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American Journal of  
Life Science and Innovation (AJLSI)

Prevalence of  Medications Errors and Factors of  Harmful Errors in Neonates and 
Paediatrics at Tertiary and Paediatric Hospitals: A Multicenter Study

Murooj Shukry1*, Mohammed Hamed Alotaibi1, Manal Bajammal1, Nissreen Althaqafi2, Saleh Alghamdi3

Volume 3 Issue 1, Year 2024
ISSN: 2833-1397 (Online)

DOI: https://doi.org/10.54536/ajlsi.v3i1.2729
https://journals.e-palli.com/home/index.php/ajlsi

Article Information ABSTRACT

Received: April 07, 2024

Accepted: May 14, 2024

Published: May 21, 2024

Medication errors (ME) are human mistakes that may occur during drug use, prescribing 
administration or monitoring. This study aimed to determine the Prevalence of  MEs among 
pediatric patients receiving tertiary treatment in pediatric speciality hospitals and describe 
their patterns and causes. Three institutions participated in a multicenter retrospective 
analysis. Reports of  all MEs involving paediatrics aged ≤13 years who were hospitalised or 
visited the Emergency Department or outpatient clinics were reviewed 1 January 2019 - 31 
December 2019. The National Coordinating Council for Medication Error Reporting and 
Prevention classification was used to categorise MEs and to define PRME (category CI) 
and patient–harming MEs (category EI). A retrospective study was conducted with a total 
of  318 MEs reported, with an overall prevalence of  0.9 per 1,000 patients. The Prevalence 
was markedly lower in the CHT (0.35 / 1,000 patients) than in KAMC and KFSH&RC 
(1.73 and 3.5). The highest rate of  MEs was observed in admitted wards (14.69 / 1,000 
patients). Most commonly, MEs occurred at the prescribing stage (81.1%), were caused by 
a physician (83.3%), and were discovered by the pharmacist (85.5%). Improper medication 
dosage (34.0%), frequency (14.2%), and duration (10.4%) were the three most common 
forms of  MEs. Paediatric inpatient treatment has a significant frequency of  MEs, accounting 
for 24% of  hospitalisations. The ME may not get to the patient if  independent double-check 
prescriptions, accurate medication reconciliation of  new prescriptions against the previously 
prescribed, and improved medicine labelling and packaging techniques are used.

Keywords

Medication Error, Neonatal, 
Prescribing Error, Prevalence, 
Pediatric, Saudi Arabia, Factors 

INTRODUCTION
Medications are active substances used to treat, prevent, 
and diagnose diseases and constitute a valuable tool for 
modern medicine. However, improper use of  medications 
can cause harmful consequences to the patient, leading 
to death (Hussain et al., 2014). From the public health 
view, medications and pharmaceutical substances are 
authorised in local markets in perspective to help improve 
the population’s health and reduce disease rates and 
related morbidity and mortality (Laatikainen et al., 2022). 
From this point, medications’ positive therapeutic and or 
preventive effect is only judged under optimal safety and 
effectiveness conditions of  usage (Maaskant et al., 2015).
Medication errors “MEs” constitute a circumstance where 
Medication is administered outside the recognised safety 
and effectiveness usage conditions. They are defined as 
human mistakes occurring in hospitals and represent 
an issue of  high priority in healthcare systems globally 
(Tajik, 2020). They may occur due to misunderstanding 
during handoffs as the healthcare providers may not be 
well affiliated across borders (Alqenae et al., 2020). These 
errors may occur during the drug’s use, prescribing, 
administration, or monitoring, thus involving all 
healthcare professionals (Abuelsoud, 2019). They may 
result in increased illness severity, other adverse effects, 
or death and are associated with unnecessary healthcare 
costs (Khaemba, 2014).

Medication errors account for 84.2% of  all medical 
errors, estimated to cause 98,000 hospital deaths and 
more than one million injuries annually, costing up 
to $ 29 billion globally. Consequently, medical errors 
rank as one of  the top causes of  death in the United 
States besides car accidents, breast cancer, and acquired 
immunodeficiency syndrome (A Truter, 2017; Khaemba, 
2014; Krzyzaniak & Bajorek, 2016).  Medication errors 
involve errors due to human or system factors. A study 
indicates (Escrivá Gracia et al., 2019) medication-related 
occurrences cause approximately 2 per 1000 hospital 
fatalities and 6-12% of  hospital admissions, respectively. 
Human-related errors are a lack of  performance and/
or compliance with procedures or protocols. Many 
factors including misunderstanding, inaccurate copying, 
lack of  knowledge, heavy workload, misjudgment, use 
of  nonstandard abbreviations, lack of  information 
about the patient, and insufficient or shortage of  staff  
(Rodziewicz et al., 2022; Thomas et al., 2019). Besides 
health professional factors, patient-related factors may 
also underlie ME, such as patients’ lack of  understanding 
of  their treatment and administration errors, related to 
prescription and incorrect dosages (Valencia Quintero et 
al., 2021). The risk of  human errors increases with an 
increased workload, stress, sleep deprivation, drugs with 
similar and complex names, and handling of  medications 
by non-pharmacists, besides non-ergonomically designed 

