Using nurses and office staff to report
prescribing errors in primary care
AMANDA G. KENNEDY1, BENJAMIN LITTENBERG2AND JOHN W. SENDERS3
1Research Assistant Professor of Medicine, Division of General Internal Medicine, University of Vermont College of Medicine, 371 Pearl
Street, Burlington, VT 05401, USA,2Henry and Carleen Tufo Professor of Medicine, Division of General Internal Medicine, University of
Vermont College of Medicine, and3Principal Scientific Consultant to the Institute for Safe Medication Practices (ISMP)
Objective. To implement a prescribing-error reporting system in primary care offices and analyze the reports.
Design. Descriptive analysis of a voluntary prescribing-error-reporting system
Setting. Seven primary care offices in Vermont, USA.
Participants. One hundred and three prescribers, managers, nurses and office staff.
Intervention. Nurses and office staff were asked to report all communications with community pharmacists regarding pre-
Main Outcome Measures. All reports were classified by severity category, setting, error mode, prescription domain and
Results. All practices submitted reports, although reporting decreased by 3.6 reports per month (95% CI, 22.7 to 24.4,
P, 0.001, by linear regression analysis). Two hundred and sixteen reports were submitted. Nearly 90% (142/165) of errors
were severity Category B (errors that did not reach the patient) according to the National Coordinating Council for
Medication Error Reporting and Prevention Index for Categorizing Medication Errors. Nineteen errors reached the patient
without causing harm (Category C); and 4 errors caused temporary harm requiring intervention (Category E). Errors invol-
ving strength were found in 30% of reports, including 23 prescriptions written for strengths not commercially available.
Antidepressants, narcotics and antihypertensives were the most frequent drug classes reported. Participants completed an exit
survey with a response rate of 84.5% (87/103). Nearly 90% (77/87) of respondents were willing to continue reporting after
the study ended, however none of the participants currently submit reports.
Conclusions. Nurses and office staff are a valuable resource for reporting prescribing errors. However, without ongoing
reminders, the reporting system is not sustainable.
Keywords: medication errors/statistics and numerical data, medical errors/statistics and numerical data, adverse drug
reaction reporting systems/classification, primary health care/methods/standards, community pharmacy services
Recent data suggest over 1.5 million preventable adverse
drug events, or injuries due to medications, occur in the
United States annually [1, 2]. In outpatients older than 65
years, preventable adverse drug events are estimated to
exceed 530 000 annually . Approximately one-third of
outpatient adverse drug events may be preventable or
ameliorable . Many adverse drug events are the result of
undetected medication errors. An error is ‘the failure of a
planned action to be completed as intended or the use of
a wrong plan to achieve an aim ’. A medication error
therefore is ‘any error occurring in the medication use
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Not all medication errors cause injury. A prescribing error
occurs when, ‘as a result of a prescribing decision or prescrip-
tion writing process, there is an unintentional significant (i)
reduction in the probability of treatment being timely and
effective or (ii) increase in the risk of harm when compared
with generally accepted practice ’. Data suggest ?2% of all
new prescriptions requires pharmacists to intervene with pre-
scribers to correct or clarify prescriptions before the medi-
cation is safely dispensed to a patient [8, 9]. This roughly
translates to 60 million pharmacist interventions during the
dispensing of 3 billion prescriptions annually in the United
States. Many of these pharmacist interventions are to correct
prescribing errors, although the exact frequency is not known
due to the lack of standards for classifying errors.
Voluntary error reporting has been proposed to help
identify and understand medication errors [1, 5], however
few studies in primary care (Internal or Family Medicine)
have been published. Most of the published reporting
systems involve clinician reporting and include all types of
medical errors [10–12]. Medication errors are frequently
reported in those systems.
The limited literature of primary care error reports suggests
a high frequency of medication errors. However, more
focused studies are needed to identify and describe the types
of medication errors detected. Although collecting errors via
self-report is difficult and likely excludes some important
errors, voluntary reporting may prove useful in primary care
as a basis for quality improvement. We describe a voluntary
error-reporting system that was developed and implemented in
our local primary care practices. The study targeted prescribing
errors, a subset of all medication errors, using existing office
systems. The primary reporters were nurses and office staff,
since they often receive the communication from the commu-
nity pharmacy that a problem has occurred.
