Reducing prescribing errors evidence scan-2012

Evidence scan:
Reducing
prescribing errors
April 2012

Identify Innovate Demonstrate Encourage

Contents

Key messages 3

1. Scope 4

2. Education and development 7

3. Expanding professional roles 11

4. Tools 15

5. Summary 25

References 29

Health Foundation evidence scans provide information to help those involved in improving the quality
of healthcare understand what research is available on particular topics.
Evidence scans provide a rapid collation of empirical research about a topic relevant to the Health
Foundation's work. Although all of the evidence is sourced and compiled systematically, they are not
systematic reviews. They do not seek to summarise theoretical literature or to explore in any depth the
concepts covered by the scan or those arising from it.
This evidence scan was prepared by The Evidence Centre on behalf of the Health Foundation.

© 2012 Health Foundation

Key messages
Medicines can do a lot of good but they also have the potential to
cause harm. Medication errors are one of the most common causes
of patient harm and prescribing accounts for a large proportion of
medication errors. This evidence scan examines strategies to reduce
prescribing errors.

Prescribing errors include mistakes or inaccuracies Educational strategies
when choosing and ordering treatments, such as
Educational initiatives tend to focus on stopping
wrong doses or illegible prescriptions.
errors before they occur. Strategies include:
Eight databases were searched and 123 studies were
–– group training sessions
included about strategies for reducing prescribing
errors, predominantly from North America. Studies –– individual education visits
about errors of omission, such as not prescribing –– letters and printed materials
a drug that might be helpful, were excluded
because it is difficult to be objective about what –– audit and error reporting systems
medications should be prescribed in any individual –– improvement projects and collaboratives.
instance. The scan does not cover other medication
All of these initiatives have had some success, but
errors such as those related to dispensing or
there is not enough evidence to say which strategies
administration.
work best.
Most studies about reducing prescribing errors
have been undertaken in hospital. The three most Professional roles
commonly researched approaches are, in order of
Studies of expanding professional roles tend to
frequency: computerised tools, training to improve
focus on how pharmacists can identify any errors
prescribing and expanding professional roles to
before patients are harmed, including:
identify errors.
–– checking for errors as prescriptions are received
Computerised tools at the pharmacy or on wards
Electronic prescribing and computerised decision –– medicine reconciliation or reviews
support have been studied extensively but there are –– individual or group education sessions.
mixed findings. Most studies suggest computerised
tools can reduce prescribing errors but some Most research suggests that engaging pharmacists
suggest unintended negative consequences. in these ways can be beneficial, but few studies have
Emerging evidence suggests that to be successful, explored the best ways to integrate pharmacists
human factors such as workflow features, tool into teams and the interprofessional factors to
design and context need to be considered. be considered. Combining education, enhanced
professional roles and computerised tools may help
to reduce prescribing errors most effectively.

THE HEALTH FOUNDATION Evidence scan: Reducing prescribing errors 3

1. Scope
Health professionals and managers are always looking for ways to
improve the quality and safety of healthcare. Medicines are a key
component of healthcare and errors relating to medication may
impact on patient safety. This evidence scan explores what is being
done to reduce prescribing errors.

1.1 Purpose 1.2 Definitions
Thousands of people in the UK take medicines
every day to help manage ongoing conditions Prescribing errors
or to help them through an emergency or crisis. Prescribing is the process whereby a doctor,
Most receive and take their prescriptions without nurse or other registered professional authorises
incident in hospital or in the community, but in a use of medications or treatments for a patient
small number of cases an error occurs, whether or and provides instructions about how and when
not it is evident to patients. those treatments should be used. Although the
term commonly refers to orders for medicines,
A medication error is a failure in the the concept can equally encompass laboratory
treatment process that leads to, or has the tests, medical imaging, psychological treatments,
potential to lead to, harm to the patient.1 eye glasses, eating and exercise regimes or
other instructions to help optimise health and
This evidence scan explores steps that have been
wellbeing.2,3
explored to minimise prescribing errors. It does not
cover the frequency or cause of prescribing errors. Prescriptions are handwritten or computerised
It focuses solely on approaches that have been used documents containing the patient’s name and
to minimise such errors. address, the date, the specific treatments prescribed
and an authorising signature. They are a way for
The scan addresses the questions:
prescribers to communicate with pharmacists or
– What approaches have been used to reduce others who in turn fill the prescription. Prescribers
prescribing errors? include doctors of various types and, in some
countries, nurse practitioners, physicians assistants,
– Have any approaches related to human factors
dentists, podiatrists, optometrists, clinical
been researched?
psychologists and clinical pharmacists also write
The scan provides a rapid collation of empirical prescriptions.4–6
research about initiatives to reduce prescribing
errors. All of the evidence has been sourced and Prescriptions can help people stay healthy or
compiled systematically, but the scan is not a manage long-term conditions or emergency
systematic review and does not seek to summarise situations. However, as with other components of
every study on this topic. healthcare, prescriptions are also subject to error
and can lead to unintended harm. Medication
This section defines prescribing errors and human errors are one of the most common patient safety
factors approaches and describes the methods used issues and prescribing errors are one of the most
to identify relevant research. The following sections common types of medication errors.7–12
outline the three broad approaches that have been
used to reduce prescribing errors: training to avoid Prescribing errors can take many forms, but
prescribing errors before they happen, expanding commonly involve incorrect doses, illegible details
professional roles to identify and rectify errors, and or ordering inappropriate medications or drugs
using tools to improve processes. that may react with other medications already
being taken.

A study to develop a definition of prescribing errors Human factors approaches address the interactions
in the UK concluded that transcription errors, between people, the work environment and
failure to communicate essential information organisational systems. This discipline seeks
and the use of drugs or doses inappropriate for to understand people’s psychological and
the individual patient were prescribing errors, physiological limitations, the demands imposed
but omissions and deviations from policies or upon people at work and how the mismatch
guidelines were not.13 Even so, some also define between the two leads to errors.
prescribing omissions as errors, for example if a
doctor fails to prescribe an antihypertensive drug In the field of healthcare, human factors approaches
for someone who could benefit from it. aim to enhance clinical performance through an
understanding of the effects of teamwork, tasks,
In this evidence scan, the focus is on active errors, equipment, workspace, culture and organisation
whereby the prescription contains a potentially on human behaviour and abilities and to apply that
harmful drug, combination or dosage rather than knowledge in clinical settings.
solely errors of omission. Studies focused only
on errors of omission were excluded unless they Human factors approaches may involve
explicitly defined such errors as ‘prescribing errors’ diagnosing issues in the interaction between
and investigated definite strategies to reduce those people and systems, identifying workload
errors. In this way, the scan uses the definition of and task interruptions and redesigning the
‘prescribing errors’ as outlined in individual studies workplace environment and team factors through
in the review. This means that studies that sought standardisation and prioritisation.
to ‘improve prescribing’ in terms of adherence to Human factors approaches tend to focus on
guidelines or increasing or decreasing the rates personnel, training and operating parameters.14–21
of prescribing some types of medicines were not More specifically, human factors solutions may
included unless the authors specifically defined include five broad approaches:22,23
these as prescribing errors. The scan focuses on
research about reducing prescribing errors rather –– training individuals to better prepare them for
than research about ‘improving prescribing’ more the work and conditions
generally. –– selecting individuals who possess the best
characteristics for the job and avoiding fatigue,
Human factors stress and burnout
All approaches to reduce prescribing errors were –– environmental design such as improved
of interest but there was a special focus on human lighting, temperature control and reduced noise
factors approaches.
–– equipment design including tools and
‘Human factors’ is a multidisciplinary field automation
incorporating contributions from psychology,
–– task design to change what staff do rather than
engineering, design, ergonomics, operations
just the devices they use. This may involve
research, aviation, continuous quality improvement
assigning some or all of tasks to other workers or
and other disciplines.
to automated processes
In general terms, a ‘human factor’ is a physical, Interventions to reduce prescribing errors all
mental, emotional or social aspect that is specific fit broadly into these human factors categories.
to humans and may influence how people interact There is potential overlap in these categories but
with the environment and people around them. interventions to reduce prescribing errors have
Human factors approaches thus study all aspects of focused largely on training individuals, selecting
the way humans relate to the world around them, individuals (pharmacist roles) and equipment and
their capabilities and limitations and how these can task design (including electronic systems). The
be used to improve performance and safety. following chapters describe research about each of
these three areas in turn.


1.3 Methods While the focus is on prescribing errors, rather
than medication errors more generally, the
The scan focused on research articles published
review screened studies related to ‘medication
in journals in the UK and internationally and was
errors’ because often studies use this terminology
completed over a two-week period.
rather than the term ‘prescribing’. Studies using
To identify relevant research, one reviewer searched this broader terminology were included if they
eight bibliographic databases for studies of any contained specific data about prescribing errors.
design in any language published between 1990
More than 10,000 articles were scanned and 123
and early January 2012. The databases comprised
studies met the inclusion criteria.
Medline, Embase, the Cochrane Library and
Controlled Trials Register, PsychLit, Google Although prescribing of all types was eligible for
Scholar, Web of Science, ScienceDirect, and the inclusion, only research about reducing errors in
Health Management Information Consortium. medication prescribing was identified in the search.
The report therefore focuses on reducing errors in
Search terms included combinations of prescribing
medication prescribing.
error, prescription error, medication error, dosing
error, dose error, human factors, task identification, Findings were extracted from all publications using
task redesign, workplace environment, situational a structured template and studies were grouped
awareness, team roles, standardisation, according to key themes to provide a narrative
prioritisation, workload interruptions, pharmacist, summary of trends.
pharmacy, computerised order entry, computerised
physician order entry, computerised pharmacist
order entry, e-prescribing and similes.
General search terms such as ‘prescribing error’
were used first to identify the largest range of
studies. When research about specific interventions
was identified, such as e-prescribing, these
interventions were then added as search terms to
ensure completeness. In this way the search strategy
was initially general, to identify research about a
wide variety of interventions, and then became
more detailed, to gain in-depth information about
the specific interventions identified.
To be eligible for inclusion, studies had to be readily
available empirical research or systematic literature
reviews which examined some type of outcome
relating to reducing prescribing errors. This may
include, for example, strategies used to reduce
errors, the benefits or costs of doing so, or increases
in reporting rates. Studies about the number and
type of prescribing errors were not included.
Studies that described potential approaches to
reducing prescribing errors but did not contain
empirical data were also excluded.


2. Education and development
Training personnel to better prepare them for tasks and work
conditions is a human factors approach. This section describes
24 studies about training and educational initiatives to reduce
prescribing errors.

Research has focused on using training and Another review of interventions to improve
development initiatives to reduce prescribing errors prescribing included 29 studies. Educational
in two distinct ways. outreach visits and audit and feedback were most
commonly studied and were found to be effective
–– The first relates to reducing errors during the for improving prescribing practice.25
prescribing process itself. Here, research has
examined one-to-one and group education and Researchers in Australia tested the value of
improvement projects to stop errors happening academic detailing to reduce simple errors when
in the first place. prescribing drugs that can be addictive in hospital.
–– Second, research has examined training and One hospital acted as a control and another
development initiatives to identify and rectify received academic detailing, where junior doctors
any errors that do occur, to minimise the chance received an educational visit and a bookmark
of them harming patients. Here, the focus tends reminder containing the requirements for selected
to be on error monitoring and reporting systems. drugs. Prescription error rates decreased from 41%
to 24% at the hospital receiving academic detailing
Research about training and development and the confidence of junior doctors in writing
initiatives is divided into these two subsections prescriptions increased. There was no change in
below. error rates at the control hospital.26
Elsewhere in Australia, a hospital used a decision
2.1 Reducing errors support tool to help with drug dosing for people
during prescribing with kidney problems. The system was introduced
to prescribers using academic detailing. There were
One-to-one education improvements in dosing for various drugs. The
Individualised education can take many forms, evaluators concluded that one-to-one education
including ‘academic detailing’ whereby a helped to introduce tools for reducing prescribing
professional is visited in their workplace for a errors.27
one-to-one education session. A Cochrane Review
examined face-to-face outreach visits by a trained Group education for trainees
person to a health professional. 18 randomised Most studies of group education sessions to reduce
trials were included, 13 of which targeted prescribing errors focus on training for medical or
prescribing. All outreach interventions included pharmacy students or registrars.
several components such as written materials or
conferences. Reminders or audit and feedback A prescribing skills course for interns was tested
were sometimes used. All studies found improved in the US. A pharmacy faculty gave two lectures,
behaviours. However, few studies examined patient attended hospital rounds and took part in clinics.
outcomes or costs.24 Interns then undertook a written exam and
clinical assessment. All interns made at least one


prescribing error on the exam, but all passed on the Another hospital in Spain tested ways to improve
second attempt and gained prescribing privileges handwritten prescriptions in neonatal units. Staff
after six months. The researchers concluded that took part in training about good prescribing
the prescribing curriculum was practical and practice and used a pocket PC automatic dosage
feasible.28 However, studies like these tend not calculation system. Incorrect prescriptions reduced
to follow up on results to examine the impact on from 40% to 12%.32
reducing prescribing errors in practice.
Other studies have examined combined training
An Objective Structured Clinical Examination for both fully qualified professionals and trainees.
(OSCE) is an assessment tool that uses lay people Researchers in England examined whether
trained to respond to questions in a standardised prescriber education in tutorials, ward-based
manner. Students’ performance is observed teaching and feedback with each new group of
and scored. An OSCE station related to the trainee medical staff could reduce prescribing
communication and management of prescription errors in intensive care. Prescribing audits before
errors was tested with third-year students at one training, immediately after training and six weeks
US medical school. In total, 77% of students said after training were fed back to prescribers with
that the OSCE station improved their awareness of their individual prescribing and error rates and
medication errors and 71% thought that they were anonymised information about other prescribers’
more comfortable communicating prescription error rates. Prescription errors decreased.33
errors to patients. Feedback about root cause
analysis, collaboration with the pharmacist for Improvement programmes
error analysis, interpersonal and communication A small number of studies have tested how
skills feedback from faculty, use of a standardised collaborative improvement projects and networks
patient and use of an actual prescription that led to of professionals may impact on prescribing errors.
a medication error were thought to be helpful.29
Thirteen hospitals in one US state took part in a
But not all types of training for students are collaborative project to improve medication safety.
successful. A hospital in Canada tested a 30-minute Teams were encouraged to make changes to their
tutorial for all fellows and residents starting in the medication processes based on evaluating their
accident and emergency (A&E) department at the medication systems and ergonomic principles and
beginning of the academic year. The tutorial was research. Before and after data from eight of the
followed by a written test. Prescribing errors were hospitals suggested a 27% decrease in medication
reviewed on 18 randomly selected days. There was errors, a 13% increase in error detection and
no difference in prescribing error rates between prevention and a 24% increase in formal written
those who attended and those who did not attend reporting of errors that reached the patient.34
the tutorial.30
A hospital in Argentina implemented strategies to
Group education for change the safety culture and reduce medication
errors in children and babies. Interventions
qualified professionals focused on promoting positive safety culture
Education to reduce prescribing errors has also
without punitive management of errors and
targeted fully qualified professionals. A neonatal
specific prescribing and drug administration
intensive care unit in Spain evaluated the effect of
recommendations. The medication error rate
educational sessions for health professionals on
decreased from 11% to 7% over a two-year period.
the number and type of prescription errors. The
Prescribing errors were not analysed separately but
prescription error rate reduced from 21% to 3%.31
were specifically targeted.35


