Types Of Medication Errors Research Articles

Medication reconciliation for patients after their discharge from intensive care unit to the hospital ward

ObjectivesThe aim of this study was to determine whether the transition of care from the intensive care unit to the ward would pose a high risk for reconciliation errors. The primary outcome of this study was to describe and quantify the discrepancies and reconciliation errors. Secondary outcomes included classification of the reconciliation errors by type of medication error, therapeutic group of the drugs involved and grade of potential severity. MethodsWe conducted a retrospective observational study of reconciliated adult patients discharged from the Intensive Care Unit to the ward. Before a patient was discharged from the intensive care unit, their last intensive care unit's prescriptions were compared with their proposed medication list in the ward. The discrepancies between these were classified as justified discrepancies or reconciliation errors. Reconciliation errors were classified by type of error, potential severity, and therapeutic group. ResultsWe found that 452 patients were reconciliated. At least one discrepancy was detected in 34.29% (155/452), and 18.14% (82/452) had at least one reconciliation errors. The most found error types were a different dose or administration route (31.79% (48/151)) and omission errors (31.79% (48/151)). High alert medication was involved in 19.20% of reconciliation errors (29/151). ConclusionsOur study shows that intensive care unit to non-intensive care unit transitions are high-risk processes for reconciliation error. They frequently occur and occasionally involve high alert medication, and their severity could require additional monitoring or cause temporary harm. Medication reconciliation can reduce reconciliation errors.

Paediatric harmful adverse drug events (PHADE).

It is well established that adverse drug events are frequent in paediatric hospital practice. The objective of this study is to systematically quantify and report the incidence of harmful adverse drug events across our institution and to identify predominant medications and error types. We prospectively compiled a validated medication safety database for paediatric inpatients within our institution over a three-and-a-half-year period. All incidences of apparent patient harm relating to medication error were investigated and analyzed to determine veracity, severity of harm, phase of medication process, error type, causative medication, and contributory factors enabling each event. We identified 59 harmful adverse drug events, with an overall rate of 15.5 per 105 patient bed days. Most events occurred during administration (n = 27) and prescribing (n = 26) phases. Almost half of all harm (49%) was associated with opioids; a broad range of medication classes accounted for other harm. Harmful events occurred in 7.3 per 105 administrations of morphine and 13.3 per 105 administrations of hydromorphone. Wrong dose was the most frequently encountered error type. This is the first study to quantify harmful adverse drug events in paediatric hospital practice. Our prospective analysis and compilation of harmful medication errors in paediatric hospital practice, reported with denominators of opioid administrations, and patient bed days, is a new standard for comparison in the long-discussed problem of paediatric harmful adverse drug events. By focusing on identified problematic drugs, error types, and contributory factors, we identify opportunities for interventions, error prevention and harm reduction.

Feedback from an Adverse Event Associated with Medication Error Type Care. Case of the Saint Padre Pio Hospital Centre in Lubumbashi in the Democratic Republic of Congo

During a recent routine vaccination session for children aged 0 to 11 months at the Padre Pio hospital center in Lubumbashi, a midwife administered twice the dose of the same injectable poliomyelitis vaccine (IPV) to an infant while only one dose was indicated. The child subsequently presented with some digestive disorders such as vomiting and diarrhea that required 24-hour observation in the emergency reception unit. The occurrence of this incident required the holding of a meeting with the various stakeholders. The aim was to examine the possible cause(s) of this incident, to be able to determine whether it was a fault attributable to the vaccinator or whether it was a systemic problem, the aim being to improve the safety of the practice of vaccination by preventing the occurrence of similar situations.

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review

ObjectivesThe aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical...

Characteristics of Patients Who Complete Suicide and Suicide Attempts While Undergoing Treatment in Norway: Findings from Compensation Claims Records.

