Look-alike Drug Research Articles

Knowledge about Administration and Regulation of High Alert Medication among Nurses in Oncology Department

Background: High alert medications (HAMs) are associated with a higher risk of causing significant patient harm when used in error. Understanding the administration and regulation of these medications within oncology settings is critical due to the complex nature of cancer treatment regimens and the severity of potential medication errors. Objective: To assess the knowledge of nurses regarding the administration and regulation of high alert medications in an oncology department of a tertiary care hospital. Methods: A descriptive cross-sectional study was conducted among 133 registered nurses in the oncology department of Jinnah Hospital, Lahore. Data were collected using a structured questionnaire designed to evaluate nurses' knowledge of HAMs. Participants included both male (29.3%) and female (70.7%) nurses, with varying educational backgrounds and work experiences. Statistical analysis was performed using SPSS version 25, focusing on descriptive statistics such as frequencies, percentages, means, and standard deviations. Results: The results revealed moderate knowledge levels among nurses with significant gaps in critical areas. Only 20.3% correctly identified the protocol for fast IV push of 1:1000 epinephrine in mild allergic reactions, and 33.8% mistakenly believed that 10% calcium gluconate and 10% calcium chloride were interchangeable. Additionally, 44.4% incorrectly used 'Amp' or 'Vial' for dose expression instead of 'mg' or 'gm', and 53.4% failed to recognize the importance of distinctive labeling on look-alike drugs. Conclusion: The study highlights the need for enhanced educational programs and standardized protocols to improve the safe administration and regulation of HAMs in oncology settings. Strengthening nurses' knowledge and practices regarding HAMs is essential to reduce medication errors and improve patient outcomes.

Enhancing Drug Management, Cost Savings, and Staff Satisfaction in Anesthesiology: A Quality Improvement Project in a Chinese Tertiary Hospital.

In alignment with China's national directive for improved drug management in anesthesiology, the Affiliated Hospital of Qingdao University initiated a quality improvement project, aiming to tackle the prevailing challenges of inefficiencies in drug administration, escalating drug costs, and the notable communication gap between pharmacists and anesthesiologists. We employed a Plan-Do-Study-Act methodology to establish a pharmacy team and execute a multidimensional pharmaceutical intervention. The interventions included the formulation of standard procedures, guidelines and regulations, assistance from an information system (including automatic dispensing cabinets and prospective prescription review system), communication feedback (via WeChat groups), and education for anesthesiology staff. The intervention spanned from April to September 2023, focusing on optimizing medication management, achieving cost savings, and enhancing the satisfaction of anesthesia team members, with an additional observation from October to December 2023. Following the interventions, improvements were observed in drug management practices. These enhancements included increased compliance with accounting procedures, more rigorous registration of controlled substances, and more effective disposal of liquid residues. There was no adverse events related to high-alert medications or look-alike drug usage errors. The introduction of automatic dispensing cabinets and a prospective prescription review system markedly improved work efficiency. The utilization of a WeChat group facilitated effective communication about unreasonable prescriptions and drug-related issues. Among the 29,061 patients who underwent surgery both before and after the interventions, significant reductions were observed both in the drug proportion and the per capita drug costs (P = 0.03, P = 0.014, respectively). The per capita drug cost decreased by 20.82%, from ¥723.43 to ¥572.78, consistently remaining below ¥600 throughout the 9-month observation period. The per capita cost of monitoring drugs including dezocine, butorphanol, haemocoagulase agkistrodon, penehyclidine, and ulinastatin experienced a significant reduction (P < 0.05). Additionally, in the satisfaction questionnaires returned, a remarkable 94.44% of anesthesiology staff expressed high satisfaction with the comprehensive pharmaceutical interventions. The quality improvement project has yielded remarkable positive outcomes, serving as a model worthy of reference and replication in similar healthcare settings.