1 Pharmaceutical Care Division, King Faisal Specialist Hospital and Research Centre, Jeddah, Saudi Arabia
2 Saudi Medication Safety Center, King Abdulaziz Medical City-National Guard Health Affairs, Jeddah, Saudi Arabia
3 Pharmaceutical Care Division, Children Hospital, Taif, Saudi Arabia
* Corresponding author’s e-mail: MuroojShukry143@outlook.com



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information systems (Khayam, 2015). A review based in 
Iran revealed that ME was the most common type of  
medical error, with Prevalence ranging from (10-80%) 
(Vaziri et al., 2019).
Suppose MEs constitute a global concern for causing 
severe medical consequences. In that case, they should 
be more seriously considered for infants and children, 
especially in developing countries where this age category 
represents a large proportion of  the population (Hmedat 
et al., 2017). MEs are prevalent in PICUs and NICUs, 
affecting medication orders and patient days (Alghamdi 
et al., 2019). While prescribing safety has become very 
clear in adults, the focus has been less on newborns, 
infants, children, and adolescents, as clinical trials are 
insufficient for children due to cost and liability problems 
and regulations that impede such studies (Al-Jeraisy et 
al., 2011). Moreover, the Prevalence of  ME in pediatric 
patients is three times higher than in adults (Brennan-
Bourdon et al., 2020). The available evidence suggests that 
MEs among pediatric patients represent a greater risk of  
death than adults, owing to additional factors that put 
children at higher risk from MEs, such as age and weight 
differences, in addition to other physiological features 
(Al-Jeraisy et al., 2011; Fernández-Llamazares et al., 2012; 
Impicciatore et al., 2001; Kaushal et al., 2001).
Further, MEs have been reported in various pediatric 
settings with an incidence rate of  1 in 6.4 medication 
orders (Dedefo et al., 2016). These involved multiple 
medications, and several risk factors have been identified in 
the literature (Antonucci & Porcella, 2014; Khayam, 2015). 
Such observations make the prevention of  MEs a priority 
for health systems and a clinical necessity, integrated under 
the broad mission of  protecting patients. Consequently, 
several efforts are undertaken to reduce drug error rates, 
using multiple human and technological tools, both at the 
healthcare provider’s and patient’s levels (Dedefo et al., 
2016; Devine EB, 2005; Fernández-Llamazares et al., 2012; 
Hussain et al., 2014; Ross et al., 2000).
Among these strategic actions is monitoring MEs, 
which has become an essential tool to improve the 
safety and efficacy of  medication dispensation systems 
by analysing the risk in case errors are reported. The 
use of  Computerised provider order entry (CPOE) was 
shown to reduce the Prevalence of  ME highly (Liu et 
al., 2023). Error reporting systems depend on the ability 
of  physicians, pharmacists, and nurses to commit error 
detection and reporting protocols (Khaemba, 2014). 
As determined in one study, nurses were less likely to 
report ME as they feared the consequences that followed 
it. Physicians were more likely to report the Me. These 
considerations are of  higher importance in the pediatric 
population. As such, we designed this study to provide 
insight into the extent and determinants of  MEs among 
neonates and children in tertiary and pediatric hospitals 
in the Western region of  Saudi Arabia. The following 
objectives were explored:

• To estimate the yearly Prevalence of  MEs among 
pediatric and neonatal patients in different types of  

hospital settings.
• To compare the Prevalence of  MEs between the 

different hospitals (tertiary and paediatrics) and different 
departments to determine the settings with the highest 
risk of  MEs.

• To describe the pattern and categorisation of  MEs 
based on the reported data;

• To investigate patient and healthcare Professional-
related factors associated with MEs and to analyse the 
predictors of  the Patient -Reaching MEs (PRME) and 
Patient-Harming MEs (PHME). 

MATERIALS AND METHODS  
Study Design
Three referral hospitals in the Western region of  Saudi 
Arabia-King AbdulAziz, medical city National Guard 
(KAMCJED, a 751-bed hospital), Children’s Hospital 
of  Taif  (CHT), and King Faisal specialist hospital and 
Research Centre Jeddah (KFSHRC, a 380-bed hospital-
were the subjects of  a multicenter retrospective study. 
Data was gathered for this investigation from 1 January 
2019 - 31 December 2019. There were no increased 
patient hazards or interventions in the research. Initially, 
the study involved all eight tertiary and pediatric hospitals 
in the Western region. Subsequently, a systematic 
sampling method was used to include all cases of  MEs 
reporting over the study period from all participating 
centres. However, due to feasibility challenges, cluster 
sampling was used to select three centres depending on 
the authors’ convenience and data accessibility. 

Ethical Considerations  
The protocol conformed with the Declaration of  
Helsinki’s ethical guidelines for using human subjects’ 
data and material in medical research. Institutional 
Review Board provided the Ethical Approval for KFSH 
with IRB study number 202068, KAIMARC with IRB 
study number RJ20/173/J, and an official approval letter 
from TAIF’s Director of  health affairs.