2.2 Reducing errors identified and randomly assigned to provider
feedback or usual care. However, after one year
after prescribing there was no difference in adverse drug events.40
One-to-one education
Various types of individualised education have Group education for trainees
also been studied for reducing the impact of errors Researchers in Canada evaluated a computer
or identifying errors before they harm patients. training module to improve third-year pharmacy
In Australia, direct feedback to clinicians was students’ ability to identify and correct prescribing
tested to reduce errors from polypharmacy or errors. The module helped increase the
drug interactions in older people. GPs were sent identification of errors.41
information about the at-risk patient, relevant In the US, first-year pharmacy students took part in
clinical guidelines and a personalised covering laboratory simulations to help identify and prevent
letter. There was a reduction in the average medication errors, including prescribing errors.
number of medications prescribed for each person Following simulations and role plays, students’
following the prescriber feedback.36 knowledge and awareness of medication errors
Similarly, researchers in Canada examined whether improved as did their confidence in recognising and
follow-up letters from pharmacists to doctors preventing errors and communicating about them.42
following inappropriate prescriptions would However, studies like these tend not to follow up to
improve prescribing for people in long-term care. examine the impact on reducing prescribing errors
The educational letters briefly described potentially in practice.
inappropriate prescriptions and suggested
alternatives. 38% of potentially inappropriate
prescriptions were changed by the doctor following
Improvement programmes
A hospital in Switzerland tested various approaches
a letter.37
for reducing the impact of adverse drug incidents.
Researchers in the US tested whether a Non punitive incident monitoring was set up in a
computerised drug review database linked to a neonatal-paediatric intensive care unit. Systems
telepharmacy intervention reduced inappropriate changes included double checking for potentially
medication use in 23,269 people aged 65 years or harmful drugs, using a standardised prescription
older. Computer alerts triggered telephone calls to form and contacting the national drug control
doctors from pharmacists with training in older agency about misleading drug labels. Most of
people’s medicine who could discuss substitution the system changes were based on minor critical
options. As a result, 24% changed to a more incidents which were only detected after a long
appropriate drug.38 period of time. They resulted in some potential
errors being caught, including prescribing errors.43
Education may also be informal and result from
interactions between staff members. Researchers in Another popular educational and improvement
the US assessed the views of pharmacy directors, method is audit and feedback. Changes are monitored
medical centre executives and pharmacists over time and prescribers and pharmacists are given
about the value of pharmacist residency training written feedback about their own performance in
programmes. Participants believed that residency comparison to others. A Cochrane Review about
programmes had many benefits and that these audit and feedback included 37 randomised trials,
outweighed costs. They thought that pharmacy including some about prescribing. Effects were
residents helped to reduce medication errors by varied. The reviewers concluded that audit and
educating prescribers and checking prescribing.39 feedback can sometimes be effective in improving
the practice of health professionals, in particular
Patients have been targeted for education in a prescribing and diagnostic test ordering, but effects
small number of instances. In one study, 913 US tend to be small.44
outpatients with potential prescribing errors were


Error monitoring and reporting
Monitoring and reporting on errors may in itself
serve to raise awareness and support improvement.
A neonatal intensive care unit in Spain tested
whether prescribing errors would decrease merely
as a result of observation and recording of errors.
The prescription error rate reduced from 33% to
19%. Rates of incorrect dosing and lack of dose
specification in prescriptions reduced significantly
but there was no change in transcription errors.45
A hospital in New Zealand conducted audits
over a 10-year period to improve the quality of
written prescriptions. Initially there was a high
rate of insufficient documentation and illegible
prescriptions. Interventions designed to address
deficiencies included feeding back audit results,
education sessions for doctors and nurses on
prescribing and medication errors and changes
to systems, such as modifying medication charts,
developing hospital-wide prescribing standards and
an alert notification system. Over time, legibility
and documentation improved.46
A system was set up to report on medication errors
at one hospital in France. 60% of medication errors
related to prescribing. The system was found to
be feasible and resulted in steps being taken to
reduce errors. Success factors included a blame-
free approach and ensuring that the system was
confidential.47


3. Expanding professional roles
Selecting appropriate personnel is an important human factors
approach. This section describes 19 studies about initiatives relating
to roles and personnel to reduce prescribing errors.

As with studies about education and development, errors decreased from 10% to 5% and the number
research has focused on using varying professional of warnings that doctors complied with increased
roles and skill mix to reduce prescribing errors in from 44% to 68%.48
two distinct ways.
–– The first relates to roles during the prescribing 3.2 Reducing errors
process to reduce the likelihood of errors after prescribing
happening in the first place. Here, research is
very limited, and has examined prescribing by Pharmacist roles
nurses versus doctors. Most studies about reducing errors after
–– Second, research has examined the use of prescriptions have been written have been
healthcare professionals to identify and rectify undertaken in hospital, particularly in the US.
any errors that do occur, to minimise the chance The most common interventions related to specific
of them harming patients. Here, the focus tends roles focusing on pharmacists.
to be on expanding the role of pharmacists to Pharmacist roles to identify prescribing errors and
perform checks and identify errors. to stop them reaching patients include:
Research about roles is divided into these two
subsections below. –– checking for errors as prescriptions are received
at the pharmacy and contacting prescribers
for clarification or amendment before filling
3.1 Reducing errors prescriptions
during prescribing –– visiting wards to review charts and provide
Few studies have examined how professional advice to prescribers about individual patients
roles can be expanded to reduce prescribing –– reconciling the medicines patients usually take
errors. One exception is a study of engaging with what they are prescribed in hospital
nurses in roles usually performed by doctors. A
hospital in Iran tested whether a collaborative –– providing medication reviews upon discharge.
prescription order entry method consisting of Each of these initiatives is explored in turn.
nurse order entry followed by doctor verification
and countersignature is as effective as a strictly Pharmacists have also run one-to-one or group
physician order entry method in reducing education sessions for prescribers but these
prescribing errors in the neonatal ward. In both interventions tend to focus on prevention rather
systems a warning and suggested change appeared than error identification. Studies of this nature were
when the dose or frequency of the prescribed covered in the previous section.
medication was incorrect. The rate of medication
errors was 40% lower for nurse order entry
compared to doctor order entry. Prescription


Checking medication orders errors was lower than before the intervention and
A number of studies have examined the value of preventable adverse drug events were reduced. The
asking pharmacists to specifically check and review intervention cost 3 Euro per monitored day but
medication orders. For instance, pharmacists at a potentially saved 26 to 40 Euro per monitored day
US hospital used an electronic system to review by preventing adverse drug events.54
all prescriptions. This alerted the prescriber and
Similarly, pharmacists reviewed prescriptions
pharmacist to dosage errors and allergies and
on the surgical wards at one hospital in Canada
reduced prescription errors.49
and provided group educational sessions for
Another US hospital examined how paediatric doctors. Doctors accepted 90% of pharmacist
clinical pharmacists intercept prescription errors. recommendations. There was a 9% decrease in drug
In total, 78% of potentially harmful prescribing costs.55
errors were intercepted by pharmacists.50
A hospital in England examined the impact of
Medicine reconciliation
Medication reconciliation, whereby a pharmacist
pharmacists on preventing prescribing errors at
checks usual medicines against planned
discharge. Routinely collected data showed that
prescribing, can take place in hospital or in primary
8% of all medication orders had an intervention
care. A systematic review of four studies examined
by a pharmacist. Pharmacists intercepted 83% of
medication reconciliation interventions in both
erroneous orders without referring to doctors.
settings. One randomised trial and one before and
Omission, drug selection and dosage errors were
after study evaluated pharmacist medication review
the most common.51
at hospital discharge. Neither found a benefit.
Researchers in the Netherlands analysed the costs Two before and after studies examining systematic
and benefits of hospital pharmacy staff detecting medication reconciliation at each primary care visit
prescribing errors. Over a five-day period, 10% of had conflicting findings.56
3,540 medication orders in two Dutch hospitals
In the UK, a pharmacist independent prescriber
contained an error. Estimated benefits amounted
completed systematic medicine reconciliation in
to 9,867 Euro compared to 285 Euro in staff time
A&E and initiated an inpatient prescription chart.
costs.52
Medicine reconciliation completed within 24 hours
But interventions involving checks by pharmacists of admission increased from 50% to 100% and
are not always successful. Researchers in France prescription chart initiation in A&E increased from
examined how pharmacy validation can be 6% to 80%. The prescribing error rate was reduced
used as a secondary filter for eliminating errors from 3.3 errors to 0.04 errors per patient.57
from a computerised order entry system. All
Elsewhere in the UK, a cost analysis of five different
prescriptions over a five-day period were analysed
strategies for preventing medication errors at
at one hospital. Pharmacy validation produced
hospital admission used models and previous
only a moderate short-term impact on potential
studies. Pharmacist reconciliation of medicines was
prescribing errors.53
found to be cost effective.58

Pharmacists on wards
Another strategy is to engage pharmacists to check
Pharmacist discharge services
One hospital in the Netherlands examined the
prescribing on hospital wards. In the Netherlands,
effect of a clinical pharmacist discharge service on
a clinical pharmacist reviewed medication orders
medication discrepancies and prescription errors in
for patients admitted to the intensive care unit and
people with heart failure. One group received usual
discussed recommendations during patient review
care by doctors and nurses. The other received a
meetings with attending doctors. Over an eight
review of discharge medication by pharmacists
and a half month period, the rate of prescribing


who alerted specialists to prescribing errors, gave medication errors to the pharmacist than to their
patients information, prepared a written overview doctor. Pharmacists acted as the final interceptors,
of discharge medication and communicated with detecting errors in prescriptions before they
community pharmacists and GPs. The pharmacist reached patients.62
discharge service was associated with fewer
medication discrepancies and prescription errors at Pharmacists may contact primary care doctors
one-month follow up (39% versus 68% of people in to clarify prescriptions or suggest changes. In the
the control group).59 US, call backs from pharmacies to 22 primary care
practices were logged over a two-week period.
Keeping records of the number and type of queries
Multifaceted hospital interventions from pharmacists helped practices develop specific
Sometimes a range of interventions involving interventions to reduce errors.63
pharmacists are implemented simultaneously. One
paediatric intensive care unit in Egypt introduced Some proactive approaches to pharmacist review
a structured medication order chart, doctor have also been tested. In Switzerland six quality
education by pharmacists, provision of dosing circles were set up whereby six community
assists and performance feedback to doctors. pharmacists reviewed the prescribing of 24 GPs.
Prescribing error rates reduced from 78% to 35%. Key elements included the review of specific
Potentially severe errors reduced from 30% to 7%.60 prescriptions, continuous quality improvement
and education, local networking and feedback of
A systematic review examined the frequency of comparative data about costs and drug choices.
medication and prescribing errors in neonatal Analysis of nine years’ worth of data found
intensive care units. In 11 studies, the highest improved quality and safety of prescribing and a
reported rate was 5.5 medication errors per 100 42% decrease in drug costs compared to a control
prescriptions, but rates varied widely between group, representing savings of US$225,000 per GP
studies partly due to differences in definitions and per year.64
methods. Dose errors were the most common.
Computerised physician order entry, participation
of pharmacists in ward rounds and pharmacist Nursing homes
review of prescriptions prior to dispensing were A review of 18 randomised trials of interventions
suggested to improve medication safety, but there to improve prescribing in nursing homes
were few high-quality evaluation data available.61 found that seven studies described educational
approaches such as outreach visits, five studies
described clinical pharmacist activities such as
Pharmacists in primary care medication reviews and two studies described
Studies are also available about the role of computerised decision support. Two studies
pharmacists in reducing prescribing errors in described multidisciplinary approaches and two
primary care. However, most of the research in this described multifaceted approaches. Improvements
area is relatively small scale and descriptive and in prescribing were found in 83% of studies. In
observational. It tends to describe interventions some cases, this included reductions in prescribing
undertaken in a small number of sites, and little errors, but most of the interventions focused on
detailed or long-term data about outcomes are improving suboptimal prescribing which was
available. outside the scope of this evidence scan.65
For instance, a US study examined the pharmacist’s
role in improving medication safety in primary care
using focus groups with pharmacists and patients.
Patients were likely to see multiple doctors but only
one pharmacist. They were more likely to report


3.3 Other human
factors issues
Other human factors issues such as fatigue,
concentration levels and stress may all have an
impact on prescribing behaviour. It has also been
suggested that temporary staff are more likely to be
associated with medication errors.66
While descriptive articles are available about
these human factors concepts and their potential
impact on safety issues, no empirical research
was identified about interventions targeting these
factors specifically to reduce prescribing errors.


4. Tools
Redesigning equipment and tasks can reduce prescribing errors.
This section describes 80 studies about tools that have been used to
reduce prescribing errors.

