PHARMACOEPIDEMIOLOGY AND PRESCRIPTION
Medication errors in hospitals in the Middle East: a systematic reviewof prevalence, nature, severity and contributory factors
Binny Thomas1,2 & Vibhu Paudyal3 & Katie MacLure4& Abdulrouf Pallivalapila1 & James McLay5 & Wessam El Kassem1
&
Moza Al Hail1 & Derek Stewart6
Received: 8 December 2018 /Accepted: 2 May 2019 /Published online: 24 May 2019# The Author(s) 2019
AbstractPurpose The aim was to critically appraise, synthesise and present the evidence of medication errors amongst hospitalisedpatients in Middle Eastern countries, specifically prevalence, nature, severity and contributory factors.Methods CINAHL, Embase, Medline, Pubmed and Science Direct were searched for studies published in English from 2000 toMarch 2018, with no exclusions. Study selection, quality assessment (using adapted STROBE checklists) and data extractionwere conducted independently by two reviewers. A narrative approach to data synthesis was adopted; data related to errorcausation were synthesised according to Reason’s Accident Causation model.Results Searching yielded 452 articles, which were reduced to 50 following removal of duplicates and screening of titles,abstracts and full-papers. Studies were largely from Iran, Saudi Arabia, Egypt and Jordan. Thirty-two studies quantified errors;definitions of ‘medication error’were inconsistent as were approaches to data collection, severity assessment, outcome measuresand analysis. Of 13 studies reporting medication errors per ‘total number of medication orders’/ ‘number of prescriptions’, themedian across all studies was 10% (IQR 2–35). Twenty-four studies reported contributory factors leading to errors. Synthesisaccording to Reason’s model identified the most common being active failures, largely slips (10 studies); lapses (9) and mistakes(12); error-provoking conditions, particularly lack of knowledge (13) and insufficient staffing levels (13) and latent conditions,commonly heavy workload (9).Conclusion There is a need to improve the quality and reporting of studies from Middle Eastern countries. A standardisedapproach to quantifying medication errors’ prevalence, severity, outcomes and contributory factors is warranted.
Keywords Medication errors . Prescribing errors . Error causation . Systematic review .Middle East
Introduction
In 1999, the ‘Institute of Medicine’ (now the NationalAcademy of Medicine) published the seminal report ‘To ErrIs Human: Building a Safer Health System’ quantifying thescale of harm associated withmedical care in the United States(US) [1]. The authors called for coordinated efforts by gov-ernments, healthcare providers and consumers and others topromote patient safety, setting a minimum goal of 50% reduc-tion in medical errors by 2004. Despite global advances inhealthcare practices, an estimated one in ten patients is stillharmed while receiving care [2]. In March 2017, the WorldHealth Organization (WHO) published ‘Medication WithoutHarm, WHO Global Patient Safety Challenge’ [3, 4]. It calledfor action to reduce patient harm which occurs as a result ofunsafe medication practices and medication errors. The aim isto ‘gain worldwide commitment and action to reduce severe,avoidable medication-related harm by 50% in the next 5 years,
Electronic supplementary material The online version of this article(https://doi.org/10.1007/s00228-019-02689-y) contains supplementarymaterial, which is available to authorized users.
* Derek Stewartd.stewart@rgu.ac.uk
1 Medication Safety and Quality Center, Pharmacy Executive Office,Hamad Medical Corporation, PO Box 3050, Doha, Qatar
2 School of Pharmacy and Life Sciences, Robert Gordon University,Aberdeen AB10 7JG, UK
3 School of Pharmacy, University of Birmingham, Birmingham B152TT, UK
4 School of Pharmacy and Life Sciences, Robert Gordon University,Aberdeen AB10 7JG, UK
5 The Institute of Applied Health Sciences, University of Aberdeen,Aberdeen AB25 2ZD, UK
6 College of Pharmacy, Qatar University, Doha PO Box 2713, Qatar
European Journal of Clinical Pharmacology (2019) 75:1269–1282https://doi.org/10.1007/s00228-019-02689-y

