Background

JMIR Pediatr Parent

pediatrics

JMIR Pediatrics and Parenting

JMIR Pediatr Parent

2561-6722

JMIR Publications

Toronto, Canada

v9i1e100070

10.2196/100070

Review

Global Prevalence and Associated Factors of Medication Errors in Hospitalized Pediatric Patients: Systematic Review and Meta-Analysis

Chen

Xiuwen

BM, MM1234*Meng

Yuan

BM14*Wei

Xueyi

BM, MM34Li

Ailian

BM, MM4He

Jiqun

BM, MM4Yue

Liqing

BM, MM, DM24

Xiangya School of Nursing, Central South University

Changsha

Hunan

ChinaNational Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University

Changsha

Hunan

ChinaXiangya Research Center of Evidence-based Healthcare, Central South University

Changsha

Hunan

ChinaTeaching and Research Section of Clinical Nursing, Xiangya Hospital Central South University

No. 87 Xiangya Road, Kaifu District

Changsha

Hunan

China

Badawy

Sherif

Chan

Chi Ming

Zhou

Le-Shan

Correspondence to Liqing Yue, BM, MM, DM, Teaching and Research Section of Clinical Nursing, Xiangya Hospital Central South University, No. 87 Xiangya Road, Kaifu District, Changsha, Hunan, 410028, China, 86 073184327394; ylq6998@163.com*

these authors contributed equally

2026

2782026

e100070

020520261007202610082026

2026

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Pediatrics and Parenting, is properly cited. The complete bibliographic information, a link to the original publication on https://pediatrics.jmir.org, as well as this copyright and license information must be included.

Background

Medication errors represent a major patient safety concern among hospitalized children due to developmental variability, weight-based dosing, and limited communication capacity. However, the global burden and determinants of pediatric inpatient medication errors remain insufficiently characterized.

Objective

This study aims to estimate the worldwide prevalence of medication errors in hospitalized pediatric patients and to identify factors associated with their occurrence.

Methods

A systematic review and meta-analysis was conducted using Embase, MEDLINE, Web of Science, PubMed, and the Cochrane Library from database inception to June 2025. Observational studies involving pediatric inpatients younger than 18 years were included. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Data were pooled using a DerSimonian-Laird random-effects model, with subgroup and sensitivity analyses performed to explore heterogeneity.

Results

A total of 61 studies from 29 countries or regions were included. The pooled prescribing error rate was 28%, the pooled administration error rate was 32%, and 54% of the hospitalized pediatric patients experienced at least one medication error. Prescribing error rates were higher in lower-middle–income countries (44%), intensive care units (34%), antibiotic prescriptions (30%), and studies conducted between 2013 and 2025 (32%). The proportion of patients experiencing medication errors was the highest in upper-middle–income countries (64%), whereas administration error rates were the highest in low-income countries (71%), although these subgroup findings should be interpreted cautiously because of limited numbers of studies and wide CIs. Factors associated with medication errors included intravenous administration (odds ratio [OR] 6.86, 95% CI 3.17‐14.83), hospital stay longer than 5 days (OR 1.94, 95% CI 1.33‐2.81), and prescription of 3 or more medications (OR 2.12, 95% CI 1.66‐2.70).

Conclusions

Medication errors are highly prevalent among hospitalized children worldwide, with substantial variation by region, income level, care setting, and time period. Errors are closely associated with treatment-related factors, underscoring the need for targeted system-level strategies to improve pediatric medication safety.

medication errorspediatric patientshospitalized childrenmeta-analysisprevalenceassociated factorssystematic review

Introduction

Medication errors have become a major public health issue threatening patient safety worldwide [1] and are particularly prominent in pediatric health care settings [2]. Children and neonates are reported to be up to 3 times more likely to experience medication errors than adults [3,4]. Due to the limited availability of pediatric-specific formulations, adult medications often require dose calculations, manipulation, or reconstitution to meet individualized pediatric treatment needs [5,6]. In addition, factors such as complex dose calculations [7], age-related differences in drug metabolism [8], the use of unlicensed or off-label medications [8], and children’s limited ability to communicate symptoms and treatment-related concerns [9] may further increase the risk of medication errors. These errors may lead to adverse drug events, prolonged hospitalization, increased health care costs, and even irreversible harm or death [10]. Medication errors impose a substantial burden on health care systems worldwide. In the United States, medication errors are associated with annual costs exceeding US $29 billion, including an estimated additional US $28 million in hospital costs each year [11,12]. In the United Kingdom, medication errors result in economic losses of £9,846,258 annually for the National Health Service and consume a considerable number of bed-days [13]. The World Health Organization has estimated that medication errors cost approximately US $42 billion globally each year [14].

The National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) [15] defines a medication error as any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of a health care professional, patient, or consumer. These events may be related to professional practice, health care products, procedures, or systems and may involve prescribing, order communication, product labeling, packaging, nomenclature, compounding, dispensing, administration, education, monitoring, and use. Medication errors thus span multiple stages of the medication-use process. Prior studies [16] suggest that most medication errors in children occur during the prescription, dispensing, and administration stages. Among them, prescription errors account for 3% to 37% of children’s medication errors, dispensing errors account for 5% to 58%, and administration errors account for 72% to 75% of children’s medication errors [17].

Although numerous studies in recent years have examined the epidemiology of pediatric medication errors [18-20], substantial methodological heterogeneity persists. Inconsistencies in the operational definitions of medication errors, variation in data collection methods, diversity of study settings, and differences in denominators have resulted in wide variability in reported prevalence, limiting comparability across studies. Gates et al [21] stratified pooled estimates of medication error prevalence in hospitalized children by ward type and use of health information technology, but their work focused solely on quantitative synthesis of prevalence without systematically addressing associated factors. This lack of root cause analysis limits the ability to develop efficient and targeted prevention strategies in clinical practice.

To address these gaps, we conducted a systematic review and meta-analysis with two primary objectives: (1) to evaluate the occurrence of medication errors among hospitalized pediatric patients worldwide and (2) to identify factors associated with medication errors in hospitalized children. By consolidating epidemiological evidence, our study aims to support the development of more precise and evidence-based strategies for the prevention of pediatric inpatient medication errors.

MethodsSearch Strategy and Study Selection

The systematic review and meta-analysis was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement (Checklist 1). A comprehensive search was conducted in Embase, Web of Science, the Cochrane Library, MEDLINE, and PubMed from database inception to June 2025. A combination of MeSH terms and free-text terms was used, including “children,” “hospitalized,” and “medication errors.” Detailed search strategies for each database are provided in Multimedia Appendix 1. This study was registered with PROSPERO (registration number: CRD42024613159).

Inclusion criteria were (1) hospitalized pediatric patients aged less than 18 years, including those in general pediatric wards, pediatric intensive care units (PICUs), and neonatal intensive care units (NICUs); (2) studies reporting the prevalence of medication errors or investigating associated factors; and (3) observational studies (cross-sectional, cohort, or case-control).

Exclusion criteria were (1) studies focusing exclusively on specific patient populations (eg, oncology patients) or specific drug categories; (2) studies based solely on voluntary reporting systems, as these systems rely on active reporting by health care professionals and may underestimate the true prevalence of medication errors; however, studies that combined voluntary reporting with active surveillance methods, such as direct observation, medical record review, or other systematic detection approaches, were included; (3) non-English publications; and (4) conference abstracts, posters, studies with insufficient data, or studies for which the full text was unavailable.

Data Extraction and Quality Assessment

Two investigators independently extracted data using Microsoft Excel, including first author, publication year, country or region, study design, ward type, study period, age range or mean age (SD) of included children, data collection methods, source of medication error definition, prevalence of medication errors (%), and associated factors. Discrepancies were resolved by a third investigator. For overlapping datasets, the study with the larger sample size was prioritized.

Cohort and case-control studies were assessed using the Newcastle-Ottawa Scale (NOS), with scores of 0 to 3, 4 to 6, and 7 to 9 indicating low, moderate, and high quality, respectively [22]. Cross-sectional studies were evaluated using the Agency for Healthcare Research and Quality (AHRQ) criteria, with scores of 0 to 3, 4 to 7, and 8 to 11 indicating low, moderate, and high quality, respectively [23]. Studies of all quality levels were included to capture all available evidence in the field.

Outcome Definitions

This study initially planned to extract data on various types of medication errors, including prescribing errors, dispensing errors, and administration errors. However, because some error types were reported in only a limited number of studies and exhibited substantial variability in reporting methods, meta-analyses were conducted only for outcomes that met the criteria for quantitative synthesis, namely the proportion of patients experiencing medication errors, prescribing error rates, and administration error rates.

