Does Tall Man lettering prevent drug name confusion errors? Incomplete and conflicting evidence suggest need for definitive study

Exploring susceptibility factors to medication dispensing errors through a retrospective study of patient-reported dispensing errors over 11 years: are dispensing errors indeed due to personal reasons for pharmacists?

BACKGROUND

Medication dispensing errors cause wastage of medicines and increase healthcare costs, with serious consequences for patients. However, few studies have systematically and completely reviewed dispensing errors, with inadequate attention to the objective regularity and risk factors for dispensing errors.

OBJECTIVES

To explore the potential causes and risk factors influencing the prevalence of medication dispensing errors.

METHODS

We collected patient-reported medication dispensing errors from a large tertiary care hospital in South China over 11 years. We assessed the characteristics of dispensing errors, labelled the causes, compared them with more than 25 million prescriptions from 2012 to 2022, identified the susceptibility factors for the occurrence of dispensing errors, and analysed the characteristics and patterns of the errors.

RESULTS

A total of 376 patient-reported dispensing errors were recorded. It took an average of 5.2 days for a patient to find an error. Only 37.5% of errors were reviewed by the patient within 24 hours. These errors directly contributed to a medication loss of US$188 406. Of the 160 recorded pharmacists, 112 (70%) committed dispensing errors. Dispensing errors were affected by the pharmacists' use of the machine, workload and the length of monthly vacation. Of the dispensing errors, 47.9% (n=180) were caused by medication packaging or names that were similar. Antibiotics (n=32, 8.5%) were the most common types of drugs dispensed incorrectly, and traditional Chinese medicines (n=31, 8.2%) and immunosuppressants (n=21, 5.6%) were the most likely to be dispensed in inaccurate quantities.

CONCLUSIONS

Organising adequate staff and using machines to prepare medicines may be necessary to reduce dispensing errors. When pharmacists have been away from work for more than 72 hours they should find their rhythm in other positions before dispensing medicines. It is more important to prioritise the differentiation of medicines with similar packaging over those with similar names when arranging drug shelving.

Frequency and influential factors on occurrence of medical errors: A three-year cross-sectional study

BACKGROUND

Despite efforts to improve patient safety, medical errors (MEs) continue to recur. Proper utilization of reported MEs can be effective in preventing their recurrence. This study investigated the errors reported in 3 years and examined the factors affecting them.

MATERIALS AND METHODS

This descriptive analytical study was conducted using the errors reported in 20 hospitals under the auspices of one of Iran's medical universities from 2018 to 2020. All reported errors were investigated by an expert panel.

RESULTS

In total, 6584 reported errors were grouped into four main categories based on the type of error. The highest reported errors were related to the management and treatment procedures. Analyses of the factors influencing medical errors revealed that 15 factors affected the occurrence of errors. An increasing trend of error was found in 9 of the 15 identified factors. Incorrect documenting of the physician's order in the nursing Kardex and noncompliance with the patient identification guide were the highest with 16.03 and 15.47%, respectively.

CONCLUSION

The most identified factor was the incorrect registration of the physician's prescription on the nursing card; therefore, it seems that the use of computerized physician order entry should be considered. Furthermore, the mere existence and training of patient safety guides cannot help prevent errors. Not only should the underlying causes of errors be carefully identified and investigated but it also requires serious determination to follow the patient's safety instructions from the highest to the lowest levels of the health system.

Linguistic Analysis of Generic-Generic Drug Name Pairs Prone to Wrong-Drug Errors for which Tall-Man Lettering is Recommended

OBJECTIVE

The Institute for Safe Medication Practices (ISMP) and the United States Food and Drug Administration (FDA) disseminated widely used lists of drug name pairs involved in wrong-drug errors, for which they recommended tall-man lettering (TML). Linguistic similarity is believed responsible for confusion of these drugs. This study aims to quantify linguistic similarity and other linguistic properties of these generic-generic name pairs.

METHODS

The FDA's Phonetic and Orthographic Computer Analysis (POCA) software was used to generate numerical similarity scores for the generic-generic name pairs on these lists and to identify conflicts between these names and the names of other marketed products. Within each pair, differences in name length and the number of identical prefix (initial) letters and suffix (final) letters were determined.