Human factors approaches are concerned with Similarly, electronic prescribing is already standard
the interface between tools and systems and the in primary care in the UK, whereas in the US this
personnel responsible for them. The majority is just beginning to get established. A great deal
of studies about re-designing equipment and of research has been undertaken in the US about
tasks to reduce prescribing errors focus on e-prescribing systems, but the findings perhaps
electronic prescribing systems (e-prescribing) merely serve to reinforce what is already standard
and computerised decision support systems. practice in the UK.
These studies tend to describe implementation of
specific systems and their outcomes, but do not
usually examine the interlinkages with workflow,
4.1 E-prescribing
interruptions and other human factors. Hospital care
Studies about computerised tools are described in E-prescribing is also known by the terms
this section because the majority of research about computerised physician order entry (CPOE),
reducing prescribing errors has focused on such computerised provider order entry or
tools. However, it is acknowledged that the research computerised pharmacist order entry (in the US
tends to focus on the technology rather than the where pharmacists may transcribe prescribers’
interface between technology and personnel. handwritten orders into a computer system). This
is an electronic process for entering instructions
Almost all of the studies focus on how tools can about patient treatment. Orders for medication,
be used to reduce errors during the prescribing equipment or other treatments are communicated
process itself, but some of the tools can also be over a computer network to various medical staff
used as a way of identifying errors after they have and departments such as pharmacy, laboratory or
occurred. radiology who are, in turn, responsible for filling
those orders.
When interpreting the findings in this section it is
important to remember that there are differences Before e-prescribing systems were available, in the
in prescribing and in the roles of pharmacists in US doctors traditionally wrote out or verbally stated
various countries. For example, electronic systems their instructions for patient care, which were then
have been set up to reduce transcription errors but transcribed by nurses or ancillary staff before being
transcription errors do not apply in the same way actioned. It was thought that such handwritten
in the UK as in the US. In the UK, doctors write notes may result in more errors and delays67 and, as
directly onto a drug chart or into an electronic a result, the US Institute of Medicine recommended
prescribing system rather than onto a piece of e-prescribing be implemented as standard.68
paper which is then transcribed by someone else.
Studies that focus on reducing transcription errors
of this nature are therefore of limited relevance to
the UK.


E-prescribing systems aim to reduce delay in in a nephrology outpatient clinic at a paediatric
accessing medication or treatment, reduce errors hospital. The overall prescribing error rate was 77%
related to handwriting or transcription, allow for handwritten items and 5% with e-prescribing.
orders to be made at the point of care or off-site and Before e-prescribing, 73% of items were missing
simplify inventory and charging processes. essential information and 12% were judged
illegible. After e-prescribing was introduced, 1% of
The systems often have decision support tools items were missing essential information and there
built in whereby the system automatically checks were no illegibility errors. The number of error-free
for duplicate or incorrect doses or tests, provides patient visits increased from 21% to 90%.73
alerts to let the prescriber know that a dose is
too high or may interact with other medications, Researchers in Canada examined the impact of
or highlights clinical guidelines or other ways to e-prescribing on medication errors and adverse
improve evidence-based treatment. This section drug events in hospitalised children over a six-year
includes studies about e-prescribing systems with period. Compared to wards using handwritten
and without inbuilt decision support tools (often orders, the computerised system was associated
the distinction is not made clear in the studies). with a 40% lower medication error rate. However,
The next subsection examines research about the there was no impact on adverse drug events.74
impacts of decision support tools themselves.
Over a four-year period, a US hospital introduced
A large number of studies have found benefits from an e-prescribing system and incorporated decision
e-prescribing, and it is commonly suggested that support features. The medication error rate
such tools can reduce prescribing errors by around (excluding missed doses) fell by 81%. Serious
a half.69,70 medication errors that were not intercepted fell by
86%. Dose errors, frequency errors, route errors,
For instance, a systematic review found that substitution errors and allergies all reduced.75
23 out of 25 studies about e-prescribing which
reported on the medication error rate found Another US hospital implemented e-prescribing
improvements. Six out of nine studies that with features designed to improve medication
analysed the effects on potential adverse events safety such as required fields, use of pick lists,
found reduced risks. Four out of seven studies enhanced workflow features, alerts and reminders
that analysed the effect on actual adverse drug and access to online reference information. The
events found reduced risks. Studies of locally system was associated with a reduced error rate.76
developed systems, those comparing e-prescribing
to handwritten prescriptions and studies using A US A&E department found that before
manual chart review to detect errors, found greater e-prescribing there were 222 prescribing errors
improvements.71 per 100 orders compared to 21 per 100 orders
afterwards.77
Another review of 12 studies compared
handwritten versus computerised prescription Another study tested e-prescribing in a US
orders. 80% of studies about e-prescribing children’s critical care unit. Before implementation,
reported fewer prescribing errors compared with there were about 2 potential adverse drug events
handwritten orders. The use of e-prescribing was per 100 orders compared to 1 per 100 orders
associated with a 66% reduction in prescribing afterwards. There was a 96% reduction in errors.78
errors in adults, but not children.72 A before and after study in a public hospital in
Studies from many parts of the world with diverse Pakistan found that prescribing errors for inpatients
health systems have found that e-prescribing were 23% during paper-based prescribing and 8%
systems can reduce prescribing errors. For example, after the introduction of e-prescribing. The error rate
researchers in England assessed e-prescribing for patients upon discharge was 17% for paper-based
prescribing and 4% after introducing e-prescribing.79


In Spain, a hospital unit using handwritten Most research focuses on the potential of
prescriptions was compared with another using e-prescribing to avoid errors during the initial
e-prescriptions. Handwritten prescriptions were prescribing process, but these tools can also be
associated with a 20% error rate compared to 9% used to identify errors after the prescription has
in electronically assisted prescriptions. Omission been entered. A US hospital aimed to reduce
errors were also lower with e-prescriptions.80 oral chemotherapy related prescribing errors
intercepted by clinical pharmacists prior to
Intensive care units at one hospital in Belgium reaching the patient. A multidisciplinary team
tested whether a computerised system could reduce identified key elements of the oral chemotherapy
the incidence and severity of prescription errors. process using healthcare failure modes and effects
One unit used a paper-based system and another analysis (HFMEA) then implemented e-prescribing
used e-prescribing. There were fewer prescription which reduced the risk of prescribing error by
errors with the computerised system (3% versus 69%.86 Pharmacists used the system to check and
27%) and fewer adverse drug events.81 amend prescriptions.
A hospital in France compared two prescribing and E-prescribing systems have also been used to try to
medication distribution systems on a paediatric reduce errors indirectly. For example, researchers
nephrology ward: a handwritten prescription in England tested whether data routinely produced
plus ward stock distribution system versus by an e-prescribing system could be used to
computerised prescription plus unit dose drug identify doctors at higher risk of making a serious
dispensing system. Over an eight-week period, the prescribing error, with the aim of intervening with
computerised prescription error rate was 11% and these doctors. 848,678 prescriptions by 381 junior
the handwritten prescription error rate was 88%.82 doctors at one hospital over a year long period
A hospital in the Netherlands tested decision were analysed. Doctors varied greatly in the extent
support and computerised order entry. The to which they triggered and responded to alerts of
proportion of prescriptions containing one or more different types. It was not possible to use data about
errors reduced from 55% to 17%.83 the number and type of alerts to identify doctors at
high risk of making serious errors.87
Some hospitals have modified or developed
specialised e-prescribing systems to target people Not all studies of e-prescribing have found
with particular conditions or to address specific favourable results. Researchers in Canada
types of errors. A systematic review of e-prescribing evaluated commercially available prescribing
in hospital paediatric care and neonatal, paediatric software in hospital outpatient clinics. Data from
or adult intensive care settings included 12 22 weeks when the system was not available
observational studies. Meta analysis found a were compared with 44 weeks when the system
decreased risk of prescription errors. There was was available. During intervention weeks, about
no reduction in adverse drug events or mortality 8% of prescriptions were electronic and the rest
rates.84 were handwritten. There was no difference in
prescription error rates88 but this may be due to the
Dose calculation errors are the most common very low uptake rate of the system.
type of medication error in children and babies.
A systematic review examined interventions to A hospital in Portugal examined an e-prescribing
reduce the risk of this type of error. 28 studies were system with a dose distribution tool. The tool
included, mostly about e-prescribing. Most studies helped to reduce medication errors related to
of e-prescribing found some reduction in errors. transcribing and patient identification, but
However, one study found increased mortality after prescription and monitoring errors remained.89
the implementation of e-prescribing.85


E-prescribing systems have sometimes been abbreviation errors. However, errors not associated
associated with negative or unexpected outcomes with abbreviations increased during the transition
too, including an increase in some types of errors.90 period.94
For instance, a systematic review of 12 studies
published between 1998 and 2007 examined A hospital in Italy compared manual prescription
e-prescribing in hospital. Nine studies found versus a computerised system. When the
reduced prescribing error rates for all or some drug computerised system was first introduced the
types, usually regarding minor errors. But several number of errors increased due to incomplete
studies reported increases in the rate of duplicate dose and incomplete prescriptions. However, after
orders and failures to discontinue drugs. This the system was modified the overall rate of errors
was attributed to inappropriate selection from a decreased.95
dropdown menu or not being able to view all active Some suggest that e-prescribing may take
medication orders concurrently. The reviewers longer than handwritten prescriptions.
concluded that evidence for e-prescribing systems Researchers in England assessed a combined
is not compelling and is limited by small sample e-prescribing, automated dispensing, barcode
sizes and poor study designs.91 patient identification and electronic medication
Researchers in the US examined hospital staffs’ administration record system in a hospital surgical
interaction with an e-prescribing system at one ward. Prescribing errors reduced from about 4% to
hospital over a two-year period. In total, 261 staff 2% of orders. However, medical staff required 15
were surveyed, 32 were interviewed and there were seconds to prescribe a regular inpatient drug before
five focus groups. The system led to 22 types of and 39 seconds after introducing the system.96
risks of medication errors such as not allowing a
coherent view of patients’ medications, mistaking Primary care
pharmacy inventory displays for dosage guidelines, Research about e-prescribing outside hospital is less
placing alerts on paper charts rather than in the frequent and sometimes less positive, though this is
system, separating functions that facilitate double standard in UK primary care.
dosing and incompatible orders and generating
A review of e-prescribing in outpatient settings
incorrect orders due to inflexible ordering formats.
included 30 studies. Only one study found reduced
These risks occurred frequently.92
prescribing errors. There were no impacts on
Another US hospital implemented a commercially adverse drug events. Three studies found reduced
available e-prescribing system to help reduce medication costs but five others did not.97
mortality among children transported for
Another study examined the impact of
specialised care. Before and after analysis found
e-prescribing in four US primary care practices.
that the tool was associated with increased rates of
There was no difference between those who used
mortality, not reductions.93
basic computerised prescribing and those using
It may take some time for the benefits of handwritten prescriptions.98
e-prescribing systems to become apparent and
However some benefits have been observed.
there may be difficulties in the transition or
An analysis of 10,172 prescriptions in primary
implementation period. An analysis of US data
care found that a basic e-prescribing system was
found that changing from using older e-prescribing
associated with reduced medication errors.99
to newer systems was associated with a reduction in
prescribing errors from 36% to 12%. Improvements Compared to when using handwritten orders, the
were mainly a result of reducing inappropriate proportion of errors reduced from 18% to 8% in
community-based US primary care. The largest
improvements were in illegibility, inappropriate
abbreviations and missing information.100


But when three primary care clinics in the US It may be that decision support is more useful at
implemented e-prescribing, a time motion study some stages of the prescribing process than others.
found that it took longer than handwritten A systematic review of 56 studies found that during
prescriptions.101 treatment initiation, decision support systems
were more effective after drug selection, rather
4.2 Decision support than before. Decision support systems were more
effective in hospital than ambulatory settings and
Decision support tools provide prompts to help when decision support was initiated automatically
prescribers avoid errors when writing or entering by the system as opposed to the user. Combining
prescriptions. This subsection focuses on decision decision support with other strategies such as
support tools or alert systems that are standalone education was no more effective than decision
systems (not part of e-prescribing) or where alert support alone.105
systems are embedded in e-prescribing tools but
their effects have been analysed separately. A Cochrane Review of 23 studies examined
whether computerised advice about drug dosage
Hospital care improved processes or outcomes. Computerised
Evidence about the benefits of decision support advice improved doses, reduced time to therapeutic
tools, such as alerts and prompts for prescribers, stabilisation and reduced the length of hospital stay.
is mixed. It had no effect on adverse reactions. There was
no evidence that integration into an e-prescribing
A systematic review of computerised drug alerts system optimised effects. Interventions usually
and prompts found that 23 out of 27 studies targeted doctors, but a few attempted to influence
suggested improved prescribing behaviour or prescribing by pharmacists and nurses.106
reduced error rates. The impact varied based on
the type of decision support. Five out of 27 studies Often, decision support is an adjunct to
reported benefits for clinical and health service e-prescribing. A paediatric intensive care unit
management outcomes.102 in Israel tested e-prescribing with or without
clinical decision support. The rate of prescription
Another systematic review reported that four errors was 2.5% without any tools and 2.4%
out of seven studies about standalone clinical once e-prescribing was introduced. There was a
decision support systems found improvements in significant reduction to less than 1% when decision
medication errors and three did not. Most studies support was added. E-prescribing decreased
were not powered to detect differences in adverse prescription errors only to a small extent, but
drug events and evaluated small ‘home grown’ adding a decision support system had more
systems rather than commercial systems.103 impact.107
A review of 87 trials of medication management A US trial tested the effectiveness of computer-
information technology found that most trials: assisted decision support in reducing potentially
inappropriate prescribing for older adults in A&E.
–– focused on clinical decision support and
63 doctors using e-prescribing were randomly
e-prescribing systems
assigned to receive, or not to receive, decision
–– took place in US hospitals support that advised against use of nine potentially
–– focused on doctors inappropriate medications and recommended
safer substitutes. The decision support group
–– studied process changes related to prescribing prescribed one or more inappropriate medications
and monitoring medication. during 3% of A&E visits by older people compared
Processes of care improved for prescribing and with 4% of visits managed by those not receiving
monitoring in hospitals. There were few studies decision support. This was a statistically significant
measuring clinical outcomes and these tended to difference.108
show limited improvements.104