The aim of this study was to identify characteristics that differentiate patients who complete suicide (SC) from patients with suicide attempts (SA) while undergoing treatment in Norway. We examined data from the Norwegian System of Patient Injury Compensation (Norsk Pasientskade Erstatning-NPE). Data were extracted from NPE case records from a 10-year period (2009-2019) for 356 individuals who attempted (n = 78) or died by (n = 278) suicide. The two groups differed significantly in the types of medical errors identified by experts. Inadequate suicide risk assessment tended to be proportionally and significantly more prevalent among SC compared to SA. There was a weak but significant trend that SA had received medication only, whereas SC had received both medication and psychotherapy. There were no significant differences with respect to age group, gender, diagnostic category, number of previous suicide attempts, inpatient/outpatient status, or category of responsible clinic. We conclude that suicide attempters and suicide completers differed in terms of identified medical errors. Focusing on the prevention of these and other types of errors could help to reduce the number of suicides of patients in treatment.

Risk management of medication errors: a novel conceptual framework

ABSTRACT Background Medication error is a common cause of patient harm. The study aims to propose a way to manage the risk of medication errors in a novel way, by identifying practice areas where mitigating patient harm should be prioritized using a risk management approach. Methods Suspected Adverse Drug Reactions (sADRs) in Eudravigilance database over three years were reviewed to identify preventable medication errors. These were classified using a new method based upon the root cause underlying pharmacotherapeutic failure. The correlation between severity of harm and type of medication error, and other clinical parameters was investigated. Results Overall, 2294 medication errors were identified from Eudravigilance, of which 1300 (57%) were due to pharmacotherapeutic failure. Most cases of preventable medication error involved prescribing (41%) and administration (39%). The variables which significantly predicted severity of medication errors were pharmacological group, patient age, number of drugs prescribed, and route of administration. The drug classes most strongly associated with harm included cardiac drugs, opioids, hypoglycaemics, antipsychotics, sedatives, and antithrombotic agents. Conclusion The findings of this study highlight the feasibility of using a novel conceptual framework to identify areas of practice at risk of pharmacotherapeutic failure where Interventions by healthcare professionals in these areas are most likely to improve medication safety.

Internet of Things as support to reduce hospital errors related to medication administration

Objective: The high potential of digital technologies to reduce medication errors in patients hospitalized or under hospital care was explored in this investigation. The study identifies common types of medication errors and their probable causes. Furthermore, examines the technological innovation solutions available that have not yet been applied in this context, specifically the Internet of Things usage to reduce medication errors through a practical example. Methods: A rapid review was applied, exploring the papers from two databases. The analysis was limited to the period from 2017 to 2021, resulting in 147 articles from the VHL Regional Portal – Virtual Health Library and 257 articles from the PubMed database. Results: There were analyzed 40 studies. An error mapping related to the theme was made. Also, the technology application and their effectiveness were identified in the studies. According to the research, inattention or distraction due to the excessive journey of work was pointed out as the main reason that leads to medication errors with the systems employed. Conclusion: There are several opportunities to improve hospital procedures with new technological approaches. By implementing innovative technologies, hospital medication errors can be managed more efficiently and reduce costs and waste with errors medication.

Medication Errors among Iranian Intensive Care Nurses: A Systematic Review.

Medication Error (ME) is a major patient safety concern in Intensive Care Units (ICUs). Critical care nurses play a crucial role in the safe administration of medication. This study was conducted to comprehensively review the literature concerning the prevalence of ME and associated factors and outcomes in Iranian ICU nurses. An extensive search of the literature was carried in international databases including PubMed, Web of Science, Scopus, and Google Scholar, as well as Persian databases such as Magiran and Scientific Information Database (SID) using ME-related keywords and the Persian equivalent of these keywords, from the first article written in this field to artcles published on March 30, 2021. The appraisal tool (AXIS tool) was used to assess the quality of the included studies. Fifteen studies were included in this systematic review. The prevalence of MEs made by ICU nurses was 53.34%. The most common types of MEs were wrong infusion rate (14.12%), unauthorized medication (11.76%), and wrong time (8.49%) errors, respectively. MEs occurred more frequently in morning work shifts (44.44%). MEs happened more frequently for heparin, vancomycin, ranitidine, and amikacin. The most important influential factor in the occurrence of MEs in ICUs was management and human factors. The prevalence of MEs made by Iranian ICU nurses is high. Therefore, nurse managers and policymakers should develop appropriate strategies, including training programs, to reduce the occurrence of MEs made by nurses in ICUs.