Leveraging code-free deep learning for pill recognition in clinical settings: A multicenter, real-world study of performance across multiple platforms

BackgroundPreventable patient harm, particularly medication errors, represent significant challenges in healthcare settings. Dispensing the wrong medication is often associated with mix-up of lookalike and soundalike drugs in high workload environments. Replacing manual dispensing with automated unit dose and medication dispensing systems to reduce medication errors is not always feasible in clinical facilities experiencing high patient turn-around or frequent dose changes. Artificial intelligence (AI) based pill recognition tools and smartphone applications could potentially aid healthcare workers in identifying pills in situations where more advanced dispensing systems are not implemented. ObjectiveMost of the published research on pill recognition focuses on theoretical aspects of model development using traditional coding and deep learning methods. The use of code-free deep learning (CFDL) as a practical alternative for accessible model development, and implementation of such models in tools intended to aid decision making in clinical settings, remains largely unexplored. In this study, we sought to address this gap in existing literature by investigating whether CFDL is a viable approach for developing pill recognition models using a custom dataset, followed by a thorough evaluation of the model across various deployment scenarios, and in multicenter clinical settings. Furthermore, we aimed to highlight challenges and propose solutions to achieve optimal performance and real-world applicability of pill recognition models, including when deployed on smartphone applications. MethodsA pill recognition model was developed utilizing Microsoft Azure Custom Vision platform and a large custom training dataset of 26,880 images captured from the top 30 most dispensed solid oral dosage forms (SODFs) at the three participating hospitals. A comprehensive internal and external testing strategy was devised, model's performance was investigated through the online API, and offline using exported TensorFlow Lite model running on a Windows PC and on Android, using a tailor-made testing smartphone application. Additionally, model's calibration, degree of reliance on color features and device dependency was thoroughly evaluated. Real-world performance was assessed using images captured by hospital pharmacists at three participating clinical centers. ResultsThe pill recognition model showed high performance in Microsoft Azure Custom Vision platform with 98.7 % precision, 95.1 % recall, and 98.2 % mean average precision (mAP), with thresholds set to 50 %. During internal testing utilizing the online API, the model reached 93.7 % precision, 88.96 % recall, 90.81 % F1-score and 87.35 % mAP. Testing the offline TensorFlow Lite model on Windows PC showed a slight performance reduction, with 91.16 % precision, 83.82 % recall, 86.18 % F1-score and 82.55 % mAP. Performance of the model running offline on the Android application was further reduced to 86.50 % precision, 75.00 % recall, 77.83 % F1-score and 69.24 % mAP. During external clinical testing through the online API an overall precision of 83.10 %, recall of 71.39 %, and F1-score of 75.76 % was achieved. ConclusionOur study demonstrates that using a CFDL approach is a feasible and cost-effective method for developing AI-based pill recognition systems. Despite the limitations encountered, our model performed well, particularly when accessed through the online API. The use of CFDL facilitates interdisciplinary collaboration, resulting in human-centered AI models with enhanced real-world applicability. We suggest that rather than striving to build a universally applicable pill recognition system, models should be tailored to the medications in a regional formulary or needs of a specific clinic, which can in turn lead to improved performance in real-world deployment in these locations. Parallel to focusing on model development, it is crucial to employ a human centered approach by training the end users on how to properly interact with the AI based system to maximize benefits. Future research is needed on refining pill recognition models for broader adaptability. This includes investigating image pre-processing and optimization techniques to enhance offline performance and operation on handheld devices. Moreover, future studies should explore methods to overcome limitations of CFDL development to enhance the robustness of models and reduce overfitting. Collaborative efforts between researchers in this domain and sharing of best practices are vital to improve pill recognition systems, ultimately enhancing patient safety and healthcare outcomes.