RESULTS

The selected pairs shared a mean of 2.5 (± 1.8) identical prefix letters and 3.2 (± 2.9) identical suffix letters. The mean POCA score 69.5 (± 9.7), indicated moderate-to-high similarity. POCA scores for individual pairs ranged from 90 (most similar) to 46 (least similar). Individual names averaged 11.2 (± 9.1) high-similarity conflicts with names of other marketed drugs.

CONCLUSIONS

POCA analysis could be a valuable tool in determining whether linguistic similarity contributes to specific wrong-drug errors. The finding of 11.2 (± 9.1) high-similarity conflicts with names of other marketed drugs is more than for candidate names USAN accepts and suggests the names on the FDA and ISMP lists are linguistically problematic.

Effects of tall man lettering on the visual behaviour of critical care nurses while identifying syringe drug labels: a randomised in situ simulation

BACKGROUND

Patients in intensive care units are prone to the occurrence of medication errors. Look-alike, sound-alike drugs with similar drug names can lead to medication errors and therefore endanger patient safety. Capitalisation of distinct text parts in drug names might facilitate differentiation of medication labels. The aim of this study was to test whether the use of such 'tall man' lettering (TML) reduces the error rate and to examine effects on the visual attention of critical care nurses while identifying syringe labels.

METHODS

This was a prospective, randomised in situ simulation conducted at the University Hospital Zurich, Zurich, Switzerland. Under observation by eye tracking, 30 nurses were given 10 successive tasks involving the presentation of a drug name and its selection from a dedicated set of 10 labelled syringes that included look-alike and sound-alike drug names, half of which had TML-coded labels.Error rate as well as dwell time, fixation count, fixation duration and revisits were analysed using a linear mixed-effects model analysis to compare TML-coded with non-TML-coded labels.

RESULTS

TML coding of syringe labels led to a significant decrease in the error rate (from 5.3% (8 of 150 in non-TML-coded sets) to 0.7% (1 of 150 in TML-coded sets), p<0.05). Eye tracking further showed that TML affects visual attention, resulting in longer dwell time (p<0.01), more and longer fixations (p<0.05 and p<0.01, respectively) on the drug name as well as more frequent revisits (p<0.01) compared with non-TML-coded labels. Detailed analysis revealed that these effects were stronger for labels using TML in the mid-to-end position of the drug name.

CONCLUSIONS

TML in drug names changes visual attention while identifying syringe labels and supports critical care nurses in preventing medication errors.

Improving safety in the operating room: Medication icon labels increase visibility and discrimination

Misreading labels, syringes, and ampoules is reported to make up a 54.4% of medication administration errors. The addition of icons to medication labels in an operating room setting could add additional visual cues to the label, allowing for improved discrimination, visibility, and easily processed information that might reduce medication administration errors. A multi-disciplinary team proposed a method of enhancing visual cues and visibility of medication labels applied to vasoactive medication infusions by adding icons to the labels. Participants were 1.12 times more likely to correctly identify medications from farther away (p < 0.001, AOR = 1.12, 95% CI: 1.02, 1.22) with icons. When icons were present, participants were 2.16 times more likely to be more confident in their identifications (p < 0.001, AOR = 2.16, 95%CI: 1.80, 2.57). Carefully designed icons may offer an additional method for identifying medications, and thus reducing medication administration errors.

SMPTall man lettering application in medication information systems as a quality and safety strategy in hospital organization

WHAT IS KNOWN AND OBJECTIVE

The technique of highlighted capital letters, Tall Man lettering, is a tool that allows differentiating the names of similar drugs in a way that contributes to reduce medication errors related to the drug identification. The objective was to implement and monitor the application protocol of the Tall Man lettering tool in drug information systems in the healthcare environment to improve the quality of care and patient safety in the pharmacy service and the medical institution.

METHODS

Scope: Tertiary general hospital with 1000 beds in which a Tall Man lettering application protocol was approved in the pharmacy service information systems.

DESIGN

Retrospective observational study.

PERIOD

2019-2021.

POPULATION

Information systems and databases used in the pharmacy service. Working group: Made up of people in charge of information areas and systems.

