Prevalence and Nature of Medication Administration Errors
Objective: To systematically review empirical evidence on the prevalence and nature of medication administration errors (MAEs) in health care settings.
Data Sources: Ten electronic databases (MEDLINE, EMBASE, International Pharmaceutical Abstracts, Scopus, Applied Social Sciences Index and Abstracts, PsycINFO, Cochrane Reviews and Trials, British Nursing Index, Cumulative Index to Nursing and Allied Health Literature, and Health Management Information Consortium) were searched (1985-May 2012).
Study Selection and Data Extraction: English-language publications reporting MAE data using the direct observation method were included, providing an error rate could be determined. Reference lists of all included articles were screened for additional studies.
Data Synthesis: In all, 91 unique studies were included. The median error rate (interquartile range) was 19.6% (8.6–28.3%) of total opportunities for error including wrong-time errors and 8.0% (5.1–10.9%) without timing errors, when each dose could be considered only correct or incorrect. The median rate of error when more than 1 error could be counted per dose was 25.6% (20.8–41.7%) and 20.7% (9.7–30.3%), excluding wrong-time errors. A higher median MAE rate was observed for the intravenous route (53.3% excluding timing errors (IQR 26.6–57.9%)) compared to when all administration routes were studied (20.1%; 9.0–24.6%), where each dose could accumulate more than one error. Studies consistently reported wrong time, omission, and wrong dosage among the 3 most common MAE subtypes. Common medication groups associated with MAEs were those affecting nutrition and blood, gastrointestinal system, cardiovascular system, central nervous system, and antiinfectives. Medication administration error rates varied greatly as a product of differing medication error definitions, data collection methods, and settings of included studies. Although MAEs remained a common occurrence in health care settings throughout the time covered by this review, potential targets for intervention to minimize MAEs were identified.
Conclusions: Future research should attend to the wide methodological inconsistencies between studies to gain a greater measure of comparability to help guide any forthcoming interventions.
Interest in adverse drug events (ADEs) and medication errors (MEs) has grown dramatically since publication of the landmark reports To Err is Human and An Organisation with a Memory. However, despite notable patient safety advances, progress has been slow. The ability to understand and apply ME research to practice has been limited by inconsistencies between study methods, which have led to variability in ME rates. For example, medication administration error (MAE) rates varied from 1.7% to 59.1% of total opportunities for error (TOE) in an early review of observational studies, with a later systematic review focusing on critical care settings finding an error rate of 3.3–72.5% of drug observations. Nurses may spend up to one third of their time on medication-related activities, and because of the relative lack of safeguards to prevent MEs, both nurses and patients are placed at high risk during this stage of the medication use process.
There are numerous methods designed to capture MEs, including self-report, incident report, chart review, direct observation, and trigger tool. It is well established that incident and self-reporting methods yield error rates that grossly underestimate the prevalence of MEs. Direct observation provides greater numbers of identified medication incidents when compared to other methods and is most suited to accurately identify the full range of administration errors, which makes it a desirable option for determining the overall MAE rate between published studies. Criticisms of the direct observation method include being labor intensive and expensive, and also being prone to modifications of subjects' behavior in the presence of the observer. However, others suggest that chart review is more time consuming and that the directly observed error rate does not change between observation lengths, multiple observers, or when the observer intervenes to prevent errors from reaching the patient.
Despite research using direct observation to identify MAEs being conducted since the 1960s, there have been few attempts to synthesize studies using only this method from multiple health care backgrounds, thus limiting attempts to understand the scale and nature of this international problem. One early multinational summary of observational data was followed in 1998 by a review of UK-based observational studies in hospitals. More recent reviews of MAEs either have not been systematic or have focused on pediatrics. One recent systematic review of MAEs based on direct observation evidence focused on critical care settings. Observational data have been included among data from other methods in some reviews. Therefore, the aim of this review was to systematically identify and assess direct observation evidence in MAE detection to determine the prevalence and nature of MAEs in health care settings.