Awareness, Practices, and Contributing Factors toward Medication Errors in Children among Pediatric Nurses

ABSTRACT Context: Medication errors are a global issue that poses a significant threat to patient safety and results in serious harm. Inconsistencies in drug formulations make children more susceptible to medication errors. Nurses have the primary responsibility of preventing medication errors in children. Aim: The aim of this study was to assess the awareness, practices, and contributing factors among pediatric nurses at a selected tertiary care hospital, Rishikesh. Setting and Design: This was a cross-sectional study conducted at a tertiary care hospital in Rishikesh (Uttarakhand), India. Methods: One hundred and twenty nurses working in pediatric care units at a tertiary care hospital in Rishikesh were involved in the study. The participants were chosen using the total enumerative sampling technique in accordance with the inclusion criteria. A self-structured questionnaire and Likert scale was used to collect the data. Statistical Analysis: The collected data were analyzed using frequency, mean and standard deviation, Pearson correlation, Fisher’s exact test, and Chi-square test using the SPSS software version 26.0. Results: Majority (70%) of the pediatric nurses were moderately aware and most of them (80%) were practicing safely toward medication errors in children. The most common reason for medication errors in children was a lack of familiarity with look-alike and sound-alike drugs. Conclusion: The study findings revealed that awareness and practices toward reporting of medication errors in children among the pediatric nurses needs to be improved by establishing the guidelines and organizing in-service education programs, the incidence of medication errors in children can be reduced.

Medication errors and associated factors as perceived by nurses working at the national referral hospital in Bhutan

Introduction: A common patient safety issue is medication error and this has remained a serious problem in hospital settings. Medication error occurs at any point of medication process: prescription, transcription, dispensing, and administration. Administration error is the most common type of error occurring among nurses; although nurses play a vital role in preventing harms of prescription and dispensing errors from reaching the patient. Therefore, this study was aimed at assessing medication errors and associated factors as perceived by nurses. Methods: The study was conducted at a tertiary hospital in Bhutan and 225 nurses participated through self-reported questionnaire. Data were double entered and validated in Epidata and analyzed using descriptive commands of statistical software STATA. Results: Among total participants of N = 225, 62.7% reported to have experienced medication error in past six months. Common type of error experienced were omission of dose; wrong time; wrong dose, and administering drugs without physician’s order. Nurses in intensive care units and those caring for pediatric patients reported experiencing more errors. The perceived factors were look-alike drugs, many patients with similar medicines, more medicines to single patient, and oral instructions and acronyms in prescription. Conclusions: The nurses reported to have experienced quite a high rate of medication error. The results may be used by relevant stakeholders in preventing and reducing medication errors, the biggest challenge of patient safety in hospital settings.

Measuring drug name similarity to prioritise the application of tall-man lettering in a computerised pharmacy dispensing system

Abstract Introduction Medicine name similarity is a contributory factor to medication errors.1 Published lists exists highlighting medicine pairs that are easily confused; locally a Look-Alike-Sound-Alike (LASA) list has expanded over time with no formal system of triage. A multipronged approach is required to address LASA risks. Tall-Man Lettering (TML) is one intervention that uses uppercase lettering for the dissimilarities in look-alike drug names to alert staff to the risk of error.1 For greatest impact, it should be reserved for pairs with the highest risk for patient safety. Aim This study aimed to use Levenshtein Distance (LD), Bigram (Bi) and Trigram (Tri) methods to prioritise medicine pairs for TML in the Pharmacy dispensing system. Objectives were to: Produce a comprehensive list of medicines pairs; Establish normalised thresholds from LD, Bi and Tri to prioritise medicine pairs for TML; and, measure drug name similarity using validated software2 applying LD, Bi and Tri to medication pairs as a method of triage for orthographic assessment. Methods Approval was obtained by the Trust Pharmacy Research Committee. The need for ethical submission was waived. A LASA list was developed combining medicine name pairs from National Pharmacy Association list3, historical local list and internal incidents where medicine name confusion was cited. Duplicated, branded and non-stock pairs were excluded. A literature search was undertaken to identify published thresholds for accuracy and sensitivity of the methods in the measure of medicine name similarity. LD measures the minimum number of edit operations needed to transform one string into another; Bi and Tri measures the frequency in which two/three similar sequential strings appear within a medicine name respectively. Two assessors independently entered medicine pairs through a validated computer program2 applying LD, Bi and Tri to measure orthographic similarity. Normalised computed similarity scores (between 0-1 where higher values represent increased drug similarity) were collated on Microsoft excel for comparison against thresholds. Results Two-hundred and twelve medicine pairs were identified for review. The literature defined Bi and Tri thresholds at ≥0.3 and ≥0.1 respectively; in absence of this for LD, in-house thresholds were assessed then defined at ≥0.6. LD identified 84 medicine pairs; Bi identified 144; and Tri identified 158; none were uniquely found by LD, four by Bi and 18 by Tri. A final look-alike list with 82 medicine pairs meeting all three thresholds was identified for TML. Discussion/Conclusion Screening using all three methods led to a 61% reduction in medicines pairs allowing prioritisation of TML as an effective intervention based on look-alike pairs with the highest risk of error. This study focused on identifying orthographic similarity in ‘look-alike’ medicine pairs only. With no single intervention available to prevent LASA errors, future work can explore other interventions. In the absence of literature around normalised LD, the definition of an in-house threshold posed to be another limitation and an area where further exploratory work should be considered. As new LASA incidents arise or the Trust catalogue increases, these methods should be applied to triage their look-alike potential, confirming if TML is an appropriate intervention.

