that they might have poor scores and wanted to improve their performance first.
Two institutions were prevented from participating as a matter of corporate
policy. Two were planning to close. Most did not give reasons. Thus, the error
rates reported likely represent a lower bound.
The doses selected for examination were a convenience sample of a medication
pass from a nursing unit identified as high volume. The typical medication
pass does not include contrast media, respiratory therapy, or most chemotherapy.
The number of doses examined was less for the nonaccredited hospitals, because
of the difficulty in anticipating medication workloads in these typically
small hospitals.
The possibility of an effect of the presence of an observer on the subjects
observed is always a concern, but it is not a severe problem when the subjects
are observed doing an activity familiar to them, such as their regular jobs,
and when the observer is trained to be unobtrusive and nonjudgmental.13, 17-20
It is possible that some errors represented intercepted prescribing errors
detected and, therefore, not followed by pharmacists or nurses. However, there
was no evidence to support this.
CONCLUSIONS
Medication errors were frequent, occurring at a rate of nearly 1 of
every 5 doses in the typical hospital and skilled nursing facility. The percentage
of errors rated potentially harmful was 7%, or more than 40 per day per 300
inpatients, on the average. Accreditation by the Joint Commission on Accreditation
of Healthcare Organizations was not associated with significantly lower error
rates. Error rates were higher in Colorado than in Georgia. Substantial variations
in error rates by facility were identified. If the rates detected are durable
over time, it should be possible to identify organizations that deserve closer
study.
The error rates are likely to be understated because of the large proportion
of facilities that declined to participate. This evidence of a high rate of
medication errors in many of the institutions in the sample supports the implications
of the Institute of Medicine report that the medication delivery and administration
systems of the nation's hospitals and skilled nursing facilities have major
systems problems. These results are especially valuable because they provide
data from primarily nonteaching sites, complementing data from large teaching
hospitals, and examine the association of accreditation with error rates.
AUTHOR INFORMATION
Accepted for publication February 13, 2002.
This study was supported by grant 500-96-P605 from the Alabama Quality
Assurance Foundation, Birmingham.
We thank Robert M. Cisneros, RPh, MS, for his valuable assistance at
16 sites in Georgia. We appreciate the input and advice of Samuel W. Kidder,
PharmD, MPH, pharmacy consultant at Health Care Financing Administration.
We thank Linda A. Pfaff, RN, MS, coordinator for operations in Georgia, for
her valuable assistance. We also thank Helen Deere-Powell, RPh; Lucian L.
Leape, MD; Loriann E. DeMartini, PharmD; G. Neil Libby, PhD, RPh; Richard
Shannon, RPh; Robert E. Pearson, RPh, MS; Tejal Gandhi, MD; Rainu Kaushal,
MD; and Jeffrey Rothschild, MD, for the various roles they played in the preparation
of the manuscript.
Corresponding author and reprints: Kenneth N. Barker, PhD, Center
for Research on Pharmacy Operations and Designs, School of Pharmacy, Auburn
University, 128 Miller Hall, Auburn, AL 36849-5506 (e-mail: barkekn{at}auburn.edu).
From the Center for Pharmacy Operations and Designs, School of Pharmacy,
Auburn University, Auburn, Ala (Drs Barker and Flynn); the School of Nursing,
University of Colorado Health Sciences Center, Denver (Dr Pepper); the Division
of General Internal Medicine and Primary Care, Brigham and Women's Hospital,
and the Center for Applied Medical Information Systems, Partners Healthcare
and Harvard Medical School, Boston, Mass (Dr Bates); and DACE Co, West Monroe,
La (Dr Mikeal).
REFERENCES
| |
1. Institute of Medicine. To Err Is Human: Building a Safer Health System. Washington, DC: National Academy Press; 1999.
2. Leape LL, Bates DW, Cullen DJ, et al. Systems analysis of adverse drug events. JAMA. 1995;274:35-43.
FREE FULL TEXT
3. Allan EL, Barker KN. Fundamentals of medication error research. Am J Hosp Pharm. 1990;47:555-571.
ABSTRACT
4. Barker KN, Heller WM, Brennan JJ, et al. The development of a centralized unit dose dispensing system, part
VI: the pilot study—medication errors and drug losses. Am J Hosp Pharm. 1964;21:609-625.