Prescribing excessive doses is a common Similarly, researchers in the US tested whether a
prescription error and can lead to adverse drug computerised alert system would reduce the rate of
reactions. In Germany, a clinical decision support errors in drug selection or dosing for people with
system was tested that provided alerts about upper renal insufficiency. A total of 32,917 people were
dose limits personalised to individual patient randomly assigned to usual care or the intervention
characteristics. Before the system was introduced group, where a computerised tool was used to alert
5% of prescriptions exceeded upper dose limits. pharmacists at the time of dispensing to possible
Afterwards, the rate of excessive doses reduced to errors in target drug selection and dosing. Of
4%, with 20% less excessive doses compared with these, 6,125 people were prescribed one or more
baseline.109 of the target drugs over a 15-month period. Alerts
helped to reduce medication errors. 33% of the
A hospital in the US used decision support tools to intervention group and 49% of the usual care group
meet the unique prescribing needs of children. An had medication errors at follow up.114
advanced dosing model was designed to interact
with an e-prescribing system to provide decision While alerts can work well to reduce prescribing
support for complex dose calculations for children. errors during the prescribing process or after
The system was flexible and could be altered over prescribing, ensuring that prescribers or
time. It was well used and found to be feasible.110 pharmacists see alerts may be an issue. Researchers
in Australia tested whether decision support
Other researchers in the US examined decision within a hospital e-prescribing system influenced
support alerts for helping avoid errors when putting medication ordering on ward rounds. 46 doctors
medication orders into an e-prescribing system. were shadowed during ward rounds and 16 were
Data for all patients at five community hospitals interviewed. Senior doctors influenced prescribing
over a six-month period were analysed. The alert decisions during ward rounds but rarely used the
system changed doctor’s behaviour and patient e-prescribing and alerts system. Junior doctors
therapy 42% of the time and reduced medication entered most medication orders into the system,
errors.111 often ignored computerised alerts and never raised
As with more generic e-prescribing systems, their occurrence with other doctors on ward
decision support tools have also been used to rounds. Doctors did not think that most features of
identify potential errors after prescribing has the decision support system were useful.115
occurred. A hospital in Japan tested an alert system Alerts are not the only type of decision support
for evaluating kidney function and checking system. Decision support tools may also include
doses of medication according to the patient’s access to clinical information and guidelines.
renal function. Discontinuation of inappropriate Researchers in France tested whether making
medication for those with poor renal function guidelines about antibiotics more accessible to
rose from 24% to 54% after the alert system was doctors would increase adherence to guidelines. In
implemented.112 this instance, a lack of adherence was specifically
Alerts targeting pharmacists have also been tested. defined as a prescribing error. One hospital
These focus on identifying errors once prescriptions changed from having guidelines available in
have been entered. In the US a computerised tool booklet format on wards to embedding these
alerted pharmacists when people aged 65 and older guidelines into an e-prescribing system. Assessment
were newly prescribed potentially inappropriate of 471 consecutive antibiotic orders for pneumonia
medications. In total, 59,680 older people were before and after the change found improvements
randomised to intervention or usual care groups. in the daily dose and the planned duration of
Alerts helped to reduce inappropriate prescriptions treatment.116
for two drugs.113 In the US, a computerised guideline increased use
of appropriate medication and decreased errors in
drug doses.117


Other researchers in the US examined three 4.3 Human factors issues
personal digital assistant (PDA)-based drug
Few studies have examined how health
information sources for reducing potential
professionals interact with e-prescribing and
medication errors. All three PDA tools were
decision support systems and the human factors
found to be feasible and one was found to be more
issues that may be influential. But there is some
effective than the others.118
evidence of scope for further work in this area.

Primary care Implementation factors
Little has been written about standalone decision
E-prescribing systems are common in the UK.
support or alert systems for reducing errors in
This contrasts with the US, where the use of
primary care. The evidence that does exist tends to
e-prescribing systems has been strongly advised
be mixed.
nationally, but rates of adoption remain relatively
The US Food and Drug Administration (FDA) low. Eight focus groups in US primary care found
issues black box warnings about medications with that e-prescribing was thought to improve the
serious risks. Doctor adherence to these warnings availability of clinical information, prescribing
is low. A system was tested for inserting black box efficiencies, coordinated care and documentation,
warning alerts about drug-drug, drug-disease and and result in safer care. Factors supporting
drug-laboratory interactions into an outpatient adoption included human factors features such
electronic health record with clinical decision as organisational support, adequate time, a shift
support. The alerts did not increase adherence to in staff workload, equipment stability, education
the black box warnings.119 about changes in patient interactions and positive
attitudes.122
On the other hand, following implementation of
alerts cautioning against prescribing certain drugs In another part of the US, a community based
to elderly people in some US outpatient clinics, integrated health system implemented a
there was a 22% reduction in exposure of elderly computerised order entry system. Strategies
patients to these drugs.120 for successful adoption included senior buy-in,
ongoing communication, a team-oriented culture,
A review of computer decision support for iterative implementation, ongoing readily accessible
improving prescribing in older adults in primary training, gaining buy-in from clinicians and
care or hospital included 10 studies. Eight of these workflow redesign.123
studies found some improvement in prescribing
including minimising drugs to avoid, optimising Workflow redesign is gaining more attention,
drug dosage or improving prescribing choices. Few but knowledge in this area remains limited.
studies reported clinical outcomes.121 Researchers in the Netherlands tested the effects
of an e-prescribing system on inter-professional
workflow. In total, 23 doctors, nurses and
pharmacists at one hospital were interviewed
and documents were reviewed. The system
reorganised existing work procedures and
impacted on workflow in positive and negative
ways. It reassigned tasks and areas of expertise
and fragmented patients’ medication-related
information, while providing limited support for
professional groups to coordinate their tasks.124


Three sites in the US implementing e-prescribing Types of alerts
identified barriers, including those relating to The effectiveness of e-prescribing systems and
human factors. Implementation barriers included decision support may sometimes be modest
previous negative experiences with technology, because clinicians often override electronic alerts.
initial and long-term cost, lost productivity, Two US teaching hospitals tested an alert that did
competing priorities, change management issues, or did not allow the information for a certain drug
functional limitations, IT requirements, waiting combination to be entered on the system. Of those
for an ‘all in one’ solution and confusion about in the intervention group, 57% did not reorder the
competing systems.125 Another study identified 15 alert-triggering drug within 10 minutes of receiving
barriers to using medication alerts at five primary an alert compared to 14% in the control group.
care clinics in the US.126 In other words, prohibiting input of some drug
combinations reduced errors of this type. However,
Design features unintended consequences included serious delays
Human factors approaches are concerned with in treatment.130
how technologies are designed to be most useful
The impact of active versus passive alerts, alerts that
and user friendly. A systematic review of 19
pop up versus those that are just inserted into the
studies examined the impact of design aspects of
online record and alerts that require the prescriber
e-prescribing systems on usability, workflow and
to acknowledge reading them have all been tested.
prescriptions. 16 studies were qualitative and three
In the US, alerts were built into an e-prescribing
used mixed qualitative and quantitative methods.
system to help doctors take account of changing
Design aspects were found to be important
kidney function when prescribing medications.
for increasing use of the systems and reducing
When treating 1,598 hospital patients with acute
prescribing errors. Such design aspects were
kidney injury, doctors received passive non-
categorised into seven groups: documentation
interactive warnings from the e-prescribing system
and data entry components, alerts, visual clues
and on printed ward round reports. An interruptive
and icons, dropdown lists and menus, safeguards,
alert was provided for contraindicated or high
screen displays and auxiliary functions.127
toxicity medications that should be avoided or
Another review of 41 randomised trials adjusted. This alert asked prescribers to modify or
examined whether design features of prescribing discontinue the orders, mark the dosing as correct
decision support systems predict successful or defer the alert to reappear next time. The active
implementation and usage. 37 studies reported alerts were associated with more modifications or
successful implementation, 25 reported discontinuations and more prompt action. Passive
changing professionals’ behaviour and five found alerts had limited response.131
improvements in patient outcomes. No design
Decision support tools may generate large numbers
feature was more prevalent in successful trials.128
of insignificant on-screen alerts presented as pop-up
Cognitive fit between the user interface and boxes. This may interrupt clinicians and limit the
clinical task may impact on whether doctors use effectiveness of these systems. A randomised trial in
e-prescribing systems. Cognitive task analysis of England compared the impact of pop-up and non-
clinical alerts for antibiotic prescribing in a US pop-up alerts on prescribing error rates. 24 junior
neonatal intensive care unit found that responses doctors, each performing 30 simulated prescribing
to alerts may be context specific and that a lack of tasks in random order, were shown pop-up alerts,
screen cues increases the cognitive effort required non-pop-up alerts or no alerts. Doctors receiving
to use a system.129 pop-up alerts were about 12 times less likely to
make a prescribing error than those not shown an
alert. Doctors shown a non-pop-up alert were about
three times less likely to make a prescribing error
than those not shown an alert.132


Similarly, researchers in the US aimed to improve 4.4 Standardised
clinician acceptance of drug alerts in 31 primary
care practices by prioritising alerts in order to medication charts
reduce workflow disruptions. Over a six-month Other tools to support the interface between health
period, 71% of alerts were non-interruptive professionals and the systems and environments
and 29% were interruptive. Two thirds of the in which they work have been researched in less
interruptive alerts were accepted.133 depth, but some studies are available.
The majority of prescribing alerts may be ignored Computerised medication charts have been tested.
because they are not seen as clinically relevant. In a system very different to that used in the UK, a
Being able to customise when alerts are seen may hospital in the Netherlands compared a medication
increase their usefulness. A Canadian study tested distribution system where the transcription of
two approaches to medication alert customisation: handwritten into printed medication orders takes
on-demand versus computer-triggered decision three to five days versus a computerised medication
support. Doctors randomised to on-demand alerts chart which was updated daily by pharmacy
activated the drug review when they considered assistants on the ward. The prescription error rate
it clinically relevant. Doctors randomised to was higher with computerised charts (50% versus
computer-triggered decision support viewed all 20%) but this was due to more administrative
alerts for electronic prescriptions in accordance errors, such as omitting the prescriber’s name and
with the severity level they selected. Customisation the date. The rate of errors with potential clinical
of computer-triggered alert systems was more significance was lower because duplicate therapy
useful in detecting prescribing problems than on- was eliminated.137
demand review. There was no difference between
In Australia, a standard medication chart
groups in prescribing errors. The majority of alerts
was developed for recording prescribing and
were ignored because the benefit was judged greater
administration of medication in hospital. Before
than the risk.134
and after audits in five sites found the prescribing
Researchers in the US tested alerts that required a error rate decreased from 20% of orders per patient
response from doctors to prevent concurrent orders to 16%.138
of warfarin and non-steroidal anti-inflammatory
After preliminary testing, the standardised
drugs. In total, 1,963 doctors were assigned to
medication chart was rolled out to 22 Australian
receive passive alerts or active alerts which required
hospitals. Prescribers were educated and baseline
a response. Active alerts had no benefits over
audit findings were presented when the chart was
passive alerts.135
introduced. Prescribing errors decreased by almost
one third.139
Workforce
A hospital in England tested computerised
prescribing with alerts over a three-month period. 4.5 Other tools
Senior doctors and those more experienced using A number of computerised and other tools have
the system were more likely to ignore a warning been tested to reduce prescribing errors, often in
message.136 conjunction with electronic prescribing. These
interventions are a mix of tools to reduce errors
during prescribing and tools to identify and
mitigate errors before they reach the patient.
One study examined the effect of regular
and expected printed educational materials
on prescribing. In Canada, 499 doctors were


randomised to receive 12 evidence-based drug receives from different hospitals nationwide.
therapy letters immediately or after 3–8 months This system was used to address the problem of
(control group). The aim was not merely to improve duplicate medications for outpatients visiting
evidence-based prescribing, but also to reduce multiple hospitals. At one hospital an e-prescribing
dosage and drug choice errors. The series of letters system was enhanced with the ability to access
influenced which drugs were prescribed to newly smart cards and alert doctors about potential
treated patients. Each letter alone did not make a duplicate medications at the time of prescribing.
significant impact, but when combined they made a Over a three-month period, 2% of all smart cards
difference.140 read contained medications that would potentially
have been duplicated without this system. Around
A hospital in the US introduced a voluntary one-third of these prescriptions were revised due to
interactive computerised worksheet for use when the alerts.146
prescribing parenteral nutrition in the neonatal
intensive care unit. The worksheet reduced the Combining more than one tool is becoming
prescribing error rate from 14% to 7%.141 popular. A US hospital system implemented a
range of clinical information technology such
Another US hospital tested a standardised as e-prescribing, pharmacy and laboratory
chemotherapy order form to reduce prescribing information systems, clinical decision support
errors and the cost of medication to reduce systems, electronic drug dispensing systems and a
vomiting and nausea. The form was associated with barcode point-of-care medication administration
fewer prescribing errors and a reduction in the system. Medication errors decreased. Most
average cost.142 prescribing errors decreased, including drug allergy
Another US hospital examined the impact of detection, excessive dosing and incomplete or
adding a medication list targeting the most unclear orders.147
common medications to an e-prescribing system in
a paediatric A&E department. The medication list
decreased errors from 24 to 13 per 100 visits.143
Elsewhere in the US, a hospital tested a system for
reconciling medications that patients take at home
with what they receive in hospital. The unintended
discrepancy rate between a patient’s home
medications and admission medication orders
was reduced from 20% to 1% using the electronic
reconciliation system.144
A hospital in Sweden tested providing a
medication report for older people discharged
into the community. 32% had one or more
medication errors compared to 66% of a
retrospective comparison group who did not
receive a medication report. Prescribing errors
were not identified separately.145
In China and Japan, patients may ‘shop around’ for
doctors or hospitals, visiting a number of doctors
for the same condition. In Taiwan, a national
insurance health smart card was adopted, which
carries information about the medications a patient


5. Summary

5.1 Key points errors, particularly if the systems do not allow the
prescriber to see the entire medication history or
Most people taking medication will benefit from
other relevant information easily.
it, but there is always the potential for errors which
may cause harm. Prescribing errors are the largest Alerts and prompts alone have generally not been
source of medication errors. A systematic review of found to reduce prescribing errors, though some
16 studies about errors in handwritten prescriptions studies have positive results.
in hospitals found that the most common causes of
error were mistakes due to inadequate knowledge Although opinion pieces and narrative articles
of the drug or the patient, memory lapses, lack are available,149 less empirical research has been
of training or experience, fatigue, stress, high published about ‘human factors’ approaches to
workload and inadequate communication between reducing prescribing errors regarding the interface
healthcare professionals.148 between personnel and the environment and
systems in which they work.
A number of strategies have been tested to reduce
prescribing errors. The most commonly researched Training staff to fulfil their roles is an important
strategy involves redesigning equipment and human factors component. There is some evidence
tasks through the use of electronic tools such as that training medical students can help them feel
e-prescribing and computerised decision support more confident about prescribing but the longer-
systems (alerts and prompts). While a great deal term impact on reducing errors remains uncertain.
has been written about e-prescribing and alert tools Other studies have examined training for fully
in hospital, and to a lesser extent in primary care, qualified doctors. This has taken the form of one-
evidence about the effectiveness and value of such to-one sessions about specific medications or
systems is mixed. In the US e-prescribing systems patients (academic detailing), group sessions and
have been mandated for widespread use, while in collaborative improvement projects and quality
the UK such tools are very common. Some research circles where groups of prescribers network, share
supports this, with findings of substantial reductions good practice and take part in practical error
in prescribing errors. In fact, it is common for the reduction initiatives.
introduction of combined e-prescribing and alert
systems to halve prescribing errors. Some research is available about expanding
pharmacist roles to target error reduction,
However, other studies suggest that the types of particularly in hospital. Research is also emerging
errors affected may be clinically insignificant and about pharmacist roles in primary care. Studies
that there may be other costs involved. While have examined reactive use of pharmacist roles,
e-prescribing systems reduce illegibility errors, such as using pharmacists to review prescriptions
such systems may take more time than handwritten for errors before medication orders are filled.
prescriptions and may introduce new types of Research is also emerging about more proactive use
of pharmacist roles, such as circulating on wards to
check prescriptions and providing education one to
one or in groups to prescribers.