To avoid misinterpretation arising from differences in units of analysis, the outcome measures were defined as follows. The prescribing error rate was defined as the proportion of prescriptions containing at least one medication error among all prescriptions, reflecting the occurrence of medication errors during the prescribing process. The administration error rate was defined as the proportion of medication administrations involving at least 1 medication error among all medication administrations, reflecting the occurrence of medication errors during the medication administration process. The proportion of patients experiencing medication errors was defined as the proportion of patients who experienced at least one medication error during the observation period among all patients, reflecting the overall burden of medication errors among hospitalized pediatric patients. Because these outcomes were calculated using different units of analysis (prescriptions, medication administrations, and patients), their estimates represent different aspects of medication error occurrence and should not be directly compared.

Statistical Analysis

All statistical analyses were performed using Stata (version 17.0). Neither the Freeman-Tukey double arcsine transformation nor the logit transformation was applied to the original proportions. For the meta-analysis of medication error occurrence among hospitalized pediatric patients, the effect size was the medication error rate. For the meta-analysis of factors associated with medication errors, the effect size was expressed as the odds ratio (OR) with its corresponding 95% CI. Given the substantial heterogeneity among the included studies (I²> 50% and P<.05), pooled estimates were calculated using the DerSimonian-Laird random-effects model. Subgroup analyses were conducted according to relevant study characteristics and associated factors to explore potential sources of heterogeneity. To assess the robustness of the findings, several sensitivity analyses were performed. A leave-one-out analysis was conducted to evaluate the influence of individual studies on the pooled estimates. In addition, restricted maximum likelihood (REML) estimation was applied within the random-effects framework to examine the impact of different τ² estimation methods on the pooled results. Publication bias was assessed using both Egger’s test and visual inspection of funnel plot symmetry. All statistical tests were 2-sided, and P<.05 was considered statistically significant.

ResultsLiterature Search

A total of 6904 records were identified through searches of Embase, Web of Science, the Cochrane Library, MEDLINE, and PubMed. After removing 1787 duplicates, 4950 records were excluded based on titles and abstracts, and 7 reports were excluded because the full text was not available. Following full-text review, 99 articles were further excluded for not meeting the inclusion criteria, leaving 61 studies for analysis. The study selection process is summarized in Figure 1.

Figure 1.

Flow diagram of the study selection process.

Characteristics and Quality Assessment of the Included Studies

Details of the included studies are provided in Table 1, with sample sizes ranging from 20 to 8691 participants. The studies were conducted across 29 countries or regions, including 14 high-income, 8 upper-middle–income, 4 lower-middle–income, and 3 low-income countries. Ward types included general pediatric wards, PICUs, and NICUs. Among the included studies, 35 were cross-sectional, with AHRQ scores ranging from 4 to 7 (Table S1 in Multimedia Appendix 2), and 26 were cohort studies, with NOS scores ranging from 3 to 9 (Table S2 in Multimedia Appendix 2). To ensure that all available data were used to estimate medication error rates, no studies were excluded based on methodological quality; all original studies reporting medication errors were included.

The types of medication errors reported in the included studies included prescribing errors, administration errors, dispensing errors, and medication reconstitution errors. However, because only a limited number of studies reported on dispensing errors and medication reconstitution errors, and substantial variability existed in outcome definitions and reporting methods, reliable quantitative synthesis was not feasible for these error types. Therefore, meta-analyses were conducted only for the proportion of patients experiencing medication errors, prescribing error rates, and administration error rates among hospitalized pediatric patients.

Table 1.

Basic characteristics of included studies.