specifically by addressing harm resulting from medicationerrors or unsafe practices due to weaknesses in healthcaresystems’. One key objective is to ‘assess the scope and natureof avoidable harm and strengthen the monitoring systems todetect and track this harm’ [3, 4].
A number of published systematic reviews have attemptedto quantify medication errors at various stages of the medica-tion use processes of prescribing, transcribing, verifying, ad-ministration, dispensing and monitoring [5–21]. These havelargely focused on secondary care inpatients, with mostreporting errors committed in targeted groups of patients in-cluding paediatrics, acute care, older people, mental healthand perioperative care. Many of these reviews also reporteddata on contributory factors leading to errors [6, 9–11, 14, 17,21]. One key limitation highlighted in many of these reviewsis the lack of a standardised approach to defining and measur-ing errors, limiting the validity of any pooling of data fromdifferent studies and different systematic reviews.Furthermore, the very different healthcare structures and pro-cesses across the world may limit the generalisability of find-ings to other contexts. Given the first objective of the WHOchallenge, there may be merit in conducting systematic re-views capturing studies from specific contexts to provide themost meaningful data which can be used to inform futurestrategies and interventions.
Given the differing healthcare systems, ethnicity, cultureand work practices of the Middle East, there may be merit inconducting systematic reviews of studies within that geo-graphical area (i.e. Bahrain, Egypt, Iran, Iraq, Israel, Jordan,Kuwait, Lebanon, Oman, Palestine, Qatar, Saudi Arabia,Syria, Turkey, United Arab Emirates and Yemen). In 2013,Alsulami et al. published a systematic review of studies up toand including 2011 on the incidence and types of medicationerrors in Middle Eastern countries and main contributory fac-tors [10]. While noting that error rates were difficult to com-pare between studies due to being expressed differently, pre-scribing errors ranged from 7.1% of prescriptions in a teachinghospital to 90.5% of prescriptions in a primary healthcarecentre. Poor knowledge of medicines was identified as a con-tributory factor for errors by doctors and nurses.
One limitation of this reviewwas the lack of any theories oferror causation in the synthesis stage. Incorporation of theoryin primary studies or systematic reviews will yield findingswhich provide more comprehensive coverage of the key in-fluential factors. The most commonly used and cited theoret-ical framework in this field is Reason’s Accident Causationmodel. This model groups error causes as follows:
1. Active failures which are unsafe acts committed by peoplewho are in direct contact with the patient or system. Theytake a variety of forms including slips and lapses (errors intask execution), mistakes (errors in planning) and proce-dural violations (rule breaking).
2. Error-producing conditions which can have adverse ef-fects of error-provoking conditions within the local work-place (e.g. time pressure, understaffing, inadequate equip-ment, fatigue and inexperience).
3. Latent failures which arise from decisions made by policymakers, leaders and top-level management [22].
Furthermore, the review highlighted that published papersfrom Middle Eastern countries were relatively few and gener-ally of poor quality. Given the advances in healthcare in recentyears, an updated systematic review incorporating error theoryis warranted.
The aim of this systematic reviewwas to critically appraise,synthesise and present the available evidence of medicationerrors amongst hospitalised patients in Middle Eastern coun-tries, specifically prevalence, nature, severity and contributoryfactors.
Methods
The systematic review protocol was developed in accordancewith the Preferred Reporting Items for Systematic Review andMeta-Analysis Protocols (PRISMA-P) guidelines [23] andregistered with the International Prospective Register ofSystematic Reviews (PROSPERO, CRD42015019693) [24].
Inclusion and exclusion criteria
Primary research studies of any design conducted in hospitalsettings in the Middle East (as defined in the introduction)which quantified medication errors (i.e. prescribing, adminis-tration or dispensing errors) published as full papers inEnglish from 2000 to the end of March 2018 were includedin the review. Studies which reported error nature, severity orassociated causative factors were also included. Studies ofadverse drug events which were not classified as errors wereexcluded, as were review articles, letters, opinion papers, ed-itorials and conference abstracts.
Search strategy
The search was conducted in Cumulative Index of Nursingand Cumulative Allied Health Literature (CINAHL), Embase,Medline, Pubmed and Science Direct. Search terms (title, ab-stract, text, keyword) were (medic* OR prescrib* ORdispens* OR administ*) AND (error* OR incident* OR mis-take*) AND (Middle East OR Bahrain OR Egypt OR Iran ORIraq OR Israel OR Jordan ORKuwait OR Lebanon OROmanOR Palestine OR Qatar OR Saudi Arabia OR Syria ORTurkey OR United Arab Emirates ORYemen). The referencelists of all identified papers were reviewed to identify addi-tional studies.
1270 Eur J Clin Pharmacol (2019) 75:1269–1282