5. Barker KN, Harris JA, Webster DB, et al. Consultant evaluation of a hospital medication system: analysis of
the existing system. Am J Hosp Pharm. 1984;41:2009-2016.
ABSTRACT
6. Barker KN, Pearson RE. Medication distribution systems. In: Brown TR, Smith MC, eds. Handbook of Institutional
Pharmacy Practice. 2nd ed. Baltimore, Md: Williams & Wilkins; 1986:341-342.
7. Flynn EA, Barker KN, Pepper GA, Bates DW, Mikeal RL. Comparison of methods for detecting medication errors in 36 hospitals
and skilled-nursing facilities. Am J Health Syst Pharm. 2002;59:436-446.
FREE FULL TEXT
8. Flynn EA, Barker KN. Medication error research. In: Cohen MR, ed. Medication Errors. Washington,
DC: American Pharmaceutical Association; 1999:6.1-6.30.
9. Barker KN, Felkey BG, Flynn EA, Carper JL. White paper on automation in pharmacy. Consult Pharm. 1998;13:256-293.
10. Healthcare QuickDisc CD-ROM Database Ver. 2.0. Chicago, Ill: Health Forum, American Hospital Association; 2000.
11. Barker KN, McConnell WE. The problems of detecting medication errors in hospitals. Am J Hosp Pharm. 1962;19:360-369.
12. Bates DW, Boyle DL, Vander Vliet MB, et al. Relationship between medication errors and adverse drug events. J Gen Intern Med. 1995;10:199-205.
ISI
| PUBMED
13. Barker KN, Kimbrough WW, Heller WM. A Study of Medication Errors in a Hospital. Fayetteville: University of Arkansas; 1966.
14. Hynniman CE, Conrad WF, Urch WA, Rudnick BR, Parker PF. A comparison of medication errors under the University of Kentucky
unit dose system and traditional drug distribution systems in four hospitals. Am J Hosp Pharm. 1970;27:802-814.
PUBMED
15. Hall KW, Ebbeling P, Brown B, Shwortz I. A retrospective-prospective study of medication errors: basis for an
ongoing monitoring program. Can J Hosp Pharm. 1985;38:141-143, 146.
PUBMED
16. Nadzam DM. A systems approach to medication use. In: Cousins DM, ed. Medication Use. Oakbrook
Terrace, Ill: Joint Commission on Accreditation of Healthcare Organizations;
1998:5.
17. Kerlinger FN, Lee HR. Foundations of Behavioral Research. 4th ed. New York, NY: Harcourt College; 2000:728-729.
18. Barker KN. Data collection techniques: observation. Am J Hosp Pharm. 1980;37:1235-1243.
ABSTRACT
19. Barker KN, Mikeal RL, Pearson RE, Illig NA, Morse ML. Medication errors in nursing homes and small hospitals. Am J Hosp Pharm. 1982;39:987-991.
ABSTRACT
20. Dean B, Barber N. Validity and reliability of observational methods for studying medication
administration errors. Am J Health Syst Pharm. 2001;58:54-59.
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Re-stocking the resuscitation trolley: how good is compliance with checking procedures?
Smith et al.
Clin Risk 2008;14:4-7.
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Medication errors: the human factor
Etchells et al.
CMAJ 2008;178:63-64.
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Medication Administration Variances Before and After Implementation of Computerized Physician Order Entry in a Neonatal Intensive Care Unit
Taylor et al.
Pediatrics 2008;121:123-128.
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Strategic approach for improving the medication-use process in health systems: The high-performance pharmacy practice framework
Vermeulen et al.
Am J Health Syst Pharm 2007;64:1699-1710.
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An observational study of medication administration errors in old-age psychiatric inpatients
Haw et al.