A voluntary outpatient prescribing-error-reporting system was
developed and implemented in a convenience sample of seven
primary care (Internal or Family Medicine) practices in
Chittenden County, Vermont. Chittenden County occupies 539
square miles of Vermont and is the only county in Vermont
federally designated as urban with a population estimate of 150
000. The community has 94.8% white with a median annual
income of US$52 843 . The practices reflect the demo-
graphics of Vermont primary care, with a median of five pre-
scribers per practice, including physicians, physician assistants
or nurse practitioners (range: 2–13 prescribers).
Vermont’s Quality Assurance law, 26 V.S.A. Sections 1442
and 1443, provided the necessary liability and confidentiality
safeguards for peer-review protections. All participating staff,
nurses, managers and prescribers (physicians, nurse prac-
titioners and physician assistants) signed statements of
consent. This project was approved by the University of
Vermont Committees on Human Research.
Nurses and office staff were asked to report all communi-
cations with pharmacists about prescribing problems for 6
months. These two groups of workers were selected as primary
reporters because they are often telephoned by community
pharmacists when there is a problem with a prescription.
Depending on the nature of the problem, the nurses and staff
will resolve the problem by reviewing the notes in the medical
chart or by communicating with the prescriber. Rarely, the com-
munity pharmacist will request to speak to the prescriber
directly. Using pharmacist communications as the signal for a
potential error is somewhat limited in scope as many medication
errors occur outside of this pathway. However, we wished to
explore the kinds of insights about prescribing errors that can
be revealed using this existing practice within primary care.
Nurses and office staff were asked to report whenever a
pharmacist telephoned the practice (i) with a question about
a prescription or (ii) to report a problem with a prescription.
Although the reporting system was designed to be a job
function of the nurses and staff, prescribers were also
encouraged to report their own errors. Reports were sub-
mitted by telephone, mail or to a research assistant who
visited each practice weekly.
We wished to explore the utility of existing office systems
serving as error reports. Therefore the nurses and office staff
were encouraged to use their usual methods for documenting
pharmacist communications as the reports. No standard report-
ing form was used and there were no incentives to report. None
of the practices used electronic medical records. Examples of
reporting methods include notes on pre-printed message forms
or medication refill forms, copies of de-identified patient chart
notes and brief notes written on self-sticking message pads.
At periodic 1-h site visits, we shared data with the prac-
tices. The intervals for the site visits were determined by
having gathered enough new reports or having completed
the 6-month study. Frequent, dangerous or unusual errors
were highlighted. A 1-page newsletter prepared by the
Principal Investigator described recent de-identified reports
submitted by the practice, as well as an overall summary of
reports submitted by other practices.
The exit process included a thank you letter, survey and a
$1.00 lottery ticket mailed to each participant with a postage-
paid return envelope. The purpose of the survey was to gather
basic information from participants regarding satisfaction with
the reporting system. The survey asked about several aspects of
the reporting system we believed were important to satisfaction,
including importance, burden, incentives, reminders and feed-
back. The majority of responses were dichotomous (yes/no) or
based on a five-point Likert scale from strongly disagree to
strongly agree. We maximized our survey response with
additional mailings at 10 and 20 days after the initial letter.
The analytic plan included analysis of the reporting system
and analysis of the submitted reports. Analysis of the
Prescribing error reporting in primary care
reporting system included descriptive statistics of the survey
results, estimation of reporting rates and linear regression
Microsoftw Excel 2002 (Microsoft Corporation, Redmond,
WA) and Stata/SE, version 9.0 (StataCorp LP, College
Station, TX) for statistical analyses. P, 0.05 was required
for statistical significance.
Analysis of submitted reports included a descriptive classi-
fication. We desired a taxonomy that would be simple for
prescribers to understand without additional patient safety
training. Although several classification systems and taxo-
nomies have been proposed and evaluated [14–20], none of
them seemed appropriate for analyzing outpatient prescribing
errors. Therefore we classified all prescription reports five
ways: (i) severity, (ii) setting, (iii) error mode, (iv) prescription
domain and (v) error-producing conditions (environmental,
team, individual or task factors that affect performance) .