Screening
Screening of titles (BT, DS), abstracts (BT, DS) and full pa-pers (BT, DS) was independently performed by two re-viewers, with disagreements resolved by consensus and re-ferred to a third reviewer (KM) whenever required.
Assessment of methodological quality
Papers were independently assessed for methodological qual-ity by two reviewers (BT and one of DS, VP, AP, JM, WEK,MAH)with disagreements resolved by consensus and referredto a third reviewer whenever required. The STROBE checklist(STrengthening the Reporting of OBservational studies inEpidemiology) was adapted as a quality assessment tool[25]. For all study designs, STROBE criteria retained werethose relating to bias with addition of criteria specific to med-ication errors (e.g. error definitions). For qualitative studies,credibility and dependability replaced validity and reliability,and transferability replaced generalisability.
Data extraction
A bespoke data extraction tool was developed and piloted toextract the following: authors, country of publication/study,year of publication, study population, setting, recruitment, er-ror quantification, nature of errors, error severity and contrib-utory factors. Data extraction was also performed by two in-dependent reviewers, as per quality assessment.
Data synthesis
Previous systematic reviews have highlighted the heterogene-ity of studies in terms of error definitions, methods of mea-surement and outcome measures [5–21]; hence, a narrativeapproach to data synthesis was selected a-priori. Data relatedto error causation were synthesised using Reason’s AccidentCausation model as a theoretical framework in terms of activefailures, error-producing conditions and latent failures [22].
Results
Study screening
Database searching and review of reference lists yielded 452articles, 110 of which were duplicates and excluded. Reviewof titles and abstracts excluded 213 papers with full-paperreview excluding a further 79. Fifty papers were included inthe quality assessment stage. The PRIMSA flowchart is givenin Fig. 1. Of the fifty studies, 48 were of a quantitative, cross-sectional design and two were qualitative in nature.
Quality assessment
Of the 50 studies, none met all 11 STROBE-related qualityassessment criteria. Thirteen studies (26%) met eight or morecriteria, 21 (42%) between five and seven criteria and theremaining 16 (32%) meeting four or less. Key limitationscentred on lack of justification for the method of samplingand sample size, and not adequately considering issues of datavalidity and reliability (quantitative studies) and trustworthi-ness (qualitative studies). Supplementary Table 1 gives thefindings of the quality assessment processes.
Characteristics of included studies
Almost half of the studies were conducted in Iran (n = 23,46%), followed by Saudi Arabia (n = 10, 20%), Egypt andJordan (n = 5 each, 10%), Turkey (n = 2, 4%) and one each(2%) from Israel, Qatar, Yemen, Palestine and Lebanon. Noneof the studies reported data from more than one country. Twothirds (n = 33, 66%) were conducted in university-affiliated oracademic hospitals, one fifth (n = 10, 20%) tertiary care non-teaching hospitals and only three (6%) in general hospitals.Three studies (6%) did not state the type of hospital and one(2%) reported an analysis of a national online database.Within each hospital, a range of specific patient groups wastargeted, mostly adults, and the most common types of wardschosen were intensive care units.
The definition of medication errors (or sub-categories ofmedication errors) was inconsistent. In the 50 studies, 17 dif-ferent definitions were given, differing in wording and con-tent. The most widely used was that of the US NationalCoordinating Council for Medication Error Reporting andPrevention (NCCMERP) [26]. Ten studies (20%) adoptednon-standardised definitions from previous studies or provid-ed their own definition. Three studies (6%) used the definitionof medication errors as per Aronson et al. [27]. Two studies(4%) on prescribing errors used the definition of the AmericanSociety of Health-System Pharmacists [28]. One study eachused definitions provided by Dean et al. [29] and the Instituteof Medicine [30]. Twelve studies (24%) did not provide anydefinition of either medication errors or the sub-category be-ing reported.
Quantifying medication errors
Of the 32 studies quantifying medication errors, the mostcommon methods of data collection were via review of med-ication charts or records (prescribing, dispensing and admin-istration) (n = 11, 31%) or by analysis of data from an error orincident monitoring system (n = 9, 28%). Only one studyemployed multiple approaches to data collection. Data collec-tion periods ranged from 20 days to 2 years. Data extraction ofthe 32 studies is provided in Supplementary Table 2.
Eur J Clin Pharmacol (2019) 75:1269–1282 1271

Inconsistencies in definitions of ‘medication error’, ‘prescrib-ing error’ etc., together with the vast range of approaches todata collection and presentation of findings, limited pooling ofdata hence a narrative approach to data synthesis wasemployed. Almost half of the studies (n = 32, 47%) quantified‘medication errors’ in general, with fewer solely reporting‘administration errors’ (n = 7, 22%) or ‘prescribing errors’(n = 6, 18%) and one (3%) reporting only transcribing errors.Three studies reported data with combinations of classifica-tions of medication errors.
The specific terms used in the studies to report medicationserrors varied and eight different denominators were used, themost frequent being ‘total number of medication orders’ or‘number of prescriptions’ (n = 13, 40%), followed by ‘numberof patients admitted’ (n = 6, 19%) and ‘total number of oppor-tunities for errors’ (n = 4, 12%). One study (3%) each used‘total number of preparations’, ‘total number of medicationsdispensed’, ‘total number of cases/records’, ‘total number ofpatient days’ and ‘total number of reports’. Four studies (13%)did not specify the denominator.
Given this marked heterogeneity, it was not possible tomake valid comparisons of the outcome measure of preva-lence. Even in studies which used the same outcome measure,the error definitions and methods of measurement varied con-siderably. The following results should therefore beinterpreted with caution.
Of the 13 studies reporting medication errors per ‘totalnumber of medication orders’/‘number of prescriptions’, themedian across all studies was 10% (IQR 2–35%). The ratesvaried from 0.18 to 56 per 100 medication orders’/‘number ofprescriptions’. Of the six studies reporting ‘number of patientsadmitted’, the median was 28% (IQR 1–35%), varying from0.15 to 40 errors per 100 patient admissions.
Nature and severity of medication errors
Almost all studies (31/32, 97%) provided data regarding thenature of the errors. For prescribing errors, the most common-ly reported included errors of omission, wrong drug, wrongdose, wrong route, incomplete order, wrong duration, drug-
Fig. 1 PRISMA flowchartdescribing systematic reviewsearch and study selection
1272 Eur J Clin Pharmacol (2019) 75:1269–1282