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

BackgroundPatients 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....

Differentiating morphological features for look-alike medicinal crude drugs

Abstract Background: Differentiation of similar-looking crude drugs when observed separately may, sometimes, be perplexing. Simple manual-based differentiation methods may be helpful in such case at the initial prospect. Materials and Methods: This study aimed at adding some new parameters to the existing methods for the identification and differentiation of crude drugs. This study included similar-looking leaves, roots and seeds/fruits, which were examined by organoleptic, macroscopic, and microscopic characters along with certain add-on parameters such as measurement of length at different points, the shape of the notch of a leaf, the distance between parallel veins, the angle made by the upper and lower end of roots, and sliding microscopy of seeds. Results: The findings proved helpful in the differentiation of similar-looking drugs by additional parameters. Conclusion: Add-on parameters may be used for other similar-looking drugs.

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

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. 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. 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. 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.

A Prospective Study on Medication Errors in an Intensive Care Unit

Any preventable event related to drugs that may cause harm to a patient is known as "medication error". Errors occur due to a lack of knowledge, poor performance, and psychological lapses. The pharmacists has a major role along with physicians, nurses, and administrators to examine and improve the healthcare system in order to ensure patient safety. The objective of this study was to determine the frequency, causes, and types of medication errors in the secondary-care intensive care unit. All medical records of intensive care unit patients, above 14 years of age, listing their co-morbid/non-co-morbid conditions, occupation, caste, and gender, were checked for medication errors for a period of 6 months at Government Head Quarters Hospital, Udhagamandalam. According to the results of this study, 116 medication errors were found in 103 patients in the intensive care unit. The number of medication errors was higher in men than in women. The most common medication errors were prescription errors, which were due to illegible handwriting; the use of lookalike drugs; and incomplete dose, dosage, and frequency. Considering the results of this study, it is important to increase awareness among healthcare professionals of varying stature about the significance of medication errors. It is also necessary to change the existing prescribing techniques and clearly differentiate lookalike drugs to avoid medication errors.

Retrospective Analysis of Look-alike and Sound-alike Drug Incidents in a Tertiary Care Hospital

Retrospective Analysis of Look-alike and Sound-alike Drug Incidents in a Tertiary Care Hospital

Dopa-responsive dystonia, DRD-plus and DRD look-alike: a pragmatic review.

