Confused Drug Names Research Articles

Improving the identification of confused drug names in Spanish

Since a drug name goes through different communication means and circumstances when it is prescribed, written, advertised, listened to, searched and administered; it tends to be confused with similar drug names that Look-Alike and Sound-Alike (LASA). LASA drug names have caused costs and damage to health. For this problem, the institutions of the United Kingdom, Canada, and the United States have implemented programs for several decades to report lists of confusing drug names pairs. Thanks to these kinds of list, it has been possible to propose new models to identify confusing drug names in English and are used to reject new drug name proposals or to alert when a confusing drug name is being dispensed. However, countries such as Spain also have published a list with the Spanish LASA drug names, and it is not clear enough whether the models previously proposed for the drug names in English are useful for the list in Spanish or if it is necessary to adjust and update them for the Spanish language. This paper focuses on updating and improving the identification of LASA drug names in Spanish. First, we update the state-of-the-art by evaluating all the individual similarity measures proposed previously and all the models that combine these measures with the list in Spanish. Second, we updated the models with new individual measures and then adjusted them with the list in Spanish to improve the identification of LASA drug names in Spanish. After that, 25 individual similarity measures and 8 models to identify confused drug names in Spanish are compared to obtain the best result and conclusions.

An orthographic and phonetic knowledge-based measure for confused drug names

A drug name could be confused because it looks or sounds like another. Nevertheless, it is not possible to know a priori the causes of the confusion. Nowadays, sophisticated similarity measures have been proposed focused on improving the score of the detection. However, when a new drug name is proposed, the Federal Drug Administration (FDA) only can reject or accept the drug name based on this value. This paper not only improves the detection of confused drug names by integrating the strengths of different similarity measures but also the orthographic and phonetic knowledge of these measures are used to give an a priori explanation of the causes of confusion. In this paper, a novel measure that integrates 24 individual measures is developed for this problem. With our proposal, each individual measure contributes to this problem. Finally, we present examples of how our proposal is used for explaining the causes of the confusion which could assist to the FDA to accept or reject a new drug name or to know the confusion causes of previously reported cases.

Combination of similarity measures based on symbolic regression for confusing drug names identification

Despite advances in medical safety, errors related to adverse drug reactions are still very common. The most common reason for a patient to develop an adverse reaction to a medication is confusion over the prescribed medication. The similarity of drug names (by their spelling or phonetic similarity) is recognized as the most critical factor causing medication confusion. Several studies have studied techniques for the identification of confusing medications pairs, the most important of which employ techniques based on similarity measures that indicate the degree of similarity that exists between two drugs names. Although it generates good results in the identification of confusing drug names, each of the similarity measures used detects to a greater or lesser degree of similarity that exists between a pair. Recent studies indicate that the optimized combination of several similarity measures can generate better results than the individual application of each one. This paper presents an optimized method of combining various similarity measures based on symbolic regression. The obtained results show an improvement in the identification of confusing drug names.

Assessment of the degree of awareness among post-graduate medical physicians and Pharmacists about look-alike, sound-alike drug and potential medication errors

Background: With thousands of drugs currently in the market, the potential for medication errors due to confusing drug names amongst practising physicians, pharmacists and patients is significant. The existence of confusing drug names is one of the most common causes of medication error. There are many look-alikes, sound-alike (LASA) combinations that could potentially result in medication errors. There is insufficient data about medication errors due to LASA. Hence, we conducted the present study to determine the degree of awareness regarding LASA drugs among post graduate medical physicians and Pharmacists.Methods: This study was a cross-sectional, questionnaire-based survey, conducted among 137 year post graduate medical residents of a tertiary care teaching hospital and 121 local pharmacists in an urban metropolitan Indian city.Results: There were 34% resident doctors and 17% pharmacists were aware of concept of LASA drugs. Only 46% resident doctors and 22% pharmacists had knowledge about the full form of LASA. Among resident doctors, 39% came across prescription errors due to LASA drugs. Only 69% of the pharmacists agreed that they consulted their doctors when they faced problems due to prescription errors due to similar looking and similar sounding drugs.Conclusions: Look-Alike, Sound-Alike (LASA) drugs are common source of medication errors. Our study suggests that there is lack of awareness about LASA drugs amongst resident doctors and pharmacists, which may contribute to occurrence of medication errors. Therefore, combined efforts by prescribers, pharmacists, organizations, manufacturers and patients is required to overcome medication errors due to LASA drugs.

An evolutionary logistic regression method to identify confused drug names

An evolutionary logistic regression method to identify confused drug names

Sound-Alike Look-Alike Confusion and Matching Medication Product Attributes: Simulated Case-Control Studies.