First author (year)	Country orregion	Study design	Ward type	Study period	Age range or mean (SD)^a	Data collection method	Source of medication error definition	Medication error rate	Associated factors
Canales-Siguero et al [24], 2025	Spain	Cross-sectional	NICU^b	2021‐2022	34 weeks (median, IQR 30‐39)	Medical record review	Reference	Prescription errors: 3.3%	Birth weight, gestational age, ward occupancy
Henry Basil et al [25], 2025	Malaysia	Cohort	NICU	2022‐2023	35 weeks (median, IQR 30‐39)	Direct observation	Reference	Administration errors: 68.0%; proportion of patients experiencing medication errors: 92.4%	Medications administered intravenously, unavailability of a protocol, the number of prescribed medications, nursing experience, nonventilated neonates, and gestational age in weeks
Badgery-Parker et al [26], 2024	Australia	Cohort	Medical, surgical, and day-stay wards	2016‐2020	0‐18 years (range^c)	Direct observation; medical record review	NR^d	Prescription errors: 18.6%; administration errors: 36.2%	Age
Westbrook et al [27], 2024	Australia	Cohort	General pediatric, medical, and surgical wards	22 weeks	0‐16 years (range)	Direct observation; medical record review	Reference	Administration errors: 37.0%	Intravenous route, early morning and weekend administrations, patient age ≥11 years, oral medications requiring solvents or diluents, and eMM^e use
Abiri et al [28], 2024	Sierra Leone	Cross-sectional	Pediatric ward	2021	0‐18 years (range)	Medical record review	NCC MERP^f	Proportion of patients experiencing medication errors: 56.1%	NR
Kwaku Wuni et al [29], 2024	Ghana	Cross-sectional	Pediatric and the newborn care units	2022	1.37 years (mean, SD 0.83)	Questionnaire survey	Reference	Proportion of patients experiencing medication errors: 65.9%	Ward type
Satir et al [30], 2023	Switzerland	Cohort	Pediatric ward	2018‐2019	0‐18 years (range)	Medical record review	Reference	Prescription errors: 49.55%	NR
Bante et al [31], 2023	Ethiopia	Cross-sectional	Pediatric ward	2020‐2021	≤28 days to ≥10 years (range)	Questionnaire survey	NCC MERP	Proportion of patients experiencing medication errors: 69.5%	Work environment, child weight, education level of medication provider, parental involvement, adherence to administration authority, length of hospital stay
Xavier et al [32], 2022	Mozambique	Cross-sectional	Pediatric ward	2020	0‐10 years (range)	Medical record review	WHO^g, BNFC^h, NICEⁱ	Prescription errors: 34.9%; proportion of patients experiencing medication errors: 36.5%	Number of prescribed medications, length of hospital stay
Akkawi et al [33], 2022	Malaysia	Cross-sectional	Pediatric ward	2019	0‐12 years (range)	Medical record review	National Antibiotic Guideline	Proportion of patients experiencing medication errors: 57.9%; prescription errors: 27.9%	Infection type, number of antibiotics used
Garedow et al [34], 2022	Ethiopia	Cross-sectional	Pediatric ward	2021	0‐18 years (range)	Medical record review	NR	Prescription errors: 19.29%	Length of hospital stay, number of prescribed medications, empirical therapy
Nasso et al [35], 2022	Italy	Cohort	Pediatrics, pediatric nephrology, and pediatric rheumatology	2019	0‐18 years (range)	Medical record review	WHO	Prescription errors: 56.0%	Ward type
Alekaw et al [36], 2022	Ethiopia	Cross-sectional	Pediatric ward	2020‐2021	1.96 years (mean, SD 3.48)	Medical record review	NR	Proportion of patients experiencing medication errors: 30.8%	Age, parental negligence, residence
Özdemir et al [37], 2021	Turkey	Cross-sectional	Pediatric ward	2016	1-226 months (range)	Database; medical record review	Reference	Proportion of patients experiencing medication errors: 40.4%	NR
Abdel-Qader et al [38], 2021	Jordan	Cross-sectional	NICU, PICU, general pediatrics, surgical ICU, pediatric surgery	2019	2.1 years (mean)	Medical record review	NR	Prescription errors: 31.5%	Number of prescribed medications
Kadmon et al [39], 2021	Israel	Cohort	PICU^j	2015‐2016	6.5 years (mean)	Database	NR	Prescription errors: 1.6%	Age, disease severity
Yehualaw et al [40], 2021	Ethiopia	Cross-sectional	Pediatric ward	2018‐2019	3.26 years (median, IQR 2‐4)	Medical record review	NR	Proportion of patients experiencing medication errors: 28.3%	Age
Bharathi et al [41], 2020	India	Cohort	NICU	2016	NR	Voluntary reporting; interview; medical record review	Reference	Proportion of patients experiencing medication errors: 22%	Polypharmacy, length of hospital stay
Brennan-Bourdon et al [42], 2020	México	Cross-sectional	PEC^k, NICU, NIMCU, PICU	2017‐2018	0‐18 years (range)	Medical record review	NCC MERP	Prescription errors: 53.5%	NR
Howlett et al [43], 2020	Ireland	Cross-sectional	PICU	2017	0‐16 years (range)	Bedside direct observation; electronic prescription system	NCC MERP	Administration errors: 13.0%	NR
McMullan et al [44], 2020	Australia	Cross-sectional	Pediatric ward	2014‐2017	0‐18 years (range)	Standardized online audit tool	NR	Prescription errors: 19.6%	Admission to nontertiary pediatric hospital, hospital location in nonmajor city
Bonafide et al [45], 2020	United States	Cohort	PICU	2016‐2017	<6 months to ≥18 years (range)	Telecommunications and electronic health record data	Investigator-defined	Administration errors: 3.2%	Mobile phone interruptions, nursing experience, nurse-to-patient ratio, patient care level
Ramírez-Camacho et al [46], 2020	México	Cross-sectional	NICU	NR	NR	Direct observation	NCC MERP	Administration errors: 34.6%	NR
Eslami et al [47], 2019	Iran	Cross-sectional	NICU	2016	NR	Medical record review	NCC MERP	Proportion of patients experiencing medication errors: 74.8%; prescription errors: 42.0%	Gestational age, length of hospital stay
Fekadu et al [48], 2019	Ethiopia	Cross-sectional	Pediatric ward	2017	1 month to 12 years (range)	Medical record and medication chart review	NR	Proportion of patients experiencing medication errors: 68.0%	Critical illness, administration route, number of prescribed medications
Palmero et al [49], 2019	Switzerland	Cross-sectional	NICU	2010-2012	33.4 weeks (mean)	Direct observation	NCC MERP	Patient-related errors: 84.8%	Number of prescribed medications, gestational age
Tang et al [50], 2018	Malaysia	Cross-sectional	Pediatric ward	NR	0.1-16.1 years (range)	Direct observation	ASHP^l	Prescription errors: 1.5%; administration errors: 10.4%	Intravenous formulations, gastrointestinal medications
Kadam et al [51], 2018	India	Cross-sectional	NICU	2013	NR	Medical record review	ASHP	Prescription errors: 65.6%	NR
Ergül et al [52], 2018	Turkey	Cross-sectional	ICU, hematology, infectious diseases, pediatric ward	2017	10‐80 months (range)	Medical record review	NR	Proportion of patients experiencing medication errors: 33.8%	NR
Baraki et al [53], 2018	Ethiopia	Cross-sectional	Pediatric ward, PICU and NICU	2016‐2017	1 day to 14 years (range)	Structured questionnaire; observation	WHO and NCC MERP	Administration errors: 62.3%	Patient age, education level of administering health care provider, availability of pharmacy preparation room, number of prescribed medications, availability of medication administration guidelines
Rishoej et al [54], 2018	Denmark	Cross-sectional	NICU, PICU and pediatric ward	2016	0‐16 years (range)	Unobtrusive direct observation; on-site recording	NCC MERP	Administration errors: 8.0%	NR
Truter et al [55], 2017	South Africa	Cross-sectional	Neonatal and pediatric ward	6 months	1 month to 19 years (range)	Direct observation; medical record and prescription review; medication error checklist	NCC MERP	Proportion of patients experiencing medication errors: 78.0%	NR
Al-Ramahi et al [56], 2017	Palestine	Cohort	Pediatric ward	2014	1‐182 months (range)	Medical record review	Drug information handbook	Prescription errors: 22.4%; proportion of patients experiencing medication errors: 40.0%	Age, weight, number of medications, length of hospital stay
Mekory et al [57], 2017	Israel	Cross-sectional	Pediatric ward	2013‐2014	0‐18 years (range)	Medical record review	ASHP	Prescription errors: 6.5%; administration errors: 11.3%	NR
Nikhithasri et al [58], 2017	India	Cross-sectional	PICU and NICU	6 months	0‐18 years (range)	Records; direct communication	NCC MERP	Prescription errors: 56.1%	NR
Khoo et al [59], 2017	Malaysia	Cross-sectional	Pediatric ward, PICU and NICU	2015	0‐18 years (range)	Drug chart review	NR	Prescription errors: 9.2%	NR
Ewig et al [60], 2017	China	Cohort	PICU	2015	3.2 years (mean, SD 3.2)	Medical record review	Institute of Medicine	310 errors per 100 admissions	NR
Tribble et al [61], 2020	United States	Cross-sectional	Medical, surgical, NICU and nonneonatal intensive care	2016‐2017	0‐17 years (range)	Electronic medical record review; standardized assessment form	NR	Proportion of patients experiencing medication errors: 25.7%; prescription errors: 21.0%	Prescription drug category, indication
Dedefo et al [62], 2016	Ethiopia	Cohort	Pediatric ward	2014	≤28 days to ≤14 years (range)	Medical record review	ASHP	Proportion of patients experiencing medication errors: 75.1%; prescription errors: 46.0%	Length of hospital stay, number of prescribed medications
Palmero et al [63], 2016	Switzerland	Cohort	NICU	8 months	33.1 weeks (mean)	Review of medical orders	Reference	Prescription errors: 28.9%	NR
Machado et al [64], 2015	Brazil	Cross-sectional	NICU	2011	NR	Medical record review	ASHP	Prescription errors: 43.5	Gestational age, weight
Glanzmann et al [65], 2015	Switzerland	Cohort	PICU	2010	NR	Medical record review	Reference	Prescription errors: 13.4%	NR
Stultz et al [66], 2014	United States	Cohort	General ward	2011	0‐17.8 years (range)	Electronic medical record; pharmacy record review	Guidelines and formularies	Prescription errors: 0.54%	NR
Sakuma et al [67], 2014	Japan	Cohort	Pediatric general ward, NICU, PICU, ICU, and emergent care unit	2009	2 years (median, IQR 0‐7)	Medical record review	NR	69.5 errors per 100 admissions	NR
Chedoe et al [68], 2012	The Netherlands	Cohort	NICU	2006	≥25 weeks (range)	Direct observation	NR	Administration errors: 48.6%	NR
Booth et al [69], 2012	United Kingdom	Cohort	PICU	32 weeks	1.65 years (median, IQR 0.35‐6.10)	Prescription review	Reference	892 errors per 1000 occupied bed-days	NR
Al-Jeraisy et al [70], 2011	Saudi Arabia	Cohort	PICU	5 weeks	0‐14 years (range)	Medical record review	Reference	Prescription errors: 56.0%	NR
Kunac et al [71], 2010	New Zealand	Cohort	NICU, postnatal ward and pediatric ward	2002	NR	Medical record review; multidisciplinary meeting; parent interview; voluntary report	NCC MERP	NR	Length of hospital stay, number of prescribed medications, administration route
Feleke et al [72], 2010	Ethiopia	Cross-sectional	Pediatric ward	2009	0‐15 years (range)	Direct observation	Reference	Administration errors: 89.9%	NR
Chua et al [73], 2010	Malaysia	Cross-sectional	General pediatric ward and pediatric oncology ward	2004‐2005	0‐16 years (range)	Direct observation	Reference	Administration errors: 11.7%	Ward type
Ghaleb et al [74], 2010	United Kingdom	Cross-sectional	Surgical, medical, intensive care (PICU or NICU), adolescent units	2004‐2006	NR	Drug chart review; unobtrusive observation	Reference	Prescription errors: 13.2%; administration errors: 19.1%	NR
Ligi et al [75], 2008	France	Cohort	NICU	2005	NR	Anonymous voluntary reporting system; independent observer verification; standardized form recording	Reference	4.9 errors per 100 admissions	NR
Campino et al [76], 2008	Spain	Cohort	NICU	NR	NR	Medical record review	Reference	Prescription errors: 32.8%; transcription error rate: 20.5%	NR
Parihar et al [77], 2008	India	Cohort	NICU, PICU, pediatric ward, and rooming-in labor ward	2005	28 weeks to 18 years (range)	Medical record review; interview	Reference	Prescription errors: 24.3%	NR
Otero et al [78], 2008	Argentina	Cross-sectional	NICU, PICU, pediatric ward	2002	0‐18 years (range)	Medical record review	ASHP	Prescription errors: 11.4%	Night shift, age, year of resident or researcher, assistant administration
Buckley et al [79], 2007	United States	Cohort	PICU	2004	0‐18 years (range)	Direct observation	Reference	Prescription errors: 14.6%	NR
Wang et al [80], 2007	United States	Cross-sectional	NICU, PICU, pediatric ward	2002	NR	Medical record review; voluntary reporting	Reference	Prescription errors: 5.1%	NR
Walsh et al [81], 2006	United States	Cohort	NICU, PICU, pediatric ward	2002‐2003	NR	Medical record review; computer log analysis; standardized form recording	Reference	Prescription errors: 1.5%	NR
Prot et al [82], 2005	France	Cross-sectional	PICU, NICU, pediatric nephrology unit, and general pediatric unit	2002‐2003	NR	Direct observation	ASHP	Administration errors: 31.3%	Drug category, administration route, nurse type, number of patient management protocols
Potts et al [83], 2004	United States	Cohort	Pediatric critical care unit	2001‐2002	6.5 years (mean, SD 12.0)	Medical record review	Investigator-defined	Prescription errors: 39.1%	NR
Kaushal et al [3], 2001	United States	Cohort	Pediatric general medical wards, general surgical wards, PICU, and NICU	1999	NR	Clinical staff reports and review of prescription sheets, medication administration records, and patient charts	Reference	Prescription errors: 5.7%	NR

^aThe age values are reported as range, mean (SD), or median (IQR) as available.

