Post on 28-Apr-2023
QUALITY AND SAFETY
Electronic prescribing reduces prescribing error in public hospitals
Ramzi Shawahna, Nisar-Ur Rahman, Mahmood Ahmad, Marcel Debray, Marjo Yliperttula and Xavier
Decleves
Aims and objectives. To examine the incidence of prescribing errors in a main public hospital in Pakistan and to assess the
impact of introducing electronic prescribing system on the reduction of their incidence.
Background. Medication errors are persistent in today’s healthcare system. The impact of electronic prescribing on reducing
errors has not been tested in developing world.
Design. Prospective review of medication and discharge medication charts before and after the introduction of an electronic
inpatient record and prescribing system.
Methods. Inpatient records (n = 3300) and 1100 discharge medication sheets were reviewed for prescribing errors before and
after the installation of electronic prescribing system in 11 wards.
Results. Medications (13,328 and 14,064) were prescribed for inpatients, among which 3008 and 1147 prescribing errors were
identified, giving an overall error rate of 22Æ6% and 8Æ2% throughout paper-based and electronic prescribing, respectively.
Medications (2480 and 2790) were prescribed for discharge patients, among which 418 and 123 errors were detected, giving
an overall error rate of 16Æ9% and 4Æ4% during paper-based and electronic prescribing, respectively.
Conclusion. Electronic prescribing has a significant effect on the reduction of prescribing errors.
Relevance to clinical practice. Prescribing errors are commonplace in Pakistan public hospitals. The study evaluated the impact
of introducing electronic inpatient records and electronic prescribing in the reduction of prescribing errors in a public hospital
in Pakistan.
Key words: hospitals, medication errors, nurses, nursing, Pakistan, prescribing errors
Accepted for publication: 18 December 2010
Introduction
Patient safety has become a nationwide priority. Conse-
quently, healthcare delivery has come under tighter scrutiny
during the last decade (Stone et al. 2009). Annually,
medication errors exact an astoundingly high financial and
human toll on society through direct injury to patients and
substantial increase in medical expenditure (Dean Franklin
et al. 2005). The medication process which can be described
in different stages of prescribing, transcribing, dispensing,
administration and monitoring, has proven to be error-prone
(Ghaleb et al. 2010). Clinical decision and prescription
Authors: Ramzi Shawahna, MPhil, PhD Student, Faculty of
Pharmacy and Alternative Medicine, The Islamia University of
Bahawalpur, Bahawalpur, Pakistan, Faculte de Pharmacie,
Universite Paris Descartes, Paris, France and Division of
Biopharmaceutics and Pharmacokinetics, Faculty of Pharmacy,
University of Helsinki, Helsinki, Finland; Nisar-Ur Rahman, PhD,
Associate Professor and Chairman, Department of Pharmacy, The
Islamia University of Bahawalpur; Mahmood Ahmad, PhD, Professor
and Dean Faculty of Pharmacy and Alternative Medicine, The
Islamia University of Bahawalpur, Bahawalpur, Pakistan; Marcel
Debray, Professor of Biostatistics, Universite Paris-Descartes, Faculte
des Sciences Pharmaceutiques et Biologiques, Departement Sante
Publique et Biostatistique, Paris, France; Marjo Yliperttula, PhD,
Professor, Division of Biopharmaceutics and Pharmacokinetics,
Faculty of Pharmacy, University of Helsinki, Helsinki, Finland;
Xavier Decleves, PhD, Associate Professor, Faculte de Pharmacie,
Universite Paris Descartes, Paris, France
Correspondence: Ramzi Shawahna, PhD Student, Faculty of
Pharmacy and Alternative Medicine, The Islamia University of
Bahawalpur, Bahawalpur-63100, Pakistan. Telephone: +92 62
9255243.
E-mail: ramzi_shawahna@hotmail.com
� 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245 3233
doi: 10.1111/j.1365-2702.2011.03714.x
writing are highly challenging, even in sophisticated health-
care facilities where medications are handled by trained and
credentialed healthcare professionals (Friedman et al. 2007).
In the UK’s National Health Service, more than 2Æ5 million
prescriptions are written every day, it has been estimated that
errors affect 11% of those prescriptions with a cost of £400
million per year (Fitzgerald 2009). Prescribing errors are
common cause of iatrogenic injury. The causes of prescribing
errors include slips of attention, failure in applying adequate
prescribing rules, insufficient knowledge of pathology, phys-
iology and clinical drug therapy. Furthermore, proper
prescribing requires adequate consideration of patient char-
acteristics, dose calculation, medication nomenclature and
dosage formulation (Dean et al. 2002a, Lesar 2002).
Universally, handwritten prescribing on a non-standardised
and cumbrous paper-based medication and discharge medi-
cation charts predominates in hospital settings. In the UK and
Australia there have been calls to standardising medication
charts, since the content and layout of these charts contrib-
uted to prescribing errors (Coombes et al. 2008). Similarly,
interventions like moving to electronic prescribing, clinical
decision support systems and clinical pharmacist interven-
tions were also recommended endeavours to effectively
reducing prescribing errors both in inpatient and outpatient
settings (Pollock et al. 2007, Stone et al. 2009). Electronic
prescribing systems as represented by the computerised
physician order entry (CPOE) are known to reduce prescrib-
ing errors. In the USA, only 5% of medical facilities use some
form of the CPOE (Stone et al. 2009). Recently, the Office of
National Coordinator for Health Information Technology
was created to ensure the establishment of electronic forms of
medical records by 2014 in all healthcare organisations in the
USA (DesRoches et al. 2010, Stone et al. 2009). Such
initiatives are highly welcomed elsewhere in the world.
