The increase in adoption of Electronic Health records (EHR) in healthcare can be overwhelming to users and pose hidden safety threats and inefficiencies if the system is not well aligned with workflows. This quality improvement study, facilitated from September 2023-April 2024, aimed to proactively test a new EHR using systems focused simulation and Human factors methods, prior to go-live, in a peri-operative children's hospital setting to improve safety, efficiency and usability of the EHR. The project was conducted at a large, academic, quaternary care children's hospital undergoing a transition from one EHR to another. Two cycles of usability testing followed by in situ simulations focused on testing the new EHR with interprofessional peri-operative team members prior to go live. Usability testing, using relevant clinical workflows, was completed over zoom using the EHR "testing" environment with individual care providers across multiple peri-operative roles. In situ simulations were facilitated in the actual peri-operative and Otolaryngology clinic spaces with full interprofessional teams. Qualitative data was collected and summarized through debriefing and recordings of the sessions. Human factors and patient safety principles were integrated throughout the recommendations. A total of 475 recommendations were made to improve the safety, efficiency, usability, and optimization of the EHR. The outcomes included a range of usability and system issues including latent safety threats and their impact on safe and quality patient care. There was a plethora of usability improvements, including some critical issues that were uncovered and mitigated prior to the go live date.

Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS.

On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review.

The studies included in this review allowed for the identification of three main "incident type" domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting.

This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.

Neonates' physiological immaturity and complex dosing requirements heighten their susceptibility to medication administration errors (MAEs), with the potential for severe harm and substantial economic impact on healthcare systems. Developing an effective risk prediction model for MAEs is crucial to reduce and prevent harm.

This national-level, multicentre, prospective direct observational study was conducted in neonatal intensive care units (NICUs) of five public hospitals in Malaysia. Randomly selected nurses were directly observed during medication preparation and administration. Each observation was independently assessed for errors. Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and expert consensus. Model performance, prioritising F1-score for MAEs, was evaluated using various measures. Feature importance was determined using the permutation-feature importance for robust comparison across ML algorithms.

A total of 1093 doses were administered to 170 neonates, with mean age and birth weight of 33.43 (SD ± 5.13) weeks and 1.94 (SD ± 0.95) kg, respectively. F1-scores for the ten models ranged from 76.15% to 83.28%. Adaptive boosting (AdaBoost) emerged as the best-performing model (F1-score: 83.28%, accuracy: 77.63%, area under the receiver operating characteristic: 82.95%, precision: 84.72%, sensitivity: 81.88% and negative predictive value: 64.00%). The most influential features in AdaBoost were the intravenous route of administration, working hours, and nursing experience.

This study developed and validated an ML-based model to predict the presence of MAEs among neonates in NICUs. AdaBoost was identified as the best-performing algorithm. Utilising the model's predictions, healthcare providers can potentially reduce MAE occurrence through timely interventions.

Owing to pharmacokinetic variations in pediatric patients, many antibiotics require weight-based dosing to ensure medication safety and antimicrobial stewardship. Despite the need for weight-based dosing, prescribers are not legally required to include the weight or diagnosis code on pediatric prescriptions that are necessary components to verify appropriateness. Clinical decision support system (CDSS) can help clinicians improve dosing appropriateness, but little is known about CDSS in a community pharmacy setting. To determine the impact of implementing CDSS in this setting, baseline information is necessary.

This study aimed to determine both the percentage of pediatric antibiotic prescriptions without optimal patient information required to evaluate weight-based dosing and the baseline percentage of prescriptions dosed outside of guideline recommendations.

A retrospective chart review was conducted at a locally owned community pharmacy in rural Southeast Missouri. Prescriptions written for patients less than 18 years old for guideline recommended antibiotics used for acute otitis media or acute pharyngitis dispensed between October 1, 2020, and May 10, 2021, were included in the analysis. Prescriptions were considered optimal if they included both patient weight and diagnosis code. Optimal prescriptions were evaluated for adherence to guideline recommended dosing. The primary outcomes included percentage of prescriptions without patient weight, diagnosis code, or both and the percentage of optimal prescriptions prescribed outside of guideline recommended dosing for the specified condition.

Of the 115 included prescriptions, 45 were missing a patient weight, diagnosis code, or both. Seventy prescriptions were considered optimal, and of those, 42 (60%) were prescribed outside of guideline recommended dosing.

Prescriptions were identified as missing important information at the time of dispensing. Of the optimal prescriptions, the majority were prescribed outside of current guideline recommended dosing, with subtherapeutic dosing being the most common.

To assess pharmacy student responses to medication problems with and without clinical decision support (CDS) alerts during simulated order verification.

Three classes of students completed an order verification simulation. The simulation randomized students to a different series of 10 orders with varying CDS alert frequency. Two of the orders contained medication-related problems. The appropriateness of the students' interventions and responses to the CDS alerts were evaluated. In the following semester for 2 classes, 2 similar simulations were completed. All 3 simulations contained 1 problem with and 1 without an alert.

During the first simulation, 384 students reviewed an order with a problem and an alert. Students exposed to prior inappropriate alerts within the simulation had less appropriate responses (66% vs 75%). Of 321 students who viewed a second order with a problem, those reviewing an order lacking an alert recommended an appropriate change less often (45% vs 87%). Among 351 students completing the second simulation, those who participated in the first simulation appropriately responded to the alert for a problem more often than those who only received a didactic debrief (95% vs 87%). Among those completing all 3 simulations, appropriate responses increased between simulations for problems with (n = 238, 72-95-93%) and without alerts (n = 49, 53-71-90%).

Some pharmacy students displayed baseline alert fatigue and overreliance on CDS alerts for medication problem detection during order verification simulations. Exposure to the simulations improved CDS alert response appropriateness and detection of problems.

Knowledge of the prevalence and characteristics of medication errors in pediatric and neonatal patients is limited. This study aimed to evaluate the incidence and medication error characteristics in a pediatric hospital over 5 years and to determine whether serial error prevention programs to optimize a computerized physician order entry (CPOE) system reduce error incidence.

We retrospectively reviewed medication errors documented between January 2015 and December 2019.

A total of 2,591,596 prescriptions were checked, and 255 errors were identified. Wrong dose prescriptions constituted the most common errors (56.9%). Medications with the highest rate of errors were antibiotics/antiviral drugs (36.9%). Oral route medications comprised the highest portion (60.8%), followed by intravenous ones (28.6%). The most common stage for medication errors was physician ordering (93.3%). Junior residents were responsible for most errors (45.9%). Most errors occurred in the pediatric ward (53.7%). In total, 221 (86.7%) errors were near misses. Only 4 errors (1.6%) were considered significant and required active monitoring or intervention. Type of error, stage of error, staff composition, and severity level of errors were significantly related to the number of errors in different years. There was a statistically significant decrease in errors per 100,000 prescriptions across different years after optimizing the CPOE system.