Background: Sound-alike look-alike (SALA) medication confusions harm as many as 250 000 Americans annually. Most preventive strategies focus on medication name similarities. Objectives: To evaluate the association between matching medication product attributes and SALA confusion. Methods: We simulated 20 000 case-control studies using the Institute for Safe Medication Practices' List of Confused Drug Names as case pairs and 4 randomly selected control pairs per case pair from the First DataBank MedKnowledge (FDBM) database. We extracted 7 product attributes for each medication from the FDBM database. We used logistic regression models to estimate the associations between matching product attributes and SALA confusion. The models included a series of univariate (unadjusted) models, a model that adjusted for all product attributes, and a model that further adjusted for medication name similarity measures. Results: Medications with a matching product attribute had increased odds of SALA confusion in the univariate analyses (odds ratio [OR] = 4.2 to 55.5). When we simultaneously adjusted for all attributes, the associations of matching package unit, package unit size, formulation, strength, therapeutic class, and manufacturer with SALA confusion were attenuated but remained elevated (OR = 1.5 to 26.5), whereas the direction of association between matching route and SALA confusion reversed (OR = 0.8). These associations persisted on adjustment for medication name similarity measures. Conclusions and Relevance: This study is the first to evaluate the association between matching medication product attributes and SALA confusion with a control group. Having matching product attributes increased the odds of SALA confusion. SALA risk reduction strategies should consider product attributes.

Using drug knowledgebase information to distinguish between look-alike-sound-alike drugs.

To extract drug indications from a commercial drug knowledgebase and determine to what extent drug indications can discriminate between look-alike-sound-alike (LASA) drugs. We extracted drug indications disease concepts from the MedKnowledge Indications module from First Databank Inc. (South San Francisco, CA) and associated them with drugs on the Institute for Safe Medication Practices (ISMP) list of commonly confused drug names. We used high-level concepts (rather than granular concepts) to represent the general indications for each drug. Two pharmacists reviewed each drug's association with its high-level indications concepts for accuracy and clinical relevance. We compared the high-level indications for each commonly confused drug pair and categorized each pair as having a complete overlap, partial overlap or no overlap in high-level indications. Of 278 LASA drug pairs, 165 (59%) had no overlap and 58 (21%) had partial overlap in high-level indications. Fifty-five pairs (20%) had complete overlap in high-level indications; nearly half of these were comprised of drugs with the same active ingredient and route of administration (e.g., Adderall, Adderall XR). Drug indications data from a drug knowledgebase can discriminate between many LASA drugs.

The establishment of the Drug Naming Committee to restrict look-alike medication names in Iran: A qualitative study.

Medication errors is a prominent issue on the health policy agenda due to its significant human and financial costs; confusing drug names are one of the most common causes of them. This issue necessitates the adoption of a mechanism to restrict such a confusion before approving drug names. Following the establishment of a committee and developed relevant criteria as mechanisms to address the issue of drug names similarity Iran, there were problems in this process. This study explores the process of adopting this mechanism. This qualitative study was conducted using date gathered through documents, observation and 31 semi-structured interviews. Ethical approval was achieved from the Research Ethics Committee in TUMS. Confidentiality was ensured at all the interviews. The interviews were recorded verbatim, transcribed and coded, using the MAXQDA ver.12 software. Data were analyzed utilizing the Health Policy Triangle Framework. High rate of medication errors and warning from the WHO about drug names in Iran pushed the issue of drug proprietary names to the FDO agenda. A National Drugs Naming Committee was established and relevant criteria were developed by the Food and Drug Organization (FDO). This committee was dissolved for four years; the function was delegated to the General Office of Trademarks Registry. However, the committee was reestablished and resumed functioning. Finings mainly indicated a higher rate of medication errors during dissolving the committee. Health policy-makers have a public responsibility for making a decision and the consequences. Regarding the process of naming drugs, it is important for policy makers to consider two issues: patient safety and trademarks rights. However, the Iran FDO adopted an approach to address these issues, a more multifaceted, integrated approach to initial naming of drugs is suggested. The committee' function is seen to hold the most promise. However the function is partially complete to ensure patient safety. Given the change experienced by altering relevant organizational authorities in the FDO, the sustainability of the committee cannot hope to be guaranteed. Yet, the need for such a guarantee is particularly important.

Characteristics That May Help in the Identification of Potentially Confusing Proprietary Drug Names.

This study aimed to provide a descriptive analysis of characteristics that are common among drug name pairs involved in name confusion medication errors. We evaluated drug name pairs that contained at least one proprietary name from the Institute for Safe Medication Practices (ISMP) List of Confused Drug Names. For each name pair, we analyzed whether the following characteristics were present: (1) the same first letter, (2) a shared letter string of at least 3 letters, and (3) similarity in the number of letters. Additionally, we obtained the combined Phonetic and Orthographic Computer Analysis (POCA) score. Ninety-nine percent of the drug name pairs reflected at least one of the 3 characteristics analyzed. Additionally, 75% of the names had a combined POCA score of ≥50%. This descriptive analysis provides some insight into characteristics that may be associated with name confusion, which should be considered when formulating and evaluating proposed proprietary drug names.