IMPLEMENTATION

Five phases were defined: organization of the working group, selection of drug names and information systems, establishment of actions, their execution, and results monitoring.

VARIABLES

Number of pharmaceutical specialties, names of active ingredient and brands incorporated in the information systems.

RESULTS AND DISCUSSION

The application of Tall Man lettering was authorized in 13/13 information systems, six have been fully implemented and seven are in the database update phase. Of the implanted systems, a total of 210 drug names have been modified (168/210 active ingredient and 42/210 brand names), corresponding to 659 pharmaceutical specialties.

WHAT IS NEW AND CONCLUSION

The application of Tall Man lettering in hospital information systems is a tool for improving the quality of the pharmacy service and guarantees the safety of medicines in the hospital.

Top-Down Processing of Drug Names Can Induce Errors in Discriminating Similar Pseudo-Drug Names by Nurses

BACKGROUND

One factor that could cause medical errors is confusing medicines with similar names. A previous study showed that nurses who have knowledge about drugs faced difficulty in discriminating a drug name from similar pseudo-drug names. To avoid such errors, finger-pointing and calling (FPC) has been recommended in Japan.

OBJECTIVES

The present study had two aims. The first was to determine whether such difficulty was due to top-down processing, rather than bottom-up processing, being applied even for pseudo-names. The other was to investigate whether FPC affected error prevention for similar drug names.

METHOD

In two experiments, nurses and non-health care professionals performed a choice reaction time task for drug names and common words, with or without FPC. Error rate and reaction time were analyzed.

RESULTS

When drug names were used, nurses showed difficulty discriminating target names from distractors. Furthermore, the error prevention effect of FPC was marginally significant for drug names. However, nurses showed no significant differences when similar drug names were used. There was no significant difference regarding the error rate for words.

CONCLUSIONS

Nurses' knowledge of drug names activates top-down processing. As a result, the processing of drug names was not as accurate and quick as that for words for nurses, which caused difficulty in discriminating similar names. FPC may be applicable to reduce confusion errors, possibly by leading individuals to process drug names using bottom-up processing.

APPLICATION

The present study advances current knowledge about error tendencies with similar drug names and the effects of FPC on error prevention.

Effects of Text Enhancement on Reduction of Look-Alike Drug Name Confusion: A Systematic Review and Meta-analysis

BACKGROUND AND OBJECTIVES

Drug name confusion induced by look-alike drug names represents a serious health care management problem in practice. Text enhancement by changing visual attributes of look-alike drug names has been proposed and widely applied in practice to mitigate drug name confusion. However, the effectiveness of text enhancement on reducing drug name confusion is yet to be determined. This study aimed to explore the effects of text enhancement on reduction of confusion caused by look-alike drug names through systematic review and meta-analysis.

METHODS

We searched 5 databases (from database inception to January 2020) for empirical studies that examined the effects of text enhancement on reduction of look-alike drug name-induced drug name confusion. The pooled outcome data were analyzed using either meta-analysis or a narrative synthesis approach.

RESULTS

From the 351 identified articles, 11 articles representing 20 individual trials were included. Five basic text enhancement methods were revealed, including Tall Man, red, boldface, contrast, and size enhancement, from which 4 Tall Man variants and 6 text enhancement combinations were derived. The meta-analysis results showed significant reduction in omission errors when using Tall Man (standardized mean difference [SMD] = -0.628, 95% confidence interval [CI]: -1.018 to -0.238, P = .002), red (SMD = -0.516, 95% CI: -1.002 to -0.030, P = .038), boldface (SMD = -1.027, 95% CI: -1.240 to -0.814, P < .001), and contrast (SMD = -0.437, 95% CI: -0.869 to -0.004, P = 0.048), as compared with lowercase. This finding was also supported in our subgroup analysis by task type for name differentiation tasks. No other significant effects of text enhancement were found for either commission errors or response time.

CONCLUSIONS

Using Tall Man, red, boldface, or contrast could help reduce omission errors (ie, wrong medication selection) caused by look-alike drug names, particularly in name differentiation tasks. However, no text enhancement could shorten name search and/or differentiation time. Our findings could facilitate the understanding of the effects of text enhancement in the prevention of confusion errors caused by look-alike drug names and promote the application of text enhancement in practice.