However, these studies tend to focus on the Summary of key themes in studies about
identification and mitigation of prescribing errors reducing prescribing errors
after they have occurred. There is very little
research about using different roles to address Factor Findings
errors during the prescribing process itself. Training One-to-one educational visits can
improve prescribing150–153
The scan suggests that there is a real gap in the
Individualised educational letters have
literature about improving the safety and reliability
shown promise154,155 as have follow-up
of prescribing in patient pathways. None of the
telephone calls from pharmacists156
solutions previously researched have focused
in-depth on patient pathways. This is a focus of Training sessions and simulations
the Health Foundation’s Safer Clinical Systems for students improve confidence in
initiative, which has the potential to make a identifying errors, but impacts on
significant contribution to the knowledge base in error reduction are uncertain157–160
this area. Education sessions for professionals
have reduced prescribing error
rates161–163
Improvement programmes
and learning networks have
positive outcomes but each varies
considerably.164–166 The process of
monitoring and reporting errors may
be a key part of this167–169
Roles Pharmacists checking medication
orders can identify prescribing
errors170–174 but not all findings are
positive175
Pharmacists circulating on wards
can identify and reduce prescribing
errors, especially when coupled with
education176,177
Medicine reconciliation by
pharmacists has mixed findings178 but
there are some positive trends179,180
Introducing pharmacist initiatives as
part of a multifaceted intervention
may work well181,182
Tools E-prescribing systems have been
found to reduce prescribing
errors,183–199 though not all studies are
positive200–207
There are mixed findings about alerts
and prompts208–210
Human factors issues such as the
design of systems, workflow, alert type
and context may be key success factors
when implementing tools to reduce
prescribing errors211–222


5.2 Caveats Quality of research
When interpreting the findings of the evidence There are also some issues with the quality of the
scan it is important to bear in mind several caveats. studies included. A number of studies have been
conducted at single sites or a small number of
homogeneous sites and include small numbers of
Scope patients and prescribers. Before and after study
The evidence scan is not exhaustive. It presents designs are common in this field and these may be
examples of studies but does not purport to subject to potential bias. A number of factors could
represent every study about reducing prescribing have affected prescribing error rates over time other
errors. The purpose is to give a flavour of available than the specific intervention being tested. For
research rather than to summarise every existing example, studies of introducing an e-prescribing
study in detail. system may note a reduction in prescribing
It is also important to note that only studies errors but it is uncertain the extent to which such
explicitly aiming to reduce prescribing errors reductions are a result of the tool itself versus the
are summarised. A number of other studies may awareness raising, education and culture change
have reduced prescribing errors as a secondary or that may have accompanied its introduction.
unexpected outcome, but if the research did not
have this as a key target it would not have been Making comparisons
included. Finally, it is difficult to make comparisons between
studies because various definitions of ‘prescribing
Quantity of research errors’ are used and the research methods vary in
Although a reasonable amount of research is design and quality.223,224
available about this topic, there are limits to Furthermore, there are differences in the healthcare
the conclusions that can be drawn. There is context in which studies took place. Much of the
insufficient comparative evidence to suggest that research is drawn from North America, where
one approach is more effective than others for prescribing practices, laws and the healthcare
reducing prescribing errors. Nor is there good systems are very different from the UK. For
evidence to be able to extrapolate about key success example, e-prescribing is almost universal in UK
factors or the settings or situations in which primary care, but is just beginning to be rolled
improvement approaches work most effectively. out in the US. Similarly, in countries such as the
The cost effectiveness of various strategies to reduce US and some parts of Europe, prescriptions are
prescribing errors is also uncertain. commonly written by doctors and then transcribed
Most research focuses on reducing prescribing by others into prescription forms or electronic
errors in hospital. Far less is known about reducing systems. In the UK, prescribers are responsible for
prescribing errors in other settings such as primary writing or inputting their own prescriptions. These
care, dentistry or mental health. A lack of evidence differences in systems and context have an impact
about settings or interventions other than those on the relevance and applicability of the research to
covered in the scan does not mean that other UK settings.
options are not useful or effective, just that few Even where comparable definitions are used and
research articles have been published about these geographic contexts can be compared, the level of
topics. detail reported in individual studies is sometimes
insufficient to provide a meaningful summary or to
extract the exact impacts of interventions. While we
can say that a particular study found a reduction in
prescription errors, the details provided are usually
not enough to be able to replicate the intervention
or roll it out more broadly.


Despite these caveats, research continues into the
most effective ways to reduce prescribing errors in
order to enhance patient safety.
It is likely that the best strategies to reduce
prescribing errors are multifaceted.

Interventions are needed at three levels
to improve prescribing: (1) improve the
training, and test the competence, of
prescribers; (2) control the environment
in which prescribers perform in order
to standardise it, have greater controls
on riskier drugs, and use technology to
provide decision support; and (3) change
organisational cultures, which do not
support the belief that prescribing is a
complex, technical, act, and that it is
important to get it right. 225
Human factors issues and the interactions between
systems, tasks and personnel have not been
explored in any depth so there is much scope for
learning in this area. As prescribing errors make up
a significant proportion of all errors in healthcare,
further work in this field has the potential to
significantly improve patient safety.


References
1 Aronson JK. Medication errors: what they are, how they happen, 20 Buetow S, Kiata L, Liew T, Kenealy T, Dovey S, Elwyn G.
and how to avoid them. QJM 2009;102(8):513-521. Approaches to reducing the most important patient errors in
2 Haas R, Maloney S, Pausenberger E, Keating JL, Sims J, Molloy E, primary health-care: patient and professional perspectives.
et al. Clinical decision making in exercise prescription for falls Health Soc Care Community 2010; 18(3):296-303.
prevention. Phys Ther (Published online January 2012). 21 Kuzel AJ, Woolf SH, Gilchrist VJ, Engel JD, LaVeist TA, Vincent
3 Joyce GF, Carrera MP, Goldman DP, Sood N. Physician C, Frankel RM. Patient reports of preventable problems and
prescribing behavior and its impact on patient-level outcomes. harms in primary health care. Ann Fam Med 2004; 2(4):333-40.
Am J Manag Care 2011;17(12):e462-471. 22 UK Civil Aviation Authority. Fundamental Human Factors
4 Griffith R, Tengnah C. Prescription of controlled drugs by non- Concepts. Gatwick Airport: Civil Aviation Authority, 2002.
medical prescribers. Br J Community Nurs 2011;16(11):558-562. 23 Wickens CD, Lee JD, Liu Y, Gorden Becker SE. An Introduction
5 American Psychological Association Division 55 (American to Human Factors Engineering, 2nd ed. Prentice Hall, 1997.
Society for the Advancement of Pharmacotherapy) Task 24 Thomson O’Brien MA, Oxman AD, Davis DA, Haynes RB,
Force on Practice Guidelines. Practice guidelines regarding Freemantle N, Harvey EL. Educational outreach visits: effects
psychologists’ involvement in pharmacological issues. Am on professional practice and health care outcomes. Cochrane
Psychol 2011;66(9):835-549. Database Syst Rev 2000;(2):CD000409.
6 McCann L, Haughey S, Parsons C, Lloyd F, Crealey G, Gormley 25 Ostini R, Hegney D, Jackson C, Williamson M, Mackson JM,
GJ, Hughes CM. Pharmacist prescribing in Northern Ireland: a Gurman K, Hall W, Tett SE. Systematic review of interventions
quantitative assessment. Int J Clin Pharm 2011;33(5):824-831. to improve prescribing. Ann Pharmacother 2009;43(3):502-513.
7 The Institute of Medicine. Preventing Medication Errors. 26 Shaw J, Harris P, Keogh G, Graudins L, Perks E, Thomas PS.
Washington, DC: The National Academies Press, 2006. Error reduction: academic detailing as a method to reduce
8 Patanwala AE, Hays DP, Sanders AB, Erstad BL. Severity incorrect prescriptions. Eur J Clin Pharmacol 2003;59(8-9):697-
and probability of harm of medication errors intercepted 699.
by an emergency department pharmacist. Int J Pharm Pract 27 Roberts GW, Farmer CJ, Cheney PC, Govis SM, Belcher TW,
2011;19(5):358-362. Walsh SA, Adams RJ. Clinical decision support implemented
9 Leape LL. Error in medicine. JAMA 1994; 272(23):1851-1857. with academic detailing improves prescribing of key renally
cleared drugs in the hospital setting. J Am Med Inform Assoc
10 Franklin BD, Reynolds M, Shebl NA, Burnett S, Jacklin 2010;17(3):308-312.
A. Prescribing errors in hospital inpatients: a three-centre
study of their prevalence, types and causes. Postgrad Med J 28 Ginzburg R, Cohrssen A. Demonstrating prescribing
2011;87(1033):739-745. competence: a successful pilot of a prescription competency
curriculum for family medicine residents. Fam Med
11 Alrwisan A, Ross J, Williams D. Medication incidents reported 2007;39(10):703-705.
to an online incident reporting system. Eur J Clin Pharmacol
2011;67(5):527-532. 29 Varkey P, Natt N. The Objective Structured Clinical Examination
as an educational tool in patient safety. Jt Comm J Qual Patient
12 Pham JC, Aswani MS, Rosen M, Lee H, Huddle M, Weeks K, Saf 2007;33(1):48-53.
Pronovost PJ. Reducing medical errors and adverse events. Annu
Rev Med 2012;63:447-463. 30 Kozer E, Scolnik D, Macpherson A, Rauchwerger D, Koren G.
The effect of a short tutorial on the incidence of prescribing
13 Dean B, Barber N, Schachter M. What is a prescribing error? errors in pediatric emergency care. Can J Clin Pharmacol
Qual Health Care 2000;9(4):232-237. 2006;13(3):e285-e91.
14 Millenson ML. The Silence. Health Affairs 2003; 22(2):103-112. 31 Campino A, Lopez-Herrera MC, Lopez-de-Heredia I, Valls-i-
15 McKay J, Bradley N, Lough M, Bowie P. A review of significant Soler A. Educational strategy to reduce medication errors in a
events analysed in general practice: implications for the quality neonatal intensive care unit. Acta Paediatr 2009;98(5):782-785.
and safety of patient care. BMC Fam Pract 2009; 10:61. 32 Pallas CR, De-la-Cruz J, Del-Moral MT, Lora D, Malalana MA.
16 Pace WD, Fernald DH, Harris DM, Dickinson LM, Araya- Improving the quality of medical prescriptions in neonatal units.
Guerra R, Staton EW, et al. Developing a taxonomy for Neonatology 2008;93(4):251-256.
coding ambulatory medical errors: a report from the ASIPS 33 Thomas AN, Boxall EM, Laha SK, Day AJ, Grundy D. An
Collaborative. In Henriksen K, Battles JB, Marks ES, Lewin educational and audit tool to reduce prescribing error in
DI (editors). Advances in Patient Safety: From Research to intensive care. Qual Saf Health Care 2008;17(5):360-363.
Implementation (Volume 2: Concepts and Methodology).
Rockville: Agency for Healthcare Research and Quality, 2005. 34 Silver MP, Antonow JA. Reducing medication errors in
hospitals: a peer review organization collaboration. Jt Comm J
17 Sandars J, Esmail A. Threats to patient safety in primary care. A Qual Improv 2000;26(6):332-340.
review of the research into the frequency and nature of error in
primary care. Manchester: University of Manchester, 2001. 35 Otero P, Leyton A, Mariani G, Ceriani Cernadas JM. Medication
errors in pediatric inpatients: prevalence and results of a
18 Rosser W, Dovey S, Bordman R, White D, Crighton E, prevention program. Pediatrics 2008;122(3):e737-e743.
Drummond N. Medical errors in primary care: results of an
international study of family practice. Can Fam Physician 2005; 36 Woodward MC, Streeton CL, Guttmann A, Killer GT, Peck
51:386-7. RW. Polypharmacy management among Australian veterans:
improving prescribing through the Australian Department of
19 Gaal S, van Laarhoven E, Wolters R, Wetzels R, Verstappen W, Veterans’ Affairs’ prescriber feedback programme. Intern Med J
Wensing M. Patient safety in primary care has many aspects: an 2008;38(2):95-100.
interview study in primary care doctors and nurses. J Eval Clin
Pract 2010; 16(3):639-43. 37 Gill SS, Misiaszek BC, Brymer C. Improving prescribing in
the elderly: a study in the long term care setting. Can J Clin
Pharmacol 2001;8(2):78-83.