drug interaction and wrong patient. Studies reporting admin-istration errors were largely related to wrong administrationtime, wrong administration route and wrong infusion rate.
Fourteen studies (43%) reported the specific medicationsmost commonly associated with errors. Most frequently re-ported therapeutic groups included anti-infectives for systemicuse, drugs used for alimentary tract and metabolism and car-diovascular drugs.
Thirteen studies (40%) reported error severity, with eightcategorising according to the NCCMERP Index [26]. Thesestudies, however, provided very little methodological detail onthe application of the index, specifically assessment of inter-rater reliability. In five studies, the most common categorywas B (near miss), with C (error occurred and reached thepatient but with no harm) in two studies and E (error occurredand may have contributed to or resulted in temporary harmand required intervention) in one study.
Contributory factors
Twenty-four studies (48%) from six Middle-Eastern countriesreported causes or contributory factors leading to medicationerrors. Approaches to data collection were largely based onquestionnaires (15/24, 63%), data from incident reporting sys-tems (n = 4, 17%), direct observation of practice (n = 2, 8%),semi-structured interviews (n = 2, 8%) and retrieval of infor-mation from patient medical records (n = 1, 4%). A total of3919 health professionals were involved in these 24 differentstudies. Notably, none of these 24 studies used any theory (e.g.behavioural, organisational) in the processes of data collectionor analysis. As described in the methods section, findingsfrom these 24 studies were categorised according toReason’s Accident Causation model [22] (Table 1), and syn-thesis of the categories is provided in Table 2. Contributoryfactors most commonly reported were active failures, largelyslips, lapses and mistakes; error-provoking conditions, partic-ularly those relating to lack of knowledge and insufficientstaffing levels and latent conditions, most commonly heavyworkload. Error-provoking conditions such as lack of experi-ence, poor documentation and look-alike drugs, or latent con-ditions of issues relating to a blame culture were rarelyreported.
Discussion
Statement of key findings
Heterogeneity in medication error definitions and scope, dif-ferences in methods of data collection and units of analysis ofthe studies included in this review limited data pooling. Mostfrequently reported was the percentage of medication errorsper total number of medication orders with a median across all
studies of 10% (IQR 2–35%). Prescribing errors were themost common type of errors reported, with dose-related errorsbeing most prevalent. Contributory factors associated withmedication errors were multifactorial. Synthesis of findingsaccording to Reason’s Accident Causation model identifiedthat active failures (slips, lapses andmistakes) weremost com-monly reported followed by error-provoking conditions (e.g.lack of knowledge, insufficient staffing), with latent failures(e.g. heavy workload) least reported.
Strengths and weaknesses
There are several strengths to this review. The protocol wasdeveloped according to the standards of PRISMA-P(Preferred Reporting Items for Systematic review and Meta-Analysis Protocols [23], registered in the PROSPERO data-base [24], and the systematic review reported according toPRISMA (Preferred Reporting Items for Systematic Reviewand Meta-Analysis) criteria [55]. The synthesis adopted atheory-driven approach based on Reason’s AccidentCausation Model [22], which could subsequently facilitatethe development of interventions. There are, however, severalweaknesses; hence, the review findings should be interpretedwith caution. Restricting the search to the English languageand excluding those written in regional languages of Arabic orPersian may have limited retrieval of potentially relevant stud-ies. It is, however, worth noting that English is the preferredlanguage of most professional organisations in the MiddleEast.
Interpretation of key findings
Although there has been an increase in the number of medi-cation errors studies originating fromMiddle East over the lastfew years, two thirds were from Iran and Saudi Arabia withnone from eight countries. While the reasons for the lack ofstudies in other countries are unknown, this does have impli-cations for the generalisability and transferability of reviewfindings and conclusions. Furthermore, there was a lack ofstudies employing a qualitative approach to explore contribu-tory factors of errors.
The majority of studies had key limitations in study designand lacked transparency in reporting key study details.Authors should be encouraged to adopt standardised reportingchecklists available from the EQUATOR (Enhancing theQUAlity and Transparency Of health Research) network[56]. This international network aims to ‘improve the reliabil-ity and value of published health research literature by pro-moting transparent and accurate reporting.’ An example is theSTROBE checklist (Strengthening the Reporting ofObservational Studies in Epidemiology) for reporting obser-vational studies [25].
Eur J Clin Pharmacol (2019) 75:1269–1282 1273