Abstract and Introduction
Abstract
Objective: To systematically review empirical evidence on the prevalence and nature of medication administration errors (MAEs) in health care settings.
Data Sources: Ten electronic databases (MEDLINE, EMBASE, International Pharmaceutical Abstracts, Scopus, Applied Social Sciences Index and Abstracts, PsycINFO, Cochrane Reviews and Trials, British Nursing Index, Cumulative Index to Nursing and Allied Health Literature, and Health Management Information Consortium) were searched (1985-May 2012).
Study Selection and Data Extraction: English-language publications reporting MAE data using the direct observation method were included, providing an error rate could be determined. Reference lists of all included articles were screened for additional studies.
Data Synthesis: In all, 91 unique studies were included. The median error rate (interquartile range) was 19.6% (8.6–28.3%) of total opportunities for error including wrong-time errors and 8.0% (5.1–10.9%) without timing errors, when each dose could be considered only correct or incorrect. The median rate of error when more than 1 error could be counted per dose was 25.6% (20.8–41.7%) and 20.7% (9.7–30.3%), excluding wrong-time errors. A higher median MAE rate was observed for the intravenous route (53.3% excluding timing errors (IQR 26.6–57.9%)) compared to when all administration routes were studied (20.1%; 9.0–24.6%), where each dose could accumulate more than one error. Studies consistently reported wrong time, omission, and wrong dosage among the 3 most common MAE subtypes. Common medication groups associated with MAEs were those affecting nutrition and blood, gastrointestinal system, cardiovascular system, central nervous system, and antiinfectives. Medication administration error rates varied greatly as a product of differing medication error definitions, data collection methods, and settings of included studies. Although MAEs remained a common occurrence in health care settings throughout the time covered by this review, potential targets for intervention to minimize MAEs were identified.
Conclusions: Future research should attend to the wide methodological inconsistencies between studies to gain a greater measure of comparability to help guide any forthcoming interventions.
Introduction
Interest in adverse drug events (ADEs) and medication errors (MEs) has grown dramatically since publication of the landmark reports To Err is Human and An Organisation with a Memory. However, despite notable patient safety advances, progress has been slow. The ability to understand and apply ME research to practice has been limited by inconsistencies between study methods, which have led to variability in ME rates. For example, medication administration error (MAE) rates varied from 1.7% to 59.1% of total opportunities for error (TOE) in an early review of observational studies, with a later systematic review focusing on critical care settings finding an error rate of 3.3–72.5% of drug observations. Nurses may spend up to one third of their time on medication-related activities, and because of the relative lack of safeguards to prevent MEs, both nurses and patients are placed at high risk during this stage of the medication use process.
There are numerous methods designed to capture MEs, including self-report, incident report, chart review, direct observation, and trigger tool. It is well established that incident and self-reporting methods yield error rates that grossly underestimate the prevalence of MEs. Direct observation provides greater numbers of identified medication incidents when compared to other methods and is most suited to accurately identify the full range of administration errors, which makes it a desirable option for determining the overall MAE rate between published studies. Criticisms of the direct observation method include being labor intensive and expensive, and also being prone to modifications of subjects' behavior in the presence of the observer. However, others suggest that chart review is more time consuming and that the directly observed error rate does not change between observation lengths, multiple observers, or when the observer intervenes to prevent errors from reaching the patient.
Despite research using direct observation to identify MAEs being conducted since the 1960s, there have been few attempts to synthesize studies using only this method from multiple health care backgrounds, thus limiting attempts to understand the scale and nature of this international problem. One early multinational summary of observational data was followed in 1998 by a review of UK-based observational studies in hospitals. More recent reviews of MAEs either have not been systematic or have focused on pediatrics. One recent systematic review of MAEs based on direct observation evidence focused on critical care settings. Observational data have been included among data from other methods in some reviews. Therefore, the aim of this review was to systematically identify and assess direct observation evidence in MAE detection to determine the prevalence and nature of MAEs in health care settings.