38 Monane M, Matthias DM, Nagle BA, Kelly MA. Improving 54 Klopotowska JE, Kuiper R, van Kan HJ, de Pont AC, Dijkgraaf
prescribing patterns for the elderly through an online drug MG, Lie-A-Huen L, et al. On-ward participation of a hospital
utilization review intervention: a system linking the physician, pharmacist in a Dutch intensive care unit reduces prescribing
pharmacist, and computer. JAMA 1998;280(14):1249-1252. errors and related patient harm: an intervention study. Crit Care
39 Fuller PD, Smith KM, Hinman RK, Gross AK, Hillebrand K, 2010;14(5):R174.
Pettit NN, Phelps PK. Value of pharmacy residency training: A 55 Ariano RE, Demianczuk RH, Danzinger RG, Richard A,
survey of the academic medical center perspective. Am J Health Milan H, Jamieson B. Economic impact and clinical benefits of
Syst Pharm 2012;69(2):158-165. pharmacist involvement on surgical wards. Can J Hosp Pharm
40 Glassman PA, Belperio P, Lanto A, Simon B, Valuck R, Sayers J, 1995;48(5):284-289.
Lee M. The utility of adding retrospective medication profiling 56 Bayoumi I, Howard M, Holbrook AM, Schabort I. Interventions
to computerized provider order entry in an ambulatory care to improve medication reconciliation in primary care. Ann
population. J Am Med Inform Assoc 2007;14(4):424-431. Pharmacother 2009;43(10):1667-1675.
41 Peeters MJ, Kamm GL, Beltyukova SA. A computer-based 57 Mills PR, McGuffie AC. Formal medicine reconciliation within
module for prescribing error instruction. Am J Pharm Educ the emergency department reduces the medication error rates
2009;73(6):101. for emergency admissions. Emerg Med J 2010;27(12):911-915.
42 Kiersma ME, Darbishire PL, Plake KS, Oswald C, Walters BM. 58 Karnon J, Campbell F, Czoski-Murray C. Model-based cost-
Laboratory session to improve first-year pharmacy students’ effectiveness analysis of interventions aimed at preventing
knowledge and confidence concerning the prevention of medication error at hospital admission (medicines
medication errors. Am J Pharm Educ 2009;73(6):99. reconciliation). J Eval Clin Pract 2009;15(2):299-306.
43 Frey B, Buettiker V, Hug MI, Waldvogel K, Gessler P, Ghelfi D, 59 Eggink RN, Lenderink AW, Widdershoven JW, van den Bemt
et al. Does critical incident reporting contribute to medication PM. The effect of a clinical pharmacist discharge service on
error prevention? Eur J Pediatr 2002;161(11):594-599. medication discrepancies in patients with heart failure. Pharm
44 Thomson O’Brien MA, Oxman AD, Davis DA, Haynes RB, World Sci 2010;32(6):759-766.
Freemantle N, Harvey EL. Audit and feedback: effects on 60 Alagha HZ, Badary OA, Ibrahim HM, Sabri NA. Reducing
professional practice and health care outcomes. Cochrane prescribing errors in the paediatric intensive care unit: an
Database Syst Rev 2000;(2):CD000259. experience from Egypt. Acta Paediatr 2011;100(10):e169-e74.
45 Campino A, Lopez-Herrera MC, Lopez-de-Heredia I, Valls- 61 Chedoe I, Molendijk HA, Dittrich ST, Jansman FG, Harting JW,
I-Soler A. Medication errors in a neonatal intensive care Brouwers JR, Taxis K. Incidence and nature of medication errors
unit. Influence of observation on the error rate. Acta Paediatr in neonatal intensive care with strategies to improve safety: a
2008;97(11):1591-1594. review of the current literature. Drug Saf 2007;30(6):503-513.
46 Gommans J, McIntosh P, Bee S, Allan W. Improving the quality 62 Brown CA, Bailey JH, Lee J, Garrett PK, Rudman WJ.
of written prescriptions in a general hospital: the influence of The pharmacist-physician relationship in the detection of
10 years of serial audits and targeted interventions. Intern Med J ambulatory medication errors. Am J Med Sci 2006; 331(1):22-4.
2008;38(4):243-248. 63 Hansen LB, Fernald D, Araya-Guerra R, Westfall JM, West
47 Thomas L, Cordonnier-Jourdin C, Benhamou-Jantelet G, D, Pace W. Pharmacy clarification of prescriptions ordered in
Divine C, Le Louet H. Medication errors management process primary care: a report from the applied strategies for improving
in hospital: a 6-month pilot study. Fundam Clin Pharmacol patient safety (ASIPS) collaborative. J Am Board Fam Med 2006;
2011;25(6):768-775. 19(1):24-30.
48 Kazemi A, Fors UG, Tofighi S, Tessma M, Ellenius J. Physician 64 Niquille A, Ruggli M, Buchmann M, Jordan D, Bugnon O. The
order entry or nurse order entry? Comparison of two nine-year sustained cost-containment impact of Swiss pilot
implementation strategies for a computerized order entry physicians-pharmacists quality circles. Ann Pharmacother 2010;
system aimed at reducing dosing medication errors. J Med 44(4):650-7.
Internet Res 2010;12(1):e5. 65 Marcum ZA, Handler SM, Wright R, Hanlon JT. Interventions
49 Jayawardena S, Eisdorfer J, Indulkar S, Pal SA, Sooriabalan D, to improve suboptimal prescribing in nursing homes: A
Cucco R. Prescription errors and the impact of computerized narrative review. Am J Geriatr Pharmacother 2010;8(3):183-200.
prescription order entry system in a community-based hospital. 66 Pham JC, Andrawis M, Shore AD, Fahey M, Morlock L,
Am J Ther 2007;14(4):336-340. Pronovost PJ. Are temporary staff associated with more severe
50 Wang JK, Herzog NS, Kaushal R, Park C, Mochizuki C, emergency department medication errors? J Healthc Qual
Weingarten SR. Prevention of pediatric medication errors by 2011;33(4):9-18.
hospital pharmacists and the potential benefit of computerized 67 Institute of Medicine. To Err Is Human: Building a Safer Health
physician order entry. Pediatrics 2007;119(1):e77-e85. System. Washington, DC: The National Academies Press, 1999.
51 Abdel-Qader DH, Harper L, Cantrill JA, Tully MP. Pharmacists’ 68 Institute of Medicine. Crossing the Quality Chasm: A New Health
interventions in prescribing errors at hospital discharge: an System for the 21st Century. Washington, DC: The National
observational study in the context of an electronic prescribing Academies Press, 2001.
system in a UK teaching hospital. Drug Saf 2010;33(11):1027-1044.
69 Donyai P, O’Grady K, Jacklin A, Barber N, Franklin BD. The
52 van den Bemt PM, Postma MJ, van Roon EN, Chow MC, effects of electronic prescribing on the quality of prescribing. Br
Fijn R, Brouwers JR. Cost-benefit analysis of the detection J Clin Pharmacol 2008;65(2):230-237.
of prescribing errors by hospital pharmacy staff. Drug Saf
2002;25(2):135-143. 70 Jimenez Munoz AB, Muino Miguez A, Rodriguez Perez
MP, Duran Garcia ME, Sanjurjo Saez M. Comparison of
53 Estellat C, Colombet I, Vautier S, Huault-Quentel J, Durieux P, medication error rates and clinical effects in three medication
Sabatier B. Impact of pharmacy validation in a computerized prescription-dispensation systems. Int J Health Care Qual Assur
physician order entry context. Int J Qual Health Care 2011;24(3):238-248.
2007;19(5):317-325.


71 Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U. 88 Dainty KN, Adhikari NK, Kiss A, Quan S, Zwarenstein M.
The effect of electronic prescribing on medication errors and Electronic prescribing in an ambulatory care setting: a cluster
adverse drug events: a systematic review. J Am Med Inform Assoc randomized trial. J Eval Clin Pract (Published online March
2008;15(5):585-600. 2011).
72 Shamliyan TA, Duval S, Du J, Kane RL. Just what the doctor 89 Mirco A, Campos L, Falcao F, Nunes JS, Aleixo A. Medication
ordered. Review of the evidence of the impact of computerized errors in an internal medicine department. Evaluation
physician order entry system on medication errors. Health Serv of a computerized prescription system. Pharm World Sci
Res 2008;43(1 Pt 1):32-53. 2005;27(4):351-352.
73 Jani YH, Ghaleb MA, Marks SD, Cope J, Barber N, Wong IC. 90 Barber N. Electronic prescribing - safer, faster, better? J Health
Electronic prescribing reduced prescribing errors in a pediatric Serv Res Policy 2010;15 Suppl 1:64-67.
renal outpatient clinic. J Pediatr 2008;152(2):214-218. 91 Reckmann MH, Westbrook JI, Koh Y, Lo C, Day RO. Does
74 King WJ, Paice N, Rangrej J, Forestell GJ, Swartz R. The effect of computerized provider order entry reduce prescribing errors for
computerized physician order entry on medication errors and hospital inpatients? A systematic review. J Am Med Inform Assoc
adverse drug events in pediatric inpatients. Pediatrics 2003;112(3 2009;16(5):613-623.
Pt 1):506-509. 92 Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR,
75 Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’Luf N, Kimmel SE, Strom BL. Role of computerized physician
Boyle D, Leape L. The impact of computerized physician order order entry systems in facilitating medication errors. JAMA
entry on medication error prevention. J Am Med Inform Assoc 2005;293(10):1197-1203.
1999;6(4):313-321. 93 Han YY, Carcillo JA, Venkataraman ST, Clark RS, Watson
76 Kuperman GJ, Teich JM, Gandhi TK, Bates DW. Patient RS, Nguyen TC, et al. Unexpected increased mortality after
safety and computerized medication ordering at Brigham and implementation of a commercially sold computerized physician
Women’s Hospital. Jt Comm J Qual Improv 2001;27(10):509-521. order entry system. Pediatrics 2005;116(6):1506-1512.
77 Aronsky D, Johnston PE, Jenkins G, Waitman LR, Frelix DW, 94 Abramson EL, Malhotra S, Fischer K, Edwards A, Pfoh ER,
Jones I, Patel NR. The effect of implementing computerized Osorio SN, et al. Transitioning between electronic health
provider order entry on medication prescribing errors in an records: effects on ambulatory prescribing safety. J Gen Intern
emergency department. AMIA Annu Symp Proc 2007:863. Med 2011;26(8):868-874.
78 Potts AL, Barr FE, Gregory DF, Wright L, Patel NR. 95 Giurdanella P, Di Denia P. Does the electronic prescription
Computerized physician order entry and medication errors in a reduce drugs errors? Comparison between electronic and
pediatric critical care unit. Pediatrics 2004;113(1 Pt 1):59-63. manual prescription. Assist Inferm Ric 2007;26(2):92-98.
79 Shawahna R, Rahman NU, Ahmad M, Debray M, Yliperttula M, 96 Franklin BD, O’Grady K, Donyai P, Jacklin A, Barber N.
Decleves X. Electronic prescribing reduces prescribing error in The impact of a closed-loop electronic prescribing and
public hospitals. J Clin Nurs 2011;20(21-22):3233-3245. administration system on prescribing errors, administration
80 Velez-Diaz-Pallares M, Delgado Silveira E, Perez Menendez- errors and staff time: a before-and-after study. Qual Saf Health
Conde C, Bermejo Vicedo T. Analysis of errors in manual Care 2007;16(4):279-284.
versus electronic prescriptions in trauma patients. Farm Hosp 97 Eslami S, Abu-Hanna A, de Keizer NF. Evaluation of outpatient
2011;35(3):135-139. computerized physician medication order entry systems: a
81 Colpaert K, Claus B, Somers A, Vandewoude K, Robays H, systematic review. J Am Med Inform Assoc 2007;14(4):400-406.
Decruyenaere J. Impact of computerized physician order entry 98 Gandhi TK, Weingart SN, Seger AC, Borus J, Burdick E,
on medication prescription errors in the intensive care unit: a Poon EG, et al. Outpatient prescribing errors and the impact of
controlled cross-sectional trial. Crit Care 2006;10(1):R21. computerized prescribing. J Gen Intern Med 2005;20(9):837-841.
82 Fontan JE, Maneglier V, Nguyen VX, Loirat C, Brion F. 99 Devine EB, Wilson-Norton JL, Lawless NM, Hansen RN, Haney
Medication errors in hospitals: computerized unit dose drug KK, Fisk AW, Sullivan SD. The impact of an ambulatory CPOE
dispensing system versus ward stock distribution system. Pharm system on medication errors. AMIA Annu Symp Proc 2008:928.
World Sci 2003;25(3):112-117. 100 Devine EB, Hansen RN, Wilson-Norton JL, Lawless NM, Fisk
83 van Doormaal JE, van den Bemt PM, Zaal RJ, Egberts AC, AW, Blough DK, et al. The impact of computerized provider
Lenderink BW, Kosterink JG, et al. The influence that electronic order entry on medication errors in a multispecialty group
prescribing has on medication errors and preventable adverse practice. J Am Med Inform Assoc 2010;17(1):78-84.
drug events: an interrupted time-series study. J Am Med Inform 101 Devine EB, Hollingworth W, Hansen RN, Lawless NM, Wilson-
Assoc 2009;16(6):816-825. Norton JL, Martin DP, et al. Electronic prescribing at the point
84 van Rosse F, Maat B, Rademaker CM, van Vught AJ, Egberts of care: a time-motion study in the primary care setting. Health
AC, Bollen CW. The effect of computerized physician order Serv Res 2010;45(1):152-171.
entry on medication prescription errors and clinical outcome 102 Schedlbauer A, Prasad V, Mulvaney C, Phansalkar S, Stanton
in pediatric and intensive care: a systematic review. Pediatrics W, Bates DW, Avery AJ. What evidence supports the use
2009;123(4):1184-1190. of computerized alerts and prompts to improve clinicians’
85 Conroy S, Sweis D, Planner C, Yeung V, Collier J, Haines L, Wong prescribing behavior? J Am Med Inform Assoc 2009;16(4):531-
IC. Interventions to reduce dosing errors in children: a systematic 538.
review of the literature. Drug Saf 2007;30(12):1111-1125. 103 Kaushal R, Shojania KG, Bates DW. Effects of computerized
86 Collins CM, Elsaid KA. Using an enhanced oral chemotherapy physician order entry and clinical decision support systems
computerized provider order entry system to reduce prescribing on medication safety: a systematic review. Arch Intern Med
errors and improve safety. Int J Qual Health Care 2011;23(1):36-43. 2003;163(12):1409-1416.
87 Coleman JJ, Hemming K, Nightingale PG, Clark IR, Dixon-
Woods M, Ferner RE, Lilford RJ. Can an electronic prescribing
system detect doctors who are more likely to make a serious
prescribing error? J R Soc Med 2011;104(5):208-218.