The incidence of medication errors decreased with extensive use of the CPOE system. Continuous application of the CPOE optimization program can effectively reduce medication errors. Further incorporation of pediatric-specific decision-making and support tools and error prevention measures into CPOE systems is needed.

Medication administration errors (MAEs) are the most common type of medication error. Furthermore, they are more common among neonates as compared with adults. MAEs can result in severe patient harm, subsequently causing a significant economic burden to the healthcare system. Targeting and prioritising neonates at high risk of MAEs is crucial in reducing MAEs. To the best of our knowledge, there is no predictive risk score available for the identification of neonates at risk of MAEs. Therefore, this study aims to develop and validate a risk prediction model to identify neonates at risk of MAEs.

This is a prospective direct observational study that will be conducted in five neonatal intensive care units. A minimum sample size of 820 drug preparations and administrations will be observed. Data including patient characteristics, drug preparation-related and administration-related information and other procedures will be recorded. After each round of observation, the observers will compare his/her observations with the prescriber's medication order, hospital policies and manufacturer's recommendations to determine whether MAE has occurred. To ensure reliability, the error identification will be independently performed by two clinical pharmacists after the completion of data collection for all study sites. Any disagreements will be discussed with the research team for consensus. To reduce overfitting and improve the quality of risk predictions, we have prespecified a priori the analytical plan, that is, prespecifying the candidate predictor variables, handling missing data and validation of the developed model. The model's performance will also be assessed. Finally, various modes of presentation formats such as a simplified scoring tool or web-based electronic risk calculators will be considered.

Prescription errors are a significant cause of iatrogenic harm in the health care system. Pediatric emergency department (ED) patients are particularly vulnerable to error. We sought to decrease prescription errors in an academic pediatric ED by 20% over a 24-month period by implementing identified national best practice guidelines.

From 2017 to 2019, a multidisciplinary, fellow-driven quality improvement (QI) project was conducted using the Model for Improvement. Four key drivers were identified including simplifying the electronic order entry into prescription folders, improving knowledge of dosing by indication, increasing error feedback to prescribers, and creating awareness of common prescription pitfalls. Four interventions were subsequently implemented. Outcome measures included prescription errors per 1000 prescriptions written for all medications and top 10 error-prone antibiotics. Process measures included provider awareness and use of prescription folders; the balancing measure was provider satisfaction. Differences in outcome measures were assessed by statistical process control methodology. Process and balancing measures were analyzed using 1-way analysis of variance and χ2 testing.

Before our interventions, 8.6 errors per 1000 prescriptions written were identified, with 62% of errors from the top 10 most error-prone antibiotics. After interventions, error rate per 1000 prescriptions decreased from 8.6 to 4.5 overall and from 20.1 to 8.8 for top 10 error-prone antibiotics. Provider awareness of prescription folders was significantly increased.

QI efforts to implement previously defined best practices, including simplifying and standardizing computerized provider order entry (CPOE), significantly reduced prescription errors. Synergistic effect of educational and technological efforts likely contributed to the measured improvement.

Acetaminophen is the most common medication prescribed in children's hospitals. The aim of the study was to estimate the frequency and risk factors for acetaminophen underdosing and overdosing in the paediatric intensive care unit (PICU).

Retrospective cohort of drug administrations in a large tertiary care PICU.

All PICU admissions, less than 18 years of age, admitted between 1 January 2008 and 1 January 2018, having received at least one dose of enteral acetaminophen.

The primary outcome was acetaminophen underdosing and overdosing, defined as doses exceeding the 10% upper and lower limits of the standard reference range (10-15 mg/kg) and 10% above daily maximum dose (75 mg/kg). A generalised estimating equation regression assessed patient risk factors for single underdosing, single overdosing and cumulative daily overdosing of acetaminophen.

Of the 147 485 doses of enteral acetaminophen administered, 7814 (5.3%) were single underdoses (1 in every 19 doses) and 4640 (3.1%) were single overdoses (1 in every 32 doses). There were 6813 cumulative overdose days (1 in every 9 patient-days). Risk factors for both underdosing and overdosing included older age and cardiac admission, whereas risk factors for cumulative overdosing were young age and cardiac admission. Electronic prescribing increased the risk of underdosing and overdosing, but decreased cumulative acetaminophen overdosing (relative risk 0.51, p=0.001).

Acetaminophen underdosing and overdosing are common in the PICU and can be detected with pharmacoepidemiology. Electronic prescribing increased the risk of single underdosing and overdosing, although it reduced the risk of cumulative overdosing.

Dosing errors can cause significant harm in paediatric healthcare settings. Our objective was to investigate the effects of paediatric dose range checking (DRC) clinical decision support (CDS) software on overdosing-related outcomes.

A before-after study and a semistructured survey of prescribers was conducted across inpatient wards (excluding intensive care) in a regional children's hospital. DRC CDS software linked to a paediatric drug formulary was integrated into an existing electronic prescribing system. The main outcome measures were; the proportion of prescriptions with overdosing errors; overdosing-related clinical incidents; severity of clinical incidents; and acceptability of the intervention.

The prescription overdosing error rate did not change significantly following the introduction of DRC CDS software: in the preintervention period 12/847 (1.4%) prescriptions resulted in prescription errors and in the postintervention period there were 9/684 (1.3%) prescription overdosing errors (n=21, Pearson χ² value=0.028, p=0.868). However, there was a significant trend towards a reduction in the severity of harm associated with reported overdosing incidents (n=60, Mann-Whitney U value=301.0, p=0.012). Prescribers reported that the intervention was beneficial and they were also able to identify factors that may have contributed to the persistence of overdosing errors.

DRC CDS software did not reduce the incidence of prescription overdosing errors in a paediatric hospital setting but the level of harm associated with the overdosing errors may have been reduced. Use of the software seemed to be safe and it was perceived to be beneficial by prescribers.

The aim of the study was to assess the impact of Electronic Health Record (EHR) implementation on medication safety in two intensive care units (ICUs).

Using a prospective pre-post design, we assessed 1254 consecutive admissions to two ICUs before and after an EHR implementation. Each medication event was evaluated with regard to medication error (error type, medication-management stage) and impact on patient (severity of potential or actual harm).

We identified 4063 medication-related events either pre-implementation (2074 events) or post-implementation (1989 events). Although the overall potential for harm due to medication errors decreased post-implementation only 2 of the 3 error rates were significantly lower post-implementation. After EHR implementation, we observed reductions in rates of medication errors per admission at the stages of transcription (0.13-0, P < 0.001), dispensing (0.49-0.16, P < 0.001), and administration (0.83-0.56, P = 0.011). Within the ordering stage, 4 error types decreased post-implementation (orders with omitted information, error-prone abbreviations, illegible orders, failure to renew orders) and 4 error types increased post-implementation (orders of wrong drug, orders containing a wrong start or stop time, duplicate orders, orders with inappropriate or wrong information). Within the administration stage, we observed a reduction of late administrations and increases in omitted administrations and incorrect documentation.