Eligibility Criteria 
The Inclusion Criteria for this study were as follows: 
A) The study included all MEs reports declared in the 
participating centres from 1 January 2019 - 31 December 
2019.  B) Only ME reports concerning neonates and 
pediatric patients under 13 years admitted or visited the 
outpatient clinics or emergency were included. 
The Exclusion Criteria were: 

A) Patients over 13 years were excluded from the study.  
B) Oncology and haematology neonates and paediatrics 

units were excluded.
C) Patients with incomplete data were excluded from 

the study.

Data Collection
A structured, precoded Excel form was used to collect all 
relevant data from both sources, i.e., the ME report and 
patient file. The form was designed by authors based on 



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the crucial ME report content and the literature review of  
relevant studies. The Excel form collected the following 
data: 

1. ME-related data including date, location (hospital, 
department), a person who reported the ME, stage 
of  error (prescribing, transcription, dispensing, 
administration, monitoring), type of  MEs (incorrect 
dose, incorrect Medication, incorrect route, expired 
Medication, missed Medication, and incorrect time), and 
medicines in the cause (name, indication, dose, route, 
higher alert [yes/no], dosage form). Further, the severity 
of  MEs was classified using the National Coordinating 
Council for Medication Error Reporting and Prevention 
(NCC MERP) categorisation (MERP, 1998)

2. Patient-related factors, including age, gender, weight, 
medical history, chronic disease, mental illness (yes/no), 
consciousness status on the day of  the ME (conscious/
unconscious), number of  medications prescribed on the 
day of  the ME, length of  hospital stay, presence of  a 
caregiver and relation to the child (mother, father)

3. Data relating to the healthcare professional in the 
cause of  the ME, including speciality (pharmacist, 
physician, nurse), grade (senior, junior), position 
(temporary, contract worker, permanent).

4. There is a predefined list of  8 contributing factors: 
missing clinical information, lack of  double-checking, 
and lack of  staff  education and training.

Besides, data regarding the number of  admissions 
by participating centres and units were collected on a 
separate sheet and used as a denominator in calculating 
ME Prevalence, as indicated later. 

Procedure
The researchers conducted the data collection onsite. ME 
reports, including electronic and handwritten reports, 
were retrieved from the participating centres’ ME 
reporting system. The corresponding patients’ files were 
reviewed to extract further relevant data not available in 

the information. Data were entered directly using the 
predesigned Excel form, which resulted in 3 separate 
databases, one for each centre. The principal investigator 
and coauthors reviewed the three partial databases for 
accuracy and completeness and then merged them into 
one final database. The final database was coded to 
respect the patient’s confidentiality before being shared 
for statistical analysis.

Study Outcomes
The study defined one primary outcome and two 
secondary outcomes. The primary outcome consisted of  
the Prevalence of  MEs, which 1,000 patients computed 
regarding the total number of  admissions and/or visits in 
each centre over the study period. The secondary outcomes 
consisted of  Patient-Reaching ME (PRME), defined 
as a ME of  NCC MERP category CI. Further, Patient-
Harming ME (PHME) was also a secondary outcome, 
expressed as a ME of  NCC MERP category EI.

Statistical Methods 
The database was edited and imported to SPSS version 
21 for statistical analysis. The results of  the descriptive 
statistics that were carried out to present the patterns of  
MEs and associated factors were presented as frequency 
and percentage. Using cross-tabulations, Chi-Square and 
Fisher’s Exact Tests were used to analyse PatientReaching 
MEs (PRME). On the contrary, the independent risk 
factors of  PRME were analysed by a technique known 
as Multivariate Binary Logistic Regression. With a 
Confidence Interval (CI) of  95%, results were presented 
as Odds Ratio (OD). A P-value < 0.05 is considered 
statistically significant for the study. 

RESULTS AND DISCUSSIONS
Prevalence of  Neonates and Pediatric Medication 
Errors by Department and Centre
The three participating centres reported 318 MEs in total, 

Figure 1: Prevalence of  Medication Errors by Department and Centres in Three Tertiary Care Hospitals of  Western 
Saudi Arabia, 2019



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including 135 (KFSH), 93 (CHT), and 90 (KAMC), and 
an overall prevalence of  0.9 per 1,000 patients. Compared 
to the two tertiary care hospitals, KAMC and KFSH, 
the rate was lower at the Children’s Hospital (0.35 per 
1,000 patients) than at both facilities (Figure 1). The 
Prevalence of  MEs varied significantly amongst hospitals. 
At the departmental level, inpatient wards had the most 
significant rates of  MEs (14.69 per 1,000 patients), 
whereas emergency departments had the lowest rates 
(0.14).

Patient-Related Factors of  ME
Table 1 illustrates the demographic and clinical features of  
individuals with MEs. The age distribution of  the 318 patients 
revealed two main age groups: 1 month-2 years (37.7%) and 
6-12 years (35.5%), with 57.2% of  the patients being male. 
60.4% of  the patients in the study had comorbidities, and 
7.2% had mental disabilities. The patient was unconscious in 
13.8% of  the instances, was taking four or more prescriptions 
in 48.1% of  the cases, and had no carer present in 30.2% of  
the cases when the medication mistakes happened.