The issue of medication errors has been extensively
explored in the developed countries; conversely, scarce data
have been reported in the developing world. In Pakistan, a
country with a complex healthcare infrastructure, little is
known about the nature of prescribing errors in public
hospitals. Recently, an orphan study reported prescribing
errors in a psychiatry ward in a public hospital (Shawahna &
Rahman 2008). We conducted the present study to investi-
gate and compare the nature and incidence of prescribing
errors in a main public hospital in Pakistan before and after
the introduction of electronic inpatient record and prescrib-
ing system. Such comparison should allow elucidating the
impact of the move to electronic inpatient record and
electronic prescribing on the incidence of prescribing errors
in public hospitals in Pakistan.
Methods
Setting
The study was conducted in a 1280-bed teaching hospital
situated in Lahore (Pakistan). The main public hospital
provided secondary and tertiary care services; however, the
tertiary services were limited to cardiac care. The hospital
operated typical Pakistani multi-ward administrative infra-
structure. Each ward was operated by a team of physicians
led by a head professor. The team included associate
professor(s), assistant professor(s), senior registrar(s) and
intern(s). The hospital admitted a patient mix of mainly
middle class and government employees’; patients were
admitted to the different wards according to their symptoms
and indications. The hospital employed 10 pharmacists who
provided classic pharmacy services including procurement of
medicines, storage and record keeping. None of those
pharmacists was involved in inpatient record auditing,
medication chart or discharge medication reviewing. In
routine practice, ward doctors handwrote medications onto
a pre-printed paper-based inpatient record. The inpatient
record contained the following information: (1) basic patient
information (name, date of birth, age, gender and address),
(2) history, (3) diagnosis, (4) treatment plan, (5) medication
chart and (6) assessment. Medications prescribed onto the
medication chart were then transcribed by the nursing staff
onto separate paper-sheets. These sheets were then used to
procure medications from the central pharmacy store. Nurs-
ing staff used the medication chart to determine drugs and
doses due and noted their administration. At the time of
patient discharge, medications to be continued were then
transcribed onto a paper-based pre-printed discharge medi-
cations sheet. Consequently, medications were then tran-
scribed onto separate sheets where they could be brought
from the central pharmacy or could be purchased from
private pharmacies.
Paper-based medication charts
We have decided to review prospectively 150 inpatient
records from each ward (n = 1650) for prescribing errors.
An inpatient case in this study refers to one particular
hospital admission. The study was conducted in the following
wards: (1) emergency, (2) ear, nose and throat (ENT), (3)
gynaecology, (4) medical 1, (5) medical 2, (6) medical 3, (7)
paediatrics, (8) pulmonary, (9) cardiology, (10) coronary care
and (11) general cardiac surgery. Inpatient records were
sampled from each ward randomly.
R Shawahna et al.
3234 � 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245
Paper-based discharge medications sheets
Consequently, 50 discharge medications sheets were reviewed
prospectively from each ward (n = 550) for prescribing and
transcribing errors. Similarly, sheets were sampled randomly.
Prescribing errors
A definition of prescribing errors and scenarios representing
prescribing error situations in Pakistan hospitals were
described in a previous study (Shawahna & Rahman 2009).
Briefly, a practitioner-led definition of prescribing error and
scenarios representing error situations were developed with
the participation of a panel of expert judges composed of 21
physicians, 19 pharmacists, five nurses, three pharmacolo-
gists and two risk managers (the demographic details of the
panel are given elsewhere (Shawahna & Rahman 2009).
Similar definitions have been used in previous studies in UK
(Dean et al. 2000, Ghaleb et al. 2005). The categories of
prescribing errors included in this study are listed in Table 1.
Data collection
A five-day pilot study was conducted in a representative ward
to assess the practicality and validate the collection protocol.
During data collection, prescribers were blinded to maintain
usual prescribing practice. However, only the head of the
ward and the hospital administration were aware of the
collection protocol.
In each ward, entire inpatient records of the recruited
inpatients were prospectively scanned twice a day (morning
and evening), throughout their stay, using a digital camera
scanner (ORITE Technology Co., Ltd, Taipei, Taiwan) by two
researcher pharmacists. Scans were then transferred to a
computer where they were enlarged and viewed. The discharge
medications sheets of 50 patients from each ward were scanned
similarly.