Development and validation of a risk prediction model for medication administration errors among neonates in the neonatal intensive care unit: a study protocol

IntroductionMedication 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,...

Preventable harm because of outpatient medication errors among children with leukemia and lymphoma: A multisite longitudinal assessment.

There is little longitudinal information about the type and frequency of harm resulting from medication errors among outpatient children with cancer. We aimed to characterize rates and types of medication errors and harm to outpatient children with leukemia and lymphoma over 7months of treatment. We recruited children taking medications at home for leukemia or lymphoma from three pediatric cancer centers. Errors were identified by chart review, in-home medication review, observation of administration, and interviews. Physician reviewers confirmed error (Fleiss' κ=0.95), harm (Fleiss' κ=0.82), and suggested interventions. Generalized linear mixed models with random effects were used to account for clustering by site. Among 131 children taking 1669 medications with 367 home visits, 408 errors were identified, including 242 with potential for harm and 39 with harm (1.0 harm per 1000 patient-days [95% CI, 0.1-9.8]). Ten percent of children were injured by errors and 42% had errors with potential for harm. Twenty-six percent of caregivers reported that miscommunication led to missed doses or overdoses at home. Children on >13 medications had significantly more serious medication errors than those on fewer medications (77% vs 61%; p=.05). Physician reviewers judged that improved communication among caregivers and between caregivers and clinicians may have prevented the most harm (66%). In this longitudinal study, 10% children with leukemia or lymphoma experienced adverse drug events because of outpatient medication errors. Improvements addressing communication with and among caregivers should be codeveloped with families and based on human-factors engineering. In this longitudinal study, medication errors in the clinic, pharmacy, or at home among children with leukemia or lymphoma over a 7-month period were common, and 10% suffered harm because of errors. Children on >13 medications had significantly more serious medication errors than those on fewer medications (77% vs 61%; p=.05). Physician reviewers judged that improved communication among caregivers and between caregivers and clinicians may have prevented the most harm (66%). Improvements addressing communication with and among caregivers should be codeveloped with families and based on human-factors engineering.

Use of complete medication history to identify and correct transitions-of-care medication errors at psychiatric hospital admission.

Methods for categorizing the scale and severity of medication errors corrected by pharmacy staff during admission medication reconciliation using complete medication history continue to evolve. We established a rating scale that is effective for generating error reports to health system quality leadership. These reports are needed to quantify the value of investment in transitions-of-care pharmacy staff. All medication errors that were reported by pharmacy staff in the admission medication reconciliation process during a period of 6 months were eligible for inclusion. Complete medication history data source was utilized by admitting providers and all pharmacist staff and a novel medication error scoring methodology was developed. This methodology included: medication error category, medication error type, potential medication error severity, and medication non-adherence. We determined that 82 medication errors were detected from 72 patients and assessed that 74 of these errors may have harmed patients if they were not corrected through pharmacist intervention. Most of these errors were dosage discrepancies and omissions. With hospital system budgets continually becoming leaner, it is important to measure the effectiveness and value of staff resources to optimize patient care. Pharmacists performing admission medication reconciliation can detect subtle medication discrepancies that may be overlooked by other clinician types. This methodology can serve as a foundation for error reporting and predicting the severity of adverse drug events.

DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgement that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, Dr.Bench, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models for diagnostic reasoning. The goal of DR. BENCH is to advance the science in cNLP to support downstream applications in computerized diagnostic decision support and improve the efficiency and accuracy of healthcare providers during patient care. We fine-tune and evaluate the state-of-the-art generative models on DR.BENCH. Experiments show that with domain adaptation pre-training on medical knowledge, the model demonstrated opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community. We also discuss the carbon footprint produced during the experiments and encourage future work on DR.BENCH to report the carbon footprint.