Table 1: Patient-Related Factors of  Medication Errors
Parameter Category Frequency Percentage
Center CHAT 93 29.2

KAMC 90 28.3
KFSH 135 42.5

Patient’s age * 0-30 days 9 2.8
31 days – 2 years 120 37.7
2-6 years 72 22.6
6-12 years 113 35.5

Gender * Male 182 57.2
Female 132 41.5

Weight * <5 kg 1 .3
510 kg 132 41.5
1120 kg 101 31.8
>20 kg 79 24.8

Comorbidity‡ Any 192 60.4
Cancer 39 12.3
Hematological disease 35 11.0
Mental disability 23 7.2
Other chronic condition 184 57.9

Consciousness state * Conscious 270 84.9
Unconscious 44 13.8

Number of  medications 13 86 27.0
46 153 48.1
7+ 73 23.0
Data not available 6 1.9

Presence of  caregiver No 96 30.2
Yes 215 67.6
Data not available 1 2.2

* Because of  missing data, not all values sum up to the total
‡ A patient may have more than one comorbidity

Patterns and Characteristics of  MEs
Most commonly, MEs occurred at the prescribing 
stage (81.1%), were caused by a physician (83.3%), and 
were discovered by the pharmacist (85.5%). Premature 
medicine dose (34.0%), incorrect frequency (14.2%), and 
duration (10.4%) were the most common types of  MEs, 
and cumulative mistakes (two or more errors in the same 
report) were discovered in 5.0% of  instances (Table 2). 
The most common medication classes involved in MEs 

were antibiotics (25.5%), followed by supplements (9.4%) 
and corticosteroids (7.2%). High-alert medications were 
involved in 17.3% of  the cases. The most common routes 
of  administration were oral (49.7%) and IV (40.3%). 
Other medication characteristics are depicted in Table 3. 
Of  the total MEs, 47 (14.8%) reached the patient (PRMEs 
rate=14.8%, NCC MERP class CF), among which only 2 
(0.6%) were harmful and required intervention (PHMEs 
rate=0.6%, NCC MERP class EF) (Table 4).



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Table 2: Characterising Patterns of  Medication Errors
Error Characteristic Category Frequency Percentage
Location General Ward 139 43.7

Outpatient clinics 89 28.0
ED 34 10.7
Pediatric ICU 31 9.7
Neonatal ICU 23 7.2
Nursery 2 0.6

Stage Prescribing 258 81.1
Transcription 3 0.9
Dispensing 33 10.4
Administration 20 6.3
Monitoring 4 1.3

Type Improper dose 108 34.0
Wrong frequency 45 14.2
Wrong duration 33 10.4
Wrong strength 24 7.5
Wrong Medication 23 7.2
Wrong rate of  infusion 20 6.3
Wrong route 15 4.7
Duplication 15 4.7
Wrong dosage form 9 2.8
ContraindicationKnown allergy 7 2.2
Delayed dispensation 5 1.6
Expired Medication 4 1.3
Monitoring error 3 0.9
Wrong patient 1 0.3
Other § 6 1.9

Cumulative error No (only one medication error) 302 95.0
Yes (2 or more) 16 5.0

Days from prescription Same day 305 95.9
One day or more later 13 4.1

Person who discovered 
the error

Pharmacist 272 85.5
Physician 2 0.6
Nurse 43 13.5
Patient 1 0.3

Professional in cause Nurse 11 3.5
Pharmacist 35 11.0
Physician 265 83.3
Data missing 7 2.2

Position Junior 74 23.3
Senior 56 17.6
Data not available 188 59.1

§ Include damaged Medication (3), wrong instruction (2 cases), and trade name in prescription (1)



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Table 3: Characteristics of  the involved Medication
Parameter Category Frequency Percentage
Class Antibiotic 81 25.5

Supplements (vitamins, minerals, etc.) 30 9.4
Corticosteroids 23 7.2
Antihypertensive 20 6.3
Electrolyte 18 5.7
Analgesic 15 4.7
Anticonvulsant  Antiepileptic 13 4.1
Laxative 10 3.1
Anti vomiting 8 2.5
Psychotropics 8 2.5
Other § 92 28.9

Route of  administration Oral 158 49.7
IV 128 40.3
IM 7 2.2
Subcutaneous 9 2.8
Topical / Local 16 5.0
Other 7 2.2

Offlabel use No 307 96.5
Yes 11 3.5

Offlabel dose No 313 98.4
Yes 5 1.6

Offlabel route No 317 99.7
Yes 1 0.3

High alert No 263 82.7
Yes 55 17.3

§ Include 29 other medication classes involving 1 (0.3%) and 7 (2.2%) cases

Table 4: Distribution of  Medication Errors According to the National Coordinating Council for Medication Error 
Reporting and Prevention (NCC MERP) classification
NCC MERP category N %
The error did not reach a patient 271 85.2
A: Circumstances or events that have the capacity to cause the error. 42 13.2
B: An error occurred, but the error did not reach the patient 229 72.0
Error reached the patient 47 14.8
C: An error occurred that reached the patient but did not cause harm. 35 11.0
D: An error occurred that reached the patient and required monitoring to confirm that it resulted in 
no harm to the patient and/or required intervention to preclude harm.