Risk factors associated with medication ordering errors

OBJECTIVE

We utilized a computerized order entry system-integrated function referred to as "void" to identify erroneous orders (ie, a "void" order). Using voided orders, we aimed to (1) identify the nature and characteristics of medication ordering errors, (2) investigate the risk factors associated with medication ordering errors, and (3) explore potential strategies to mitigate these risk factors.

MATERIALS AND METHODS

We collected data on voided orders using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. Interviews were informed by the human factors-based SEIPS (Systems Engineering Initiative for Patient Safety) model to characterize the work systems-based risk factors contributing to ordering errors; chart reviews were used to establish whether a voided order was a true medication ordering error and ascertain its impact on patient safety.

RESULTS

During the 16-month study period (August 25, 2017, to December 31, 2018), 1074 medication orders were voided; 842 voided orders were true medication errors (positive predictive value = 78.3 ± 1.2%). A total of 22% (n = 190) of the medication ordering errors reached the patient, with at least a single administration, without causing patient harm. Interviews were conducted on 355 voided orders (33% response). Errors were not uniquely associated with a single risk factor, but the causal contributors of medication ordering errors were multifactorial, arising from a combination of technological-, cognitive-, environmental-, social-, and organizational-level factors.

CONCLUSIONS

The void function offers a practical, standardized method to create a rich database of medication ordering errors. We highlight implications for utilizing the void function for future research, practice and learning opportunities.

A mixed methods analysis of lithium-related patient safety incidents in primary care

BACKGROUND

Lithium is a drug with a narrow therapeutic range and has been associated with a number of serious adverse effects. This study aimed to characterise primary care lithium-related patient safety incidents submitted to the National Reporting and Learning System (NRLS) database with respect to incident origin, type, contributory factors and outcome. The intention was to identify ways to minimise risk to future patients by examining incidents with a range of harm outcomes.

METHODS

A mixed methods analysis of patient safety incident reports related to lithium was conducted. Data from healthcare organisations in England and Wales were extracted from the NRLS database. An exploratory descriptive analysis was undertaken to characterise the most frequent incident types, the associated chain of events and other contributory factors.

RESULTS

A total of 174 reports containing the term 'lithium' were identified. Of these, 41 were excluded and, from the remaining 133 reports, 138 incidents were identified and coded. Community pharmacies reported 100 incidents (96 dispensing related, two administration, two other), general practitioner (GP) practices filed 22 reports and 16 reports originated from other sources. A total of 99 dispensing-related incidents were recorded, 39 resulted from the wrong medication dispensed, 31 the wrong strength, 8 the wrong quantity and 21 other. A total of 128 contributory factors were identified overall; for dispensing incidents, the most common related to medication storage/packaging (n = 41), and 'mistakes' (n = 22), whereas no information regarding contributory factors was provided in 41 reports.

CONCLUSION

Despite the established link between medication packaging and the risk of dispensing errors, our study highlighted storage and packaging as the most commonly described contributory factors to dispensing errors. The absence of certain relevant data limited the ability to fully characterise a number of reports. This highlighted the need to include clear and complete information when submitting reports. This, in turn, may help to better inform the further development of interventions designed to reduce the risk of incidents and improve patient safety.

The problem of look-alike, sound-alike name errors: Drivers and solutions

Look-alike or sound-alike (LASA) medication names may be mistaken for each other, e.g. mercaptamine and mercaptopurine. If an error of this sort is not intercepted, it can reach the patient and may result in harm. LASA errors occur because of shared linguistic properties between names (phonetic or orthographic), and potential for error is compounded by similar packaging, tablet appearance, tablet strength, route of administration or therapeutic indication. Estimates of prevalence range from 0.00003 to 0.0022% of all prescriptions, 7% of near misses, and between 6.2 and 14.7% of all medication error events. Solutions to LASA errors can target people or systems, and include reducing interruptions or distractions during medication administration, typographic tweaks, such as selective capitalization (Tall Man letters) or boldface, barcoding, and computerized physician order entry.