Medical errors and patient safety: Strategies for reducing errors using artificial intelligence

Background: Medical errors remain a significant challenge in healthcare, contributing to adverse patient outcomes, increased costs, and extended hospitalizations. These errors encompass diagnostic inaccuracies, medication mistakes, surgical errors, and communication breakdowns. The global prevalence of medical errors underscores the urgent need for effective strategies to enhance patient safety. Aim: This article explores the role of Artificial Intelligence (AI) in reducing medical errors and improving patient safety. It aims to evaluate how AI technologies can mitigate various types of medical errors, and the challenges associated with their implementation. Methods: The study reviews current literature on AI applications in healthcare, focusing on diagnostic support, medication safety, surgical precision, and patient monitoring. It analyzes the effectiveness of AI-driven systems in reducing errors across different medical disciplines and examines the integration challenges, including ethical and regulatory concerns. Results: AI technologies, including machine learning algorithms and decision support systems, have demonstrated significant potential in enhancing diagnostic accuracy, preventing medication errors, and improving surgical outcomes. AI-driven systems have shown promising results in real-time patient monitoring, early detection of adverse events, and optimizing healthcare management. However, challenges related to data privacy, algorithm transparency, and integration into clinical workflows persist.

Assessment of the Medication Administration Errors in the Tertiary Hospital in Saudi Arabia: A prospective Observational Study

Medication errors can occur at any of the three steps of the medication use process: prescribing, dispensing and administration. Drug administration errors were the second most frequent type of medication error, after prescribing errors, but the latter were often intercepted; hence, administration errors were more probably to reach the patients. Therefore, this study was conducted to determine the frequency and types of drug administration errors in a Taif hospital ward. Prospective study based on a disguised observation technique in nine wards in a general hospital in Taif, Saudi Arabia (800 beds). A pharmacist accompanied nurses and witnessed the preparation and administration of drugs to all patients during the three drug rounds on each of six days per ward. The main outcomes were the number, type and clinical importance of errors and associated risk factors. The drug administration error rate was calculated. Relationships between the drug dose frequency, dosage form and types of medication administration error were measured. A total of 7105 medications administered by 250 nursing staff members to 700 patients were observed. Observers intervened in seven administrations. There are 1769 medication administration errors confirmed. The most common medication administration errors were drug preparation error (40.56%, n =727) then, improper dose error (18.58%, n=333); the most common drug class error was Antibiotic (38.9%, n =399) then Analgesic and anti-inflammatory drugs (17%, n =176). The most drug dose frequency had Drug preparation error was seen in a drug used three times a day by 484. MAEs were more likely to occur in the evening shift compared to the morning and afternoon shifts. The study indicates that the frequency of drug administration errors in developing countries such as Malaysia is similar to that in developed countries.

Using machine learning to predict nosocomial infections and medical accidents in a NICU

Adult studies have shown that nursing overtime and unit overcrowding are associated with increased adverse patient events but there exists little evidence for the Neonatal Intensive Care Unit (NICU). We investigate the main determinants of nosocomial infections and medical accidents in a NICU using state-of-the-art machine learning techniques. Our analysis focuses on a retrospective study on the 7,438 neonates admitted in the CHU de Québec NICU (capacity of 51 beds) from 10 April 2008 to 28 March 2013. Daily administrative data on nursing overtime hours, total regular hours, number of admissions, patient characteristics, as well as information on nosocomial infections and on the timing and type of medical errors were retrieved from various hospital-level datasets. We use a generalized mixed effects regression tree model with random effects (GMERT-RI) to elaborate predictions trees for the two outcomes. Neonates' characteristics and daily exposure to numerous covariates are used in the model. GMERT-RI is suitable for binary outcomes and is a recent extension of the standard tree-based method. The model allows to determine the most important predictors. Diagnosis-related group level, regular hours of work, overtime, admission rates, birth weight and occupation rates are the main predictors for both outcomes. On the other hand, gestational age, C-Section, multiple births, medical/surgical and number of admissions are poor predictors. The GMERT-RI algorithm is a powerful tool. It is well suited to unearth potential correlations in the context of unbalanced panel data and discrete health outcomes, two common features of clinical data. In the particular setting of a NICU, we find that institutional features (overtime hours, occupancy rates, etc.) are just as important drivers as neonate-specific medical conditions in predicting medical accidents and health care associated infections. From an operational point of view, prediction trees can complement traditional management tools in preventing undesirable health outcomes in the NICU.

Knowledge and perception of medication errors among health care workers in Ahmadu Bello University Teaching Hospital Zaria, Kaduna State, North-west Nigeria.