10 3.1

E: An error occurred that may have contributed to or resulted in temporary harm to the patient and 
required intervention.

1 0.3

F: An error occurred that may have contributed to or resulted in temporary harm to the patient and 
required initial or prolonged hospitalisation.

1 0.3

Factors Contributing to MEs
A lack of  staff  education and training regarding ME was 
identified in most cases (90.2%). The next three most 
frequently identified factors included an absence of  

double-checking (66.8%), missing clinical information 
of  the patient (48.1%), and missing drug information 
(32.0%) (Figure 2).



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Factors Associated with PRME
PRMEs occurred at the same frequency in the three 
hospitals (P = 0.241). PRMEs rate was significantly 
higher in ICU (31.5% vs. up to 13.5%, P = 0.001) and 
in the case of  unconscious patients (38.6% vs. 11.1%, 
P < 0.001) and absence of  caregiver (29.2% vs. 8.8%, P 
< 0.001), compared to their counterparts respectively; 
and the rate increased with the number of  prescribed 
medications (P = 0.008). Expectedly, the later the stage 
of  ME occurrence, the more likely it reached the patient 
(P < 0.001), and PRMEs occurred less likely in MEs 
occurring on the day of  prescription of  the involved 

Medication (13.1% vs. 53.8%, P < 0.001). Further, MEs 
caused by nurses were more likely to be PRMEs (72.2% 
vs. up to 34.3%, P < 0.001), whereas those discovered 
by a pharmacist were less likely to be PRMEs (10.3% vs. 
39.5% or more, P < 0.001). High-alert medications were 
associated with a higher PRME rate (32.7% vs. 11.1%, 
P < 0.001). Three among the eight contributing factors 
increased the PRMEs’ risk significantly, including drug 
name, label, filling, and package problem (39.5% vs. 
11.4%, P < 0.001), staffing shortage and workload (29.4% 
vs. 13.0%, P = 0.011), and lack of  doublechecking (20.3% 
vs. 3.8%, P < 0.001) (Table 5).

Figure 2: Contributing Factors in Medication Errors

Table 5: Factors Associated with Medication Errors Reaching the Patient
Factor Category Error reached the patient P-value

No Yes
N % N %

Center/Department factors
Center CHT 83 89.2 10 10.8

KAMC 78 86.7 12 13.3
KFSH 110 81.5 25 18.5 .241

Hospital type Children 83 89.2 10 10.8
Tertiary 187 83.6 37 16.4 .193

Department General Ward 122 86.5 19 13.5
ICU 37 68.5 17 31.5
ED 33 97.1 1 2.9
Outpatient 79 88.8 10 11.2 .001*

Patient-related factors
Age 030 days 5 55.6 4 44.4

31 days - 2 years 100 83.3 20 16.7
2-6 years 64 88.9 8 11.1
6-12 years 98 86.7 15 13.3 .057

Gender Male 159 87.4 23 12.6
Female 108 81.8 24 18.2 .277



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Weight <5 kg 0 0.0 1 100.0
5 10 kg 111 84.1 21 15.9
1120 kg 86 85.1 15 14.9
>20 kg 69 87.3 10 12.7 .107

Cancer No 237 84.9 42 15.1
Yes 34 87.2 5 12.8 1.000F

Haematological disease No 236 84.9 42 15.1
Yes 30 85.7 5 14.3 1.000F

Mental disability No 248 85.5 42 14.5
Yes 18 78.3 5 21.7 .362F

Other chronic condition No 112 87.5 16 12.5
Yes 153 83.2 31 16.8 .291

Consciousness state Conscious 240 88.9 30 11.1
Unconscious 27 61.4 17 38.6 <.001*

Number of  medications 13 78 90.7 8 9.3
46 133 86.9 20 13.1
7+ 54 74.0 19 26.0 .008*

Presence of  caregiver No 68 70.8 28 29.2
Yes 196 91.2 19 8.8 <.001*

Factor related to the medication error
Stage Prescribing 235 91.1 23 8.9

Transcription 3 100.0 0 0.0
Dispensing 24 72.2 9 27.3
Administration 7 35.0 13 65.0
Monitoring 2 50.0 2 50.0 <.001*

Cumulative error No 259 85.8 43 14.2
Yes (2 or more) 12 75.0 4 25.0 .270

Days from prescription Same day 265 68.9 40 13.1
One day or more later 6 46.2 7 53.8 <.001*

Person who discovered the 
error

Pharmacist 244 89.7 28 10.3
Physician 0 0.0 2 100.0
Nurse 26 60.5 17 39.5
Patient 1 100.0 0 0.0 <.001*

Professional in cause Nurse 3 27.3 8 72.7
Pharmacist 23 65.7 12 34.3
Physician 240 90.6 25 9.4 <.001*

Position Junior 67 90.5 7 9.5
Senior 46 82.1 10 17.9
Data not available 151 84.4 28 15.6 .332

Factors Associated with the Medication
Route of  administration Oral 136 86.1 22 13.9

Parenteral (IV, IM, s/c) 121 84.0 23 16.0
Topical / Local 14 87.5 2 12.5 .852

High alert No 234 89.0 29 11.0
Yes 37 67.3 18 32.7 <.001*

Medication duration Temporary 188 85.5 32 14.5
Chronic 78 83.9 15 16.1 .720



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Contributing factors
Factor 1 No 146 88.0 20 12.0