Dopa-responsive dystonia (DRD) and DRD plus are diseases of the dopamine pathway with sizeable genetic diversity and myriad presentations. DRD has onset in childhood or adolescence with focal dystonia, commonly affecting lower limb, diurnal fluctuations with evening worsening of symptoms and a demonstrable sleep benefit. DRD "plus" has "atypical features" which include infantile onset, psychomotor delay, cognitive abnormalities, oculogyric crisis, seizures, irritability, spasticity, hypotonia, ptosis, hyperthermia and cerebellar dysfunction. Neurodegeneration, however, is not a feature of either DRD or DRD-plus disorders. Tetrahydrobiopterin (BH4), a key cofactor, deficiency leads to inadequate dopamine and serotonin synthesis. Norepinephrine deficiency may coexist, depending on the enzyme defect. Hyperphenylalaninemia (HPA) is a clue for BH4 paucity. However, HPA is conspicuously absent in autosomal-dominant guanosine triphosphate cyclohydrolase 1 deficiency and sepiapterin reductase deficiency. DRD look-alike is a group of neurodegenerative disorders involving the nigrostriatal dopaminergic system, which could present with dystonia responsive to dopaminergic drugs or neurodegenerative or non-neurodegenerative disorders without involving the nigrostriatal dopaminergic system yet responsive to levodopa. Although levodopa is the mainstay of therapy, response to this drug can be unsatisfactory in DRD plus and DRD look-alike and other drugs are tried. Simultaneous management of HPA leads to remarkable improvement in both motor and cognitive functions. The aim of this review is to help neurology practitioners in treating patients with DRD, DRD-plus and DRD look-alike as many of them have excellent outcome with appropriate therapy.

Effects of Text Enhancement, Identical Prescription-Package Names, Visual Cues, and Verbal Provocation on Visual Searches of Look-Alike Drug Names: A Simulation and Eye-Tracking Study.

Simulation and eye tracking were used to examine the effects of text enhancement, identical prescription-package names, visual cues, and verbal provocation on visual searches of look-alike drug names. Look-alike drug names can cause confusion and medication errors, which jeopardize patient safety. The effectiveness of many strategies that may prevent these problems requires evaluation. We conducted two experiments that were based on a four-way, repeated-measures design. The within-subject factors were text enhancement, identical prescription-package names, visual cues, and verbal provocation. In Experiment 1, 40 nurses searched for and selected a target drug from an array of drug packages on a pharmacy shelf mock-up. In Experiment 2, the eye movements of another 40 nurses were tracked while they performed a computer-based drug search task. Text enhancement had no significant effect on the drug search. Nurses selected the target drugs more quickly and easily when the prescriptions and drug packages shared identical drug name formats. The use of a visual cue to direct nurses' attention facilitated their visual searches and improved their eye gaze behaviors. The nurses reported greater mental effort if they were provoked verbally during the drug search. Efficient and practical strategies should be adopted for designs that facilitate accurate drug search. Among these strategies are using identical name appearances on drug prescriptions and packages, using a visual cue to direct nurses' attention, and avoiding rushing nurses while they are concentrating. The findings aim to inspire recommendations for work system designs that will improve the visual search of look-alike drug names.

Visual differentiation and recognition memory of look-alike drug names: effects of disfluent format, text enhancement and exposure time

Three computer-based experiments were conducted to examine whether disfluent format, enhanced text, and increased exposure time improve the accuracy of visual differentiation and recognition memory of look-alike drug names. A three-way, repeated-measures look-alike drug name differentiation test assessed the visual differentiation accuracy of 30 nursing students (Experiment 1) and 15 nurses (Experiment 2). A two-way, repeated-measures recognition memory test examined the recognition memory accuracy of 15 nurses for look-alike drug names (Experiment 3). We found that making drug names disfluent did not significantly improve differentiation (Experiment 2) or memory accuracy (Experiment 3), but even impaired differentiation accuracy (Experiment 1). Enhanced text and longer exposure time significantly improved differentiation accuracy (Experiments 1 and 2). However, the enhanced text did not improve recognition memory (Experiment 3). We suggest that making look-alike drug names disfluent is not favourable. Enhanced text and longer exposure times are effective in supporting visual differentiation of look-alike drug names.Practitioner Summary: Confusion arising from look-alike drug names may compromise patient safety. Three experiments examined the effects of disfluent format, text enhancement and increased exposure time on visual and memory performances. Making drug names more difficult to read did not improve performance. Enhancing text design and increasing exposure (i.e. reading) time improved visual differentiation between medications, but did not improve the recognition of medications from memory.Abbreviations: SEEV: Salience-effort-expectancy-value; FDA: Food and Drug Administration; ANOVA: analysis of variance; SD: standard deviation, DF: disfluent format; TE: text enhancement; ET: exposure time.