^bNICU: neonatal intensive care unit.

^crange: minimum-maximum.

^dNR: not reported.

^eeMM: electronic medication management system.

^fNCC MERP: National Coordinating Council for Medication Error Reporting and Prevention.

^gWHO: World Health Organization.

^hBNFC: British National Formulary for Children.

ⁱNICE: National Institute for Health and Care Excellence.

^jPICU: pediatric intensive care unit.

^kPEC: pediatric emergency care.

^lASHP: American Society of Health-System Pharmacists.

Meta-Analysis of Prescribing Errors in Hospitalized Pediatric Patients

Based on the denominators used in the included studies, we reported prescribing and administration error rates as well as the proportion of patients experiencing medication errors. Thirty-five studies [3,24,26,30,32-35,38,39,42,44,47,49-51,56-59,61-66,70,74,76-81,83] reported prescribing error rates in hospitalized children. Significant heterogeneity was observed among studies (I²=99%; P<.001). Sensitivity analyses using a leave-one-out approach indicated that no single study significantly influenced the overall results. Therefore, a random-effects model was applied. The pooled prescribing error rate among hospitalized pediatric patients was 28% (95% CI 24%‐31%; Figure 2).

Figure 2.

Forest plot of prescribing error rates in hospitalized pediatric patients. Weights are from random effects model. DL: DerSimonian-Laird [3,24,26,30,32-35,38,39,42,44,47,49-51,56-59,61-66,70,74,76-81,83].

Subgroup Analysis of Prescribing Error Rates in Hospitalized Pediatric Patients

Subgroup analyses of prescribing error rates are presented in Table 2, with the number of included studies reported for each category. When stratified by study design, prescribing error rates were comparable between cohort studies (28%, 95% CI 23%‐34%) and cross-sectional studies (27%, 95% CI 22%‐32%). By country income level, prescribing error rates appeared higher in lower-middle–income countries (44%, 95% CI 24%‐64%) compared with low-income countries (33%, 95% CI 16%‐51%), although the number of studies in each subgroup was limited and CIs were wide. Regional subgroup analysis showed variability across geographic areas. The highest estimate was observed in Latin America and the Caribbean (36%, 95% CI 8%‐64%), whereas North America showed a lower estimate (12%, 95% CI 7%‐17%). However, these differences should be interpreted cautiously due to limited study numbers and substantial uncertainty. Regarding ward type, intensive care units showed higher prescribing error rates (34%, 95% CI 29%‐39%). For prescription types, antibiotic medications showed slightly higher error rates (30%, 95% CI 24%‐36%) compared with nonantibiotic medications (27%, 95% CI 24%‐31%). For temporal trends, studies conducted between 2013 and 2025 showed higher prescribing error rates (32%, 95% CI 26%‐38%) compared with those conducted between 2000 and 2012 (18%, 95% CI 14%‐22%). However, this finding should be interpreted cautiously given potential differences in study settings, definitions, and data collection methods across time periods.

Table 2.

Subgroup analysis of prescribing error rates in hospitalized pediatric patients.

Subgroup	Studies, n	Events, n	Total, n	Prevalence (95% CI)	P value	I² (%)
Study design
Cross-sectional	19	13,398	89,380	0.27 (0.22-0.32)	<.001	99.85
Cohort	16	21,618	157,676	0.28 (0.23-0.34)	<.001	99.95
Country income level
High income	20	27,301	206,580	0.23 (0.19-0.27)	<.001	99.95
Upper-middle income	7	4227	25,689	0.27 (0.16-0.38)	<.001	99.82
Lower-middle income	5	2489	5919	0.44 (0.24-0.64)	<.001	99.67
Low income	3	803	2244	0.33 (0.16-0.51)	NA^a	NA
Study location
Europe	8	3857	32,939	0.35 (0.15-0.55)	<.001	99.92
Asia	11	5897	28,962	0.31 (0.23-0.39)	<.001	99.89
Africa	3	803	2244	0.33 (0.16-0.51)	NA	NA
North America	7	8011	105,964	0.12 (0.07-0.17)	<.001	99.94
Latin America and the Caribbean	3	2101	5602	0.36 (0.08-0.64)	NA	NA
Australia and New Zealand	2	14,047	75,000	0.19 (0.18-0.19)	NA	NA
Special regions	1	213	949	0.22 (0.20-0.25)	NA	NA
Ward type
Intensive care unit	17	10,070	89,161	0.34 (0.29-0.39)	<.001	99.91
General ward	11	17,588	83,764	0.30 (0.20-0.40)	<.001	99.87
Mixed wards	7	7358	74,131	0.10 (0.05-0.14)	<.001	99.84
Prescription type
Antibiotic prescriptions	7	5971	26,589	0.30 (0.24-0.36)	<.001	98.81
Non-antibiotic prescriptions	28	29,045	220,467	0.27 (0.24-0.31)	<.001	99.94
Study period
2000‐2012	11	6027	87,882	0.18 (0.14-0.22)	<.001	99.87
2013‐2025	18	25,435	142,088	0.32 (0.26-0.38)	<.001	99.92
NA	6	3554	17,086	0.33 (0.17-0.50)	<.001	99.90

^aNA: not applicable.

Meta-Analysis of the Proportion of Patients Experiencing Medication Errors

Based on the denominators used in the included studies, we reported the proportion of patients experiencing medication errors, as well as prescribing and administration error rates. Eighteen studies [25,28,29,31-33,36,37,40,41,47,48,52,55,56,61-63] reported the proportion of patients with medication errors. The meta-analysis showed significant heterogeneity across studies (I²=99%; P<.001). A sensitivity analysis using the leave-one-out method indicated that no single study had a substantial impact on the pooled results. Therefore, a random-effects model was applied. The analysis estimated that 54% of the hospitalized pediatric patients experienced at least 1 medication error (95% CI 42%‐67%), as shown in Figure 3.

Figure 3.

Forest plot of the proportion of hospitalized pediatric patients experiencing medication errors. Weights are from random effects model. DL: DerSimonian-Laird [25,28,29,31-33,36,37,40,41,47-49,52,55,56,61,62].

Subgroup Analysis of the Proportion of Patients Experiencing Medication Errors

The results of the subgroup analysis are presented in Table 3, with the number of studies reported for each subgroup. By study design, similar proportions were observed in cohort studies (57%, 95% CI 24%‐90%) and cross-sectional studies (54%, 95% CI 41%‐67%). By national income level, upper-middle–income countries showed a higher proportion (64%, 95% CI 50%‐77%) than other groups, but the number of studies was small. By region, estimates varied from 26% (95% CI 25%‐26%) to 85% (95% CI 78%‐90%). These results should be interpreted with caution due to limited studies and wide CIs. By prescription type, nonantibiotic prescriptions showed a higher proportion (66%, 95% CI 53%‐79%). By time period, the estimates were similar between 2018 and 2025 (55%, 95% CI 37%‐72%) and 2000‐2017 (52%, 95% CI 34%‐69%).

Table 3.

Subgroup analysis of the proportion of hospitalized pediatric patients experiencing medication errors.

Subgroup	Studies, n	Events, n	Total, n	Prevalence (95% CI)	P value	I² (%)
Study design
Cross-sectional	14	4972	15,401	0.54 (0.41-0.67)	<.001	99.30
Cohort	4	551	1072	0.57 (0.24-0.90)	<.001	99.48
Country income level
High income	2	3166	11,948	0.27 (0.26-0.28)	NA^a	NA
Upper-middle income	7	945	1412	0.64 (0.50-0.77)	<.001	97.32
Lower-middle income	2	219	669	0.31 (0.28-0.35)	NA	NA
Low income	7	1193	2444	0.52 (0.36-0.68)	<.001	98.64
Study location
Europe	1	139	164	0.85 (0.78-0.90)	NA	NA
Asia	6	564	1055	0.54 (0.28-0.80)	<.001	99.07
Africa	9	1633	3070	0.56 (0.43-0.70)	<.001	98.60
North America	1	3027	11,784	0.26 (0.25-0.26)	NA	NA
Special regions	1	160	400	0.40 (0.35-0.45)	NA	NA
Prescription type
Antibiotic prescriptions	7	3653	13,523	0.36 (0.28-0.44)	<.001	95.93
Nonantibiotic prescriptions	11	1870	2950	0.66 (0.53-0.79)	<.001	98.51
Study period
2010‐2017	9	4000	13,559	0.52 (0.34-0.69)	<.001	99.33
2018‐2025	8	1346	2687	0.55 (0.37-0.72)	<.001	99.08
NA	1	177	227	0.78 (0.72-0.83)	NA	NA

^aNA: not applicable.