Severity was assigned using the National Coordinating
Council for Medication Error Reporting and Prevention
(NCC MERP) Index for Categorizing Medication Errors
. Likely settings included the provider office, pharmacy
or with the patient. Error modes included omission (failure
to carry out the necessary steps in the prescription) ,
commission (failure to prescribe correctly) , no error or
indeterminate. Prescription domain included drug, strength,
route, dose, formulation etc. Error-producing conditions
were grouped by important themes, such as confusion with
abbreviations and illegibility.
A pharmacist (AK) and a physician (BL) independently
assigned each report a severity category, setting, error mode
and prescription domain. Since a goal of the study was to
understand what can be learned using existing office systems,
only the submitted reports were considered in the analysis.
For example, errors were classified as reaching a patient only
if the report specifically mentioned the patient being
involved. Most reports did not include reasons why errors
occurred. There was moderate agreement among AK and
BL (kappa 0.552,standard
P , 0.001).
Categorization discrepancies were resolved through discus-
sion until consensus was reached.
One hundred and three people from five Internal Medicine
practices and two Family Medicine practices participated. They
included 31 physicians, 8 nurse practitioners, 2 physician
assistants, 26 non-prescribing nurses, 10 medical assistants, 20
office staff and 6 non-physician office managers. Data
collection occurred from June 2004 through July 2005.
All practices contributed reports. The majority were sub-
mitted directly to the research assistant. Only seven reports
(3.5%) were contributed by prescribers. Total reports per
practice varied from 10 to 62 reports (median 32 reports per
practice). Although the intervention was designed to be
6 months, the end date for practices was greater than
6 months due to scheduling difficulties for exit sessions with
the practices (median 32 weeks, range 28–44 weeks).
Table 1 describes estimated reporting rates by practice.
Total prescriptions written and total pharmacy calls per prac-
tice were not collected. On the basis of the United States
physician productivity statistics and ambulatory care survey
data, it is estimated that the average prescriptions written per
visit is 1.7  with an average of 84 visits per week by
family physicians and general internists . Combining
these estimates yields an average of 146 prescriptions written
per week by family and internal medicine physicians. The
average rate of pharmacy calls to the office for clarifications
is 2% . These averages were used to estimate reporting
rates for each of the seven practices, which ranged from 3.1
to 8.6% with a median reporting rate of 6.1%.
1 IM2 3610499
2 FM5 32 23332
3 IM5 44 32081
4 IM3 28 12249
5 IM9 32 41997
6 IM13 44 83411
7 FM4 28 16332
Table 1 Reporting rates by practice
aIM, internal medicine; FM, family medicine.
bAlthough the intervention was designed to be 6 months, the end date for practices was greater than 6 months due to scheduling
difficulties for exit sessions with the practices.
cOn the basis of an average of 146 prescriptions per provider per week [25–26].
dOn the basis of 2% of prescriptions requiring callbacks [8.]
A. G. Kennedy et al.
Overall reporting statistically decreased by 3.6 reports per
month (95% CI, 22.7 to 24.4, P , 0.001). See Fig. 1.
After the study end date, only one report was received. None
of the practices have continued to submit reports.
The reports described 216 near-misses or errors, 116 unique
drugs and 2 non-drug products (lancets and test strips for
patients with diabetes). Over 65% (141/216) of the reports
identified the medication by trade name rather than generic
name. Antidepressants (38/216), narcotics (32/216) and anti-
hypertensives (24/216) were the most frequent drug classes
reported. Bupropion was the individual drug most often
reported (12/216), followed by levothyroxine (6/216) and
metoprolol (6/216). Twenty percent of near-misses or errors
(43/216) concerned ‘high-alert’ medications or medications
that ‘have a high risk of causing injury when they are
misused ’ (Table 2).
Table 3 describes the severity classification of all 216 near-
misses or errors according to the National Coordinating
Council for Medication Error Reporting and Prevention
(NCC MERP) Index for Categorizing Medication Errors.