Special Considerations for Proprietary Name Review: Focus on Products That May Be Prescribed "As Directed".

Look-alike and sound-alike (LASA) drug name similarity is often cited as a major factor contributing to wrong drug errors. When present on a prescription, differing product characteristics or directions for use may help health care professionals differentiate between 2 LASA drug names. However, evidence suggests that 1% to 5% of prescriptions include only the signa "use as directed" rather than explicit directions for use. Using nationally projected US outpatient physician survey data, we analyzed drug classes and their associated signa to identify products commonly prescribed with the signa "as directed." The following categories of products are commonly associated with the signa "as directed": (1) inhalers; (2) oral contraceptives; (3) one-time treatments, such as those for lice or scabies; (4) prepackaged items with specific directions for use on the package; (5) migraine medications; (6) erectile dysfunction medications; (7) bowel evacuation/colonoscopy preparations; (8) topical medications; (9) medications with regimens that may change frequently (eg, warfarin, insulin); (10) otic products; (11) transdermal products; (12) products administered vaginally; (13) products administered rectally; and (14) products that may be titrated upon initiation. Prescriptions for these products with the signa "as directed" may not include product characteristics or other directions for use. The potential for products to be prescribed with the signa "as directed" should be taken into consideration when evaluating the safety of proposed proprietary names. Sponsors and the FDA can use the results of our research to aid in the selection and review of proposed proprietary names, respectively.

Drug identification by the patient: Perception of patients, physicians and pharmacists

Faced with the increasing number of pharmaceutical products on the market, several pharmacovigilance notifications regarding confusion between look-alike and sound-alike drugs have been reported. This study of perception among patients, family physicians and pharmacists aims to evaluate drug identification factors and the risk of errors of confusion for patients. Patients were systematically approached in randomly selected pharmacies within the Midi-Pyrénées region in France and invited to complete a questionnaire. Two other questionnaires were respectively sent to family physicians and pharmacists in the same region asking for their opinion on patients' perception of the identification of prescribed medicines. Of the 768 patients interviewed, most report identifying their medications by name (brand name: 50%; generic: 21%), while a smaller number cite physical appearance (box: 16%, tablet: 7% and blister packaging: 3%). In practice the factors considered most likely to cause confusion by patients relate to drug appearance (look-alike tablets: 28%, look-alike boxes: 20% and look-alike blister packaging: 13%). In contrast, look-alike and sound-alike names (generic and brand names combined) were cited in 31% of cases. Physicians (n=345) and pharmacists (n=198) understimate that patients identify their treatment by name (physicians: 46%; pharmacists: 26% vs. patients: 71%), reporting instead that problems arise mainly from the appearance of medicines (physicians: identification: 52% and risk factors for confusion: 74%; pharmacists: identification: 74% and risk factors for confusion: 83%; versus patients: identification: 26%; risk factors for confusion: 61%). Our study highlights the critical role of medication name in identifying drugs among patients. However, confusion of look-alike tablets or pills figures prominently among fears surrounding medication errors. Despite several notifications of pharmacovigilance, this issue appears to be underestimated within the body of medical literature. Proper identification of medicines by patients is essential to improving medication safety and therapeutic compliance. Concrete measures can be undertaken to reach this goal.

Look-Alike Drugs: Avoiding Potential Medical Errors.

Look-Alike Drugs: Avoiding Potential Medical Errors.

The performance improvement of look-alike sound-alike drug pre-dispensing

Medical dispensing errors are common and classic human errors in the hospitals. Look-alike, Sound-alike (LASA) drug is one of the main issues in pre-dispensing and dispensing processes. The data in this study were observed from one middle size hospital in Thailand. In 2017, there were two months that the medication errors KPI (0.065 and 0.061) were greater than the accepted error threshold (0.06). The SBAR (Situation, Background, Assessment, and Recommendation) solution and HOSxP software were applied for three months in the pre-dispensing process to reduce the medical dispensing errors. The results showed that the medication errors KPI (0.04, 0.03, and 0.02, respectively) were reduced dramatically. Therefore, implementation of the SBAR and HOSxP software is recommended as a fast and easy solution for medical dispensing errors.