Meta-Analysis of Medication Administration Errors in Hospitalized Pediatric Patients

Fifteen studies [25-27,43,45,46,50,53,54,57,68,72-74,82] reported medication administration error rates in hospitalized pediatric patients. Meta-analysis revealed substantial heterogeneity among studies (I²=99%; P<.001). Sensitivity analysis using the leave-one-out method indicated that no single study significantly influenced the overall estimate. Therefore, a random-effects model was applied, showing that the pooled rate of medication administration errors was 32% (95% CI 21%‐44%; Figure 4).

Figure 4.

Forest plot of medication administration error rates in hospitalized pediatric patients. Weights are from random effects model. DL: DerSimonian-Laird [25-27,43,45,46,50,53,54,57,68,72-74,82].

Subgroup Analysis of Medication Administration Error Rates in Hospitalized Pediatric Patients

The results of the subgroup analysis for administration error rates are presented in Table 4, with the number of studies reported for each subgroup. By study design, cohort studies showed higher estimates (39%, 95% CI 15%‐62%) than cross-sectional studies (29%, 95% CI 16%‐42%). By national income level, low-income countries showed a higher estimate (71%, 95% CI 69%‐73%) compared with other groups, but the number of studies was limited. By region, estimates varied across areas, ranging from 3% (95% CI 3%‐3%) to 71% (95% CI 69%‐73%). These differences should be interpreted cautiously due to limited data and uncertainty. By ward type, ICU (33%, 95% CI 10%‐57%) and general wards (33%, 95% CI 18%‐48%) showed similar estimates. By study period, estimates were 40% (95% CI 20%‐60%) and 29% (95% CI 11%‐47%), with overlapping CIs.

Table 4.

Subgroup analysis of medication administration error rates in hospitalized pediatric patients.

Subgroup	Studies, n	Events, n	Total, n	Prevalence (95% CI)	P value	I² (%)
Study design
Cross-sectional	10	2655	9718	0.29 (0.16-0.42)	<.001	99.64
Cohort	5	12,284	250,218	0.39 (0.15-0.62)	<.001	99.94
Country income level
High income	9	12,761	255,240	0.23 (0.11-0.35)	<.001	99.87
Upper-middle income	4	1203	3227	0.31 (0.04-0.58)	<.001	99.74
Low income	2	975	1469	0.71 (0.69-0.73)	NA^a	NA
Study location
Europe	5	1267	5502	0.24 (0.14-0.33)	<.001	98.61
Asia	4	882	2355	0.25 (−0.00-0.51)	<.001	99.77
Africa	2	975	1469	0.71 (0.69-0.73)	NA	NA
North America	1	7633	238,540	0.03 (0.03-0.03)	NA	NA
Latin America and the Caribbean	1	325	939	0.35 (0.32-0.38)	NA	NA
Australia and New Zealand	2	3757	10,274	0.37 (0.36-0.37)	NA	NA
Ward type
Intensive care unit	5	8985	241,906	0.33 (0.10-0.57)	<.001	99.86
General ward	6	4192	12,611	0.33 (0.18-0.48)	<.001	99.72
Mixed wards	4	1762	5419	0.30 (0.10-0.50)	<.001	99.65
Study period
2000‐2012	5	1414	5354	0.40 (0.20-0.60)	<.001	99.69
2013‐2025	7	11,266	248,168	0.29 (0.11-0.47)	<.001	99.91
NA	3	2259	6414	0.27 (0.12-0.42)	NA	NA

^aNA: not applicable.

Meta-Analysis of Factors Associated With Medication Errors in Hospitalized Pediatric Patients

Intravenous administration, hospital stay longer than 5 days, and prescription of 3 or more medications were identified as factors associated with medication errors in hospitalized pediatric patients. Among these, intravenous administration was most strongly associated with medication errors (OR 6.86, 95% CI 3.17‐14.83), followed by the prescription of 3 or more medications (OR 2.12, 95% CI 1.66‐2.70) and a hospital stay longer than 5 days (OR 1.94, 95% CI 1.33‐2.81; Table 5).

Table 5.

Meta-analysis of factors associated with medication errors in hospitalized pediatric patients.

Associated factors	Number of studies	Minimum OR^a	Maximum OR	Pooled OR (95% CI)	I² (%)	P value for heterogeneity
Route of administration (intravenous vs oral)	4	3.36	21.18	6.86 (3.17-14.83）	89.4	<.001
Sex (male vs female)	3	0.82	1.09	1.00 (0.80-1.26）	0.0	.61
Hospital stay (>5 days vs <5 days)	5	1.34	3.46	1.94 (1.33-2.81）	63.1	.03
Number of prescribed medications (≥3 vs 1‐2)	5	1.24	7.02	2.12 (1.66-2.70）	40.1	.15
Weight (>20 kg vs <10 kg)	2	1.13	1.7	1.28 (0.63-2.58)	0.0	.60
Night shift	2	0.81	3.06	1.52 (0.41-5.60)	94.9	<.001

^aOR: odds ratio.

Sensitivity Analysis

Sensitivity analyses were conducted using a leave-one-out approach. Sequential exclusion of each study did not materially change the pooled estimates for prescribing errors, proportion of patients experiencing medication errors, or administration errors, indicating the robustness of the findings (Figures S1-S3 in Multimedia Appendix 3). To assess the robustness of the pooled estimates, the results obtained using the DerSimonian-Laird method were further validated using the REML method. The pooled effect estimates derived from the 2 methods were highly consistent, with substantial overlap in the corresponding 95% CIs, indicating good robustness of the study findings (Figures S4-S6 in Multimedia Appendix 3).

Publication Bias

Because fewer than 10 studies were included for each factor associated with medication errors, publication bias was only assessed for medication error rates. Multiple methods were used to evaluate publication bias. Funnel plots for prescribing errors, administration errors, and proportion of patients with medication errors showed asymmetry (Figures 7-9 in Multimedia Appendix 3). Egger tests indicated potential publication bias for prescribing errors and administration errors (z=9.39; P<.001 and z=2.25; P=.03). However, nonparametric trim-and-fill analyses (imputed studies=0) suggested that no additional studies were needed. Therefore, the observed asymmetry likely reflects true heterogeneity between studies rather than solely publication bias.

DiscussionPrincipal Findings

This systematic review and meta-analysis evaluated the overall medication error rates and associated factors in hospitalized pediatric patients. We found that 54% of the pediatric inpatients experienced a medication error, which is lower than the 69.5% reported by Bante et al [31]. The pooled prescribing error rate was 28%, comparable to the 28.9% reported by Palmero et al [63] but higher than the 17.5% reported by Koumpagioti et al [84]. The administration error rate was 32%, slightly lower than the 37% reported by Westbrook et al [27]. These discrepancies may be attributable to differences in the characteristics of the included populations, definitions of medication errors, and data collection methods. Furthermore, intravenous administration, hospital stay longer than 5 days, and prescription of 3 or more medications were identified as factors associated with medication errors, providing clinically relevant insights for error prevention strategies.

This systematic review and meta-analysis evaluated prescribing error rates, the proportion of patients experiencing medication errors, administration error rates, and associated factors among hospitalized pediatric patients. Notably, substantial heterogeneity was observed across all primary outcomes, and therefore the pooled estimates should be interpreted with caution. The observed heterogeneity may be attributable to methodological and clinical differences among the included studies [85]. Specifically, studies varied in their units of analysis, with some reporting errors per patient and others per prescription or medication administration. Variations were also identified in medication error detection methods, including direct observation, medical record review, and database-based data extraction, which may differ in their ability to identify errors. Furthermore, the included studies were conducted across diverse clinical settings, such as general pediatric wards, NICUs, and PICUs, where patient characteristics, medication-use processes, and risk profiles may differ substantially. In addition, definitions of medication errors were not uniform across studies. Collectively, these factors may have contributed to the high level of heterogeneity and may limit the generalizability of the pooled findings [86]. Therefore, the results of this review should be interpreted as reflecting the overall trends across the included studies rather than precise estimates applicable to all pediatric inpatient settings.