Table1
Classificationof
medicationerrorcontributory
factors
Author(s),year,
country
Methodology
Setting,participants,
number
Classificationof
contributory
factorsas
perReason’sAccidentC
ausatio
nmodel[22]
Abdar
etal.2014,
Iran
[31]
Cross-sectio
nalsurvey
Setting—4academ
ichospitals
Participants—nurses
No.of
participants—238
Error-producing
conditions
•Insufficient
staff
•Nurse
fatig
ue•Illegiblehandwriting
•Nurse
workload
Latentfailures
•Su
pervisoryissues
•Not
consideringnurses’view
s
Alietal.2017,
SaudiA
rabia
[32]
Retrospectiv
eanalysis
from
incident
reportingsystem
Setting—tertiary
care
hospital
Participants—not
relevant
No.of
participants—not
relevant
Activefailu
res
•Slips—
look-alik
esound-alikemedications
Error-producing
conditions
•Miscommunicationof
drug
orders
Latentfailures
•Lackof
educationalactivities
Alshaikhetal.
2013,S
audi
Arabia[33]
Retrospectiv
eanalysis
from
incident
reportingsystem
Setting—academ
ichospital
Participants—not
relevant
No.of
errorsreported—
949
Duration—
1year
Error-producing
conditions
•Lackof
know
ledge
•Illegiblehandwriting
Latentfailures
•Performance
deficit
Al-Sh
araetal.
2011,Jordan
[34]
Cross-sectio
nalsurvey
Setting
-NS
Participants—nurses
No.of
participants—126
Activefailu
res
•Slips—
soundalike
•Mistake—prescribingwrong
dosage
•Violatio
n—usingabbreviations
Error-producing
conditions
•Heavy
workload
•Unfam
iliarity
ofnurses
with
patients’medical
conditions
•Unfam
iliarity
with
theuseof
medications
Al-Youssifetal.
2013,S
audi
Arabia[35]
Cross-sectio
nalsurvey
Setting—government
hospital
Participants—nurses
No.of
participants—253
Activefailu
res
•Lapse—dispensing
wrong
drug
•Mistake—wrong
packaging
•Violatio
n—poor
adherence
toprotocol
Error-producing
conditions
•Illegibleprescriptio
n•Po
orcommunication
Latentfailures
•Ph
armacistsnotavailable24
h
Al-Tehewy,etal.
2016,E
gypt
[36]
Prospective
observationalstudy
Setting—academ
ichospital
Participants—nurses
No.of
participants—28
Error-producing
conditions
•Heavy
workload
•Patient
condition
(illiteracy,elderly)
Latentfailures
•Po
orstaffing
•Lackof
policyandprocedures
•Low
commitm
ento
fhospitaladm
inistration
towards
patient
safety
Bagheri-N
esam
ietal.2015,Iran
[37]
Cross-sectio
nalsurvey
Setting—12
academ
ichospitals
Participants—nurses
No.of
participants—190
Activefailu
res
•Slips—
selectingwrong
medication
•Lapse—failedtoputcorrect
labelson
medications
•Mistake—deliv
ered
incorrectm
edicationdoses
Error-producing
conditions
•Ph
ysicians’medicationordersillegible
•Manypatientsreceivingsimilarmedications
•Lim
itedknow
ledgeof
medications
LatentF
ailures
•poor
communication
•Lim
itedaccess
tomedicationinform
ation.
•Medicationexpertsnotavailable.
Cheragi
etal.
2013,
Iran
[38]
Cross-sectio
nalsurvey
Setting—academ
icParticipants—nurses
No.of
participants—237
Activefailu
res
•Slips—
wrong
patient,
•Lapse—failure
togive
medication
Error-producing
conditions
•Large
varietyofdrugsinthemedicationcabinet•So
und
alikemedications•To
obusy
andtired
from
excessive
work(nurses)
LatentF
ailures
•lack
oftraining
•lack
ofstaffing
1274 Eur J Clin Pharmacol (2019) 75:1269–1282

Tab
le1
(contin
ued)
Author(s),year,
country
Methodology
Setting,participants,
number
Classificationof
contributory
factorsas
perReason’sAccidentC
ausatio
nmodel[22]
•Mistake—prescribing
wrong
dosage
and
infusion
rate
•Violatio
n—usingacronyms
ofmedicationnames
Dibbi,etal.2006,
SaudiA
rabia
[39]
Retrospectiv
echart
review
Setting—generalh
ospital
Participants—not
relevant
No.of
participants-2
627
Activefailu
res
•Slips—
choosing
wrong
medication(lookalikeandsoundalike)
Error-producing
conditions
•Lackof
know
ledge
•Performance
deficit
Ehsanietal.2013,
Iran
[40]
Cross-sectio
nalsurvey
Setting—academ
ichospital
Participants—nurses
No.of
participants—94
Activefailu
res
•Slips—
choosing
wrong
medication(lookalikeand
soundalike)
•Violatio
n—using
abbreviatednames
Error-producing
conditions
•Fatig
uefrom
hard
work•
Illegibility
•Insufficient
pharmacologicalknow
ledge
Latentfailures
•Highpatient
-to-
nurseratio
•Insufficient
education/training
Farzietal.2017,
Iran
[41]
Semi-structured
individualinterviews
Setting—academ
ichospitals
Participants—physicians,
nurses
andclinical
pharmacists
No.of
participants—19
Activefailu
res
•Slips—
look
alike,sound
alike
•Mistake—incomplete
medicationorders
Error-producing
conditions
•Lackof
know
ledgeof
healthcare
team
•Lackof
professionalcommunication
•Lackof
medicationreconciliation
•Interruptio
n/talkingwhilemedicationadministration
•Lackof
pharmaceuticalknow
ledge
Latentfailures
•Lackof
monitoring
orsupervisorymechanism
s•
Weakprofessionalcollaboratio
nbetween
healthcare
team
•Lackof
managem
entd
ecisions
•Lackof
adequatestaffing
Fathietal.2017,
Iran
[42]
Cross-sectio
nalsurvey
Setting—7academ
ichospitals
Participants—nurses
No.of
participants—500
ActiveFailu
res
•Slips—
look
alike,sound
alike
•Mistake—wrong
labelling
Error-producing
conditions
•Inappropriatebehavior
ofpatients
•Fatig
uefrom
hard
work
•Ph
onecallorders
•Highnumberof
patients
•Noisy
environm
ent
Latentfailures
•Lackof
monitoring
orsupervisorymechanism
s•
Shortage
ofnursingstaff
•Lackof
drug
inform
ationresources
Gorgich
etal.
2016,Iran55]
Cross-sectio
nalsurvey
Setting—academ
ichospitals
Participants—nurses
No.of
participants—327
Activefailu
res
•Violatio
n—unreadable
orders
Error-producing
conditions
•Fatig
uedueto
high
workload
•Large
numberof
critically
illpatients
•Po
orphysicalenvironm
ent(light,tem
perature)
•Po
orcommunicationbetweenteam
mem
bers
Latentfailures
•Low
ratio
ofnurses
topatients
•Failure
inem
phasisingtheim
portance
ofrecordingandreportingthemedicationerrors
•Blameculture
Güneş
etal.2014,
Turkey[43]
Cross-sectio
nalsurvey
Setting—2government
hospitals
Participants—nurses
No.of
participants—243
Activefailu
res
•Lapse—physicians
notw
ritingdrug
route
•Mistake—prescribinginteractingdrugs
•Violatio
n—physicians
notw
ritingtheorderor
notintim
e
Error-producing
conditions
•Interruptionby
telephoneetc.whilepreparing
medication
•Po
ormathematicalskillsford
rugdose
calculation
Ham
moudi
etal.
2017,S
audi
Arabia[44]
Cross-sectio
nalsurvey
Setting—tertiary
care
hospital
Participants—nurses
No.of
participants—367
Error-producing
conditions
•Illegibilityof
patients’records
•Wrong
medicationpreparationby
pharmacists
Latentfailures
Low
staffing
Mrayyan
etal.
2007,Jordan
[45]
Cross-sectio
nalsurvey
Setting—11
government
and11
private
hospitals
Participants—nurses
No.of
participants—799
Activefailu
res
•Slips—
nurses
confused
bydifferenttypes
andfunctions
ofinfusion
devices
•Lapse—nursefails
tocheckthepatient
namewith
medicationadministrationrecord
Error-producing
conditions
•Nursesdistracted
byotherpatients,co-w
orkersor
eventson
unit
Cross-sectio
nalsurvey
Activefailu
res
Error-producing
conditions
Eur J Clin Pharmacol (2019) 75:1269–1282 1275