Yes 125 82.2 27 17.8 .151
Factor 2 No 187 86.6 29 13.4

Yes 84 82.4 18 17.6 .322
Factor 3 No 246 86.3 39 13.7

Yes 24 75.0 8 25.0 .088
Factor 4 No 248 88.6 32 11.4

Yes 23 60.5 15 39.5 <.001*
Factor 5 No 247 87.0 37 13.0

Yes 24 70.6 10 29.4 .011*
Factor 6 No 28 90.3 3 9.7

Yes 242 84.6 44 15.4 .396
Factor 7 No 260 85.8 43 14.2

Yes 11 73.3 4 26.7 .252F
Factor 8 No 101 96.2 4 3.8

Yes 169 79.7 43 20.3 <.001*

Factor 1: Clinical information missing; 
Factor 2: Drug information missing; 
Factor 3: Lookalike and sound-alike medication; 
Factor 4: Drug name, label, filling, and package problem; 
Factor 5: Staffing shortage and workload problem; 
Factor 6: Lack of  staff  education and training problem
Factor 7: Patient education problem; 
Factor 8: No double-check.

The test used: F Fisher’s exact test; otherwise, the chi-
square test was used.
* Statistically significant result
Inappropriate drug labelling or packaging (OR=3.13, 
p=0.042) and a lack of  double-checking (OR=5.49, 
p=0.016) were independently linked with the incidence 
of  PRME, according to multivariate regression analysis 
(Table 6). 

Table 6: Predictors of  Medication Errors Reaching the Patient
Predictor Level OR 95%CI P-Value
Department General Ward Ref .193

ICU 0.35 0.07 1.65 .183
ED 0.37 0.04 3.31 .374
Outpatient 2.01 0.65 6.26 .227

Consciousness state Conscious Ref
Unconscious 2.95 0.67 13.07 .155

Number of  medications 13 Ref .970
46 0.90 0.31 2.65 .852
7+ 0.99 0.27 3.71 .994

Presence of  caregiver No Ref
Yes 0.39 0.13 1.16 .090

Days from prescription Same day Ref
One day or more later 12.43 2.15 71.70 .005*

Professional in cause Nurse 7.71 1.37 43.49 .021
Pharmacist 1.08 0.34 3.46 .892
Physician Ref  .061

High alert No Ref
Yes 2.46 0.95 6.38 .064

Factor 4 No Ref
Yes 3.13 1.04 9.41 .042*



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Factor 5 No Ref
Yes 1.55 0.50 4.87 .450

Factor 8 No Ref
Yes 5.49 1.37 21.94 .016*

Multivariate Binary Logistic Regression. 
Dependent factor: patient-reaching medication error 
(yes).
OR: Odds ratio; 
95% CI: 95% confidence interval
Ref: Category used as a reference in the calculation of  
OR
Factor 4: Drug name, label, filling, and package problem
Factor 5: Staffing shortage and workload problem
Factor 8: No double check
* Statistically significant result 

DISCUSSION
MEs in the pediatric population constitute a significant 
health issue. They comprise a high risk of  iatrogenic 
injury, which exposes increased morbidity, mortality, 
health expenditure, and legal issues. In the current 
multicenter investigation, we examined the trends and 
characteristics of  all MEs reports from the two tertiary 
hospitals and the Children’s Hospital in Saudi Arabia’s 
Western area over the course of  a year. Findings showed 
that ME Prevalence ranged between 0.35 and 3.5 /1,000 
patients, depending on the hospital, and between 0.14 and 
14.7 / 1,000 patients, depending on the department. Most 
MEs consisted of  prescribing errors made by doctors and 
reported by the pharmacist.
Nevertheless, 14.8% of  the MEs reached the patient 
was predicted by several risk factors, notably earlier 
prescription date, absence of  doublechecking, and 
inappropriate drug labelling or package, with a 3 to 12-
fold risk of  occurrence. On the other hand, the rate of  
PHMEs was very low (0.6%), not enabling inferential 
analysis.

The Prevalence of  MEs – Rates and Calculation 
Methods
Various calculation methods have been used to estimate 
the Prevalence of  MEs in literature, making the 
comparative analysis difficult across different studies and 
settings. The Prevalence of  MEs was determined in the 
current study by considering all hospital admissions or 
visits during the same period, depending on whether the 
ME occurred in an inpatient or outpatient environment. 
This calculation method adjusts for the patient flow in 
the given hospital or department while disregarding the 
number of  medication orders. 
A systematic review by Alghamdi et al. found that, in 
every 100 medication orders, approximately 15 MEs were 
reported in pediatric intensive care units and up to 78 
in neonatal ICU (Santesteban et al., 2015). Prescription 
mistakes were observed in between 5.7% and 35.2% 
of  prescriptions or medicine orders for newborn care, 