One hundred and sixty-five were errors, 49 were near-misses
(Category A) and 2 problems did not contain enough infor-
mation to determine a severity category. Nearly 90% (142/
165) of the errors were Category B, errors that did not reach
the patient. Nineteen errors reached the patient without
causing harm (C), and four errors caused temporary harm
requiring intervention (E). We did not observe any errors in
NCC MERP Categories D, F, G, H or I. Examples of
reports by category are also presented in Table 3.
Table 4 describes the setting, error mode, prescription
domain and error-producing conditions of the submitted
reports. The majority of near-misses or errors originated
within prescribers’ offices. Only 16 near-misses or errors ori-
ginated within the pharmacy or patient environment.
Fifty-five percent of errors were commissions (90/165). The
remaining errors (75/165) were omissions. Issues with
strength were found in over 30% (66/216) of near-misses or
errors, including 23 prescriptions written for strengths not
Illegibility was the most frequent error-producing con-
dition. Approximately 45% (22/49) of the near-misses
(Category A reports) were reports of ‘illegible handwriting’.
Three Category B reports also had evidence of illegibility.
Confusion due to look-alike or sound-alike medication
names was found in 5% (12/216) of near-misses or errors.
Other error-producing conditions included multiple formu-
lations available (11/216), calculations or decimal points
(7/216), unusual schedules such as weekly doses or tapers
(6/216), confusion due to abbreviations (3/216), use of mul-
tiple pharmacies (3/216) or multiple prescribers (2/216) and
the availability of multiple drugs within a therapeutic class
(5/216). An example of multiple drugs within a therapeutic
class is prescribing ‘Actos 4 mg’. The available strengths for
Actos (pioglitazone) are 15, 30 and 45 mg. However, the
available strengths for Avandia (rosiglitazone), a different
drug within the same therapeutic class, are 2, 4 and 8 mg.
Since the trade names are similar and both drugs belong to
the same therapeutic class, it is possible there was confusion
with the correct prescribing information for each drug.
Codeine alone or in combination
Hydrocodone in combination
Oxycodone alone or in combination
Table 2 Frequently reported medications or high-alert
aInstitute for Safe Medication Practices ‘high-alert’ medications .
‘High alert’ medications ‘have a high risk of causing injury when
they are misused ’.
bPercent of 216 near-misses or errors.
Figure 1 Linear regression of reporting over time
Prescribing error reporting in primary care
A 49 Circumstances or events that have the capacity to cause error
Table 3 Frequency and examples of submitted reports according to the National Coordinating Council for Medication Error
Reporting and Prevention (NCC MERP) Index for Categorizing Medication Errors 
Description (includes severity definition followed by examplesb)
† Was on Cozaar (losartan) 1 BID, called in as 1 daily. Correct? Per chart change to 1 daily
† Amitriptyline 25 mg or 10 mg? 10 mg as was called in
† We called in the script for Celexa (citalopram) to the pharmacy, but they want to know if you
are decreasing to 10 mg or increasing to 30 mg. She currently takes 20 mg. MD to restart her
at 10 mg
An error occurred but the error did not reach the patientB 142
† Please clarify directions for Premarin (conjugated estrogens) vaginal cream. Apply QD ? 7
days then BID. Should it be QD ? 7 then 2 ? /week? Per MD, yes
† Actonel (risedronate) 35 mg. Written for QD. Pharmacist asked to change that dose to QWeek.
† Pt received script written with wrong dose. Written for Synthroid (levothyroxine) 150 mg, but
should have been 50 mg. Error was taken care of
An error occurred that reached the patient but did not cause patient harmC 19
† Fluoxetine called to pharmacy. Should have been paroxetine. Pt did not take fluoxetine. Med
changed to paroxetine
† Pt takes Toprol XL (metoprolol) 100 mg QD and is noted in her chart. I accidentally gave her
Rx for 50 mg. She called us to get a new Rx
† Pt brought prednisone bottle in. She was concerned that the pharmacy had filled the Rx
incorrectly. However, after speaking with the pharmacist and having them fax the copy to us, it
is apparent that the Rx was written incorrectly. Directions should read 20 mg 2 PO daily
(not QID) Pt was clear that she had been told to take two pills daily. This was then verified by
the chart note and also by a phone call to the provider
An error occurred that reached the patient and required monitoring to confirm that it resulted in no
harm to the patient and/or required intervention to preclude harm
An error occurred that may have contributed to or resulted in temporary harm to the patient and
† Patient prescribed Synthroid (levothyroxine) 0.25 mg. Pharmacy filled prescription as 0.025 mg.