104 McKibbon KA, Lokker C, Handler SM, Dolovich LR, Holbrook 119 Yu DT, Seger DL, Lasser KE, Karson AS, Fiskio JM, Seger
AM, O’Reilly D, et al. The effectiveness of integrated health AC, Bates DW. Impact of implementing alerts about
information technologies across the phases of medication medication black-box warnings in electronic health records.
management: a systematic review of randomized controlled Pharmacoepidemiol Drug Saf 2011;20(2):192-202.
trials. J Am Med Inform Assoc 2012;19(1):22-30. 120 Smith DH, Perrin N, Feldstein A, Yang X, Kuang D, Simon SR,
105 Pearson SA, Moxey A, Robertson J, Hains I, Williamson M, et al. The impact of prescribing safety alerts for elderly persons
Reeve J, Newby D. Do computerised clinical decision support in an electronic medical record: an interrupted time series
systems for prescribing change practice? A systematic review of evaluation. Arch Intern Med 2006;166(10):1098-1104.
the literature (1990-2007). BMC Health Serv Res 2009;9:154. 121 Yourman L, Concato J, Agostini JV. Use of computer decision
106 Durieux P, Trinquart L, Colombet I, Nies J, Walton R, support interventions to improve medication prescribing in
Rajeswaran A, et al. Computerized advice on drug dosage older adults: a systematic review. Am J Geriatr Pharmacother
to improve prescribing practice. Cochrane Database Syst Rev 2008;6(2):119-129.
2008;(3):CD002894. 122 Devine EB, Williams EC, Martin DP, Sittig DF, Tarczy-Hornoch
107 Kadmon G, Bron-Harlev E, Nahum E, Schiller O, Haski G, P, Payne TH, Sullivan SD. Prescriber and staff perceptions of
Shonfeld T. Computerized order entry with limited decision an electronic prescribing system in primary care: a qualitative
support to prevent prescription errors in a PICU. Pediatrics assessment. BMC Med Inform Decis Mak 2010;10:72.
2009;124(3):935-940. 123 Devine EB, Wilson-Norton JL, Lawless NM, Hansen RN,
108 Terrell KM, Perkins AJ, Dexter PR, Hui SL, Callahan CM, Miller Hollingworth W, Fisk AW, Sullivan SD. Implementing an
DK. Computerized decision support to reduce potentially ambulatory e-prescribing system: strategies employed and
inappropriate prescribing to older emergency department lessons learned to minimize unintended consequences. In:
patients: a randomized, controlled trial. J Am Geriatr Soc Henriksen K, Battles JB, Keyes MA, Grady ML (eds). Advances
2009;57(8):1388-1394. in Patient Safety: New Directions and Alternative Approaches
109 Seidling HM, Schmitt SP, Bruckner T, Kaltschmidt J, Pruszydlo (Vol. 4: Technology and Medication Safety). Rockville: Agency for
MG, Senger C, et al. Patient-specific electronic decision support Healthcare Research and Quality, 2008.
reduces prescription of excessive doses. Qual Saf Health Care 124 Niazkhani Z, Pirnejad H, van der Sijs H, de Bont A, Aarts J.
2010;19(5):e15. Computerized provider order entry system - does it support the
110 Ferranti JM, Horvath MM, Jansen J, Schellenberger P, Brown T, inter-professional medication process? Lessons from a Dutch
DeRienzo CM, Ahmad A. Using a computerized provider order academic hospital. Methods Inf Med 2010;49(1):20-27.
entry system to meet the unique prescribing needs of children: 125 Halamka J, Aranow M, Ascenzo C, Bates DW, Berry K, Debor G,
description of an advanced dosing model. BMC Med Inform et al. E-prescribing collaboration in Massachusetts: early
Decis Mak 2011;11:14. experiences from regional prescribing projects. J Am Med
111 Saxena K, Lung BR, Becker JR. Improving patient safety by Inform Assoc 2006;13(3):239-244.
modifying provider ordering behavior using alerts (CDSS) in 126 Russ AL, Zillich AJ, McManus MS, Doebbeling BN, Saleem JJ.
CPOE system. AMIA Annu Symp Proc 2011;2011:1207-1216. A human factors investigation of medication alerts: barriers to
112 Matsumura Y, Yamaguchi T, Hasegawa H, Yoshihara K, prescriber decision-making and clinical workflow. AMIA Annu
Zhang Q, Mineno T, Takeda H. Alert system for inappropriate Symp Proc 2009;2009:548-552.
prescriptions relating to patients’ clinical condition. Methods Inf 127 Khajouei R, Jaspers MW. The impact of CPOE medication
Med 2009;48(6):566-573. systems’ design aspects on usability, workflow and medication
113 Raebel MA, Charles J, Dugan J, Carroll NM, Korner EJ, Brand orders: a systematic review. Methods Inf Med 2010;49(1):3-19.
DW, Magid DJ. Randomized trial to improve prescribing safety 128 Mollon B, Chong J Jr, Holbrook AM, Sung M, Thabane L, Foster
in ambulatory elderly patients. J Am Geriatr Soc 2007;55(7):977- G. Features predicting the success of computerized decision
985. support for prescribing: a systematic review of randomized
114 Bhardwaja B, Carroll NM, Raebel MA, Chester EA, Korner controlled trials. BMC Med Inform Decis Mak 2009;9:11.
EJ, Rocho BE, et al. Improving prescribing safety in patients 129 Sheehan B, Kaufman D, Stetson P, Currie LM. Cognitive analysis
with renal insufficiency in the ambulatory setting: the Drug of decision support for antibiotic prescribing at the point of
Renal Alert Pharmacy (DRAP) program. Pharmacotherapy ordering in a neonatal intensive care unit. AMIA Annu Symp
2011;31(4):346-356. Proc 2009;2009:584-588.
115 Baysari MT, Westbrook JI, Richardson KL, Day RO. The 130 Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S,
influence of computerized decision support on prescribing Leonard CE, Pifer E. Unintended effects of a computerized
during ward-rounds: are the decision-makers targeted? J Am physician order entry nearly hard-stop alert to prevent a drug
Med Inform Assoc 2011;18(6):754-759. interaction: a randomized controlled trial. Arch Intern Med
116 Westphal JF, Jehl F, Javelot H, Nonnenmacher C. Enhanced 2010;170(17):1578-1583.
physician adherence to antibiotic use guidelines through 131 McCoy AB, Waitman LR, Gadd CS, Danciu I, Smith JP, Lewis JB,
increased availability of guidelines at the time of drug ordering et al. A computerized provider order entry intervention
in hospital setting. Pharmacoepidemiol Drug Saf 2011;20(2):162- for medication safety during acute kidney injury: a quality
168. improvement report. Am J Kidney Dis 2010;56(5):832-841.
117 Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, 132 Scott GP, Shah P, Wyatt JC, Makubate B, Cross FW. Making
Bates DW. Effects of computerized physician order entry on electronic prescribing alerts more effective: scenario-based
prescribing practices. Arch Intern Med 2000;160(18):2741-2747. experimental study in junior doctors. J Am Med Inform Assoc
118 Galt KA, Rule AM, Houghton B, Young DO, Remington G. 2011; 18(6):789-798.
Personal digital assistant-based drug information sources: 133 Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ,
potential to improve medication safety. J Med Libr Assoc Blumenfeld B, et al. Improving acceptance of computerized
2005;93(2):229-236. prescribing alerts in ambulatory care. J Am Med Inform Assoc
2006;13(1):5-11.


134 Tamblyn R, Huang A, Taylor L, Kawasumi Y, Bartlett G, Grad R, 151 Ostini R, Hegney D, Jackson C, Williamson M, Mackson JM,
et al. A randomized trial of the effectiveness of on-demand Gurman K, et al. Systematic review of interventions to improve
versus computer-triggered drug decision support in primary prescribing. Ann Pharmacother 2009;43(3):502-513.
care. J Am Med Inform Assoc 2008;15(4):430-438. 152 Shaw J, Harris P, Keogh G, Graudins L, Perks E, Thomas PS.
135 Strom BL, Schinnar R, Bilker W, Hennessy S, Leonard CE, Error reduction: academic detailing as a method to reduce
Pifer E. Randomized clinical trial of a customized electronic incorrect prescriptions. Eur J Clin Pharmacol 2003;59(8-9):697-
alert requiring an affirmative response compared to a control 699.
group receiving a commercial passive CPOE alert: NSAID- 153 Roberts GW, Farmer CJ, Cheney PC, Govis SM, Belcher TW,
warfarin co-prescribing as a test case. J Am Med Inform Assoc Walsh SA, et al. Clinical decision support implemented
2010;17(4):411-415. with academic detailing improves prescribing of key renally
136 Anton C, Nightingale PG, Adu D, Lipkin G, Ferner RE. cleared drugs in the hospital setting. J Am Med Inform Assoc
Improving prescribing using a rule based prescribing system. 2010;17(3):308-312.
Qual Saf Health Care 2004;13(3):186-190. 154 Woodward MC, Streeton CL, Guttmann A, Killer GT, Peck
137 van Gijssel-Wiersma DG, van den Bemt PM, Walenbergh-van RW. Polypharmacy management among Australian veterans:
Veen MC. Influence of computerised medication charts on improving prescribing through the Australian Department of
medication errors in a hospital. Drug Saf 2005;28(12):1119-1129. Veterans’ Affairs’ prescriber feedback programme. Intern Med J
138 Coombes ID, Stowasser DA, Reid C, Mitchell CA. Impact of a 2008;38(2):95-100.
standard medication chart on prescribing errors: a before-and- 155 Gill SS, Misiaszek BC, Brymer C. Improving prescribing in
after audit. Qual Saf Health Care 2009;18(6):478-485. the elderly: a study in the long term care setting. Can J Clin
139 Coombes ID, Reid C, McDougall D, Stowasser D, Duiguid M, Pharmacol 2001;8(2):78-83.
Mitchell C. Pilot of a National Inpatient Medication Chart in 156 Monane M, Matthias DM, Nagle BA, Kelly MA. Improving
Australia: improving prescribing safety and enabling prescribing prescribing patterns for the elderly through an online drug
training. Br J Clin Pharmacol 2011;72(2):338-349. utilization review intervention: a system linking the physician,
140 Dormuth CR, Maclure M, Bassett K, Jauca C, Whiteside C, pharmacist, and computer. JAMA 1998;280(14):1249-1252.
Wright JM. Effect of periodic letters on evidence-based drug 157 Peeters MJ, Kamm GL, Beltyukova SA. A computer-based
therapy on prescribing behaviour: a randomized trial. CMAJ module for prescribing error instruction. Am J Pharm Educ
2004;171(9):1057-1061. 2009;73(6):101.
141 Brown CL, Garrison NA, Hutchison AA. Error reduction 158 Ginzburg R, Cohrssen A. Demonstrating prescribing
when prescribing neonatal parenteral nutrition. Am J Perinatol competence: a successful pilot of a prescription competency
2007;24(7):417-427. curriculum for family medicine residents. Fam Med
142 Sano HS, Waddell JA, Solimando DA Jr, Doulaveris P, Myhand 2007;39(10):703-705.
R. Study of the effect of standardized chemotherapy order forms 159 Kiersma ME, Darbishire PL, Plake KS, Oswald C, Walters BM.
on prescribing errors and anti-emetic cost. J Oncol Pharm Pract Laboratory session to improve first-year pharmacy students’
2005;11(1):21-30. knowledge and confidence concerning the prevention of
143 Sard BE, Walsh KE, Doros G, Hannon M, Moschetti W, Bauchner medication errors. Am J Pharm Educ 2009;73(6):99.
H. Retrospective evaluation of a computerized physician order 160 Varkey P, Natt N. The Objective Structured Clinical Examination
entry adaptation to prevent prescribing errors in a pediatric as an educational tool in patient safety. Jt Comm J Qual Patient
emergency department. Pediatrics 2008;122(4):782-787. Saf 2007;33(1):48-53.
144 Agrawal A, Wu WY. Reducing medication errors and improving 161 Campino A, Lopez-Herrera MC, Lopez-de-Heredia I, Valls-i-
systems reliability using an electronic medication reconciliation Soler A. Educational strategy to reduce medication errors in a
system. Jt Comm J Qual Patient Saf 2009;35(2):106-114. neonatal intensive care unit. Acta Paediatr 2009;98(5):782-785.
145 Midlov P, Holmdahl L, Eriksson T, Bergkvist A, Ljungberg 162 Pallas CR, De-la-Cruz J, Del-Moral MT, Lora D, Malalana MA.
B, Widner H, et al. Medication report reduces number of Improving the quality of medical prescriptions in neonatal units.
medication errors when elderly patients are discharged from Neonatology 2008;93(4):251-256.
hospital. Pharm World Sci 2008;30(1):92-98. 163 Thomas AN, Boxall EM, Laha SK, Day AJ, Grundy D. An
146 Hsu MH, Yeh YT, Chen CY, Liu CH, Liu CT. Online detection educational and audit tool to reduce prescribing error in
of potential duplicate medications and changes of physician intensive care. Qual Saf Health Care 2008;17(5):360-363.
behavior for outpatients visiting multiple hospitals using 164 Silver MP, Antonow JA. Reducing medication errors in
national health insurance smart cards in Taiwan. Int J Med hospitals: a peer review organization collaboration. Jt Comm J
Inform 2011;80(3):181-189. Qual Improv 2000;26(6):332-340.
147 Mahoney CD, Berard-Collins CM, Coleman R, Amaral JF, 165 Otero P, Leyton A, Mariani G, Ceriani Cernadas JM. Medication
Cotter CM. Effects of an integrated clinical information system errors in pediatric inpatients: prevalence and results of a
on medication safety in a multi-hospital setting. Am J Health prevention program. Pediatrics 2008;122(3):e737-e743.
Syst Pharm 2007;64(18):1969-1977.
166 Frey B, Buettiker V, Hug MI, Waldvogel K, Gessler P, Ghelfi D,
148 Tully MP, Ashcroft DM, Dornan T, Lewis PJ, Taylor D, Wass et al. Does critical incident reporting contribute to medication
V. The causes of and factors associated with prescribing error prevention? Eur J Pediatr 2002;161(11):594-599.
errors in hospital inpatients: a systematic review. Drug Saf
2009;32(10):819-836. 167 Campino A, Lopez-Herrera MC, Lopez-de-Heredia I, Valls-
I-Soler A. Medication errors in a neonatal intensive care
149 Yan J. The human factor in medication-error reduction. Am J unit. Influence of observation on the error rate. Acta Paediatr
Health Syst Pharm 2003;60(14):1417. 2008;97(11):1591-1594.
150 Thomson O’Brien MA, Oxman AD, Davis DA, Haynes RB,
Freemantle N, Harvey EL. Educational outreach visits: effects
on professional practice and health care outcomes. Cochrane
Database Syst Rev 2000;(2):CD000409.