Development and exploratory analysis of software to detect look-alike, sound-alike medicine names

BACKGROUND

'Look-alike, sound-alike' (LASA) medicines may be confused by prescribers, pharmacists, nurses and patients, with serious consequences for patient safety. The current research aimed to develop and trial software to proactively identify LASA medicines by computing medicine name similarity scores.

METHODS

Literature review identified open-source software from the United States Food and Drug Administration for screening of proposed medicine names. We adapted and refined this software to compute similarity scores (0.0000-1.0000) for all possible pairs of medicines registered in Australia. Two-fold exploratory analysis compared: RESULTS: Screening of the Australian medicines register identified 7,750 medicine pairs with at least moderate (arbitrarily ≥0.6600) name similarity, including many oncology, immunomodulating and neuromuscular-blocking medicines. Computed similarity scores and resulting risk categories demonstrated a modest correlation with the manually-calculated similarity scores (r = 0.324, p < 0.002, 95 % CI 0.119-0.529). However, agreement between the resulting risk categories was not significant (Cohen's kappa = -0.162, standard error = 0.063).

CONCLUSIONS

The software (LASA v2) has potential to identify pairs of confusable medicines. It is recommended to supplement incident reports in risk-management programs, and to facilitate pre-screening of medicine names prior to brand/trade name approval and inclusion of medicines in formularies.

Prevention strategies to identify LASA errors: building and sustaining a culture of patient safety

BACKGROUND

Potential look-alike, sound-alike (LASA) errors in outpatient and inpatient prescriptions have been widely described worldwide. However, most strategies of reducing drug name confusion have been only focused on the processes of prescribing and dispensing, often following local rules.

MAIN TEXT

An illustrative recent example about this topic is given: the antidepressant Brintellix® (vortioxetine) (Takeda Pharmaceuticals USA, Inc.) and the antiplatelet medication Brilinta® (ticagrelor) (AstraZeneca LP). Revision of the initiatives that are currently applied to prevent potential LASA errors in different countries around the world and debate about the emerging strategies that could be implemented in short and mid-term. At present, a common policy worldwide on the authorization of unique names for innovative medicines does not exist. The implication of authorities in topdown strategies and the importance of developing an international health policy on the authorization of unique names for innovative medicines are highlighted in the following piece of opinion.

CONCLUSIONS

Building and sustaining a culture of patient safety should be considered as a global top-down strategy which involved all the elements in the system (regulatory bodies, manufacturers and suppliers). The precedent established by the FDA in prevention strategies to identify and avoid LASA errors has been extremely important and should lead to international discussion. Coordinated international efforts are urgently needed in this area for the sake of patients' safety.

Identification of potential drug name confusion errors in the Sentinel System

PURPOSE

In July 2015, the US Food and Drug Administration (FDA) published a drug safety communication regarding errors in prescribing and dispensing of the antidepressant Brintellix (vortioxetine) and the antiplatelet Brilinta (ticagrelor) that arose due to proprietary drug name confusion. Brintellix is indicated for major depressive disorder; Brilinta is indicated to reduce cardiovascular death, myocardial infarction, and stroke in patients with acute coronary syndrome or history of myocardial infarction. Brintellix was renamed to Trintellix in May 2016. Using Brilinta and Brintellix as a proof-of-concept feasibility use case, we assessed whether drug name confusion errors between the pair could be identified in electronic health care data via the combination of a claims-based algorithm and limited manual claims data review.

METHODS

Using data from the Sentinel System, we defined potential errors as Brintellix users without an on- or off-label indication for Brintellix, without a dispensing for a drug with the same indications as Brintellix, and with an on- or off-label indication for Brilinta between -365 and +30 days after index Brintellix dispensing; the reverse was done for Brilinta. We manually reviewed claims profiles of potential cases.

RESULTS

We identified 27 (0.1%) potential errors among 21 208 Brintellix users; 16 appeared to be likely errors based on claims profile review. Fifty-one (0.3%) of the 16 779 Brilinta users were identified as potential errors, and four appeared to be likely errors.

CONCLUSIONS

A claims-based algorithm combined with manual review of claims profiles could identify potential drug name confusion errors, and narrow down likely errors that warrant further investigation.