The ethics of this study were approved by the board of
advance studies and research of the Islamia University of
Bahawalpur. A risk management panel composed of two
physicians and two pharmacists reviewed the scans for
prescribing errors. Each reviewer viewed the scans indepen-
dently and gave a score of 0 for ‘no error situation’ or 1 in
case of error. Disputed ratings were arbitrated by a risk
manager with pharmaceutical background. Decision on error
was based on official monographs, product specifications
given by manufacturers and decisions rendered by the panel
of expert judges. Each medication prescribed was reviewed
against all error categories. Thus, one medication prescribed
could be associated with more than one error. All medica-
tions prescribed during the entire study, including regular and
once only medications, were considered in the analysis. This
included medications prescribed on day 0 (the day of
inpatient admission). In the consecutive days, only newly
prescribed drugs were included in the analysis. Thus, each
medication prescribed was counted only once. Error analysis
of medications prescribed was carried out in the same day
after each scanning, the time lag between scanning and
analysis was between one and three hours. Errors identified in
the morning scanning were communicated immediately to the
ward head or senior during the duty hours and error
reporting was initiated. Errors identified after the evening
scanning were discussed in the next day either with the head
of the ward or senior before the morning dose; otherwise, a
serious error was reported immediately and corrective action
was pursued.
Severity of error
Severity of error was classified according to a classification by
the National Coordinating Council for Medication Error
Reporting and Prevention as modified in a previous study
(van den Bemt et al. 2002). Similarly, reviewers classified
errors into six categories: A1, A2, B, C, D and E. Disputed
ratings were arbitrated by the risk manager. Seniority of
prescribers was defined by experience in years as either junior
(< 4) or senior (‡4).
Seminar and newsletters
Doctors, nurses and pharmacists were invited to series of
seminars on medication errors (including prescribing errors).
Attendants were educated on error scenarios and their
consequences. Throughout the study, newsletters concerning
such errors and safe prescribing practice circulated the
different wards of the hospital. Newsletters contained error
examples and recommendations for safe prescribing.
Electronic medication charts
Inpatient record was coded on spreadsheet software package
(EXCEL, Microsoft). The following sections of the previous
paper-based record (1) patient basic information, (2) history,
(3) diagnosis, (4) treatment plan and (5) assessment, were
retained. The section of medication chart was modified to
contain a space for indispensable information to be consulted
at the time of medication prescription. Information concern-
ing medication history, allergy, renal and hepatic function
was included in the medication chart section. Crossable
boxes indicating instructions on dose, frequency and route of
Quality and safety Electronic prescribing reduces prescribing error in public hospitals
� 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245 3235
Table 1 Prescribing errors identified during the two phases of the study
Error
category Example
Paper-based profile Electronic profile
p
Number of
errors
(percentage
of all errors)
Total
(percentage)
Number of
errors
(percentage
of all errors)
Total
(percentage)
(1) Dosing error A dosing error was
considered when
one or a combina
tion of these
scenarios occurred:
1179 (39Æ2%) 583 (50Æ8%) < 0Æ001
(a) The dose or
frequency is
sub-therapeutic
or toxic
(especially
when the drug
has a narrow
therapeutic
range).
A patient was
prescribed half
tablet of
Theophylline
50 mg q12h.
322 (10Æ7%) 150 (13Æ1%)
(b) The dose/regi-
men was not
altered after
the steady-state
serum
concentration
was believed to
be achieved
when such an
objective was
stipulated
while
prescribing the
drug or regimen
A patient was
prescribed with
Amoxicillin 1 g
injection q12h
followed by
500 mg tablets
q12h
419 (13Æ9%) 162 (14Æ1%)
(c) The dose/
regimen was
inappropriate
for the patient’s
renal function
according to
the Pakistan
National
Formulary,
summary of
product
characteristics,
or reference
sources
A doctor prescribed
Ketorolac 30 mg
q6h for a patient on
dialysis
342 (11Æ4%) 201 (17Æ5%)
(d) The dose written
in ‘milligrams’
when
‘micrograms’
were intended
A doctor prescribed
Digoxin in 250 mg
instead of 250 lg
96 (3Æ2%) 70 (6Æ1%)
R Shawahna et al.