Int J Qual Health Care 2007;19:210-216.
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ASHP Long-Range Vision for the Pharmacy Work Force in Hospitals and Health Systems: Ensuring the Best Use of Medicines in Hospitals and Health Systems
Am J Health Syst Pharm 2007;64:1320-1330.
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Educational interventions to reduce use of unsafe abbreviations
Abushaiqa et al.
Am J Health Syst Pharm 2007;64:1170-1173.
ABSTRACT
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Comparative costs of ertapenem and piperacillin-tazobactam in the treatment of diabetic foot infections
Tice et al.
Am J Health Syst Pharm 2007;64:1080-1086.
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Nursing Home Error and Level of Staff Credentials
Scott-Cawiezell et al.
Clin Nurs Res 2007;16:72-78.
ABSTRACT
Assessment of adverse drug events among patients in a tertiary care medical center
Johnston et al.
Am J Health Syst Pharm 2006;63:2218-2227.
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Harm Resulting from Inappropriate Telephone Triage in Primary Care
Hildebrandt et al.
J Am Board Fam Med 2006;19:437-442.
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Compelling features of a safe medication-use system.
Kelly and Rucker
Am J Health Syst Pharm 2006;63:1461-1468.
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Pharmacy Clarification of Prescriptions Ordered in Primary Care: A Report from the Applied Strategies for Improving Patient Safety (ASIPS) Collaborative
Hansen et al.
J Am Board Fam Med 2006;19:24-30.
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A Review of Medication Administration Errors Reported in a Large Psychiatric Hospital in the United Kingdom
Haw et al.
Psychiatr. Serv. 2005;56:1610-1613.
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{beta} blockers for elective surgery in elderly patients: population based, retrospective cohort study
Redelmeier et al.
BMJ 2005;331:932.
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Detection and Prevention of Medication Errors Using Real-Time Bedside Nurse Charting
Nelson et al.
J. Am. Med. Inform. Assoc. 2005;12:390-397.
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Standard Drug Concentrations and Smart-Pump Technology Reduce Continuous-Medication-Infusion Errors in Pediatric Patients
Larsen et al.
Pediatrics 2005;116:e21-e25.
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Factors predictive of intravenous fluid administration errors in Australian surgical care wards
Han et al.
Qual Saf Health Care 2005;14:179-184.
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Clinical Relevance of Automated Drug Alerts From the Perspective of Medical Providers
Spina et al.
American Journal of Medical Quality 2005;20:7-14.
ABSTRACT
Use of Incident Reports by Physicians and Nurses to Document Medical Errors in Pediatric Patients
Taylor et al.
Pediatrics 2004;114:729-735.
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Design of a safer approach to intravenous drug infusions: failure mode effects analysis
Apkon et al.
Qual Saf Health Care 2004;13:265-271.
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Adverse drug events and medication errors: detection and classification methods
Morimoto et al.
Qual Saf Health Care 2004;13:306-314.
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Nutropin or Neupogen? A medication error resulting in leukocytosis
Wiener et al.
The Annals of Pharmacotherapy 2004;38:721-721.
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The role of structured observational research in health care
Carthey
Qual Saf Health Care 2003;12:ii13-16.
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Minerva
BMJ 2003;327:E178-178.
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The Effect of Computerized Physician Order Entry on Medication Errors and Adverse Drug Events in Pediatric Inpatients
King et al.
Pediatrics 2003;112:506-509.
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Reducing Medication Errors and Increasing Patient Safety: Case Studies in Clinical Pharmacology
Benjamin
J Clin Pharmacol 2003;43:768-783.
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"At least Mom will be safe there": the role of resident safety in nursing home quality
Kapp
Qual Saf Health Care 2003;12:201-204.
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Medication Errors Detected in Infusions
Anton and Ferner
Arch Intern Med 2003;163:982-982.
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JCAHO Accreditation And Quality Of Care For Acute Myocardial Infarction
Chen et al.
Health Aff (Millwood) 2003;22:243-254.
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Minerva
BMJ 2002;325:724-724.
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