Automated detection of wrong-drug prescribing errors

BACKGROUND

To assess the specificity of an algorithm designed to detect look-alike/sound-alike (LASA) medication prescribing errors in electronic health record (EHR) data.

SETTING

Urban, academic medical centre, comprising a 495-bed hospital and outpatient clinic running on the Cerner EHR. We extracted 8 years of medication orders and diagnostic claims. We licensed a database of medication indications, refined it and merged it with the medication data. We developed an algorithm that triggered for LASA errors based on name similarity, the frequency with which a patient received a medication and whether the medication was justified by a diagnostic claim. We stratified triggers by similarity. Two clinicians reviewed a sample of charts for the presence of a true error, with disagreements resolved by a third reviewer. We computed specificity, positive predictive value (PPV) and yield.

RESULTS

The algorithm analysed 488 481 orders and generated 2404 triggers (0.5% rate). Clinicians reviewed 506 cases and confirmed the presence of 61 errors, for an overall PPV of 12.1% (95% CI 10.7% to 13.5%). It was not possible to measure sensitivity or the false-negative rate. The specificity of the algorithm varied as a function of name similarity and whether the intended and dispensed drugs shared the same route of administration.

CONCLUSION

Automated detection of LASA medication errors is feasible and can reveal errors not currently detected by other means. Real-time error detection is not possible with the current system, the main barrier being the real-time availability of accurate diagnostic information. Further development should replicate this analysis in other health systems and on a larger set of medications and should decrease clinician time spent reviewing false-positive triggers by increasing specificity.

Is an Indication-Based Prescribing System in Our Future?

Indication-based prescribing has many potential benefits, including preventing errors by reducing medication choices and assisting with medication reconciliation.

Challenges and innovations of delivering medicines to older adults

Older adults with multimorbidity, polypharmacy, and complex health needs are the major consumer of health care. Ensuring that medicines are used safely, effectively, and delivered efficiently in this population is challenging. In this context, the approach to medicines delivery should seek to overcome some of the difficulties of delivering medicines to older people, and ensure each medication is delivered by the optimal and most convenient route for the patient in question. However, this poses significant obstacles, as the development of medicines suitable for use in older populations does not often account for complex health needs, potential challenges in relation to drug disposition, safety of excipients and limitations with practical usability of dosage forms. The objective of this review is to summarise and discuss current challenges and novel approaches to delivering medications to older adults.

Preventing dispensing errors by alerting for drug confusions in the pharmacy information system-A survey of users

Introduction

Drug confusion is thought to be the most common type of dispensing error. Several strategies can be implemented to reduce the risk of medication errors. One of these are alerts in the pharmacy information system.

Objective

To evaluate the experiences of pharmacists and pharmacy technicians with alerts for drug name and strength confusion.

Methods

In May 2017, a cross-sectional survey of pharmacists and pharmacy technicians was performed in community pharmacies in the Netherlands using an online questionnaire.

Results

Of the 269 respondents, 86% (n = 230) had noticed the alert for drug name confusion, and 26% (n = 67) for drug strength confusion. Of those 230, 9% (n = 20) had experienced that the alert had prevented dispensing the wrong drug. For drug strength confusion, this proportion was 12% (n = 8). Respondents preferred to have an alert for drug name and strength confusion in the pharmacy information system. ‘Alert fatigue’ was an important issue, so alerts should only be introduced for frequent confusions or confusions with serious consequences.

Conclusion

Pharmacists and pharmacy technicians were positive about having alerts for drug confusions in their pharmacy information system and experienced that alerts contributed to the prevention of dispensing errors. To prevent alert fatigue, it was considered important not to include all possible confusions as a new alert: the potential contribution to the prevention of drug confusion should be weighed against the risk of alert fatigue.

A systematic literature review on strategies to avoid look-alike errors of labels

PURPOSE

Unclear labeling has been recognized as an important cause of look-alike medication errors. The aim of this literature review is to systematically evaluate the current evidence on strategies to minimize medication errors due to look-alike labels.

METHODS

A literature search of PubMed and EMBASE for all available years was performed independently by two reviewers. Original studies assessing strategies to minimize medication errors due to look-alike labels focusing on readability of labels by health professionals or consumers were included. Data were analyzed descriptively due to the variability of study methods.