according to three distinct studies, according to a second 
comprehensive analysis by Santesteban et al.. Another study 
that assessed the efficacy of  a pharmacy-led intervention 
to reduce MEs in the pediatric ward of  a Maternity 
and Children’s Hospital, focusing on prescribing errors, 
found that an intervention was necessary for 10% of  the 
medical orders (Jennifer L Costello 1, 2007). The rates 
reported in the previously mentioned studies, including 
the two systematic reviews, can be considered consistent 
with each other, highlighting an alarming frequency of  
MEs. These figures seem to be less reflected when using 
the number of  hospital admissions as a denominator, 
such as in the present study. 
Another calculation method that is reported in the 
literature uses the patient’s parameter as the denominator. 
In the comprehensive systematic review by Alghamdi 
et al., four studies-two in the PICU and two in the 
NICU-used this calculating approach and found that 
the prevalence rates of  ME were, respectively, 6.4 to 9.1 
and 4 to 35.1 per 1,000 patient days (Santesteban et al., 
2015). Compared to the 5.1 to 40.1 per 1,000 hospital 
admissions rates found in in-patient departments in the 
present study, the prevalence rates reported in Alghamdi 
et el. systematic reviews are likely to be considerably 
high; this is by assuming that patient days are higher than 
the raw number of  hospital admissions. Although such 
a comparison is inaccurate due to dissimilar calculation 
methods, this appearing discrepancy may indicate a 
probable underreporting of  MEs in our study population. 
In an intriguing method, Costello et al. demonstrated that 
underreporting of  MEs is a significant limitation of  the 
voluntary reporting strategy in an exciting approach. A 
pharmacist-led intervention would considerably enhance 
reporting of  MEs while decreasing the severity of  the 
errors reported (Dedefo et al., 2016).
Other calculation methods were used in reporting MEs. 
For example, a study by Morriss et al. reported MEs 
prevalence in NICU regarding medication doses and 
found 69.5 MEs per 1,000 doses (Izadpanah et al., 2018); 
such a method may be more applicable for dispensing 
errors. Based on nurses‘ self-reports, another study from 
Iran estimated the number of  MEs involving nurses 
during the past month in pediatric emergency wards. The 
authors found that nurses caused an average of  ~42 cases 
of  MEs in the studied month (Emily Beth Devine 1 & 
Kerm Henriksen 1, 2005). Along with this variation in 
calculating techniques, Alghamdi et al. also emphasised 
a serious lack of  uniformity in the definition of  MEs 
used by various research, with several of  them employing 
definitions that were created locally (Santesteban et al., 
2015). Such heterogeneity in MEs’ definition and reporting 
systems and the calculation method of  MEs prevalence 



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hinders the comparisons between the different studies 
and settings. This indicates the need for a standardised 
practice using evidence-based, homogenous strategies. 

Types and Patterns of  MEs
The current investigation showed doctors were the most 
frequently implicated in MEs, with prescription mistakes 
accounting for over 80% of  cases. Second, nurses were 
engaged in 13.5% of  the instances, particularly while 
giving medicine, which accounted for 10.4% of  all MEs. 
Hence, the study revealed that the most common type of  
MEs was improper medication dose, strength, frequency, 
or duration, probably indicating the challenging aspect 
of  the prescribing practice in the pediatric population. 
Given the frequency of  prescribing errors in the pediatric 
population, several authors were concerned about 
studying this particular type of  ME separately in pediatric 
settings. 
Further, the authors reported that dosing errors were 
the most commonly reported subtype (Santesteban et al., 
2015). A study from South Africa consistently showed 
that the leading cause of  ME in children was dose error, 
especially in intensive care, as this category of  patients 
requires an individual description of  drugs according 
to age, body, and weight. Medicines that increased the 
risk of  error were antibiotics, sedatives, analgesics, 
bronchodilators, and cardiovascular medications 
(Khayam, 2015). A Saudi study in the general pediatric 
ward and PICU of  King Abdulaziz Medical City, Riyadh, 
found that 56% of  the medication orders contained 
prescribing errors, and 79% were classified as harmful. 
Additionally, dose and frequency errors accounted for 
approximately 50% of  the cases, and an overdose was 
detected in 18.7% of  the prescribing errors. 
Conversely, a study conducted in neonatal care settings 
in the Tertiary Care Hospital of  South India showed 
significantly higher rates of  administration errors reported 
in some of  the included studies, accounting for up to 
77% of  MEs (B Prakadeesh Bharathi, 2020). In contrast, 
other studies reported relatively higher rates of  drug 
preparation errors (Morriss et al., 2009). In sum, MEs can 
happen at any point throughout the administration of  a 
medicine, from prescription to administration and follow-
up, and drug interactions can even cause them. Thus, all 
healthcare professionals are responsible for detecting and 
prospectively managing the factors causing MEs when 
dealing with a patient (Abuelsoud, 2019).