3236 � 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245
Table 1 (Continued)
Error
category Example
Paper-based profile Electronic profile
p
Number of
errors
(percentage
of all errors)
Total
(percentage)
Number of
errors
(percentage
of all errors)
Total
(percentage)
(2) The maximum
dose was not
specified when
the medication
was prescribed
as ‘S.O.S’
(Latin ‘si opus
sit’, meaning:
when needed)
A patient was
prescribed Injection
Voltaren 75 mg
(Dicolfenac
Sodium) as S.O.S
without indicating
the maximum dose
274 (9Æ1%) 11 (1%) < 0Æ001
(3) The name of the
medication was
misspelled,
leading to
confusion
(writing
illegibly was
not considered
a prescribing
error, even when
hardly
readable)
A doctor misspelled
tablet Zantac
(ranitidine) with Z
looked like X
without indicating
the dose, the order
could be confused
with Xanax
(Alprazolam)
281 (9Æ3%) 14 (1Æ2%) < 0Æ001
(4) Ambiguous
medication
order
The medication
order was consid-
ered ambiguous
when one or a
combination of
these scenarios
occurred:
667 (22Æ2%) 231 (20Æ1%) < 0Æ001
(a) The order was
completely
unclear or leads
to confusion
A patient was pre-
scribed with Syrup
Lomogel, a name
which never ex-
isted, when asked
he said it was
Somogel (Ligno-
caine)
155 (5Æ2%) 21 (1Æ8%)
(b) The order was
not rewritten in
full when a
change was
made, thus fail-
ing to provide a
requisite neat
and clean order
A doctor crossed a
medication
prescribed then
ticked it with � as
still valid
75 (2Æ5%) 14 (1Æ2%)
(c) The route of
drug adminis-
tration was not
stated when
more than one
route was
applicable
A doctor recom-
mended nebulisa-
tion with
Salbutamol, nei-
ther the duration
nor the frequency
were specified
152 (5Æ1%) 82 (7Æ1%)
Quality and safety Electronic prescribing reduces prescribing error in public hospitals
� 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245 3237
Table 1 (Continued)
Error
category Example
Paper-based profile Electronic profile
p
Number of
errors
(percentage
of all errors)
Total
(percentage)
Number of
errors
(percentage
of all errors)
Total
(percentage)
(d) The medication
was prescribed
using a non-
standard abbre-
viation and/or
nomenclature
A doctor prescribed
‘Diclo’ 75 mg a
short for Dicloran
75 mg (Diclofenac
Sodium) ‘Diclo’
could be misunder-
stood with Diclo-P
(Diclofenac
Potassium)
285 (9Æ5%) 114 (9Æ9%)
(5) Dosage form
error
A dosage form was
considered when
one or a
combination of
these scenarios
occurred:
205 (6Æ8%) 92 (8%) <0Æ01
(a) The medication
was prescribed
in a dosage
form that was
not available
commercially
A doctor prescribed
capsule Risek
40 mg (Omepra-
zole), Risek is
available in two
dosage forms, one
is capsule 20 mg
and the other is
infusion 40 mg
93 (3Æ1%) 20 (1Æ7%)
(b) The medication
was prescribed
in a dosage
form that
couldn’t be
administered
to the patient
in his/her
clinical
situation
An unconscious
patient was
prescribed with
tablet Lasix 40 mg
(Furosemide)
112 (3Æ7%) 72 (6Æ3%)
(6) A medication
wasn’t
prescribed
when the
patient’s clinial
condition
required a
medication
A patient had severe
cough attacks with
no cough remedy
prescribed
57 (1Æ9%) 62 (5Æ4%) ns
(7) The medication
prescribed was
contraindicated
for the clinical
condition of the
patient
A doctor prescribed
tablet Tegral
200 mg (Carba-
mazepine) for
anaemic patient
141 (4Æ7%) 73 (6Æ4%) <0Æ05
R Shawahna et al.
3238 � 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245
administration were added. Similar changes were made to the
discharge medications sheet. A comprehensive standard
medication information database was constructed using
standard sources (Anderson et al. 2002, Joint Formulary
Committee 2007). The database provided proprietary and
non-proprietary (brand and generic) names, dosage forms,
available strengths, adverse drug reactions, contraindications,
drug–drug, drug–food and drug–disease interactions for all
medications on the hospital’s formulary and commonly
prescribed medications. The electronic inpatient record was
interfaced to the database. The system did not provide
interactive alerts (pop-ups) neither decision support such as
drug interaction or allergy checks. All relevant information
was listed and physicians needed to consult them at the time
of prescribing.
The new electronic inpatient record was sent to a collective
of 20 doctors, 20 nurses and 10 pharmacists to elicit their
opinions and suggestions on the design. Suggestions rendered
by the collective were considered and the electronic inpatient
record was modified accordingly, later, the final design was
sent for approval to a total of 200 doctors, 200 nurses and
100 pharmacists, employed by the hospital or similar settings
in Lahore. After approval, doctors were given a one-day
training workshop on the new electronic inpatient record and
discharge medications sheet. Doctors were provided hand-
held computers and were asked to prescribe medications onto
the medication chart in the electronic inpatient records and
similarly for discharge medications (onto the electronic
discharge medications sheets).
Consequently, identical sample size (150 inpatient cases
and 50 discharge medications sheets) from the same wards
were recruited to investigate prescribing errors. Electronic
inpatient records were printed and reviewed for prescribing
errors by the same panel. Prints were treated like the scans in
the paper-based inpatient records analysis. The time lag
between scanning and analysis was shorter than that of scans
0Æ5–1Æ5 hours. Similarly, errors identified were communicated
to the head of the ward or senior and corrective action was
pursued.
Analysis
The null hypothesis to be tested was that the electronic
inpatient record and discharge medications sheet interfaced
to the medication information database would not have any
effect on the incidence of prescribing errors. The numerator
of this study was the number of errors identified and the
denominator was the number of medications prescribed.
Error rates in different wards were compared after normal-
ising the absolute error number to the number of medications
prescribed for each inpatient in the each ward.
Prescribing errors data were treated with Graphpad Prism
4Æ0 (GraphPad Software Inc., San Diego, CA, USA). Statis-
tical significance (p < 0Æ05) was tested with Kruskal–Wallis
test (95% CI) for non-normally distributed data. Paper-based
and electronic prescribing data were compared with Mann–
Whitney U-test. Nominal data were compared using the v2 or
Fisher’s exact test and odds ratios (OR) with (95% CIs).