Patient alerted MD who wrote a new prescription. Patient went to a second pharmacy with the
correct prescription and again received 0.025 mg. Pt suffered 1 month without correct Rx and
felt lethargic and swollen
† Pt reports that insulin she picked up yesterday is clear—usually cloudy. Advised to check with
pharmacy. Pharmacy reports discrepancy with what we called in and what they heard. Will give
her Novolog Mix (insulin aspart 70/30) syringes. Pt received regular insulin rather than mix. Pt
called and had a headache all day. Also hungry. Advised to check blood sugars throughout day.
Will go pick up correct insulin from pharmacy and take as prescribed
† Ortho-Cyclen (ethinyl estradiol/norgestimate) received. Thinks pills are different. Different
color and also experiencing moodiness, diarrhea and heavy period. Per pharmacy,
Ortho-Cyclen (ethinyl estradiol/norgestimate) dispensed. Rx changed to Ortho-Cept (ethinyl
An error occurred that may have contributed to or resulted in temporary harm to the patient and
required initial or prolonged hospitalization
An error occurred that may have contributed to or resulted in permanent patient harm
An error occurred that required intervention necessary to sustain life
An error occurred that may have contributed to or resulted in the patient’s death
aThe 49 Category A reports do not meet the definition of ‘error’. Two reports could not be classified and are listed as unknown. Therefore,
of the 216 identified problems, only 165 are classified as errors.
bThe examples of submitted reports included the following changes from the original reports: limited editing for ease of reading; generic
names added in parentheses in reports that only include trade names.
A. G. Kennedy et al.
84.5% (87/103) of participants completed the exit survey
(Table 5). The survey respondents included 33 prescribers,
24 nurses, 6 medical assistants, 16 office staff and 5 man-
agers. Three respondents did not list their profession.
Seventy-eight (68/87) were female and 92% (80/87) were
white. Over 90% of respondents stated that the reporting
system is important to patient care (77/85) and will improve
patient care (76/84). Nearly 60% (60/87) of respondents
stated a standard reporting form would have improved the
system. Interestingly, respondents were undecided about
having more reminders and feedback. Approximately half of
respondents agreed or were neutral that more reminders and
feedback were needed. Nearly 90% (77/87) were willing to
continue participating in the reporting system after the study
ended. Only two respondents indicated that the reporting
system was burdensome to them and to their office. One
respondent was neutral about personal burden, but indicated
that the system was burdensome to their office.
We successfully implemented a prescribing-error-reporting
system in busy outpatient primary care practices using exist-
ing office systems. This approach required minimal provider
and staff effort. The system was easily transferable from
practice to practice, despite operational differences in hand-
ling pharmacist communications. Providers, nurses, office
staff and managers overwhelmingly accepted the system, with
most willing to continue their involvement. However, none
of the practices have continued to send reports.
It is unclear why the reporting system failed to work
beyond the study. Although there was disagreement among
the survey respondents about the need for more reminders
and feedback, it is likely that some intervention is required to
keep participants active. Given the decrease in submitted
reports over time during the study, it is likely that our remin-
ders and feedback were insufficient to create a sustainable
Errors in strength, dosage form  and decimal point or
calculations  have been reported in the literature for
more than 30 years. Why should these types of errors still be
reported? First, reporting is important for local surveillance
Multiple problems or entire prescription
Look-alike or sound-alike medication names
Multiple formulations available
Calculations or decimal points required
Unusual dosing schedules
Multiple options within a therapeutic class
Confusion with abbreviations
Use of multiple pharmacies
Table 4 Taxonomy for classifying prescribing errors
1 Was a burden to me 77(90.6)
2 Was a burden to my
3 Is important to
4 Will improve patient
5 Should have had
more incentives for
6 Should have had
more incentives for
7 Should have had
8 Should have
Table 5 Survey responses
The prescription error
1(1.2) 77 (90.6) 85
2 (2.4)76(90.5) 84
66 (78.6)2(2.4) 84
62 (73.8)3 (1.2) 84
38 (45.2) 26(31.0)84
42 (51.2)23 (28.0)82
Participants who responded neutral to the eight survey questions
are as follows: 1) six, 2) ten, 3) seven, 4) six, 5) sixteen, 6) nineteen,
7) twenty, 8) seventeen.