168 Gommans J, McIntosh P, Bee S, Allan W. Improving the quality 184 Jimenez Munoz AB, Muino Miguez A, Rodriguez Perez
of written prescriptions in a general hospital: the influence of 10 MP, Duran Garcia ME, Sanjurjo Saez M. Comparison of
years of serial audits and targeted interventions. Intern Med J medication error rates and clinical effects in three medication
2008;38(4):243-248. prescription-dispensation systems. Int J Health Care Qual Assur
169 Thomas L, Cordonnier-Jourdin C, Benhamou-Jantelet G, 2011;24(3):238-248.
Divine C, Le Louet H. Medication errors management process 185 Shamliyan TA, Duval S, Du J, Kane RL. Just what the doctor
in hospital: a 6-month pilot study. Fundam Clin Pharmacol ordered. Review of the evidence of the impact of computerized
2011;25(6):768-775. physician order entry system on medication errors. Health Serv
170 Jayawardena S, Eisdorfer J, Indulkar S, Pal SA, Sooriabalan D, Res 2008;43(1 Pt 1):32-53.
Cucco R. Prescription errors and the impact of computerized 186 Jani YH, Ghaleb MA, Marks SD, Cope J, Barber N, Wong IC.
prescription order entry system in a community-based hospital. Electronic prescribing reduced prescribing errors in a pediatric
Am J Ther 2007;14(4):336-340. renal outpatient clinic. J Pediatr 2008;152(2):214-218.
171 Wang JK, Herzog NS, Kaushal R, Park C, Mochizuki C, 187 King WJ, Paice N, Rangrej J, Forestell GJ, Swartz R. The effect
Weingarten SR. Prevention of pediatric medication errors by of computerized physician order entry on medication errors
hospital pharmacists and the potential benefit of computerized and adverse drug events in pediatric inpatients. Pediatrics
physician order entry. Pediatrics 2007;119(1):e77-e85. 2003;112(3 Pt 1):506-509.
172 Abdel-Qader DH, Harper L, Cantrill JA, Tully MP. Pharmacists’ 188 Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’Luf N,
interventions in prescribing errors at hospital discharge: an et al. The impact of computerized physician order entry
observational study in the context of an electronic prescribing on medication error prevention. J Am Med Inform Assoc
system in a UK teaching hospital. Drug Saf 2010;33(11):1027- 1999;6(4):313-321.
1044. 189 Kuperman GJ, Teich JM, Gandhi TK, Bates DW. Patient
173 van den Bemt PM, Postma MJ, van Roon EN, Chow MC, safety and computerized medication ordering at Brigham and
Fijn R, Brouwers JR. Cost-benefit analysis of the detection Women’s Hospital. Jt Comm J Qual Improv 2001;27(10):509-521.
of prescribing errors by hospital pharmacy staff. Drug Saf 190 Aronsky D, Johnston PE, Jenkins G, Waitman LR, Frelix DW,
2002;25(2):135-143. Jones I, et al. The effect of implementing computerized provider
174 Eggink RN, Lenderink AW, Widdershoven JW, van den Bemt order entry on medication prescribing errors in an emergency
PM. The effect of a clinical pharmacist discharge service on department. AMIA Annu Symp Proc 2007:863.
medication discrepancies in patients with heart failure. Pharm 191 Potts AL, Barr FE, Gregory DF, Wright L, Patel NR.
World Sci 2010;32(6):759-766. Computerized physician order entry and medication errors in a
175 Estellat C, Colombet I, Vautier S, Huault-Quentel J, Durieux P, pediatric critical care unit. Pediatrics 2004;113(1 Pt 1):59-63.
Sabatier B. Impact of pharmacy validation in a computerized 192 Shawahna R, Rahman NU, Ahmad M, Debray M, Yliperttula M,
physician order entry context. Int J Qual Health Care Decleves X. Electronic prescribing reduces prescribing error in
2007;19(5):317-325. public hospitals. J Clin Nurs 2011;20(21-22):3233-3245.
176 Klopotowska JE, Kuiper R, van Kan HJ, de Pont AC, Dijkgraaf 193 Velez-Diaz-Pallares M, Delgado Silveira E, Perez Menendez-
MG, Lie-A-Huen L, et al. On-ward participation of a hospital Conde C, Bermejo Vicedo T. Analysis of errors in manual
pharmacist in a Dutch intensive care unit reduces prescribing versus electronic prescriptions in trauma patients. Farm Hosp
errors and related patient harm: an intervention study. Crit Care 2011;35(3):135-139.
2010;14(5):R174.
194 Colpaert K, Claus B, Somers A, Vandewoude K, Robays H,
177 Ariano RE, Demianczuk RH, Danzinger RG, Richard A, Decruyenaere J. Impact of computerized physician order entry
Milan H, Jamieson B. Economic impact and clinical benefits of on medication prescription errors in the intensive care unit: a
pharmacist involvement on surgical wards. Can J Hosp Pharm controlled cross-sectional trial. Crit Care 2006;10(1):R21.
1995;48(5):284-289.
195 Fontan JE, Maneglier V, Nguyen VX, Loirat C, Brion F.
178 Bayoumi I, Howard M, Holbrook AM, Schabort I. Interventions Medication errors in hospitals: computerized unit dose drug
to improve medication reconciliation in primary care. Ann dispensing system versus ward stock distribution system. Pharm
Pharmacother 2009;43(10):1667-1675. World Sci 2003;25(3):112-117.
179 Mills PR, McGuffie AC. Formal medicine reconciliation within 196 van Doormaal JE, van den Bemt PM, Zaal RJ, Egberts AC,
the emergency department reduces the medication error rates Lenderink BW, Kosterink JG, et al. The influence that electronic
for emergency admissions. Emerg Med J 2010;27(12):911-915. prescribing has on medication errors and preventable adverse
180 Karnon J, Campbell F, Czoski-Murray C. Model-based cost- drug events: an interrupted time-series study. J Am Med Inform
effectiveness analysis of interventions aimed at preventing Assoc 2009;16(6):816-825.
medication error at hospital admission (medicines 197 Collins CM, Elsaid KA. Using an enhanced oral chemotherapy
reconciliation). J Eval Clin Pract 2009;15(2):299-306. computerized provider order entry system to reduce prescribing
181 Alagha HZ, Badary OA, Ibrahim HM, Sabri NA. Reducing errors and improve safety. Int J Qual Health Care 2011;23(1):
prescribing errors in the paediatric intensive care unit: an 36-43.
experience from Egypt. Acta Paediatr 2011;100(10):e169-e74. 198 van Rosse F, Maat B, Rademaker CM, van Vught AJ, Egberts
182 Chedoe I, Molendijk HA, Dittrich ST, Jansman FG, Harting JW, AC, Bollen CW. The effect of computerized physician order
Brouwers JR, et al. Incidence and nature of medication errors entry on medication prescription errors and clinical outcome
in neonatal intensive care with strategies to improve safety: a in pediatric and intensive care: a systematic review. Pediatrics
review of the current literature. Drug Saf 2007;30(6):503-513. 2009;123(4):1184-1190.
183 Donyai P, O’Grady K, Jacklin A, Barber N, Franklin BD. 199 Conroy S, Sweis D, Planner C, Yeung V, Collier J, Haines L, et al.
The effects of electronic prescribing on the quality of Interventions to reduce dosing errors in children: a systematic
prescribing. Br J Clin Pharmacol 2008;65(2):230-237. review of the literature. Drug Saf 2007;30(12):1111-1125.


200 Ammenwerth E, Schnell-Inderst P, Machan C, Siebert U. 213 Niazkhani Z, Pirnejad H, van der Sijs H, de Bont A, Aarts J.
The effect of electronic prescribing on medication errors and Computerized provider order entry system - does it support the
adverse drug events: a systematic review. J Am Med Inform Assoc inter-professional medication process? Lessons from a Dutch
2008;15(5):585-600. academic hospital. Methods Inf Med 2010;49(1):20-27.
201 Dainty KN, Adhikari NK, Kiss A, Quan S, Zwarenstein M. 214 Halamka J, Aranow M, Ascenzo C, Bates DW, Berry K,
Electronic prescribing in an ambulatory care setting: a cluster Debor G, et al. E-prescribing collaboration in Massachusetts:
randomized trial. J Eval Clin Pract (Published online March early experiences from regional prescribing projects. J Am Med
2011). Inform Assoc 2006;13(3):239-244.
202 Mirco A, Campos L, Falcao F, Nunes JS, Aleixo A. Medication 215 Khajouei R, Jaspers MW. The impact of CPOE medication
errors in an internal medicine department. Evaluation systems’ design aspects on usability, workflow and medication
of a computerized prescription system. Pharm World Sci orders: a systematic review. Methods Inf Med 2010;49(1):3-19.
2005;27(4):351-352. 216 Mollon B, Chong J Jr, Holbrook AM, Sung M, Thabane L,
203 Barber N. Electronic prescribing - safer, faster, better? J Health Foster G. Features predicting the success of computerized
Serv Res Policy 2010;15 Suppl 1:64-67. decision support for prescribing: a systematic review of
204 Reckmann MH, Westbrook JI, Koh Y, Lo C, Day RO. Does randomized controlled trials. BMC Med Inform Decis Mak
computerized provider order entry reduce prescribing errors for 2009;9:11.
hospital inpatients? A systematic review. J Am Med Inform Assoc 217 Sheehan B, Kaufman D, Stetson P, Currie LM. Cognitive analysis
2009;16(5):613-623. of decision support for antibiotic prescribing at the point of
205 Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel ordering in a neonatal intensive care unit. AMIA Annu Symp
SE, et al. Role of computerized physician order entry systems in Proc 2009;2009:584-588.
facilitating medication errors. JAMA 2005;293(10):1197-1203. 218 McCoy AB, Waitman LR, Gadd CS, Danciu I, Smith JP, Lewis JB,
206 Han YY, Carcillo JA, Venkataraman ST, Clark RS, Watson et al. A computerized provider order entry intervention
RS, Nguyen TC, et al. Unexpected increased mortality after for medication safety during acute kidney injury: a quality
implementation of a commercially sold computerized physician improvement report. Am J Kidney Dis 2010;56(5):832-841.
order entry system. Pediatrics 2005;116(6):1506-1512. 219 Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S,
207 Gandhi TK, Weingart SN, Seger AC, Borus J, Burdick E, Leonard CE, et al. Unintended effects of a computerized
Poon EG, et al. Outpatient prescribing errors and the impact of physician order entry nearly hard-stop alert to prevent a drug
computerized prescribing. J Gen Intern Med 2005;20(9):837-841. interaction: a randomized controlled trial. Arch Intern Med
2010;170(17):1578-1583.
208 Schedlbauer A, Prasad V, Mulvaney C, Phansalkar S, Stanton
W, Bates DW, et al. What evidence supports the use of 220 Scott GP, Shah P, Wyatt JC, Makubate B, Cross FW. Making
computerized alerts and prompts to improve clinicians’ electronic prescribing alerts more effective: scenario-based
prescribing behavior? J Am Med Inform Assoc 2009;16(4):531- experimental study in junior doctors. J Am Med Inform Assoc
538. 2011; 18(6):789-798.

209 Kaushal R, Shojania KG, Bates DW. Effects of computerized 221 Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ,
physician order entry and clinical decision support systems Blumenfeld B, et al. Improving acceptance of computerized
on medication safety: a systematic review. Arch Intern Med prescribing alerts in ambulatory care. J Am Med Inform Assoc
2003;163(12):1409-1416. 2006;13(1):5-11.

210 McKibbon KA, Lokker C, Handler SM, Dolovich LR, Holbrook 222 Tamblyn R, Huang A, Taylor L, Kawasumi Y, Bartlett G, Grad R,
AM, O’Reilly D, et al. The effectiveness of integrated health et al. A randomized trial of the effectiveness of on-demand
information technologies across the phases of medication versus computer-triggered drug decision support in primary
management: a systematic review of randomized controlled care. J Am Med Inform Assoc 2008;15(4):430-438.
trials. J Am Med Inform Assoc 2012;19(1):22-30. 223 Hofer TP, Kerr EA, Hayward RA. What is an error? Eff Clin
211 Devine EB, Williams EC, Martin DP, Sittig DF, Tarczy-Hornoch Pract 2000; 3(6):261-9.
P, Payne TH, et al. Prescriber and staff perceptions of an 224 Tully MP, Ashcroft DM, Dornan T, Lewis PJ, Taylor D,
electronic prescribing system in primary care: a qualitative Wass V. The causes of and factors associated with prescribing
assessment. BMC Med Inform Decis Mak 2010;10:72. errors in hospital inpatients: a systematic review. Drug Saf
212 Devine EB, Wilson-Norton JL, Lawless NM, Hansen RN, 2009;32(10):819-836.
Hollingworth W, Fisk AW, et al. Implementing an ambulatory 225 Barber N, Rawlins M, Dean Franklin B. Reducing prescribing
e-prescribing system: strategies employed and lessons learned error: competence, control, and culture. Qual Saf Health Care
to minimize unintended consequences. In: Henriksen K, Battles 2003;12 Suppl 1:i29-32.
JB, Keyes MA, Grady ML (eds). Advances in Patient Safety: New
Directions and Alternative Approaches (Vol. 4: Technology and
Medication Safety). Rockville: Agency for Healthcare Research
and Quality, 2008.


The Health Foundation is an independent charity working
to continuously improve the quality of healthcare in the UK.

We want the UK to have a healthcare system of the highest
possible quality – safe, effective, person-centred, timely,
efficient and equitable. We believe that in order to achieve
this, health services need to continually improve the way
they work.

We are here to inspire and create the space for people, teams,
organisations and systems to make lasting improvements to
health services.

Working at every level of the healthcare system, we aim to
develop the technical skills, leadership, capacity, knowledge,
and the will for change, that are essential for real and lasting
improvement.

The Health Foundation
90 Long Acre
London WC2E 9RA
T 020 7257 8000
F 020 7257 8001
E info@health.org.uk
For more information, visit:
www.health.org.uk
Follow us on Twitter:
www.twitter.com/HealthFdn
Sign up for our email newsletter:
www.health.org.uk/enewsletter
Registered charity number: 286967
Registered company number: 1714937
© 2012 The Health Foundation

Reducing prescribing errors evidence scan-2012

More Related Content

What's hot (20)

Viewers also liked (7)

Similar to Reducing prescribing errors evidence scan-2012 (20)

More from docenciaaltopalancia (20)

Recently uploaded (20)

Reducing prescribing errors evidence scan-2012