Tab
le1
(contin
ued)
Author(s),year,
country
Methodology
Setting,participants,
number
Classificationof
contributory
factorsas
perReason’sAccidentC
ausatio
nmodel[22]
Mrayyan
etal.
2012,Jordan
[46]
Setting—academ
ichospitals
Participants—nurses
No.of
participants—212
•Mistake—inaccuraterateof
totalp
arenteraln
utritio
n•Po
orquality
ordamaged
medicationlabels
•Fear
ofdisciplinaryactions
Pawluketal.
2017,Q
atar
[35]
Retrospectiv
eanalysis
from
incident
reportingsystem
Setting—tertiary
care
hospital
Participants—not
relevant
No.of
participants—201
Activefailu
res•Lapse—missing
documentatio
n•Mistake—errorin
calculation
•Violatio
n—im
proper
useof
hospitalp
rotocol
Pazokian
etal.
2014,Iran[47]
Semi-structured
individualinterviews
Setting—academ
ichospital
Participants—nurses
No.of
participants—20
Activefailu
res
•Mistake—prescribing
wrong
medications
Error-producing
conditions
•Po
odocumentation
•Po
orknow
ledge
Latentfailures
•Lackof
attentionof
managerstostaffp
hysicaland
psychologicalissuesleadingto
decrease
innurses’motivation
•Riskmanagem
entstrategiesinsufficient
Shahrokhietal.
2013,Iran[48]
Cross-sectio
nalsurvey
Setting—academ
ichospitals
Participants—nurses
No.of
participants—150
Activefailu
res
•Mistake—incorrect
transcription
Error-producing
conditions
•Excessive
workload•
Inadequatepharmacological
know
ledge
•Sh
ortage
oftim
e
Latentfailures
•Low
nurseto
patient
ratio
•Inadequatenumberof
staffin
each
working
shift
•Similardrug
packing
Shehataetal.
2015,E
gypt
[49]
Retrospectiv
eanalysis
from
incident
reportingsystem
Setting—government
andprivatehospitals
Participants—not
relevant
No.of
participants—
1200
reports
Activefailu
res
•Lapse—lack
ofdocumentatio
n
Error-producing
conditions
•Lackof
know
ledgeandexperience
•Excessive
workloadanddistractions
•Incompleteprescribinginstructions
•Illegiblehandwritin
g
Latentfailures
•Lackof
drug
inform
ationresources
Shohanietal.
2018,Iran[50]
Cross-sectio
nalsurvey
Setting—academ
ichospital
Participants—nurses
No.of
participants—120
Error-producing
conditions
•Lackof
awarenessof
drug
•Fatigue
andworkload
•Lackof
patient
inform
ation
•Noisy
working
environm
ent
•Heavy
workload
Latentfailures
•Lackof
motivationam
ongstn
urses
•Lackof
drug
protocol
•Lackof
training
Toruneretal.
2012,T
urkey
[51]
Cross-sectio
nalsurvey
Setting—4tertiary
care
hospitals
Participants—nurses
No.of
participants—124
Activefailu
res
•Mistake—readingthe
prescriptionin
wrong
way
Error-producing
conditions
•Longworking
hours•Highpatient–nurse
ratio
•Lackof
patient
inform
ation
Latentfailures
•Unavailabilityof
medications
inappropriateform
s•Po
orworkenvironm
ent
Vazin
etal.2012,
SaudiA
rabia
[52]
Prospective
observationalstudy
Setting—academ
ichospitals
Participants—patients
No.of
participants—38
Activefailu
res
•Slips—
mem
orylapses
•Lapse—faulty
dose
checking
(missing)
•Mistake—preparationerror
•Violatio
n—violating
hospitalrules
Error-producing
conditions
•Lackof
drug
know
ledge
•Lackof
interactionwith
otherservices
•Lackof
patient
inform
ation
Latentfailures
•Po
ordrug
stocking
anddeliv
ery
1276 Eur J Clin Pharmacol (2019) 75:1269–1282