Assessment of Frequency and Causes of Medication Errors in Pediatrics and Emergency Wards of Teaching Hospitals Affiliated to Tehran University of Medical Sciences (24 Hospitals).

Introduction and Objective: Medical errors and adverse events are among the major causes of avoidable deaths and costs incurred on health systems all over the world. Medical errors are among the main challenges threatening the safety of patients in all countries and one of the most common types of medical errors is medication errors. This study aimed to determine the frequency, type, and causes of medication errors in the emergency and pediatric wards of hospitals affiliated to Tehran University of Medical Sciences in 2017.Materials and Methods: This study was a cross-sectional descriptive study which was conducted on 423 nurses working in teaching hospitals affiliated to Tehran University of Medical Sciences in 2017. The subjects were selected using the stratified sampling method. A total of 49 teaching hospitals in Tehran are affiliated to Tehran University of Medical Sciences and they are divided into two groups of general and specialized hospitals. Of all, 10 general hospitals and 14 specialized hospitals were randomly selected. The required data was collected using a three-part questionnaire. Using the SPSS software (version 18), the collected data was analyzed by means of ANOVA, Pearson Correlation Coefficient, and t-test and the results were reported as frequency, percentage, mean, and standard deviation.Results: According to the results of this study, the mean total number of medication errors that occurred within one month in the pediatric and emergency wards was roughly 41.9 cases, as stated by the nurses. The mean number of medication errors was higher in men than in women. Also, the two variables of gender and the type of shift work were related to medication errors; specifically, it was higher first in the evening and night shifts and then in the morning and evening shifts, respectively. Also, the number was higher in night shifts than in the morning shifts. The most common types of medication errors were: administration of the drugs at the wrong time, using a wrong technique of administration, wrong dosage, forgetting the dosage of the drug, administrating additional doses, administrating the drug to a wrong patient, and following the oral orders of physicians. On the other hand, the most common causes of medication errors in clinical wards were the following: illegible physician orders, shortage of manpower and high workload, incomplete physician orders, the use of lookalike and sound-alike drugs, absence of pharmacist/pharmaceutical expert in the ward, lack of dosage forms appropriate for children, and lack of adequate training regarding drug therapy.Discussion and Conclusion: Considering the results of this study, it is necessary to reduce the workload and working hours of nurses, increase medical staff’s awareness of the significance of medication errors, revise the existing techniques of drug prescription, and update the indices of human resource in hospitals. It is also necessary to correct the process of naming and selecting the dosage forms of drugs by the industry.

Exploring the potential for using drug indications to prevent look-alike and sound-alike drug errors

ABSTRACTBackground: Look-alike, sound-alike (LASA) drug names are a cause of medication errors with resulting patient harm and healthcare costs. This study assessed to which extent the use of the generic drug name, therapeutic class, health problem, and the U.S. Food and Drug Administration (FDA)-approved indications might be used to differentiate LASA drug pairs.Research design and methods: We collected information about LASA drug pairs reported by the FDA to have look-alike sound-alike similarities. To assess potential for differentiating LASA drug pairs, we compared the following drug characteristics: generic name, therapeutic class, health problem, and FDA-approved indication.Results: For the 33 FDA reported LASA drug pairs we identified a total of 432 FDA-approved indications. Using the generic name, therapeutic class, health problem and drug indication we were able to differentiate 8 (24.2%), 24 (72.7%), 25 (75.8%) and 26 (78.8%), respectively of the 33 LASA drug pairs. Using the generic name, therapeutic class, and health problem we were able to distinguish 31 (7.2%), 212 (49.1%), and 269 (62.3%), respectively of the 432 FDA-approved indications for the LASA drug pairs.Conclusions: Including the FDA-approved indication in the drug prescription may be used to differentiate LASA drug pairs and thus, prevent wrong drug medication errors.