Subgroup analyses across prescription, patient, and administration dimensions highlight the complexity and heterogeneity of pediatric medication errors. Geographic and economic differences were observed, with higher estimates reported in Latin America and the Caribbean, Africa, and low- to middle-income countries. These findings may reflect differences in health care resources, reporting practices, and system development; however, they should be interpreted cautiously given the limited number of studies in several subgroups and substantial heterogeneity. Lower estimates observed in North America may be associated with more developed medication safety systems; however, this explanation remains speculative and was not directly examined in the included studies. Temporal trends showed higher prescribing error rates and a higher proportion of patients experiencing medication errors in more recent studies, which may reflect improved detection and reporting practices over time. In contrast, administration error rates appeared to decrease in recent years. PICUs consistently showed higher error rates, likely reflecting the complexity and high-risk nature of these clinical settings [87,88].

The findings of this study suggest that medication errors in hospitalized pediatric patients are closely associated with treatment-related factors, including intravenous administration, hospital stay longer than 5 days, and the prescription of 3 or more medications. However, as this study is based on observational data, the results reflect statistical associations rather than causal relationships, and residual confounding cannot be excluded.

Intravenous administration was associated with a higher likelihood of medication errors compared with oral administration (OR 6.86). This finding suggests that intravenous delivery may be an important factor associated with medication errors. Similar results have been reported by Westbrook et al [27], who observed 5137 medication administration events in hospitalized children and found that intravenous medications were associated with higher error rates, reaching 77.4% for injections and 64.7% for infusions. The complexity of intravenous medication processes, including dose calculation, dilution, preparation, and infusion rate adjustment, may contribute to the increased risk of errors. Pediatric dosing, which is often based on body weight or body surface area [62], further increases complexity. Rajakumar et al [89] also reported that such individualized dosing increases the risk of medication errors. In addition, pediatric doses are often substantially lower than adult doses, and even minor calculation or measurement errors may result in clinically significant dosing deviations, potentially leading to serious harm [74].

Patients with a hospital stay longer than 5 days were more likely to experience medication errors compared with those with a stay of 5 days or less (OR 1.94). This finding is consistent with the study by Bante et al [31], which also reported that longer hospital stays were associated with a higher risk of medication errors. Longer hospitalization may be associated with increased exposure to medications, resulting in a greater number of prescribing and administration processes, which in turn increases the opportunity for errors. However, any assumption that prolonged hospitalization directly leads to reduced adherence to verification procedures should be interpreted with caution, as this was not directly examined in the included studies.

Children receiving 3 or more prescribed medications had a higher likelihood of medication errors compared with those receiving fewer than 3 medications (OR 2.12). However, evidence regarding the underlying mechanisms of this association remains limited. This finding may be related to increased complexity in medication management as the number of prescriptions increases, including prescribing, verification, dispensing, and administration processes. It may also reflect greater exposure to medication-related procedures in patients receiving multiple drugs. In addition, patients receiving polypharmacy are often clinically more complex, which may contribute to higher observed error rates. Nevertheless, these explanations remain speculative, as the included studies did not provide sufficient information on drug classes, dosing frequency, or disease severity. Therefore, further research is needed to clarify the relationship between polypharmacy and medication errors and to explore potential underlying mechanisms.

Limitations

This study has several limitations. Although uniform inclusion criteria were applied, the definitions and classification systems for medication errors varied across the included studies. This may have led to misclassification bias, as certain error types may have been included in some studies but excluded in others. In addition, only studies published in English were included, which may have resulted in the omission of relevant evidence published in other languages, potentially limiting the global generalizability of the findings. Substantial heterogeneity was observed across the included studies, which may limit the interpretability and generalizability of the pooled estimates. Furthermore, only 3 outcomes were quantitatively synthesized, including the proportion of patients experiencing medication errors, prescription error rates, and administration error rates. Although some studies reported additional types of medication errors, such as dispensing errors and reconstitution errors, the limited number of studies and inconsistencies in reporting prevented meaningful meta-analysis of these outcomes. Therefore, the findings may not fully capture the overall burden of all types of medication errors in hospitalized pediatric patients. Future studies should adopt more standardized reporting frameworks for different types of medication errors to enable more comprehensive evidence synthesis.

Conclusions

The findings of this study indicate that medication errors remain a significant patient safety concern among hospitalized pediatric populations worldwide. Prescription errors and administration errors are the most frequently reported and relatively well-evidenced types of medication errors, and a substantial proportion of hospitalized pediatric patients experience at least 1 medication error. Intravenous administration, hospital stay longer than 5 days, and the prescription of 3 or more medications may be associated with an increased risk of medication errors, suggesting that treatment-related factors are closely linked to medication safety in pediatric inpatients. However, these findings should be interpreted as observational associations rather than causal relationships. These results highlight the importance of strengthening medication safety practices in pediatric inpatient settings to improve treatment quality and patient safety. Future research is needed to further investigate less-studied types of medication errors, such as dispensing and reconstitution errors, to provide a more comprehensive understanding of the overall burden and to support the development of targeted interventions.

The authors declare that no generative AI or AI-assisted technologies were used in the writing, data analysis, or any other aspect of this manuscript. All content was produced solely by the authors.

Funding

This work was supported by the Scientific Research Project of the Chinese Nursing Association (ZHKYQ202414) and the Research Project of Natural Science Foundation of Hunan Province (HS1593060461). The authors alone are responsible for the design, writing, and content of this paper. The funding bodies were not involved in the decision to submit this research for publication.

XC, YM, and LY have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. XC, YM, XW, AL, JH, and LY were involved in drafting the manuscript or revising it critically for important intellectual content. XC, YM, XW, AL, JH, and LY gave final approval of the version to be published. XC, YM, XW, AL, JH, and LY agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

XC and YM contributed equally to this work and share first authorship.

None declared.

Abbreviations

AHRQ

Agency for Healthcare Research and Quality

NCC MERP

National Coordinating Council for Medication Error Reporting and Prevention

NICU

neonatal intensive care unit

NOS

Newcastle-Ottawa Scale

odds ratio

PICU

pediatric intensive care unit

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

REML

restricted maximum likelihood

References1

Alqenae

Steinke

Keers

Prevalence and nature of medication errors and medication-related harm following discharge from hospital to community settings: a systematic review

Drug Saf202006436517537

10.1007/s40264-020-00918-3

32125666

Lesar

Briceland

Delcoure

Parmalee

Masta-Gornic

Pohl

Medication prescribing errors in a teaching hospital

JAMA19900522631723292334

2325234

Kaushal

Bates

Landrigan

Medication errors and adverse drug events in pediatric inpatients

JAMA200104252851621142120

10.1001/jama.285.16.2114

11311101

Simpson

Lynch

Grant

Alroomi

Reducing medication errors in the neonatal intensive care unit

Arch Dis Child Fetal Neonatal Ed200411896F480F482

10.1136/adc.2003.044438

15499135

Fei

LiFang

Retrospective analysis of medication errors involving 106 pediatric patients at our hospital

Pract Pharm Clin Remedies2019228863866

10.14053/j.cnki.ppcr.201908019

Qin

FengTing

XuMei

Best evidence on prevention of drug administration errors in pediatric inpatients

Chinese Health Quality Management20243115357

10.13912/j.cnki.chqm.2024.31.1.12

Bannan

Tully

Bundle interventions used to reduce prescribing and administration errors in hospitalized children: a systematic review

J Clin Pharm Ther201606413246255

10.1111/jcpt.12398

27145467

Kozer

Berkovitch

Koren

Medication errors in children

Pediatr Clin North Am20061253611551168

10.1016/j.pcl.2006.09.005

17126688

Manias

Cranswick

Newall

Medication error trends and effects of person-related, environment-related and communication-related factors on medication errors in a paediatric hospital

J Paediatr Child Health201903553320326

10.1111/jpc.14193

30168236

Ghasemi

Babamiri

Pashootan

A comprehensive method for the quantification of medication error probability based on fuzzy SLIM

PLoS One2022172e0264303

10.1371/journal.pone.0264303

35213625

Bates

Spell

Cullen

The costs of adverse drug events in hospitalized patients

JAMA199701222774307

10.1001/jama.1997.03540280045032

9002493

Phillips

Christenfeld

Glynn

Increase in US medication-error deaths between 1983 and 1993

Lancet199802283519103643644

10.1016/S0140-6736(98)24009-8

9500322

Elliott

Camacho

Jankovic

Sculpher

Faria

Economic analysis of the prevalence and clinical and economic burden of medication error in England

BMJ Qual Saf20210230296105

10.1136/bmjqs-2019-010206

32527980

WHO calls for urgent action by countries for achieving medication without harm

World Health Organization2022

2026-08-15

https://www.who.int/news/item/16-09-2022-who-calls-for-urgent-action-by-countries-for-achieving-medication-without-harm

About medication errors

NCC MERP (National Coordinating Council for Medication Error Reporting and Prevention)2025-09-02

https://www.nccmerp.org/about-medication-errors

Ruano

Villamañán

Pérez

Herrero

Álvarez-Sala

New technologies as a strategy to decrease medication errors: how do they affect adults and children differently?