Table2
Synthesisof
causativefactors A
ctivefailu
reError-provoking
conditions
Author(s),year,country
Slip
Lapse
Mistake
Violatio
nLackof
know
ledge
Insufficient
staff
Patient
condition(s)
Poor
communication
Lackof
experience
Distractio
nsLookalike
drugs
Abdar
etal.2014,Iran
[31]

Alietal.2017,SaudiA
rabia[32]
✓✓
Alshaikhetal.2013,SaudiA
rabia
[33]

Al-Sh
araetal.2011,Jordan
[34]
✓✓
✓✓

Al-Tehewy,etal.2016,Egypt
[36]
✓✓
Al-Youssifetal.2013,SaudiA
rabia
[53]
✓✓
✓✓

Bagheri-N
esam
ietal.2015,Iran[37]
✓✓
✓✓
Cheragi
etal.2013,Iran
[38]
✓✓
✓✓
✓✓

Dibbi,etal.2006,S
audi
Arabia[39]
✓✓
Ehsanietal.2013,Iran[40]
✓✓

Farzietal.2017,Iran[41]
✓✓
✓✓
✓✓
Fathietal.2017,Iran[42]
✓✓
✓✓

Gorgich
etal.2016,Iran
[54]
✓✓
✓✓
Güneş
etal.2014,Turkey[43]
✓✓
✓✓

Ham
moudi
etal.2017,Saudi
Arabia
[44]

Mrayyan
etal.2007,Jordan
[45]
✓✓

Mrayyan
etal.2012,Jordan
[46]

Pawluketal.2017,Qatar
[35]
✓✓

Pazokian
etal.2014,Iran
[47]
✓✓

Shahrokhi
etal.2013,Iran
[48]
✓✓
✓✓
Shehataetal.2015,Egypt
[49]
✓✓
Shohanietal.2018,Iran[50]
✓✓
✓✓
Toruneretal.2012,Turkey[51]
✓✓
✓✓
Vazin
etal.2012,SaudiA
rabia[52]
✓✓
✓✓
✓✓
Totaln
umberof
studies
109
127
1313
36
17
2
Error-provoking
conditions
Latentconditio
ns
Author(s),year,country
Poor
Docum
entatio
nIllegible
orders
Heavy
workload
Lackof
training
Organisation
factors
Blame
cultu
reSupervisory
issues
Organisationalp
olicy
issues
Inform
ationresource
issues
Abdar
etal.2014,Iran
[31]
✓✓
✓✓
Alietal.2017,SaudiA
rabia[32]

Alshaikhetal.2013,SaudiA
rabia
[33]

Eur J Clin Pharmacol (2019) 75:1269–1282 1277

Tab
le2
(contin
ued)
Al-Sh
araetal.2011,Jordan
[34]

Al-Tehewy,etal.2016,Egypt
[36]
✓✓

Al-Youssifetal.2013,Saudi
Arabia[53]
✓✓
Bagheri-N
esam
ietal.2015,Iran
[37]
✓✓
✓✓
Cheragi
etal.2013,Iran
[38]

Dibbi,etal.2006,S
audi
Arabia
[39]
Ehsanietal.2013,Iran[40]
✓✓

Farzietal.2017,Iran[41]
✓✓
Fathi
etal.2017,Iran
[42]
✓✓
Gorgich
etal.2016,Iran
[54]
✓✓
✓✓
Güneş
etal.2014,Turkey[43]
Ham
moudi
etal.2017,Saudi
Arabia[44]

Mrayyan
etal.2007,Jordan
[45]
Mrayyan
etal.2012,Jordan
[46]

Pawluketal.2017,Qatar
[35]
Pazokian
etal.2014,Iran
[47]
✓✓
Shahrokhi
etal.2013,Iran
[48]

Shehataetal.2015,Egypt
[49]
✓✓

Shohanietal.2018,Iran[50]
✓✓

Toruneretal.2012,Turkey[51]
✓✓
Vazinetal.2012,SaudiA
rabia[52]

Totaln
umberof
studies
17
94
42
74
4
Atickindicatesthatthespecificcausitive
factor
was
reported
1278 Eur J Clin Pharmacol (2019) 75:1269–1282