Policies and Procedures for Sound-alike and Look-alike Medications

ABSTRACT The sound-alike and look-alike (SALA) drug is one of the most common causes of medication error and is of fear globally. Thus, the probability for error due to confusing drug names is very high. The SALA drug names have become a significant challenge to pharmacists, nurses, patients, and doctors. With thousands of drugs currently on the market, the probability for error is significant. The purpose of this article is to formulate a policy that would help in minimizing medication errors arising with the use of SALA medications. How to cite this article Singh S, Singh AR. Policies and Procedures for Sound-alike and Look-alike Medications. Int J Res Foundation Hosp Healthc Adm 2017;5(1):15-20.

Cognitive tests predict real-world errors: the relationship between drug name confusion rates in laboratory-based memory and perception tests and corresponding error rates in large pharmacy chains

BackgroundDrug name confusion is a common type of medication error and a persistent threat to patient safety. In the USA, roughly one per thousand prescriptions results in the wrong drug...

Tallman lettering as a strategy for differentiation in look-alike, sound-alike drug names: The role of familiarity in differentiating drug doppelgangers

Tallman lettering, capitalizing the dissimilar portions of easily confused drug names, is one strategy for reducing medication errors. We assessed the efficacy of Tallman lettering in a visually complex environment using a change detection method with healthcare providers and laypeople. In addition, the effect of familiarity with the drug name was assessed using a subset of responses collected from healthcare providers.Both healthcare providers and laypeople detected changes in confusable pairs of drug names more often (P < 0.0001) and more quickly (P < 0.05) when changes were presented in Tallman lettering, though the benefits were more pronounced for healthcare providers (p < 0.05). Familiarity with both drug names in a confusable pair mitigated the benefit of Tallman lettering. Results are discussed in terms of bottom-up and top-down attentional systems for processing of information in the context of the varied healthcare environments.

Medication Errors Associated with Look-alike/Sound-alike Drugs: A Brief Review

The existence of confusing drug names is one of the most common causes of medication errors. There are many types of medication errors: wrong drug, wrong dose, wrong route of administration, wrong patient etc. Misreading medication names that look similar is a common mistake. These look-alike medication names may also sound alike and can lead to errors associated with verbal prescriptions. Similar sounding drugs may produce confusion and may lead to unintended interchange of drugs causing harm to patients or even patient death. The main aim of the study was to evaluate medication errors related to look alikesound alike drug names and to find out the strategies to prevent these medication errors.J Enam Med Col 2015; 5(2): 110-117

Detection and prediction limits for identifying highly confusable drug names from experimental data

ABSTRACTConfusions between drug names that look and sound alike are common, costly, harmful, and difficult to prevent. One prevention strategy is to screen proposed new drug names for confusability before approving them. Widespread acceptance of preapproval tests of confusability is compromised by the lack of experimental designs and statistical methods to support valid inferences about whether a proposed new name is unacceptably confusing. One way of identifying confusing names is to conduct memory and perception experiments on a set of drug names which would include both the new name and a set of control names (e.g., names already on the market). The experiment would yield an observed error rate for every name. Inferences about the acceptability of the new name can be made by comparing the error rate of the new name to the distribution of error rates of the control names. We describe four memory and perception experiments on drug names, carried out using clinicians as participants. Each experiment included drug names designated as test and control names. We demonstrate how to use a combination of logistic regression, Poisson prediction limits, and highly assured credible intervals to identify and apply a threshold for identifying unacceptably confusing names. Our models show an excellent fit to the data. These experimental designs and analytic methods should be useful in the preapproval testing of proposed new drug names and in similar regulatory scenarios where it is necessary to draw inferences about the comparative safety or effectiveness of new vs. old products.

Systematic derivation of an Australian standard for Tall Man lettering to distinguish similar drug names.

Confusion between similar drug names can cause harmful medication errors. Similar drug names can be visually differentiated using a typographical technique known as Tall Man lettering. While international conventions exist to derive Tall Man representation for drug names, there has been no national standard developed in Australia. This paper describes the derivation of a risk-based, standardized approach for use of Tall Man lettering in Australia, and known as National Tall Man Lettering. A three-stage approach was applied. An Australian list of similar drug names was systematically compiled from the literature and clinical error reports. Secondly, drug name pairs were prioritized using a risk matrix based on the likelihood of name confusion (a four-component score) vs. consensus ratings of the potential severity of the confusion by 31 expert reviewers. The mid-type Tall Man convention was then applied to derive the typography for the highest priority drug pair names. Of 250 pairs of confusable Australian drug names, comprising 341 discrete names, 35 pairs were identified by the matrix as an 'extreme' risk if confused. The mid-type Tall Man convention was successfully applied to the majority of the prioritized drugs; some adaption of the convention was required. This systematic process for identification of confusable drug names and associated risk, followed by application of a convention for Tall Man lettering, has produced a standard now endorsed for use in clinical settings in Australia. Periodic updating is recommended to accommodate new drug names and error reports.