Electronic Health Record implementation in two ICUs was associated with both improvement and worsening in rates of specific error types. Further safety improvements require a nuanced understanding of how various error types are influenced by the technology and the sociotechnical work system of the technology implementation. Recommendations based on human factors engineering principles are provided for reducing medication errors.

Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to miss important risk factors associated with medication ordering errors. Previously, we described a dataset of CPOE-based medication voiding accompanied by univariable and multivariable regression analyses. However, these traditional techniques require expert guidance and may perform poorly compared to newer approaches. In this paper, we update that analysis using machine learning (ML) models to predict erroneous medication orders and identify its contributing factors. We retrieved patient demographics (race/ethnicity, sex, age), clinician characteristics, type of medication order (inpatient, prescription, home medication by history), and order content. We compared logistic regression, random forest, boosted decision trees, and artificial neural network models. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). The dataset included 5,804,192 medication orders, of which 28,695 (0.5%) were voided. ML correctly classified voids at reasonable accuracy; with a positive predictive value of 10%, ~20% of errors were included. Gradient boosted decision trees achieved the highest AUROC (0.7968) and AUPRC (0.0647) among all models. Logistic regression had the poorest performance. Models identified predictive factors with high face validity (e.g., student orders), and a decision tree revealed interacting contexts with high rates of errors not identified by previous regression models. Prediction models using order-entry information offers promise for error surveillance, patient safety improvements, and targeted clinical review. The improved performance of models with complex interactions points to the importance of contextual medication ordering information for understanding contributors to medication errors.

This study aimed to evaluate the effect of a closed-loop computerised physician order entry (CPOE) system on prescribing in a general paediatric unit in Hong Kong. We studied the effect of the CPOE system on medication prescribing error and the characteristics of these errors before and after the implementation of the system.

This was a single-site, prospective, observational study at a public hospital's general paediatric unit in Hong Kong, conducted during the pre- and post-implementation of the system from March to April 2019 and 2020, respectively. Collected data included the number of medication orders processed, the number of prescribing errors identified, and the characteristics of errors, such as the severity, children's age group, drug formulation, and drug class.

The prescribing error rate was significantly reduced from 6.7% to 3.9% after CPOE implementation. The causes of prescribing errors were found to be significantly different, as the implementation eradicated handwriting-related errors and reduced dosage selection-related errors. However, we found that CPOE increased other causes of error, such as missing entry of patient information that might affect the dispensing process, thus delaying patients in receiving their medications on time.

The CPOE system significantly reduced prescribing errors and altered some of the characteristics of these errors. Poor system design or inadequate user training could result in the creation of new causes of error.

Due to the diversity of reports and on the rates of medications errors (MEs) in Saudi Arabia, we performed the first meta-analysis to determine the rate of medications errors in Saudi Arabia using meta-analysis in the hospital settings.

We conducted a systematic literature search through August 2019 using PubMed, EMBASE, CINAHL, PsycINFO, and Google Scholar to identify all observational studies conducted in hospital settings in Saudi Arabia that reported the rate of MEs. A random-effects models were used to calculate overall MEs, as well as prescribing, dispensing, and administration error rates. The I2 statistics were used to analyze heterogeneity.

Sixteen articles were included in this search. The total incidence of MEs in Saudi Arabia hospitals was estimated at 44.4%. Prescribing errors, dispensing errors, and adminstration errors incidents represent 40.2%, 28.2%, and 34.5% out of the total number of reported MEs, respectively. However, between-study heterogeneity was also generally found to be >90% (I-squared statistic).

This study demonstrates the MEs common in health facilities. Additional efforts in the field are needed to improve medication management systems in order to prevent patient harm incidents.

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Health information technology (HIT) is known to reduce prescribing errors but may also cause new types of technology-generated errors (TGE) related to data entry, duplicate prescribing, and prescriber alert fatigue. It is unclear which component behaviour change techniques (BCTs) contribute to the effectiveness of prescribing HIT implementations and optimisation. This study aimed to (i) quantitatively assess the HIT that reduces prescribing errors in hospitals and (ii) identify the BCTs associated with effective interventions.

Articles were identified using CINAHL, EMBASE, MEDLINE, and Web of Science to May 2020. Eligible studies compared prescribing HIT with paper-order entry and examined prescribing error rates. Studies were excluded if prescribing error rates could not be extracted, if HIT use was non-compulsory or designed for one class of medication. The Newcastle-Ottawa scale was used to assess study quality. The review was reported in accordance with the PRISMA and SWiM guidelines. Odds ratios (OR) with 95% confidence intervals (CI) were calculated across the studies. Descriptive statistics were used to summarise effect estimates. Two researchers examined studies for BCTs using a validated taxonomy. Effectiveness ratios (ER) were used to determine the potential impact of individual BCTs.

Thirty-five studies of variable risk of bias and limited intervention reporting were included. TGE were identified in 31 studies. Compared with paper-order entry, prescribing HIT of varying sophistication was associated with decreased rates of prescribing errors (median OR 0.24, IQR 0.03-0.57). Ten BCTs were present in at least two successful interventions and may be effective components of prescribing HIT implementation and optimisation including prescriber involvement in system design, clinical colleagues as trainers, modification of HIT in response to feedback, direct observation of prescriber workflow, monitoring of electronic orders to detect errors, and system alerts that prompt the prescriber.

Prescribing HIT is associated with a reduction in prescribing errors in a variety of hospital settings. Poor reporting of intervention delivery and content limited the BCT analysis. More detailed reporting may have identified additional effective intervention components. Effective BCTs may be considered in the design and development of prescribing HIT and in the reporting and evaluation of future studies in this area.

We evaluated the nutrient intakes of very low birthweight (VLBW) infants weighing less than 1500 g and tested the hypothesis that using a triple-chamber parenteral nutrition (PN) solution, containing lipids, glucose and amino acids, would improve protein intake.

This retrospective cohort study comprised 953 VLBW infants born in 2005-2013 at a gestational age of less than 32 + 0/7 weeks and admitted to the neonatal care unit at Helsinki Children's Hospital, Finland. The infants were divided into four groups according their birth year and PN regime. Nutrient intakes were obtained from computerised medication administration records.

In 2012-2013, when a triple-chamber PN solution was used, infants were more likely to reach the target parenteral protein intake of 3.5 g/kg/d, and reach it 3-7 days earlier, compared with infants who received individual PN or standard two-in-one PN solutions in 2005-2011. In addition, infants in the triple-chamber group had the highest median energy intake (90 kcal/kg/d) during the first week. They also had higher median protein intakes in weeks one, two and three (3.1, 3.4 and 3.7 g/kg/d) than infants born in 2005-2011 (P < .05).

Using a triple-chamber PN solution was associated with improved protein intake, and the protein target was more likely to be achieved.