Inter-rater agreement was determined by Fleiss’ generalised
kappa, using a free Excel spreadsheet-based program, the
program is available at http://www.ccit.bcm.tmc.edu/jking/
homepage/genkappa.doc.
Table 1 (Continued)
Error
category Example
Paper-based profile Electronic profile
p
Number of
errors
(percentage
of all errors)
Total
(percentage)
Number of
errors
(percentage
of all errors)
Total
(percentage)
(8) The medication
prescribed
interacted with
another
concomitant
medication
A doctor prescribed
Voltaren 75 mg
(Dicofenac Sodium)
for a patient on
Warfarin
99 (3Æ3%) 43 (3Æ7%) <0Æ05
(9) The medication
prescribed was
not clinically
indicated for
the patient
A doctor prescribed
tablet Convul
200 mg (Carba-
mazepine) for a
patient with no
indication
105 (3Æ5%) 38 (3Æ3%) <0Æ01
Quality and safety Electronic prescribing reduces prescribing error in public hospitals
� 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245 3239
Results
Prescribing errors
During the paper-based prescribing phase, there were 13,328
medications prescribed with a median of 5 and a range of
(2–12). Similarly, there were 14,064 medication prescribed
onto the electronic inpatient records reviewed with a median
of 5 and a range of (2–11). Table 1 shows prescribing errors
identified during the two phases of the study. Table 2 shows
error rates calculated for each ward during the paper-based
and electronic prescribing. Throughout the paper-based
prescribing phase, 3008 errors were identified giving an
overall error rate of 22Æ6%, while error rates varied between
different wards ranging from 40Æ8% to 8%. During electronic
prescribing, there were 1147 identified prescribing errors,
giving an overall error rate of 8Æ2%, similarly, error rates
varied between different wards ranging from 19Æ4–5Æ1%. The
difference in both rates (paper-based and electronic) was
statistically significant (p < 0Æ01). The inter-rater reliability
among the risk management panel was good (j = 64, CI:
60Æ2–67Æ8). Throughout the study, there were 380 (2Æ9%)
omissions of prescriber’s signature these were not considered
as prescribing errors.
Dosing errors dominated prescribing errors types in both
phases of the study. The incidence of dosing errors was
witnessed in all wards at variable rates. The difference in
rates was statistically significant in emergency, paediatrics,
cardiology and general cardiac surgery wards (p < 0Æ05).
Similarly, ambiguous medication orders occurred also at
variable rates in the different wards investigated. For exam-
ple, 9Æ8% of medications ordered in the pulmonary ward
were ambiguous. However, the ambiguity of orders was
reduced in electronic prescribing phase (p < 0Æ01). During
the paper-based prescribing, 9Æ3% of medications ordered
were misspelled, however, the occurrence of misspelling
errors was reduced to 1Æ2% after the installation of electronic
prescribing (p < 0Æ01). Similarly, 9Æ3% of medications were
prescribed as S.O.S (si opus sit) or (when needed) without
indicating the maximal daily dose, such errors were reduced
to 1% in the electronic prescribing phase (p < 0Æ01). The
rest of error types were also reduced, while the reduction was
barely significant.
Table 3 shows the severity of errors identified during the
both phases. During paper-based prescribing, 46% of errors
were in the act of writing; while the rest 54% were errors in
the clinical decision. Electronic prescribing significantly
reduced the error rate to 12Æ2% errors in the act of writing
and 87Æ8% errors in the clinical decision (p < 0Æ01). During
the paper-based prescribing 62Æ9% of errors were rated
minor errors (i.e. without clinical consequences: A1 through
B) against 39Æ7% in the electronic prescribing phase. A type
A1 prescribing error had a likelihood of eight times to occur
in the paper-based context than in the electronic one, while a
type E error had a 0Æ4 times more likely to occur in the paper-
based context.
Table 4 shows the pharmacological classification of med-
ications prescribed throughout the study. During the paper-
based prescribing phase, out of the 13,328 medications
prescribed, 3050 (28Æ88%) were antibiotics. Antibiotic class
had the highest error percentage (39% of total errors).
Likewise, in the electronic prescribing, antibiotics were the
commonest prescribed class.
Discharge medication sheet
During paper-based prescribing, 2480 medications were
prescribed with a median of 4 and a range of (2–9), while
2790 medications were prescribed during the electronic
prescribing phase with a median of 5 and a range of 2–11.
A total of 418 errors were identified in the paper-based
prescribing giving an overall error rate of 16Æ9%, the number
of errors was reduced to 123 errors giving an overall error
rate of 4Æ4% during electronic prescribing. Table 5 shows
prescribing errors detected in discharge medications sheets
during the two phases of the study. Omission errors prevailed
during the paper-based prescribing phase, followed by
misspelling errors. However, the omission errors remained
the highest in the electronic prescribing followed by dosing
errors. Omission, misspelling and ambiguous order errors
were significantly reduced in electronic prescribing phase
(p < 0Æ05).