World J Pediatr2016021212834

10.1007/s12519-015-0067-6

26684316

Miller

Robinson

Lubomski

Rinke

Pronovost

Medication errors in paediatric care: a systematic review of epidemiology and an evaluation of evidence supporting reduction strategy recommendations

Qual Saf Health Care200704162116126

10.1136/qshc.2006.019950

17403758

Alghamdi

Keers

Sutherland

Ashcroft

Prevalence and nature of medication errors and preventable adverse drug events in paediatric and neonatal intensive care settings: a systematic review

Drug Saf201912421214231436

10.1007/s40264-019-00856-9

31410745

Liu

Shih

Chiang

Reducing medication errors in children's hospitals

J Patient Saf2023041193151157

10.1097/PTS.0000000000001087

36728168

Henry Basil

Premakumar

Mhd Ali

Mohd Tahir

Mohamed Shah

Prevalence, causes and severity of medication administration errors in the neonatal intensive care unit: a systematic review and meta-analysis

Drug Saf202212451214571476

10.1007/s40264-022-01236-6

36192535

Gates

Baysari

Gazarian

Raban

Meyerson

Westbrook

Prevalence of medication errors among paediatric inpatients: systematic review and meta-analysis

Drug Saf201911421113291342

10.1007/s40264-019-00850-1

31290127

Wells

Shea

O’Connell

The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses

The Ottawa Hospital Research Institute2025-09-15

https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp

Rostom

Dubé

Cranney

Celiac disease. Appendix D: Quality Assessment Forms

2004

2025-09-15

Agency for Healthcare Research and Quality (US)

https://www.ncbi.nlm.nih.gov/books/NBK35156

Canales-Siguero

García-Muñoz

Caro-Teller

Electronic prescribing in the neonatal intensive care unit: analysis of prescribing errors and risk factors

J Med Syst2025021849126

10.1007/s10916-025-02161-8

39964641

Henry Basil

Premakumar

Mhd Ali

Prevalence and factors associated with medication administration errors in the neonatal intensive care unit: a multicentre, nationwide direct observational study

J Adv Nurs202502812820833

10.1111/jan.16247

38803148

Badgery-Parker

Fitzpatrick

Mumford

Raban

Westbrook

Child age and risk of medication error: a multisite children's hospital study

J Pediatr202409272114087

10.1016/j.jpeds.2024.114087

38705229

Westbrook

Woods

Risk factors associated with medication administration errors in children: a prospective direct observational study of paediatric inpatients

Drug Saf202406476545556

10.1007/s40264-024-01408-6

38443625

Abiri

Ninka

Coker

An assessment of medication errors among pediatric patients in three hospitals in Freetown Sierra Leone: findings and implications for a low-income country

Pediatric Health Med Ther202415145158

10.2147/PHMT.S451453

38567243

Kwaku Wuni

Suntaa Saanwie

Kofi Dzotsi

Medication administration errors among children admitted at a Regional Hospital in Northern Ghana

Int J Africa Nurs Sci202421100795

10.1016/j.ijans.2024.100795

Satir

Pfiffner

Meier

Caduff Good

Prescribing errors in children: what is the impact of a computerized physician order entry?

Eur J Pediatr202306182625672575

10.1007/s00431-023-04894-5

36933016

Bante

Mersha

Aschalew

Ayele

Medication errors and associated factors among pediatric inpatients in public hospitals of gamo zone, southern Ethiopia

Heliyon20230494e15375

10.1016/j.heliyon.2023.e15375

37123938

Xavier

Victor

Cumaquela

Vasco

Rodrigues

OAS

Inappropriate use of antibiotics and its predictors in pediatric patients admitted at the Central Hospital of Nampula, Mozambique

Antimicrob Resist Infect Control202206211179

10.1186/s13756-022-01115-w

35655272

Akkawi

Taffour

Al-Shami

Evaluation of antibiotic prescribing pattern and appropriateness among hospitalized pediatric patients: findings from a Malaysian teaching hospital

Infect Dis Rep20221117146889899

10.3390/idr14060089

36412747

Garedow

Tesfaye

Evaluation of antibiotics use and its predictors at pediatrics ward of Jimma Medical Center: hospital based prospective cross-sectional study

Infect Drug Resist20221553655375

10.2147/IDR.S381999

36110127

Nasso

Scarfone

Pirrotta

Appropriateness of antibiotic prescribing in hospitalized children: a focus on the real-world scenario of the different paediatric subspecialties

Front Pharmacol202213890398

10.3389/fphar.2022.890398

35694248

Alekaw

Derebe

Melese

Yismaw

Antibiotic prescription pattern, appropriateness, and associated factors in patients admitted to pediatric wards of Tibebe Ghion Specialized Hospital, Bahir Dar, North West Ethiopia

Infect Drug Resist20221566596669

10.2147/IDR.S380897

36411756

Özdemir

Kara

Büyükçam

Evaluation of medication errors in pediatric patients using antibiotics

Turk J Pediatr2021636970977

10.24953/turkjped.2021.06.004

35023646

Abdel-Qader

Ismael

Albassam

Antibiotics use and appropriateness in two Jordanian children hospitals: a point prevalence study

J Pharm Health Serv Res20210510122166172

10.1093/jphsr/rmaa014

Kadmon

Shifrin

Pinchover

Nahum

Risk factors for electronic prescription errors in pediatric intensive care patients

Pediatr Crit Care Med202006216557562

10.1097/PCC.0000000000002303

32343112

Yehualaw

Taferre

Bantie

Demsie

Appropriateness and pattern of antibiotic prescription in pediatric patients at Adigart General Hospital, Tigray, Ethiopia

Biomed Res Int202120216640892

10.1155/2021/6640892

33937405

Bharathi

Raj

Saldanha

Suman Rao

Devi

Medication errors in neonatal intensive care unit of a tertiary care hospital in South India: a prospective observational study

Indian J Pharmacol2020524260265

10.4103/ijp.IJP_611_19

33078726

Brennan-Bourdon

Vázquez-Alvarez

Gallegos-Llamas

Koninckx-Cañada

Marco-Garbayo

Huerta-Olvera

A study of medication errors during the prescription stage in the pediatric critical care services of a secondary-tertiary level public hospital

BMC Pediatr2020125201549

10.1186/s12887-020-02442-w

33278900

Howlett

Brereton

Breatnach

Cleary

P19 Direct observational study of infusion errors associated with smart-pump technology in paediatric intensive care

Arch Dis Child2020091059e15

10.1136/archdischild-2020-NPPG.28

McMullan

Hall

James

Antibiotic appropriateness and guideline adherence in hospitalized children: results of a nationwide study

J Antimicrob Chemother2020031753738746

10.1093/jac/dkz474

31697335

Bonafide

Miller

Localio

Association between mobile telephone interruptions and medication administration errors in a pediatric intensive care unit

JAMA Pediatr20200211742162169

10.1001/jamapediatrics.2019.5001

31860017

Ramirez-Camacho

Lara Ake

Puga Tuyub

Torres-Romero

Medication errors of intravenous therapy in the neonatal intensive care unit of a second-level hospital in southeastern Mexico

Lat Am J Pharm2020

2026-08-15

393604611

https://www.latamjpharm.org/resumenes/39/3/LAJOP_39_3_1_25.pdf

Eslami

Aletayeb

SMH

Kouti

Hardani

Identifying medication errors in neonatal intensive care units: a two-center study

BMC Pediatr20191022191365

10.1186/s12887-019-1748-4

31638939

Fekadu

Abdisa

Fanta

Medication prescribing errors among hospitalized pediatric patients at Nekemte Referral Hospital, western Ethiopia: cross-sectional study

BMC Res Notes20190716121421

10.1186/s13104-019-4455-1

31311587

Palmero

Di Paolo

Stadelmann

Pannatier

Sadeghipour

Tolsa

Incident reports versus direct observation to identify medication errors and risk factors in hospitalised newborns

Eur J Pediatr2019021782259266

10.1007/s00431-018-3294-8

30460407

Tang

Wimmer

Akkawi

Ming

Ibrahim

Incidence and pattern of medication errors in a general paediatric ward in a developing nation

Res Social Adm Pharm201803143317319

10.1016/j.sapharm.2017.03.053

28365153

Kadam

Gohil

Kabra

Ahmed

Avasthi

Sharma

Prescription errors in NICU: prevalence and results of an intervention program

Perinatology2018

2026-08-15

1912935

https://www.perinatology.in/prescription-errors-in-nicu-prevalence-and-results-of-an-intervention-program

Ergül

Gökçek

Çelik

Torun

Assessment of inappropriate antibiotic use in pediatric patients: point-prevalence study