The risk of medication errors is high in paediatric inpatient settings. However, estimates of the prevalence of medication errors have not accounted for heterogeneity across studies in error identification methods and definitions, nor contextual differences across wards and the use of electronic or paper medication charts.

Our aim was to conduct a systematic review and meta-analysis to provide separate estimates of the prevalence of medication errors among paediatric inpatients, depending on hospital ward and the use of electronic or paper medication charts, that address differences in error identification methods and definitions.

We systematically searched five databases to identify studies published between January 2000 and December 2018 that assessed medication error rates by medication chart audit, direct observation or a combination of methods.

We identified 71 studies, 19 involved paediatric wards using electronic charts. Most studies assessed prescribing errors with few studies assessing administration errors. Estimates varied by ward type. Studies of paediatric wards using electronic charts generally reported a reduced error prevalence compared to those using paper, although there were some inconsistencies. Error detection methods impacted the rate of administration errors in studies of multiple wards, however, no other difference was found. Definition of medication error did not have a consistent impact on reported error rates.

Medication errors are a frequent occurrence in paediatric inpatient settings, particularly in intensive care wards and emergency departments. Hospitals using electronic charts tended to have a lower rate of medication errors compared to those using paper charts. Future research employing controlled designs is needed to determine the true impact of electronic charts and other interventions on medication errors and associated harm among hospitalized children.

To assess risk factors for electronic prescription errors in a PICU.

A database of electronic prescriptions issued by a computerized physician order entry with clinical decision support system was analyzed to identify risk factors for prescription errors.

Of 6,250 prescriptions, 101 were associated with errors (1.6%). The error rate was twice as high in patients older than 12 years than in patients children 6-12 and 0-6 years old (2.4% vs 1.3% and 1.2%, respectively, p < 0.05). Compared with patients without errors, patients with errors had a significantly higher score on the Pediatric Index of Mortality 2 (-3.7 vs -4.5; p = 0.05), longer PICU stay (6 vs 3.1 d; p < 0.0001), and higher number of prescriptions per patient (40.8 vs. 15.7; p < 0.0001). In addition, patients with errors were more likely to have a neurologic main admission diagnosis (p = 0.008) and less likely to have a cardiologic diagnosis (p = 0.03) than patients without errors.

Our findings suggest that older patient age and greater disease severity are risk factors for electronic prescription errors.

Children admitted to paediatric and neonatal intensive care units may be at high risk from medication errors and preventable adverse drug events.

The objective of this systematic review was to review empirical studies examining the prevalence and nature of medication errors and preventable adverse drug events in paediatric and neonatal intensive care units.

Seven electronic databases were searched between January 2000 and March 2019.

Quantitative studies that examined medication errors/preventable adverse drug events using direct observation, medication chart review, or a mixture of methods in children ≤ 18 years of age admitted to paediatric or neonatal intensive care units were included.

Data on study design, detection method used, rates and types of medication errors/preventable adverse drug events, and medication classes involved were extracted.

Thirty-five unique studies were identified for inclusion. In paediatric intensive care units, the median rate of medication errors was 14.6 per 100 medication orders (interquartile range 5.7-48.8%, n = 3) and between 6.4 and 9.1 per 1000 patient-days (n = 2). In neonatal intensive care units, medication error rates ranged from 4 to 35.1 per 1000 patient-days (n = 2) and from 5.5 to 77.9 per 100 medication orders (n = 2). In both settings, prescribing and medication administration errors were found to be the most common medication errors, with dosing errors the most frequently reported error subtype. Preventable adverse drug event rates were reported in three paediatric intensive care unit studies as 2.3 per 100 patients (n = 1) and 21-29 per 1000 patient-days (n = 2). In neonatal intensive care units, preventable adverse drug event rates from three studies were 0.86 per 1000 doses (n = 1) and 0.47-14.38 per 1000 patient-days (n = 2). Anti-infective agents were commonly involved with medication errors/preventable adverse drug events in both settings.

Medication errors occur frequently in critically ill children admitted to paediatric and neonatal intensive care units and may lead to patient harm. Important targets such as dosing errors and anti-infective medications were identified to guide the development of remedial interventions.

Increased use of health information technology (HIT) has been advocated as a medication error reduction strategy. Evidence of its benefits in the pediatric setting remains limited. In 2012, electronic prescribing (ICCA, Philips, United Kingdom) and standard concentration infusions (SCIs)-facilitated by smart-pump technology-were introduced into the pediatric intensive care unit (PICU) of an Irish tertiary-care pediatric hospital.

The aim of this study is to assess the impact of the new technology on the rate and severity of PICU prescribing errors and identify technology-generated errors.

A retrospective, before and after study design, was employed. Medication orders were reviewed over 24 weeks distributed across four time periods: preimplementation (Epoch 1); postimplementation of SCIs (Epoch 2); immediate postimplementation of electronic prescribing (Epoch 3); and 1 year postimplementation (Epoch 4). Only orders reviewed by a clinical pharmacist were included. Prespecified definitions, multidisciplinary consensus and validated grading methods were utilized.

A total of 3,356 medication orders for 288 patients were included. Overall error rates were similar in Epoch 1 and 4 (10.2 vs. 9.8%; p = 0.8), but error types differed (p < 0.001). Incomplete and wrong unit errors were eradicated; duplicate orders increased. Dosing errors remained most common. A total of 27% of postimplementation errors were technology-generated. Implementation of SCIs alone was associated with significant reductions in infusion-related prescribing errors (29.0% [Epoch 1] to 14.6% [Epoch 2]; p < 0.001). Further reductions (8.4% [Epoch 4]) were identified after implementation of electronically generated infusion orders. Non-infusion error severity was unchanged (p = 0.13); fewer infusion errors reached the patient (p < 0.01). No errors causing harm were identified.

The limitations of electronic prescribing in reducing overall prescribing errors in PICU have been demonstrated. The replacement of weight-based infusions with SCIs was associated with significant reductions in infusion prescribing errors. Technology-generated errors were common, highlighting the need for on-going research on HIT implementation in pediatric settings.

Background Prescribing errors in children are common due to individualization of dosage regimen. It potentially has a great impact especially in this vulnerable population. Objective To determine the prevalence and common types of prescribing errors in a Malaysian pediatric outpatient department and to determine the factors contributing to prescribing errors. Setting Pediatric Outpatient Department and Outpatient Pharmacy at a tertiary care hospital in Malaysia. Method This is a prospective, cross sectional observational study where all new prescriptions received by the outpatient pharmacy from patients attending pediatric out-patient clinic were included for analysis. Descriptive statistics and logistic regression were used to analyze the data. Main outcome measure Frequency, types, potential clinical consequences and contributing factors of prescribing errors. Results Two hundred and fifty new prescriptions with 493 items were analyzed. There were 13 per 100 prescriptions with at least one prescribing error and 7.3% of the total items were prescribed incorrectly. The most common types of prescribing error were, an ambiguous prescription (61.1%) followed by an unrecommended dose regimen (13.9%). Logistic regression analysis showed that the risk of a prescribing error significantly increased when the prescription was written by a house officer (OR 4.72, p = 0.029). Errors were judged to be potentially non-significant (33.3%), significant (36.1%), or serious (30.6%). Conclusion The experience of prescribers is an important factor that contributes to prescribing errors in pediatrics. Many of the errors made were potentially serious and may impact on the patients' well-being.