Table 2 Error rates calculated during the paper-based and electronic
prescribing phases
Ward
Error rate (%) (95% CI)
pPaper-based Electronic
Medical
emergency
40Æ8 (30Æ2–49) 19Æ4 (10Æ8–23Æ9) <0Æ001
Paediatrics 34 (27Æ3–38Æ4) 6 (4–11) <0Æ001
Pulmonary 31Æ3 (24–36Æ2) 8Æ9 (6Æ2–12Æ4) <0Æ001
ENT 27 (23Æ7–33Æ4) 7Æ8 (6Æ2–17) <0Æ01
Gynaecology 26Æ6 (22–32Æ1) 7 (4–11Æ2) <0Æ01
Medical 1 19Æ3 (11–29Æ3) 8Æ3 (7Æ2–8Æ9) <0Æ05
Medical 2 19Æ6 (13Æ2–24Æ5) 6Æ3 (5Æ2–7Æ1) <0Æ05
Medical 3 25 (17–29Æ7) 6 (2–8) <0Æ01
Cardiology 19Æ8 (13Æ9–27Æ6) 6 (5Æ5–10Æ6) <0Æ05
General cardiac
surgery
9Æ6 (5Æ2–13Æ4) 5Æ3 (4Æ2–9Æ3) ns
Coronary care 8 (4–12) 5Æ1 (3Æ8–10) ns
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3240 � 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245
Table
3Sev
erit
yra
ting
of
pre
scri
bin
ger
rors
iden
tified
thro
ugh
out
the
study
Sev
erit
y
rati
ng
Des
crip
tion
Exam
ple
from
this
study
Paper
-base
dpro
file
Ele
ctro
nic
pro
file
pO
dds
rati
o
(95%
CI)
Num
ber
of
erro
rs
(n)
Per
centa
ge
(%)
Num
ber
of
erro
rs
(n)
Per
centa
ge
(%)
Pro
ble
mord
ers:
Err
ors
inth
eact
of
wri
ting
A1
Apre
scri
bin
ger
ror
has
bee
nm
ade,
but
the
erro
ris
so
min
or
that
the
med
icati
on
ord
er
cannot
be
mis
under
stood
Adoct
or
pre
-
scri
bed
capsu
le
Ris
ek40
mg
(Om
epra
zole
),
Ris
ekis
avail
able
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odosa
ge
form
s,one
is
capsu
le20
mg
nd
the
oth
eris
infu
sion
40
mg
650
21Æ6
80
6Æ9
7<
0Æ0
19Æ0
(7–11Æ3
)
A2
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scri
bin
grr
or
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nm
ade,
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eca
nnot
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inis
ter
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icati
on
wit
hout
havin
gto
gath
er
addit
ional
info
rmat
ion
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unco
nsc
ious
pati
ent
was
pre
scri
bed
wit
h
table
tL
asi
x40
mg
(Furo
sem
ide)
733
24Æ4
60
5Æ2
3<
0Æ0
113Æ6
(10Æ4
–17Æ7
)
Err
ors
inth
e
clin
ical
dec
isio
n
BA
pre
scri
bin
ger
ror
has
bee
nm
ade
but
adm
inis
trat
ion
to
the
pati
ent
wil
l
have
no
clin
ical
conse
quen
ces
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or
pre
scri
bed
‘Dic
lo’
75
mg
a
short
for
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lora
n
75
mg
(Dic
lofe
nac
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diu
m)
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lo’
could
be
mis
un
der
stood
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h
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lo-P
(Dic
lofe
nac
Pota
ssiu
m)
510
17Æ0
315
27Æ4
6<
0Æ0
11Æ7
(1Æ5
–2)
CA
pre
scri
bin
ger
ror
has
bee
nm
ade,
that
could
pote
nti
all
y
resu
ltin
the
nee
d
for
an
incr
ease
d
freq
uen
cyof
pati
ent
monit
ori
ng
Apati
ent
was
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scri
bed
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phyllin
e
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)
Quality and safety Electronic prescribing reduces prescribing error in public hospitals
� 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245 3241
Error tendency
In both paper-based and electronic phases of the study, junior
doctors tended to make more errors than their seniors. A junior
doctor had likelihood to make a prescribing error 58Æ7% more
than a senior doctor (OR 1Æ58, 95% CI: 1Æ46–1Æ71) in the
paper-based phase, however, the likelihood decreased to
46Æ3% (OR 1Æ46, 95% CI: 1Æ4–1Æ51) in the electronic phase.
Discussion
Our present study reports the incidence of prescribing errors
in a busy hospital in Pakistan comparing error rates of paper-
based and electronic prescribing. Prescribing errors resulting
in patient harm (iatrogenic injury) are common in today’s
healthcare system. The dilemma is multifaceted in nature.