Medication errors (MEs) are important, often overlooked forms of medical error that are associated with morbidity and mortality in health care settings. Among health care workers, knowledge, attitude and perception may affect reporting of MEs. The aim of this study was to determine the level of knowledge and perception of MEs among health care workers in Ahmadu Bello University Teaching Hospital, Zaria. A cross-sectional study was conducted among 138 health care workers selected randomly through a stratified sampling. Their responses were collected using pre-tested, self-administered questionnaires and analysed using the Statsitical Package for the Social Sciences. The numerical variables were summarised as means and standard deviations, whereas categorical variables were presented as frequencies and percentages. The Chi-square test was used to test for associations at P < 0.05. All the respondents heard of MEs and up to 108 (78.3%) correctly defined them. While only 121 (87.7%) respondents had fair to good knowledge about MEs, all of them had good perception of them. Major types of MEs known by the respondents were knowledge-based errors (79.7%), rule-based errors (52.9%), action-based errors (67.4%) and memory-based errors (55.8%). The causes of MEs identified were communication problems (88.4%), poor organizational knowledge transfer (63.8%), heavy workload (80.4%) and not reading instructions carefully (63.0%). There was no statistically significant association between the level of knowledge on MEs and sociodemographic characteristics of the respondents. Knowledge and perceptions of MEs were good among our respondents. Adequate mechanism (s) should be put in place to encourage the reporting of MEs whenever they occur to promote patient safety and improve health outcomes.

Knowledge and Attitudes Regarding Medication Errors among Nurses: A Cross-Sectional Study in Major Jeddah Hospitals.

Medication error is a multifactorial problem that mainly involves missing or bypassing the administration, which may have life-threatening impacts on the patient. Nevertheless, there is a dearth of information on medication errors among nurses in Saudi Arabia. This study investigates the knowledge and attitudes toward medication errors and their associated factors among nurses in Saudi Arabia. A cross-sectional study was conducted in four major public hospitals by recruiting a total of 408 nurses using cluster random sampling and proportional stratified sampling techniques. Data were gathered using an online self-administered questionnaire from January to March 2022. Descriptive statistics, Chi-square tests, and binary logistic regression models were performed to analyze the data. The prevalence of medication error among the nurses was 72.1%, only 41.2% were reported, while wrong doses (46.9%) were the most common type of medication error. Approximately 55% and 50% of the respondents demonstrated good knowledge and a positive attitude toward medication errors, respectively. The prevalence of medication error was associated with age groups of less than 25, and 25-35 years old, King Fahad and King Abdulaziz hospitals, no history of attending an MER training course, poor knowledge, and negative attitude. These findings reflect a high prevalence of medication error among nurses in Saudi Arabia, and the factors identified could be considered in mitigating this important health problem.

Your clinical pharmacist can save your life, the impact of pharmacist's intervention.

Patient safety and adverse event analysis are of paramount importance in the management of patient medication, given the significant economic burden they place on a country's healthcare system. Medication errors fall into the category of preventable adverse drug therapy events and are therefore of key importance from a patient safety perspective. Our study aims to identify the types of medication errors associated with the medication dispensing process and to determine whether automated individual medication dispensing with pharmacist intervention significantly reduces medication errors, thereby increasing patient safety, compared to traditional, ward base medication dispensing (by a nurse). A prospective, quantitative, double-blind point prevalence study was conducted in three inpatient internal medicine wards of Komló Hospital in February 2018 and 2020. We analyzed data from comparisons of prescribed and non-prescribed oral medications in 83 and 90 patients per year aged 18 years or older with different diagnoses treated for internal medicine on the same day and in the same ward. In the 2018 cohort, medication was traditionally dispensed by a ward nurse, while in the 2020 cohort, it used automated individual medication dispensing with pharmacist intervention. Transdermally administered, parenteral and patient-introduced preparations were excluded from our study. We identified the most common types of errors associated with drug dispensing. The overall error rate in the 2020 cohort was significantly lower (0.9%) than in the 2018 cohort (18.1%) (p < 0.05). Medication errors were observed in 51% of patients in the 2018 cohort, i.e. 42 patients, of which 23 had multiple errors simultaneously. In contrast, in the 2020 cohort, a medication error occurred in 2%, i.e. 2 patients (p < 0.05). When evaluating the potential clinical consequences of medication errors, in the 2018 cohort, the proportion of potentially significant errors was 76.2% and potentially serious errors 21.4%, whereas in the 2020 cohort, only three medication errors were identified in the potentially significant category due to pharmacist intervention, which was significantly lower (p < 0.05). Polypharmacy was detected in 42.2% of patients in the first study and in 12.2% (p < 0.05) in the second study. Automated individual medication dispensing with pharmacist intervention is a suitable method to increase the safety of hospital medication, reduce medication errors, and thus improve patient safety.