Risks and vulnerabilities of the medication-use process in nonpediatric institutions that also serve pediatric patients are reviewed, and guidance on risk mitigation strategies is provided.

There are many risks and vulnerabilities in the medication-use process as it relates to pharmacotherapy for pediatric patients admitted to adult institutions. Mitigation of these risks is critical and should encompass various available resources and strategies. Special emphasis should be placed on use of technology to improve overall safety. Available literature recommends optimization of technology and resource use, institutional support for pediatric pharmacists' involvement in managing pediatric medication use, and provision of early exposure to pediatric patients in pharmacist training programs as additional methods of mitigating risks associated with pediatric medication use in adult institutions. Adult hospitals that provide care for pediatric patients should assess their processes in order to identify hospital-specific interventions to promote pediatric medication safety.

Pediatric medication safety frameworks in U.S. adult institutions vary widely. Treating pediatric patients involves risks in all areas of the medication-use process. Optimizing technology, utilizing external resources, supporting a pediatric pharmacist, and providing early-career exposure to pediatric patients are methods to mitigate risks in institutions that primarily serve adult patients.

Problems arising from medicines usage are recognised as a key patient safety issue. Children are a particular concern, given that they are more likely than adults to experience medication-related harm. While previous reviews have provided an estimate of prevalence in this population, these predate recent developments in the delivery of paediatric care. Hence, there is a need for an updated, focussed and critical review of the prevalence and nature of drug-related problems in hospitalised children in the UK, in order to support the development and targeting of interventions to improve medication safety.

Nine electronic databases (Medline, Embase, CINAHL, PsychInfo, IPA, Scopus, HMIC, BNI, The Cochrane library and clinical trial databases) were searched from January 1999 to April 2019. Studies were included if they were based in the UK, reported on the frequency of adverse drug reactions (ADRs), adverse drug events (ADEs) or medication errors (MEs) affecting hospitalised children. Quality appraisal of the studies was also conducted.

In all, 26 studies were included. There were no studies which specifically reported prevalence of adverse drug events. Two adverse drug reaction studies reported a median prevalence of 25.6% of patients (IQR 21.8-29.9); 79.2% of reactions warranted withdrawal of medication. Sixteen studies reported on prescribing errors (median prevalence 6.5%; IQR 4.7-13.3); of which, the median rate of dose prescribing errors was 11.1% (IQR 2.9-13). Ten studies reported on administration errors with a median prevalence of 16.3% (IQR 6.4-23). Administration technique errors represented 53% (IQR 52.7-67.4) of these errors. Errors detected during medicines reconciliation at hospital admission affected 43% of patients, 23% (Range 20.1-46) of prescribed medication; 70.3% (Range 50-78) were classified as potentially harmful. Medication errors detected during reconciliation on discharge from hospital affected 33% of patients and 19.7% of medicines, with 22% considered potentially harmful. No studies examined the prevalence of monitoring or dispensing errors.

Children are commonly affected by drug-related problems throughout their hospital journey. Given the high prevalence and risk of patient harm,, there is a need for a deeper theoretical understanding of paediatric medication systems to enable more effective interventions to be developed to improve patient safety.

The aim of this research was to ascertain the effectiveness of current electronic prescribing (EP) systems to prevent a standardised set of paediatric prescribing errors likely to cause harm if they reach the patient.

Semistructured survey.

UK hospitals using EP in the paediatric setting.

Number and type of erroneous orders able to be prescribed, and the level of clinical decision support (CDS) provided during the prescribing process.

90.7% of the erroneous orders were able to be prescribed across the seven different EP systems tested. Levels of CDS varied between systems and between sites using the same system.

EP systems vary in their ability to prevent harmful prescribing errors in the hospital paediatric setting. Differences also occur between sites using the same system, highlighting the importance of how a system is set up and optimised.

Computerized physician order entry (CPOE) has grown since the early 1990s. While many systems serve adult patients, systems for pediatric and neonatal populations have lagged. Adapting adult CPOE systems for pediatric use may require significant modifications to address complexities associated with pediatric care such as daily weight changes and small medication doses.

This article aims to review the neonatal intensive care unit (NICU) CPOE literature to characterize trends in the introduction of this technology and to identify potential areas for further research.

Articles pertaining to NICU CPOE were identified in MEDLINE using MeSH terms "medical order entry systems," "drug therapy," "intensive care unit, neonatal," "infant, newborn," etc. Two physician reviewers evaluated each article for inclusion and exclusion criteria. Consensus judgments were used to classify the articles into five categories: medication safety, usability/alerts, clinical practice, clinical decision Support (CDS), and implementation. Articles addressing pediatric (nonneonatal) CPOE were included if they were applicable to the NICU setting.

Sixty-nine articles were identified using MeSH search criteria. Twenty-two additional articles were identified by hand-searching bibliographies and 6 articles were added after the review process. Fifty-five articles met exclusion criteria, for a final set of 42 articles. Medication safety was the focus of 22 articles, followed by clinical practice (10), CDS (10), implementation (11), and usability/alerts (4). Several addressed more than one category. No study showed a decrease in medication safety post-CPOE implementation. Within clinical practice articles, CPOE implementation showed no effect on blood glucose levels or time to antibiotic administration but showed conflicting results on mortality rates. Implementation studies were largely descriptive of single-hospital experiences.

CPOE implementation within the NICU has demonstrated improvement in medication safety, with the most consistent benefit involving a reduction in medication errors and wrong-time administration errors. Additional research is needed to understand the potential limitations of CPOE systems in neonatal intensive care and how CPOE affects mortality.

The risk of dose errors is high in paediatric inpatient settings. Computerized provider order entry (CPOE) systems with clinical decision support (CDS) may assist in reducing the risk of dosing errors. Although a frequent type of medication error, the prevalence of dose errors is not well described. Dosing error rates in hospitals with or without CPOE have not been compared.

Our aim was to conduct a systematic review assessing the prevalence and impact of dose errors in paediatric wards with and without CPOE and/or CDS.

We systematically searched five databases to identify studies published between January 2000 and December 2017 that assessed dose error rates by medication chart audit or direct observation.