Prescribing errors in the US and UK’s healthcare systems have
been extensively studied with fewer reports described the
situation in European and Australian context (Dean FranklinTable
3(C
onti
nued
)
Seve
rity
rati
ng
Des
crip
tion
Exam
ple
from
this
study
Paper
-base
dpro
file
Ele
ctro
nic
pro
file
p
Odds
rati
o
(95%
CI)
Num
ber
of
erro
rs
(n)
Per
centa
ge
(%)
Num
ber
of
erro
rs
(n)
Per
centa
ge
(%)
DA
pre
scri
bin
g
erro
rhas
bee
n
made,
that
could
pote
nti
all
y
resu
ltin
dam
age
toth
epati
ent
Adoct
or
pre
scri
bed
table
tT
egra
l
200
mg
(Car
bam
aze
pin
e)
for
anae
mic
pati
ent
415
13Æ8
215
18Æ7
4<
0Æ0
12Æ1
(1Æ8
–2Æ4
)
EA
pre
scri
bin
g
erro
rhas
bee
n
made,
that
could
pote
nti
all
y
resu
ltin
the
dea
thof
the
pati
ent
Adoct
or
pre
scri
bed
Dig
oxin
in
250
mg
inst
ead
of
250
lg
230
7Æ6
170
14Æ8
2<
0Æ0
51Æ4
(1Æ2
–1Æ8
)
Table 4 Pharmacological classification of medications prescribed
during the study
Pharmacological class
Absolute number
Paper-based Electronic
Antibiotics 3050 3500
Analgesics 2800 3100
Diuretics 2500 1540
Sedatives-hypnotics 1300 1214
Antiasthmatics 700 862
Antihypertensives 624 953
Beta-blockers 490 450
Hormones and steroids 430 250
Histamine antagonists 308 732
Antiepileptics 241 412
Anticoagulants 230 370
Antipsychotics 200 180
Nitrates 150 210
Others 305 291
Table 5 Prescribing errors detected in the discharge medications
sheets during the study
Paper-based Electronic
p
Absolute
number
of errors
Error
rate
(95% CI)
Absolute
number
of errors
Error
rate
(95% CI)
Omission 152 6Æ1 (5–10Æ2) 50 1Æ8 (0Æ9–2Æ6) <0Æ01
Misspelling 103 4Æ2 (3Æ6–8) 11 0Æ4 (0Æ2–1Æ1) <0Æ01
Ambiguous
order
54 2Æ2 (1Æ2–3Æ9) 12 0Æ4 (0Æ1–0Æ9) <0Æ05
Dosing error 71 2Æ9 (2Æ1–5Æ3) 40 1Æ4 (0Æ8–2Æ6) ns
Dosage form 38 1Æ5 (0Æ7–2Æ9) 10 0Æ4 (0Æ1–0Æ8) ns
R Shawahna et al.
3242 � 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245
et al. 2005, Lewis et al. 2009). The structure of healthcare
system in Pakistan is different from those in the US and
Europe. Therefore, it was necessary to investigate the nature
of prescribing errors in such environments. Error reporting
system is severely underdeveloped in Pakistan public hospi-
tals and errors often go unnoticed.
Our study is the first extensive report from Pakistan
hospitals. The hospital chosen for this study operated typical
Pakistani administrative and operative system. The multi-ward
specialties and cases included in this study make it possible to
generalise the results to similar institutions in Pakistan. The
overall prescribing error rate reported in our study (Table 2)
before intervention (during paper-based prescribing) (22Æ6%)
was higher than rates reported in other studies in UK and the
USA. The error rate ranged between (0Æ4–15Æ4%) in the US and
(7Æ4–18Æ7%) in the UK (Dean Franklin et al. 2005, Lewis et al.
2009). Recently, a study conducted in UK showed an error rate
of 13Æ2% in paediatric settings (Ghaleb et al. 2010). Our results
could be essentially different since error rate depends on
definition, methodology, setting investigated and depth of
investigation, furthermore, it has been proven observer depen-
dent (Dean Franklin et al. 2005, Lewis et al. 2009). Prescribing
errors identified in our study concerned 66% of the inpatients
during the paper-based prescribing phase against 42% during
electronic prescribing. Such result was consistent with studies
conducted abroad. Studies showed that prescribing errors
occurred in 40Æ5–100% of inpatient cases studied, likewise,
consistent results were observed across the wards included in
those studies (Kaushal et al. 2001). The highest error rate
(40Æ8%) occurred in the Medical emergency ward (Table 2).
Potts et al. (2004) reported a comparative rate (39Æ1%) in a
study conducted at a paediatric intensive care unit (ICU). In our
study setting, the dynamicity of the emergency ward and
diversity of cases admitted could also further complicate the
situation (Dean et al. 2002a). The variability in error rate
across different wards could be attributed to differential
experience, since the demographics of prescribers and their
background were largely different across wards. Consistent
with our findings, former research revealed that the majority of
prescribing errors are related to dose (Table 1). In a study
conducted by Dean et al., 54% of the identified errors were
related to dose (Dean et al. 2002b). Clarity of medication
orders concerned 5% of the errors identified in this study.
Failing in communicating clear orders often led to dangerous
consequences, such as depriving the patient from the benefits of
the right medication and subjecting the patient to the dangers
of unnecessary medication. In a communication, the American
Hospital Association and collaborating associations strongly
discouraged the use of non-standard nomenclature, abbrevia-
tion and symbols. Such usage frequently results in the misin-
terpretation of the intent of the order (Pollock et al. 2007).
Prescribing errors dominated in the most prescribed medica-
tion class (antibiotics) (Table 4). Our findings were consistent
with previous research (Thomsen et al. 2007, Lewis et al.