As noted in previous systematic reviews [5–21], manystudies either did not define terms such as ‘medication errors’,‘prescribing errors’ etc. or used non-standardised definitions.There was also variation in the methods used and the durationof data collection. To further advance this field of research, theadoption of standardised definitions and methodologiesshould be encouraged. This would enable analytical ap-proaches such as meta-analyses and provide more robust andgeneralisable findings to inform practice.
Few studies reported the severity of errors, often pro-viding little methodological detail. In a systematic re-view of tools used in error severity estimation,Garfield et al. highlighted that of the 40 tools assessed,only two were deemed to have acceptable validity andreliability [57].
Despite these issues around standardisation, it is evi-dent from this systematic review that medication errorsremain prevalent in hospitals in the Middle East. Forthose reporting medication errors, the median ‘totalnumber of medication orders’/ ‘number of prescriptions’across all studies was 10% (IQR 2–35% and range of0.18–56%). While differences in methodology, settingsand patient populations limit comparisons to other sys-tematic reviews; these figures are similar to those re-ported by Alsulami et al. in a systematic review ofMiddle Eastern studies up to 2011 [10]. The prevalenceof medication errors in the Middle East would appear toremain largely unchanged and at a similar level to thosereported from around the world [5–21].
None of the 24 studies in this review and only twoprevious systematic reviews analysed causative factorsaccording to Reason’s theory. In a review of prescribingerrors in hospitalised patients, Tully et al. reported thatthe active failure most frequently cited was a mistakedue to inadequate knowledge of the drug or the patient.There were issues of lack of training or experience,fatigue, stress, high workload and inadequate communi-cation between healthcare professionals [9]. In a system-atic review of medication administration error studies,Keers et al. reported that slips and lapses were the mostcommon unsafe acts [11]. Our synthesis of study find-ings according to Reason’s Theory is similar in thatactive failures of slips, lapses and mistakes were mostcommon. Error-provoking conditions included lack ofknowledge and insufficient staff. It is possible that othercontributory factors may have been identified if the pri-mary studies had used Reason’s Theory in data collec-tion and analysis. Using a theoretical framework in pri-mary research would ensure that all possible explana-tions underlying medication errors are identified [58].Given the accumulation of evidence from this and othersystematic reviews, a standardised, theory-informed ap-proach should be adopted. This is fundamental to the
key stated WHO objective of assessing and scopingthe nature of avoidable medication-related harm [3, 4].
Policy makers, leaders, practitioners and other relevantstakeholders must continue working towards minimising thekey-identified contributory factors where possible.
Further research
There is a need for consensus-based research to defineand standardise medication error definitions, approachesto data collection and outcome measures. Furthermore,theoretically informed qualitative research which allowsin-depth exploration of contributory factors leading tomedication errors is warranted. The findings from stud-ies such as these would facilitate the development, test-ing, evaluation and monitoring of interventions aimingto reduce avoidable medication-related harm. There isevidence that consideration of theory allows comprehen-sive identification of the key issues to be targeted aspart of intervention development leading to more effec-tive and sustainable interventions compared to morepragmatic approaches [58].
Conclusion
While there has been a clear increase in the number of publi-cations from selected Middle Eastern countries, there is needto improve the quality and reporting of studies. A standardisedapproach to quantifying medication errors’ prevalence, sever-ity, outcomes and contributory factors is warranted.
Acknowledgements Open Access funding provided by the QatarNational Library. The authors would like to acknowledge the contributionof Doua Al Saad to quality assessment.
Author contributions Binny Thomas reviews conception, protocol de-sign, data collection, analysis, interpretation, drafting manuscript.
Vibhu Paudyal reviews conception, protocol design, data collection,analysis, interpretation, reviewing and approving final manuscript.
Katie MacLure reviews conception, protocol design, data collection,analysis, interpretation, reviewing and approving final manuscript.
Abdulrouf Pallivalapila: data collection, analysis, reviewing and ap-proving final manuscript.
James McLay, reviews conception, protocol design, interpretation,reviewing and approving final manuscript.
Wessam El Kassem: data collection, analysis, reviewing and approv-ing final manuscript.
Moza Al Hail: data collection, analysis, reviewing and approving finalmanuscript.
Derek Stewart reviews conception, protocol design, data collection,analysis, interpretation, reviewing and approving final manuscript.
All authors agree to be accountable for all aspects of the work inensuring that questions related to the accuracy or integrity of any part ofthe work are appropriately investigated and resolved.
Eur J Clin Pharmacol (2019) 75:1269–1282 1279

Funding This systematic review was undertaken as part of the self-funded PhD at Robert Gordon University, UK.
Open Access This article is distributed under the terms of the CreativeCommons At t r ibut ion 4 .0 In te rna t ional License (h t tp : / /creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided you giveappropriate credit to the original author(s) and the source, provide a linkto the Creative Commons license, and indicate if changes were made.
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Medication errors in hospitals in the Middle East: a systematic review of prevalence, nature, severity and contributory factors

Abstract
Abstract
Abstract
Abstract
Abstract
Introduction
Methods

Inclusion and exclusion criteria
Search strategy
Screening
Assessment of methodological quality
Data extraction
Data synthesis

Results

Study screening
Quality assessment
Characteristics of included studies
Quantifying medication errors
Nature and severity of medication errors
Contributory factors

Discussion

Statement of key findings
Strengths and weaknesses
Interpretation of key findings
Further research

Conclusion
References

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