We identified 39 studies, nine of which involved paediatric wards using CPOE with or without CDS. Studies of paediatric wards using paper medication charts reported approximately 8-25% of patients experiencing a dose error, and approximately 2-6% of medication orders and approximately 3-8% of dose administrations contained a dose error, with estimates varying by ward type. The nine studies of paediatric wards using CPOE reported approximately 22% of patients experiencing a dose error, and approximately 1-6% of medication orders and approximately 3-8% of dose administrations contained a dose error. Few studies provided data for individual wards. The severity and prevalence of harm associated with dose errors was rarely assessed and showed inconsistent results.

Dose errors occur in approximately 1 in 20 medication orders. Hospitals using CPOE with or without CDS had a lower rate of dose errors compared with those using paper charts. However, few pre/post studies have been conducted and none reported a significant reduction in dose error rates associated with the introduction of CPOE. Future research employing controlled designs is needed to determine the true impact of CPOE on dosing errors among children, and any associated patient harm.

Results of a systematic review of published data on the effect of computerized prescriber order entry (CPOE) with clinical decision support on medication error (ME) and adverse drug event (ADE) rates are presented.

Literature searches of MEDLINE, Embase, and other databases were conducted to identify English- and Spanish-language articles on selected CPOE outcomes published from 1995 through 2016; in addition, 5 specific journals were searched for pertinent articles published during the period 2010-16. Publications on controlled prospective studies and before-and-after studies that assessed MEs and/or ADEs as main outcomes were selected for inclusion in the review.

Nineteen studies met the inclusion criteria. Data on MEs and ADEs could not be pooled, mainly due to heterogeneity in outcome definitions and study methodologies. The reviewed evidence indicated that CPOE implementation led to an overall reduction in errors at the prescription stage of the medication-use process (relative risk reduction, 0.29 [95% confidence interval, 0.10-0.85]; I ² = 99%) and reductions in most types of prescription errors, but CPOE also resulted in the emergence of other types of errors.

CPOE reduces the overall ME rate in the prescription process, as well as specific types of errors, such as wrong dose or strength, wrong drug, frequency, administration route, and drug-drug interaction errors. The implementation of CPOE can lead to new errors, such as wrong drug selection from drop-down menus.

Medication-related clinical decision support (CDS) alerts have been shown to be effective at reducing adverse drug events (ADEs). However, these alerts are frequently overridden, with limited data linking these overrides to harm. Dose-range checking alerts are a type of CDS alert that could have a significant impact on morbidity and mortality, especially in the intensive care unit (ICU) setting.

We performed a single-center, prospective, observational study of adult ICUs from September 2016 to April 2017. Targeted overridden alerts were triggered when doses greater than or equal to 5% over the maximum dose were prescribed. The primary outcome was the appropriateness of the override, determined by two independent reviewers, using pre-specified criteria formulated by a multidisciplinary group. Overrides which resulted in medication administration were then evaluated for ADEs by chart review.

The override rate of high dose-range alerts in the ICU was 93.0% (total n = 1525) during the study period. A total of 1418 overridden alerts from 755 unique patients were evaluated for appropriateness (appropriateness rate 88.8%). The most common medication associated with high dose-range alerts was insulin regular infusion (n = 262, 18.5%). The rates of ADEs for the appropriately and inappropriately overridden alerts per 100 overridden alerts were 1.3 and 5.0, respectively (p < 0.001).

Overriding high dose-range CDS alerts was found to be common and often appropriate, suggesting that more intelligent dose checking is needed. Some alerts were clearly inappropriately presented to the provider. Inappropriate overrides were associated with an increased risk of ADEs, compared to appropriately overridden alerts.

The purpose of this paper is to present a review of health information system (HIS)-induced errors and its management. This paper concludes that the occurrence of errors is inevitable but it can be minimised with preventive measures. The review of classifications can be used to evaluate medical errors related to HISs using a socio-technical approach. The evaluation could provide an understanding of errors as a learning process in managing medical errors.

A literature review was performed on issues, sources, management and approaches to HISs-induced errors. A critical review of selected models was performed in order to identify medical error dimensions and elements based on human, process, technology and organisation factors.

Various error classifications have resulted in the difficulty to understand the overall error incidents. Most classifications are based on clinical processes and settings. Medical errors are attributed to human, process, technology and organisation factors that influenced and need to be aligned with each other. Although most medical errors are caused by humans, they also originate from other latent factors such as poor system design and training. Existing evaluation models emphasise different aspects of medical errors and could be combined into a comprehensive evaluation model.

Overview of the issues and discourses in HIS-induced errors could divulge its complexity and enable its causal analysis.

This paper helps in understanding various types of HIS-induced errors and promising prevention and management approaches that call for further studies and improvement leading to good practices that help prevent medical errors.

Classification of HIS-induced errors and its management, which incorporates a socio-technical and multi-disciplinary approach, could guide researchers and practitioners to conduct a holistic and systematic evaluation.

Literature is limited on pediatric anti-infective medication errors. There is a pressing need for additional research, as studies suggest high rates of overall pediatric medication errors and known harmful side effect profiles for anti-infective medications with narrow dosing ranges. This study aimed to identify risk factors related to harmful anti-infective medication errors in pediatric patients.

A retrospective chart review of all voluntary error reports involving anti-infective medication errors and pediatric patients (0 to < 22 years old) reported June 2014-December 2015 was conducted. Error reports were generated using the hospital's general error reporting system and a pharmacy-based patient surveillance reporting system and were stratified based on the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) Medication Error Index. Harmful errors were compared to nonharmful errors using Fisher's exact test.

Of 338 anti-infective medication-related error reports, 13.6% of voluntarily reported errors reached the patient and 1.5% resulted in harm to the patient and required additional monitoring, interventions, and/or prolonged hospitalization. Antibacterials comprised 93.8% of all error reports, with beta-lactams (63.0%), macrolides (6.5%) and glycopeptides (6.2%) the most common classes. When using Fisher's exact test to compare harmful and nonharmful medication errors, the risk factor significantly associated with harmful errors was anti-infective class (p = 0.001).

Voluntarily reported anti-infective medication errors within the pediatric patient population often reached the patient, and specific anti-infective medications are potentially of higher risk. Further investigation and additional quality and patient safety strategies may be needed for these higher-risk profile medications.

To identify and understand the factors that contribute to medication errors associated with the use of computerized provider order entry (CPOE) in pediatrics and provide recommendations on how CPOE systems could be improved.

We conducted a systematic literature review across 3 large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Three independent reviewers screened the titles, and 2 authors then independently reviewed all abstracts and full texts, with 1 author acting as a constant across all publications. Data were extracted onto a customized data extraction sheet, and a narrative synthesis of all eligible studies was undertaken.

A total of 47 articles were included in this review. We identified 5 factors that contributed to errors with the use of a CPOE system: (1) lack of drug dosing alerts, which failed to detect calculation errors; (2) generation of inappropriate dosing alerts, such as warnings based on incorrect drug indications; (3) inappropriate drug duplication alerts, as a result of the system failing to consider factors such as the route of administration; (4) dropdown menu selection errors; and (5) system design issues, such as a lack of suitable dosing options for a particular drug.