Turk Pediatri Ars2018035311723

10.5152/TurkPediatriArs.2018.5644

30083070

Baraki

Abay

Tsegay

Gerensea

Kebede

Teklay

Medication administration error and contributing factors among pediatric inpatient in public hospitals of Tigray, northern Ethiopia

BMC Pediatr20181010181321

10.1186/s12887-018-1294-5

30305080

Rishoej

Almarsdóttir

Thybo Christesen

Hallas

Juel Kjeldsen

Identifying and assessing potential harm of medication errors and potentially unsafe medication practices in paediatric hospital settings: a field study

Ther Adv Drug Saf20180999509522

10.1177/2042098618781521

30181859

Truter

Schellack

Meyer

Identifying medication errors in the neonatal intensive care unit and paediatric wards using a medication error checklist at a tertiary academic hospital in Gauteng, South Africa

S Afr J Child Health20171115

10.7196/SAJCH.2017.v11i1.1101

Al-Ramahi

Hmedat

Alnjajrah

Manasrah

Radwan

Alkhatib

Medication dosing errors and associated factors in hospitalized pediatric patients from the South Area of the West Bank - Palestine

Saudi Pharm J201709256857860

10.1016/j.jsps.2017.01.001

28951670

Mekory

Bahat

Bar-Oz

Tal

Berkovitch

Kozer

The proportion of errors in medical prescriptions and their executions among hospitalized children before and during accreditation

Int J Qual Health Care2017061293366370

10.1093/intqhc/mzx031

28340029

Nikhithasri

Ramya

Kishore

Assessment of medication errors in pediatricinpatient department of a private hospital

Int J Curr Pharm Sci20179670

10.22159/ijcpr.2017v9i6.23432

Khoo

Tan

Choo

Bt Abdul Shukor

INC

Teh

Paediatric in-patient prescribing errors in Malaysia: a cross-sectional multicentre study

Int J Clin Pharm201706393551559

10.1007/s11096-017-0463-1

28417303

Ewig

CLY

Cheung

Kam

Wong

Knoderer

Occurrence of potential adverse drug events from prescribing errors in a pediatric intensive and high dependency unit in Hong Kong: an observational study

Pediatric Drugs201708194347355

10.1007/s40272-017-0222-8

28353155

Tribble

Lee

Flett

Appropriateness of antibiotic prescribing in United States children's hospitals: a national point prevalence survey

Clin Infect Dis2020115718e226e234

10.1093/cid/ciaa036

31942952

Dedefo

Mitike

Angamo

Incidence and determinants of medication errors and adverse drug events among hospitalized children in West Ethiopia

BMC Pediatr20160771681

10.1186/s12887-016-0619-5

27387547

Palmero

Di Paolo

Beauport

Pannatier

Tolsa

A bundle with a preformatted medical order sheet and an introductory course to reduce prescription errors in neonates

Eur J Pediatr2016011751113119

10.1007/s00431-015-2607-4

26272253

Machado

APC

Tomich

CSF

Osme

Prescribing errors in a Brazilian neonatal intensive care unit

Cad Saude Publica201512311226102620

10.1590/0102-311X00194714

26872237

Glanzmann

Frey

Meier

Vonbach

Analysis of medication prescribing errors in critically ill children

Eur J Pediatr2015101741013471355

10.1007/s00431-015-2542-4

25899070

Stultz

Porter

Nahata

Sensitivity and specificity of dosing alerts for dosing errors among hospitalized pediatric patients

J Am Med Inform Assoc20141021e2e21925

10.1136/amiajnl-2013-002161

24496386

Sakuma

Ida

Nakamura

Adverse drug events and medication errors in Japanese paediatric inpatients: a retrospective cohort study

BMJ Qual Saf2014102310830837

10.1136/bmjqs-2013-002658

24742779

Chedoe

Molendijk

Hospes

Van den Heuvel

Taxis

The effect of a multifaceted educational intervention on medication preparation and administration errors in neonatal intensive care

Arch Dis Child Fetal Neonatal Ed201211976F44955

10.1136/fetalneonatal-2011-300989

22491014

Booth

Sturgess

Taberner-Stokes

Peters

Zero tolerance prescribing: a strategy to reduce prescribing errors on the paediatric intensive care unit

Intensive Care Med201211381118581867

10.1007/s00134-012-2660-7

22885650

Al-Jeraisy

Alanazi

Abolfotouh

Medication prescribing errors in a pediatric inpatient tertiary care setting in Saudi Arabia

BMC Res Notes201108144294

10.1186/1756-0500-4-294

21838929

Kunac

Kennedy

Austin

Reith

Risk factors for adverse drug events and medication errors in paediatric inpatients: analysis by admission characteristics

J Pharm Pract Res201012404290293

10.1002/j.2055-2335.2010.tb00562.x

Feleke

Girma

Medication administration errors involving paediatric in-patients in a hospital in Ethiopia

Trop J Pharm Res201094401407

10.4314/tjpr.v9i4.58942

Chua

Omar

Drug administration errors in paediatric wards: a direct observation approach

Eur J Pediatr2010051695603611

10.1007/s00431-009-1084-z

19823870

Ghaleb

Barber

Franklin

Wong

ICK

The incidence and nature of prescribing and medication administration errors in paediatric inpatients

Arch Dis Child201002952113118

10.1136/adc.2009.158485

20133327

Ligi

Arnaud

Jouve

Tardieu

Sambuc

Simeoni

Iatrogenic events in admitted neonates: a prospective cohort study

The Lancet2008023719610404410

10.1016/S0140-6736(08)60204-4

Campino

Lopez-Herrera

Lopez-de-Heredia

Valls-I-Soler

Medication errors in a neonatal intensive care unit. Influence of observation on the error rate

Acta Paediatr200811971115911594

10.1111/j.1651-2227.2008.00982.x

18710435

Parihar

Passi

Medical errors in pediatric practice

Indian Pediatr200807457586589

18695280

Otero

Leyton

Mariani

Ceriani Cernadas

Committee

Medication errors in pediatric inpatients: prevalence and results of a prevention program

Pediatrics2008091223e7372743

10.1542/peds.2008-0014

18762510

Buckley

Erstad

Kopp

Theodorou

Priestley

Direct observation approach for detecting medication errors and adverse drug events in a pediatric intensive care unit

Pediatr Crit Care Med20070382145152

10.1097/01.PCC.0000257038.39434.04

17273111

Wang

Herzog

Kaushal

Park

Mochizuki

Weingarten

Prevention of pediatric medication errors by hospital pharmacists and the potential benefit of computerized physician order entry

Pediatrics2007011191e7785

10.1542/peds.2006-0034

17200262

Walsh

Adams

Bauchner

Medication errors related to computerized order entry for children

Pediatrics200611118518721879

10.1542/peds.2006-0810

17079557

Prot

Fontan

Alberti

Drug administration errors and their determinants in pediatric in-patients

Int J Qual Health Care200510175381389

10.1093/intqhc/mzi066

16115809

Potts

Barr

Gregory

Wright

Patel

Computerized physician order entry and medication errors in a pediatric critical care unit

Pediatrics2004011131 Pt 15963

10.1542/peds.113.1.59

14702449

Koumpagioti

Varounis

Kletsiou

Nteli

Matziou

Evaluation of the medication process in pediatric patients: a meta-analysis

J Pediatr (Rio J)2014904344355

10.1016/j.jped.2014.01.008

24726455

Chen

Chan

Efficacy and safety of intracameral amphotericin B for fungal keratitis: a systematic review and meta-analysis

Int J Antimicrob Agents202605675107707

10.1016/j.ijantimicag.2025.107707

41616875

Chen

Chan

Cyclosporine in uveitis: a game changer or a risky choice? A systematic review and meta-analysis of its efficacy and safety

Br J Ophthalmol202512151101816

10.1136/bjo-2025-327349

40812793

Poon

Keohane

Yoon

Effect of bar-code technology on the safety of medication administration

N Engl J Med20100563621816981707

10.1056/NEJMsa0907115

20445181

Marufu

RNBower

RNHendron

Manning

Nursing interventions to reduce medication errors in paediatrics and neonates: systematic review and meta-analysis

J Pediatr Nurs202262e139e147

10.1016/j.pedn.2021.08.024

34507851

Rajakumar

Rajah

Thanimalai

Mokhtar

FBM

Ramachandram

Intravenous medication administration errors in hospitalised patients: an updated systematic review

J Eval Clin Pract202506314e70167

10.1111/jep.70167

40545841

Multimedia Appendix 1

The detailed search strategies for each database.

Multimedia Appendix 2

Quality assessment of the included cross-sectional and cohort studies

Multimedia Appendix 3

Sensitivity analyses, forest plots based on the restricted maximum likelihood random-effects model, and funnel plots for medication error outcomes.

Checklist 1

PRISMA 2020 checklist.