Identification and Prevention of  MEs Risk 
The identification of  the associated risk factors 
constitutes a critical step in the prevention of  MEs. As 
revealed in the current study, among the predominantly 
identified factors is the lack of  staff  education and 
training regarding MEs, found in 90.2% of  the cases. 
This was followed by an absence of  doublechecking 
by the involved healthcare professional, accounting for 
two-thirds of  the cases. Both issues can be corrected 
using education and awareness interventions targeting 

the concerned health professionals. Several factors for 
MEs are highlighted in the literature, such as multiple 
medications and extended stays in the hospital, which 
increase the risk of  both MEs and drug interactions 
(Antonucci & Porcella, 2014). Further, neonatal care 
settings, notably NICU, are reportedly more exposed to 
MEs than other pediatric settings (B Prakadeesh Bharathi, 
2020). Another study in Us revealed that Physicians were 
had an increased likelihood of  major medical errors due 
to poor well-being, burn-out and low work unit safety 
grades (Tawfik et al., 2018).  
On the other hand, the current study has the particularity 
to have analysed factors associated with PRME. 
Finding such variables may be crucial for lowering the 
incidence and severity of  MEs and related morbidity and 
mortality. Among these factors, we highlight the patient’s 
consciousness status and the caregiver’s presence or 
absence. The clinical significance of  these two patient-
related factors reflects the potential contribution of  
the patients or their caregivers in managing the risks of  
MEs. The adjusted regression model highlighted three 
other factors: the absence of  doublechecking, which was 
demonstrated to increase the risk of  PRME by 5.5 times 
in an independent fashion, and inadequate drug labelling 
and packaging, which increased the same risk by 3.1 times. 
This demonstrates the importance of  enhancing the 
standards of  routine practice among health professionals 
regarding medication use. 
Prevention of  medication errors should be set in health 
authorities and institutions. Of  the several efforts 
undertaken to reduce ME rates, a significant focus is 
on using information technology to mitigate the human 
factor most commonly underlying MEs (Dedefo et al., 
2016; Fernández-Llamazares et al., 2012; Khaemba, 2014). 
Another encouraged strategy is to increase the number of  
clinical pharmacists and enhance their training, as these 
constitute physicians’ backup in ensuring the medical 
prescription’s safety and adequacy (Devine EB, 2005). 
Reports showed that the role of  clinical pharmacists in 
reducing MEs is prominent, owing to their crucial role 
in identifying issues related to the safe use of  drugs and 
the potential health problems that arise from the misuse 
(Dedefo et al., 2016). From a larger perspective, reducing 
MEs requires setting high-level caution at all stages of  the 
mediation process, such as packaging, labelling, description, 
copying, dispensing, and administration in the internal and 
external patient settings. Particular attention should be 
drawn to managing home medication, which increases the 
risk of  human error by involving a third party, including 
parents and caregivers. Hence, physicians should assess 
the family environment before prescribing for children to 
ensure correct administration (Hussain et al., 2014).

STRENGTHS AND LIMITATIONS
The present study is limited by the reliance on a voluntary 
ME reporting system, resulting in under-reporting and 
underestimating ME prevalence rates. The study also 
overlooked potential measures for assessing and improving 



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ME reporting practises, which may differ between centres 
and departments. Further, the retrospective design made 
collecting relevant data a challenging task, notably the 
assessment of  the contributing factors. Nevertheless, the 
present study has the advantage of  being conducted in 
three referral centres and including all MEs reported in the 
study year. It supports the good external validity of  the 
findings and provides a reliable baseline figure for further 
interventional studies in any of  the three included centres.

CONCLUSION
Paediatric inpatient care has a significant frequency of  
MEs, especially in tertiary care facilities, which account for 
24% of  hospital admissions. Implementing a systematic 
doublecheck of  prescriptions, including recent and older 
ones, and improving drug labelling and packaging methods 
may prevent many MEs. The adequate education of  health 
professionals and caregivers regarding MEs should be 
considered a high-priority tool to improve medication safety 
among pediatric patients. Further corrective measures and 
awareness-raising campaigns should be implemented in all 
referral hospitals to modify the risk factors and improve 
medication practice at all stages.

Abbreviations
Children’s Hospital in Taif  (CHT), Confidence Interval 
(CI), Emergency Department (ED), Factor (F), 
Intramuscular (IM), Intravenous (IV), Intensive Care 
Unit (ICU), Institutional Review Board (IRB), Kingdom 
of  Saudi Arabia (KSA), King Abdul-Aziz Medical City 
(KAMC), King Faisal Specialist Hospital and Research 
Center (KFSH&RC), Medication Error (ME), The National 
Coordinating Council for Medication Error Reporting and 
Prevention (NCCMERP), Neonatal Intensive Care Unit 
(NICU), Odds Ratio (OR), Patient Harm medication error 
(PH-ME), Pediatric Intensive Care Unit (PICU), Patient 
Reaching medication error (PR-ME), Subcutaneous (S/C), 
Statistical Package for the Social Sciences (SPSS) 

Funding Source
The Author (s) received no financial support for the 
research, authorship, and/or publication of  this article.
Ethical approval was obtained from the Institutional Review 
Board of  KFSH, KAIMARC and an official approval letter 
from the Director of  Health Affairs in TAIF.

Acknowledgement
Quality department staff  at King Faisal Specialist Hospital 
and Research Center-Jeddah, King Abdul-Aziz Medical 
City-Jeddah and Children’s Hospital-Taif. Presented at 
ASHP Mid-year on December 7th, 2021 and at Pediatric 
Pharmacy Association on May 4th, 2022.
 
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