2009). Electronic prescribing significantly reduced prescribing
errors in different severity classes. However, the impact of
electronic prescribing was more pronounced on minor errors
(i.e. errors without clinical consequences A1 through B)
(Table 3). In our study, the impact of electronic prescribing
brought a significant reduction in the occurrence of prescribing
errors in all wards other than the general cardiac surgery ward
and the coronary care unit (Table 2). Similar significance
reduction was observed with all types of prescribing errors
identified other than ‘not prescribing a medication when
clinically needed’ (Table 1). Previous reports recommended
the use of computerised medication order systems to reduce
prescribing errors both, occurrence and severity (Bizovi et al.
2002, Potts et al. 2004, Colpaert et al. 2006). Our study was
conducted in two-phase design (paper-based and electronic) in
the same wards by the same researchers, recruiting the same
number of patients. Such design is supposed to ensure a
consistent approach. Furthermore, the same risk management
panel rated the prescribing errors. Moreover, the panel
consisted of internal and external reviewers. Such consistent
approach strengthen the rejection of the hull hypothesis (that
there will be no effect of computerised prescribing on the
number of prescribing errors), however, the reduction of
prescribing errors was due to the computerisation of the
medication and discharge medication charts, in addition to
educating prescribers on medication errors.
The tendency of junior doctors to make more errors than
senior prescribers was evident in our study. Although the
literature is inconclusive, in a study conducted by Caruba et al.,
(2010) prescribing errors occurred at higher rates on the first
day of inpatient hospital stay, the study connected the higher
error rates with the fact that on admittance, patients were
received by junior doctors who took their medication history
(Caruba et al. 2010). Coombes et al. (2008) described an
existing culture where prescription writing is seen as a low-risk
chore undertaken by junior doctors where orders were often
incomplete, ambiguous or illegible. Consistently, junior doc-
tors performed more clerical duties than their seniors. Previous
research showed that prescribing errors could be reduced by
reporting and educating prescribers on the issue of prescribing
errors. In a study conducted by Shawet al. (2003), academic
detailing was shown to significantly affect the error rates
identified in the settings investigated (41% vs. 24%). Pharma-
cist interventions and interdisciplinary team medication rec-
onciliation could result in minimising the occurrence of
prescribing and transcription errors (Barber et al. 1997). In a
Quality and safety Electronic prescribing reduces prescribing error in public hospitals
� 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245 3243
previous study, van den Bemt et al. (2002) investigated the
cost–benefit and concluded that pharmacy staff interventions
resulted in higher benefits than the costs related to time
investment. Similarly, modification of medication charts could
bring beneficial results in this domain as show in the study of
Coombes et al. (2008).
Limitations
Our study has several limitations. First, the tertiary cardiac
care services had a separate administration; however, in this
analysis we treated the data as they came from one hospital.
A multicenter study would have permitted to draw a more
solid conclusion. Second, albeit the rating by a risk-manage-
ment panel, the possibility of leaving some prescribing errors
undetected cannot be excluded, therefore, this study could be
an underestimation of prescribing errors in the study setting.
Third, throughout the study, data collectors had the oppor-
tunity to observe and interview prescribers, nursing staff and
patients. Conversely, neither error context nor situations (like
workload) leading to error were formally investigated.
Fourth, our study design had pre and postphases. Such
design has less control on bias as compared with a rando-
mised controlled trial design. Finally, the electronic inpatient
record along with the interfaced database did not provide
‘flags’ if the dose was inappropriate or a possible interaction.
A system with flags and warnings is expected to have a better
impact on the reduction of such errors.
Conclusion
Our results indicate that prescribing errors are highly
prevalent in public hospitals in Pakistan in the existing
environment. Such high prevalence serves as a call to action
to minimise these errors. We have shown that incidence of
prescribing errors can be reduced by information technology
and educating prescribers and nursing staff on medication
errors and safe prescribing. However, this fact has been
shown in previous studies conducted elsewhere. We believe
that improving the electronic system with flags and warnings
could further reduce prescribing errors. Furthermore, previ-
ous studies showed that ward-based pharmacist interventions
and clinical recommendations resulted in reduction of similar
errors (Barber et al. 1997). In the current settings, dispensary-
based pharmacists physically do not see the patient; neither
have full access to patient data. However, once an erroneous
order reaches the pharmacy, it is almost sure that the error
will reach the patient if a checking point was not installed.
Relevance to clinical practice
Prescribing errors are commonplace in Pakistan public
hospitals. The present study tested the impact of moving to
electronic patient records and prescribing helps in reducing
the incidence of such errors. In 2004, Pakistan universities
started a brand new clinically oriented PharmD program.
New graduates are expected to initiate clinical pharmacy
services in hospitals. There is a clear need to investigate the
role of the ward pharmacist in minimising such errors in
Pakistan hospitals.
Acknowledgements
The authors would like to thank the hospital superintendent
and professors of the wards involved in this study for
granting permission and cooperative behaviour.
Contributions
Study design: RS, NR; data collection and analysis: RS, NR,
MD, MA, MY, XD and manuscript preparation: RS, NR,
MD, XD.
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Quality and safety Electronic prescribing reduces prescribing error in public hospitals
� 2011 Blackwell Publishing Ltd, Journal of Clinical Nursing, 20, 3233–3245 3245