Descriptive study for look-alike and sound-alike medicines based on local language peculiarities

The existence of confusing drug names was considered one of the leading causes of medication errors. The aim of the study was to summarize the potential sources for errors associated with look-alike and sound-alike (LASA) medicines, to provide overview of the most common patterns and offer recommendations of safety strategies to help mitigate the problem. A standardized questionnaire was conducted for a period of 1 month in 2011. The collected data was statistically analyzed with Microsoft Excel 2007 and SPSS version 17.0. The total of 93 questionnaires was obtained. Two hundred and twenty-three (223) unique LASA drug pairs were identified with prevalence to brand names. The modal Bulgarian brand medicine name had 7 letters, 3 syllables and 1 word. The average generic drug name had 10 letters, 4 syllables and 1 word. The top 15 highest risk-potential pairs from both groups were classified by the values of the Dice coefficient and analyzed by possible impact on prescription and pharmacy practice. The results of the study showed that look-alike medicine names were a possible source of medication errors and threat to patient safety in everyday practice. Safety strategies were needed to reduce the risk of medication errors in community and hospital pharmacies related to LASA drugs.

Automatic identification of confusable drug names

Many hundreds of drugs have names that either look or sound so much alike that doctors, nurses and pharmacists can get them confused, dispensing the wrong one in errors that can injure or even kill patients. We propose to address the problem through the application of two new methods-one based on orthographic similarity ("look-alike"), and the other based on phonetic similarity ("sound-alike"). In order to compare the effectiveness of the new methods for identifying confusable drug names with other known similarity measures, we developed a novel evaluation methodology. We show that the new orthographic measure (BI-SIM) outperforms other commonly used measures of similarity on a set containing both look-alike and sound-alike pairs, and that a new feature-based phonetic approach (ALINE) outperforms orthographic approaches on a test set containing solely sound-alike pairs. However, an approach that combines several different measures achieves the best results on two test sets. Our system is currently used as the basis of a system developed for the U.S. Food and Drug Administration for detection of confusable drug names.

Pharmacist's attitudes towards dispensing errors: their causes and prevention.

To assess the attitudes of pharmacists towards the issue of dispensing errors. A postal survey was undertaken among all Tasmanian-registered pharmacists residing in Australia. The anonymous questionnaire sought opinions on whether the risk of dispensing errors and the actual numbers of errors are increasing, the major factors contributing to the occurrence of dispensing errors, factors that can best minimize the risk of dispensing errors, the number of prescription items that one pharmacist can safely dispense in a day and whether Australia should have a regulatory maximum dispensing load, and an estimation of the number of recent errors at the pharmacist's workplace. Completed questionnaires were received from 209 pharmacists (50% response rate). Most pharmacists (82%) believed that the risk of dispensing errors is increasing. The principal contributing factors nominated were: high prescription volumes, pharmacist fatigue, pharmacist overwork, interruptions to dispensing, and similar or confusing drug names. The main factors identified as being important in reducing the risk of dispensing errors were: having mechanisms for checking dispensing procedures, having a systematic dispensing workflow, checking the original prescription (duplicate) when dispensing repeats, improving the packaging and labelling of drug products, having drug names that are distinctive, counselling patients at the time of supply, keeping one's knowledge of drugs up-to-date, avoiding interruptions, reducing workloads on pharmacists, improving doctors' handwriting, and privacy when counselling patients. Most pharmacists (72%) stated that they were aware of dispensing errors that had left the pharmacy undetected, in their place of practice during the past 6 months. The median number of such dispensing errors that they were aware of was three. A median of 150 was nominated as the maximum number of prescription items that can be safely dispensed per 9-h day (i.e. 17 items per hour) by or in the presence of one pharmacist. Most pharmacists (58%) stated that there should be a regulatory guideline for the safe dispensing load in Australia. Dispensing errors are occurring in numbers well above reports to regulatory authorities or professional indemnity insurance companies, and seem to be accepted as part of practice. High prescription volumes, pharmacist fatigue and overwork appear to be important factors. The profession needs to be proactive and standards must be set appropriately high (i.e. zero error tolerance).

Confusing drug names

The majority of drugs have at least two names (the proprietary or brand name, and the non-proprietary or generic name) but some have many more when different brand names are adopted by different manufacturers. Often the names do not indicate the nature of the drug or drug mixture, and, by looking or sounding similar or by incorporating numbers or letters, may lead to confusion.1 Errors in prescribing, dispensing and administration inevitably follow.