RESULTS

Sixteen studies were included. Thirteen studies were performed in a laboratory and three in a healthcare setting. Eleven studies evaluated Tall Man lettering, i.e., capitalizing parts of the drug name, two color-coding, and three studies other strategies. In six studies, lower error rates were found for the Tall Man letter strategy; one showed significantly higher error rates. Effects of Tall Man lettering on response time were more varied. A study in the hospital setting did not show an effect on the potential look-alike sound-alike error rate by introducing Tall Man lettering. Color-coding had no effect on the prevention of syringe-swaps in one study.

CONCLUSIONS

Studies performed in laboratory settings showed that Tall Man lettering contributed to a better readability of medication labels. Only few studies evaluated other strategies such as color-coding. More evidence, especially from real-life setting is needed to support safe labeling strategies.

Drugs and their names

Over recent years, drug names have become more complex and difficult to pronounce (e.g. axicabtagene ciloleucel, ▼rurioctocog alfa pegol, ▼riociguat ).1,2 Although some rules underpin drug nomenclature, there are several examples of drugs with similar sounding names that are easily confused. In addition, the different levels of nomenclature (chemical, generic and brand names) may cause confusion for patients. Here, we provide an overview of how drugs get their names, the patterns that can be seen in naming conventions and highlight the risk associated with drugs with similar looking or similar sounding names.

A systematic review of the types and causes of prescribing errors generated from using computerized provider order entry systems in primary and secondary care

Objective

To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems.

Materials and Methods

We conducted a systematic review of the literature published between January 2004 and June 2015 using three large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Studies that reported qualitative data about the types and causes of these errors were included. A narrative synthesis of all eligible studies was undertaken.

Results

A total of 1185 publications were identified, of which 34 were included in the review. We identified 8 key themes associated with CPOE-related prescribing errors: computer screen display, drop-down menus and auto-population, wording, default settings, nonintuitive or inflexible ordering, repeat prescriptions and automated processes, users' work processes, and clinical decision support systems. Displaying an incomplete list of a patient's medications on the computer screen often contributed to prescribing errors. Lack of system flexibility resulted in users employing error-prone workarounds, such as the addition of contradictory free-text comments. Users' misinterpretations of how text was presented in CPOE systems were also linked with the occurrence of prescribing errors.

Discussion and Conclusions

Human factors design is important to reduce error rates. Drop-down menus should be designed with safeguards to decrease the likelihood of selection errors. Development of more sophisticated clinical decision support, which can perform checks on free-text, may also prevent errors. Further research is needed to ensure that systems minimize error likelihood and meet users' workflow expectations.

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

BACKGROUND

Drug 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 being filled, and most of these errors involve drug names that look or sound alike. Prior to approval, drug names undergo a variety of tests to assess their potential for confusability, but none of these preapproval tests has been shown to predict real-world error rates.

OBJECTIVES

We conducted a study to assess the association between error rates in laboratory-based tests of drug name memory and perception and real-world drug name confusion error rates.

METHODS

Eighty participants, comprising doctors, nurses, pharmacists, technicians and lay people, completed a battery of laboratory tests assessing visual perception, auditory perception and short-term memory of look-alike and sound-alike drug name pairs (eg, hydroxyzine/hydralazine).

RESULTS

Laboratory test error rates (and other metrics) significantly predicted real-world error rates obtained from a large, outpatient pharmacy chain, with the best-fitting model accounting for 37% of the variance in real-world error rates. Cross-validation analyses confirmed these results, showing that the laboratory tests also predicted errors from a second pharmacy chain, with 45% of the variance being explained by the laboratory test data.

CONCLUSIONS

Across two distinct pharmacy chains, there is a strong and significant association between drug name confusion error rates observed in the real world and those observed in laboratory-based tests of memory and perception. Regulators and drug companies seeking a validated preapproval method for identifying confusing drug names ought to consider using these simple tests. By using a standard battery of memory and perception tests, it should be possible to reduce the number of confusing look-alike and sound-alike drug name pairs that reach the market, which will help protect patients from potentially harmful medication errors.