Prescribing error reporting in primary care
and education. Our data suggest feedback to providers about
prescribing bupropion and strengths not commercially avail-
able would be useful for local quality improvement efforts.
Second, reporting promotes a discussion of error. Since the
majority of reports concerned circumstances or errors that
did not reach the patient, prescribers discussed the errors
without fear of litigation. Third, reporting is hypothesis-
generating for strategies that may then undergo rigorous
testing. For example, error reports stimulated us to develop
and test a modified prescribing form . Reports may be
used to stimulate other ‘basic science’ research into the
understudied nature of error . Lastly, reporting can help
evaluate new technology after implementation. Although
computerized technology is widely promoted as a means of
reducing prescribing errors, these systems do not prevent all
types of prescribing errors, have induced new errors and
have questionable generalizability [32–35]. Reporting systems
detect unanticipated errors and can guide revisions of new
The strengths of this reporting system include simple
design, outpatient focus, easy translation to multiple primary
care offices and minimal disruption of the office. The system
allows for local surveillance of prescribing errors, promotes a
discussion of errors among prescribers, nurses and office
staff, and generates ideas for future research.
The limitations of this study include low reporting rates,
inability to capture many important errors, small sample size,
geographic restriction to one state and limited follow-up
analysis with participants. We did not have patient infor-
mation or the prescribers’ perspectives on the circumstances
surrounding the error. A more detailed survey or semi-
structured interviews would have enhanced our understand-
ing of the strengths and weaknesses of our system. It is
unknown if this system would transfer well to specialty prac-
tices. We do not know if any of the practices have made
changes or conducted quality improvement projects based on
the feedback received from the reporting system. Finally,
since none of the practices in this study use electronic pre-
scribing technology, it is unknown if the detected errors
would be similar or different.
Nurses and office staff may not have fully understood the
complexities of the prescriptions and pharmacology of the
medications well enough to submit complete reports.
Additionally since the nurses and office staff were often
intermediate parties, many of the reports did not contain the
resolution of the problem. These limitations are recognized,
however there was still interesting and useful information
contained in the submitted reports.
As with all voluntary reporting systems, the true error rate
is unknown, the ability to capture important errors is unpre-
dictable and the reporting rates are consistently low. We only
have data that participants deemed ‘reportable’. For example,
participants may have felt a heightened awareness around
narcotics compared with other classes of drugs, contributing
to higher reporting. However, errors involving another class
of drugs may have been more frequent or more dangerous.
Additionally many medication errors, such as administration
errors or errors corrected with the patient at the pharmacy
before dispensing are not detected by this system and are
therefore never reported.
These data are not rich enough to further specify errors
mechanisms. For example, one report described a prescrip-
tion for 120 tablets of oxycodone, but the pharmacist dis-
pensed 180 tablets. With more data, we may be able to
determine if this error was a substitution of 180 for 120 or a
repetition of a count of 60. This insight is important as
different solutions are required depending on the mechanism
of the error.
Nurses and office staff are a valuable resource for report-
ing prescribing errors in primary care practices. However,
without ongoing reminders, the reporting system is not sus-
tainable. Important information about outpatient prescribing
errors is available using existing office systems. Simple taxo-
nomies for outpatient prescription errors may be useful to
primary care practices who wish to conduct local quality
improvement efforts, although further study is required to
explore the effectiveness of these efforts and applicability to
practices with electronic prescribing systems.
The authors wish to thank all of the participants in this
study for their time so that we all may learn more about pre-
scribing errors. This work was supported by funding from
grant number 1 K08 HS013891 from the Agency for
Healthcare Research and Quality.
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Accepted for publication 28 March 2008
Prescribing error reporting in primary care