This review highlights 5 key factors that contributed to the occurrence of CPOE-related medication errors in pediatrics. Dosing support is the most important. More advanced clinical decision support that can suggest doses based on the drug indication is needed.

An enhanced dose range checking (DRC) system was developed to evaluate prescription error rates in the pediatric intensive care unit and the pediatric cardiovascular intensive care unit.

An enhanced DRC system incorporating "soft" and "hard" alerts was designed and implemented. Practitioner responses to alerts for patients admitted to the pediatric intensive care unit and the pediatric cardiovascular intensive care unit were retrospectively reviewed.

Alert rates increased from 0.3% to 3.4% after "go-live" (P < 0.001). Before go-live, all alerts were soft alerts. In the period after go-live, 68% of alerts were soft alerts and 32% were hard alerts. Before go-live, providers reduced doses only 1 time for every 10 dose alerts. After implementation of the enhanced computerized physician order entry system, the practitioners responded to soft alerts by reducing doses to more appropriate levels in 24.7% of orders (70/283), compared with 10% (3/30) before go-live (P = 0.0701). The practitioners deleted orders in 9.5% of cases (27/283) after implementation of the enhanced DRC system, as compared with no cancelled orders before go-live (P = 0.0774). Medication orders that triggered a soft alert were submitted unmodified in 65.7% (186/283) as compared with 90% (27/30) of orders before go-live (P = 0.0067). After go-live, 28.7% of hard alerts resulted in a reduced dose, 64% resulted in a cancelled order, and 7.4% were submitted as written.

Before go-live, alerts were often clinically irrelevant. After go-live, there was a statistically significant decrease in orders that were submitted unmodified and an increase in the number of orders that were reduced or cancelled.

Medication errors represent a significant but often preventable cause of morbidity and mortality in neonates. The objective of this systematic review was to determine the effectiveness of interventions to reduce neonatal medication errors.

A systematic review was undertaken of all comparative and noncomparative studies published in any language, identified from searches of PubMed and EMBASE and reference-list checking. Eligible studies were those investigating the impact of any medication safety interventions aimed at reducing medication errors in neonates in the hospital setting.

A total of 102 studies were identified that met the inclusion criteria, including 86 comparative and 16 noncomparative studies. Medication safety interventions were classified into six themes: technology (n = 38; e.g. electronic prescribing), organizational (n = 16; e.g. guidelines, policies, and procedures), personnel (n = 13; e.g. staff education), pharmacy (n = 9; e.g. clinical pharmacy service), hazard and risk analysis (n = 8; e.g. error detection tools), and multifactorial (n = 18; e.g. any combination of previous interventions). Significant variability was evident across all included studies, with differences in intervention strategies, trial methods, types of medication errors evaluated, and how medication errors were identified and evaluated. Most studies demonstrated an appreciable risk of bias. The vast majority of studies (>90%) demonstrated a reduction in medication errors. A similar median reduction of 50-70% in medication errors was evident across studies included within each of the identified themes, but findings varied considerably from a 16% increase in medication errors to a 100% reduction in medication errors.

While neonatal medication errors can be reduced through multiple interventions aimed at improving the medication use process, no single intervention appeared clearly superior. Further research is required to evaluate the relative cost-effectiveness of the various medication safety interventions to facilitate decisions regarding uptake and implementation into clinical practice.

The positive impact of computerized physician order entry (CPOE) systems on prescription safety must be considered in light of the persistence of certain types of medication-prescription errors. We performed a systematic review, based on the PRISMA statement, to analyze the prevalence of prescription errors related to the use of CPOE systems.

We searched MEDLINE, EMBASE, CENTRAL, DBLP, the International Clinical Trials Registry, the ISI Web of Science, and reference lists of relevant articles from March 1982 to August 2017. We included original peer-reviewed studies which quantitatively reported medication-prescription errors related to CPOE. We analyzed the prevalence of medication-prescription errors according to an adapted version of the National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) taxonomy and assessed the mechanisms responsible for each type of prescription error due to CPOE.

Fourteen studies were included. The prevalence of CPOE systems-related medication errors relative to all prescription medication errors ranged from 6.1 to 77.7% (median = 26.1% [IQR:17.6-42,1]) and was less than 6.3% relative to the number of prescriptions reviewed. All studies reported "wrong dose" and "wrong drug" errors. The "wrong dose" error was the most frequently reported (from 7 to 67.4%, median = 31.5% [IQR:20.5-44.5]). We report the associated mechanism for each type of medication described (those due to CPOE or those occurring despite CPOE).

We observed very heterogeneous results, probably due to the definition of error, the type of health information system used for the study, and the data collection method used. Each data collection method provides valuable and useful information concerning the prevalence and specific types of errors related to CPOE systems.

The reporting of prescription errors should be continued because the weaknesses of CPOE systems are potential sources of error. Analysis of the mechanisms behind CPOE errors can reveal areas for improvement.

To conduct a systematic review and meta-analysis of the impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay (LOS), and mortality in intensive care units (ICUs).

We searched for English-language literature published between January 2000 and January 2016 using Medline, Embase, and CINAHL. Titles and abstracts of 586 unique citations were screened. Studies were included if they: (1) reported results for an ICU population; (2) evaluated the impact of CPOE or the addition of CDSSs to an existing CPOE system; (3) reported quantitative data on medication errors, ICU LOS, hospital LOS, ICU mortality, and/or hospital mortality; and (4) used a randomized controlled trial or quasi-experimental study design.

Twenty studies met our inclusion criteria. The transition from paper-based ordering to commercial CPOE systems in ICUs was associated with an 85% reduction in medication prescribing error rates and a 12% reduction in ICU mortality rates. Overall meta-analyses of LOS and hospital mortality did not demonstrate a significant change.

Critical care settings, both adult and pediatric, involve unique complexities, making them vulnerable to medication errors and adverse patient outcomes. The currently limited evidence base requires research that has sufficient statistical power to identify the true effect of CPOE implementation. There is also a critical need to understand the nature of errors arising post-CPOE and how the addition of CDSSs can be used to provide greater benefit to delivering safe and effective patient care.

To assess the prolonged impact of computerized physician order entry (CPOE) on medication prescription errors in pediatric intensive care patients.

This observational study was conducted at a pediatric intensive care unit in which a CPOE (Metavision, iMDsoft, Israel) with a limited clinical decision support system was implemented between 2004 and 2007. Since then, no changes were made to the systems. We analyzed 2500 electronic prescriptions (1250 prescriptions from 2015 and 1250 prescriptions from 2016). Prescription errors were identified by a pediatric intensive care physician and classified as potential adverse drug events, medication prescription errors, or rule violations. Their prevalence was compared with the rate in 2007, reported in a previous study from the same unit. A randomly selected 10% of the prescriptions were also analyzed by the pediatric intensive care unit pharmacist, and the level of agreement was determined.