Occurrence and types of medication error and its associated factors in a reference teaching hospital in northeastern Iran: a retrospective study of medical records

BackgroundMedication errors are categorized among the most common medical errors that may lead to irreparable damages to patients and impose huge costs on the health system. A correct understanding of the prevalence of medication errors and the factors affecting their occurrence is indispensable to prevent such errors. The purpose of this study was to investigate the prevalence and types of medication errors among nurses in a hospital in northeastern Iran.MethodsThe present descriptive-analytical research was conducted on 147 medical records of patients admitted to the Department of Internal Medicine at a hospital in northeastern Iran in 2019, selected by systematic sampling. The data were collected through a researcher-made checklist containing the demographic profiles of the nurses, the number of doctor's orders, the number of medication errors and the type of medication error, and were finally analyzed using STATA version 11 software at a significance level of 0.05.ResultsBased on the findings of this study, the mean prevalence of medication error per each medical case was 2.42. Giving non-prescription medicine (47.8%) was the highest and using the wrong form of the drug (3.9%) was the lowest medication error. In addition, there was no statistically significant relationship between medication error and the age, gender and marital status of nurses (p > 0.05), while the prevalence of medication error in corporate nurses was 1.76 times higher than that of nurses with permanent employment status (IRR = 1.76, p = 0.009). The prevalence of medication error in the morning shift (IRR = 0.65, p = 0.001) and evening shift (IRR = 0.69, p = 0.011) was significantly lower than that in the night shift.ConclusionEstimating the prevalence and types of medication errors and identified risk factors allows for more targeted interventions. According to the findings of the study, training nurses, adopting an evidence-based care approach and creating interaction and coordination between nurses and pharmacists in the hospital can play an effective role in reducing the medication error of nurses. However, further research is needed to evaluate the effectiveness of interventions to reduce the prevalence of medication errors.

Medication safety in the perioperative setting: A comparison of methods for detecting medication errors and adverse medication events.

The purpose of this study was to evaluate perioperative medication-related incidents (medication errors (MEs) and/or adverse medication events (AMEs)) identified by 2 different reporting methods (self-report and direct observation), and to compare the types and severity of incidents identified by each method. We compared perioperative medication-related incidents identified by direct observation in Nanji et al's 2016 study[1] to those identified by self-report via a facilitated incident reporting system at the same 1046-bed tertiary care academic medical center during the same 8-month period. Incidents, including MEs and AMEs were classified by type and severity. In 277 operations involving 3671 medication administrations, 193 MEs and/or AMEs were observed (5.3% incident rate). While none of the observed incidents were self-reported, 10 separate medication-related incidents were self-reported from different (unobserved) operations that occurred during the same time period, which involved a total of 21,576 operations and approximately 280,488 medication administrations (0.004% self-reported incident rate). The distribution of incidents (ME, AME, or both) did not differ by direct observation versus self-report methodology. The types of MEs identified by direct observation differed from those identified by self-report (P = .005). Specifically, the most frequent types of MEs identified by direct observation were labeling errors (N = 37; 24.2%), wrong dose errors (N = 35; 22.9%) and errors of omission (N = 27; 17.6%). The most frequent types of MEs identified by self-report were wrong dose (N = 5; 50%) and wrong medication (N = 4; 40%). The severity of incidents identified by direct observation and self-report differed, with self-reported incidents having a higher average severity (P < .001). The procedure types associated with medication-related incidents did not differ by direct observation versus self-report methodology. Direct observation captured many more perioperative medication-related incidents than self-report. The ME types identified and their severity differed between the 2 methods, with a higher average incident severity in the self-reported data.