The rate of prescription errors increased from 1.4% in 2007 to 3.2% in 2015 (P = .03). Following revision of the clinical decision support system tools, prescription errors decreased to 1% in 2016 (P < .0001). The potential adverse drug event rate dropped from 2% in 2015 to 0.7% in 2016 (P = .006), and the medication prescription error rate, from 1% to 0.2% (P = .01). The agreement between the 2 reviewers was excellent (k = 0.96).

The rate of prescription errors may increase with time from implementation of a CPOE. Repeated surveillance of prescription errors is highly advised to plan strategies to reduce them. This approach should be considered in quality improvement of computerized information systems in general.

To provide ICU clinicians with evidence-based guidance on safe medication use practices for the critically ill.

PubMed, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, and ISI Web of Science for relevant material to December 2015.

Based on three key components: 1) environment and patients, 2) the medication use process, and 3) the patient safety surveillance system. The committee collectively developed Population, Intervention, Comparator, Outcome questions and quality of evidence statements pertaining to medication errors and adverse drug events addressing the key components. A total of 34 Population, Intervention, Comparator, Outcome questions, five quality of evidence statements, and one commentary on disclosure was developed.

Subcommittee members were assigned selected Population, Intervention, Comparator, Outcome questions or quality of evidence statements. Subcommittee members completed their Grading of Recommendations Assessment, Development, and Evaluation of the question with his/her quality of evidence assessment and proposed strength of recommendation, then the draft was reviewed by the relevant subcommittee. The subcommittee collectively reviewed the evidence profiles for each question they developed. After the draft was discussed and approved by the entire committee, then the document was circulated among all members for voting on the quality of evidence and strength of recommendation.

The committee followed the principles of the Grading of Recommendations Assessment, Development, and Evaluation system to determine quality of evidence and strength of recommendations.

This guideline evaluates the ICU environment as a risk for medication-related events and the environmental changes that are possible to improve safe medication use. Prevention strategies for medication-related events are reviewed by medication use process node (prescribing, distribution, administration, monitoring). Detailed considerations to an active surveillance system that includes reporting, identification, and evaluation are discussed. Also, highlighted is the need for future research for safe medication practices that is specific to critically ill patients.

Health information technology (HIT) interventions may improve neonatal patient safety but may also introduce new errors. The objective of this review was to evaluate the evidence for use of HIT interventions to improve safety in neonatal care. Evidence for improvement exists for interventions like computerized provider order entry in the neonatal population, but is lacking for several other interventions. Many unique applications of HIT are emerging as technology and use of the electronic health record expands. Future research should focus on the impact of these interventions in the neonatal population.

Medication voiding is a computerised provider order entry (CPOE)-based discontinuation mechanism that allows clinicians to identify erroneous medication orders. We investigated the accuracy of voiding as an indicator of clinician identification and interception of a medication ordering error, and investigated reasons and root contributors for medication ordering errors.

Using voided orders identified with a void alert, we conducted interviews with ordering and voiding clinicians, followed by patient chart reviews. A structured coding framework was used to qualitatively analyse the reasons for medication ordering errors. We also compared clinician-CPOE-selected (at time of voiding), clinician-reported (interview) and chart review-based reasons for voiding.

We conducted follow-up interviews on 101 voided orders. The positive predictive value (PPV) of voided orders that were medication ordering errors was 93.1% (95% CI 88.1% to 98.1%, n=94). Using chart review-based reasons as the gold standard, we found that clinician-CPOE-selected reasons were less reflective (PPV=70.2%, 95% CI 61.0% to 79.4%) than clinician-reported (interview) (PPV=86.1%, 95%CI 78.2% to 94.1%) reasons for medication ordering errors. Duplicate (n=44) and improperly composed (n=41) ordering errors were common, often caused by predefined order sets and data entry issues. A striking finding was the use of intentional violations as a mechanism to notify and seek ordering assistance from pharmacy service. Nearly half of the medication ordering errors were voided by pharmacists.

We demonstrated that voided orders effectively captured medication ordering errors. The mismatch between clinician-CPOE-selected and the chart review-based reasons for error emphasises the need for developing standardised operational descriptions for medication ordering errors. Such standardisation can help in accurately identifying, tracking, managing and sharing erroneous orders and their root contributors between healthcare institutions, and with patient safety organisations.

This study aims to determine the prevalence and factors associated with unrounded doses ordered via a computerized prescriber order entry (CPOE) system among children during a 1-week reference period.

This retrospective, cross-sectional study included children younger than 18 years admitted during a 7-day period. An unrounded dose was defined as an unrounded actual dose (eg, dose calculated to the tenths place for non-neonatal intensive care (non-NICU) patients and dose calculated to the hundredth place for NICU patients) or unrounded volume per dose [eg, <0.1 mL for non-NICU patients and <0.01 mL for NICU patients]. A multilevel logistic regression model was used to determine the prevalence and factors associated with unrounded doses via a CPOE system with adjustment for clustering effects.

A total of 395 patients were admitted with 391 receiving medications. The overall prevalence of unrounded doses was 30% among the 2426 doses administered. Patients on the NICU team had the highest prevalence of unrounded doses. The odds of an unrounded dose were 4% (adjusted odds ratio, 0.96; 95% confidence interval, 0.94-0.98) lower with each additional kilogram increase in weight after controlling for age, route, scheduled versus as-needed administration, and cluster effects.

The prevalence of unrounded doses was higher than in previous studies. It was higher in smaller children after controlling for age, medication-related variables, and clustering. Future studies should focus on the role of CPOE in preventing unrounded and unmeasurable doses and if these strategies affect clinical outcomes (eg, adverse drug events).

Medication order voiding allows clinicians to indicate that an existing order was placed in error. We explored whether the order voiding function could be used to record and study medication ordering errors.

We examined medication orders from an academic medical center for a 6-year period (2006-2011; n  = 5 804 150). We categorized orders based on status (void, not void) and clinician-provided reasons for voiding. We used multivariable logistic regression to investigate the association between order voiding and clinician, patient, and order characteristics. We conducted chart reviews on a random sample of voided orders ( n  = 198) to investigate the rate of medication ordering errors among voided orders, and the accuracy of clinician-provided reasons for voiding.

We found that 0.49% of all orders were voided. Order voiding was associated with clinician type (physician, pharmacist, nurse, student, other) and order type (inpatient, prescription, home medications by history). An estimated 70 ± 10% of voided orders were due to medication ordering errors. Clinician-provided reasons for voiding were reasonably predictive of the actual cause of error for duplicate orders (72%), but not for other reasons.

Medication safety initiatives require availability of error data to create repositories for learning and training. The voiding function is available in several electronic health record systems, so order voiding could provide a low-effort mechanism for self-reporting of medication ordering errors. Additional clinician training could help increase the quality of such reporting.