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BackgroundPatient misunderstanding of prescription drug label instructions is a common cause of unintentional misuse of medication and adverse health outcomes. Those with limited literacy and English proficiency are at greater risk.ObjectiveTo test the effectiveness of a patient-centered drug label strategy, including a Universal Medication Schedule (UMS), to improve proper regimen use and adherence compared to a current standard.DesignTwo-arm, multi-site patient-randomized pragmatic trial.ParticipantsEnglish- and Spanish-speaking patients from eight community health centers in northern Virginia who received prescriptions from a central-fill pharmacy and who were 1) ≥30 years of age, 2) diagnosed with type 2 diabetes and/or hypertension, and 3) taking ≥2 oral medications.InterventionA patient-centered label (PCL) strategy that incorporated evidence-based practices for format and content, including prioritized information, larger font size, and increased white space. Most notably, instructions were conveyed with the UMS, which uses standard intervals for expressing when to take medicine (morning, noon, evening, bedtime).Main MeasuresDemonstrated proper use of a multi-drug regimen; medication adherence measured by self-report and pill count at 3 and 9 months.Key ResultsA total of 845 patients participated in the study (85.6 % cooperation rate). Patients receiving the PCL demonstrated slightly better proper use of their drug regimens at first exposure (76.9 % vs. 70.1 %, p = 0.06) and at 9 months (85.9 % vs. 77.4 %, p = 0.03). The effect of the PCL was significant for English-speaking patients (OR 2.21, 95 % CI 1.13–4.31) but not for Spanish speakers (OR 1.19, 95 % CI 0.63–2.24). Overall, the intervention did not improve medication adherence. However, significant benefits from the PCL were found among patients with limited literacy (OR 5.08, 95 % CI 1.15–22.37) and for those with medications to be taken ≥2 times a day (OR 2.77, 95 % CI 1.17–6.53).ConclusionsA simple modification to pharmacy-generated labeling, with minimal investment required, can offer modest improvements to regimen use and adherence, mostly among patients with limited literacy and more complex regimens.Trial Registration (ClinicalTrials.gov): NCT00973180, NCT01200849

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

Background: Med Guides are the only Food and Drug Administration-regulated source of written patient information distributed with prescriptions drugs. Despite their potential value, studies have found them to have limited utility. Objective: To evaluate the effectiveness of patient-centered strategies for the design of Med Guides to improve comprehension. Design: A cross-sectional, randomized trial. Setting: Two primary care clinics in Chicago, Illinois; one based in a public university hospital and the other within a private academic medical center. Patients: A total of 1003 adults aged 18–85 years. Intervention: The format and layout of content from 3 typical Med Guides (by reading difficulty, length, exposure) were modified several ways to promote information accessibility. Working with patients, the 3 most preferred versions were evaluated. The first used 2 columns to organize content (Column), a second mimicked over-the-counter \"Drug Facts\" labeling (Drug Facts), and the third followed health literacy best practices using a simple table format (Health Literacy prototype). Measures: Tailored comprehension assessment of content from 3 representative Med Guides. Results: Comprehension was significantly greater for all 3 prototypes compared with the current standard (all P<0.001). The Health Literacy prototype consistently demonstrated the highest comprehension scores, and in multivariable analyses, outperformed both the Drug Facts [β = -4.43, 95% confidence interval (CI), -6.21 to -2.66] and Column (β = -4.04, 95% CI, -5.82 to -2.26) prototypes. Both older age (older than 60 y: β = -10.54, 95% CI, -15.12 to -5.96), low and marginal literacy skills were independently associated with poorer comprehension (low: β = -31.92, 95% CI, -35.72 to -28.12; marginal: β = -12.91, 95% CI, -16.01 to -9.82). Conclusions: The application of evidence-based practices to the redesign of Med Guides significantly improved patient comprehension. Although some age and literacy disparities were reduced with the Health Literacy format in particular, both older age and low literacy remained independently associated with poorer comprehension. More aggressive strategies will likely be needed to gain assurances that all patients are informed about their prescribed medications. Trial Registration: Clinical Trials.Gov #NCT01731405.

Inhaler technique can be corrected with training, but skills drop off quickly without repeated training. The aim of our study was to explore the effect of novel inhaler technique labels on the retention of correct inhaler technique. In this single-blind randomized parallel-group active-controlled study, clinical pharmacists enrolled asthma patients using controller medication by Accuhaler [Diskus] or Turbuhaler. Inhaler technique was assessed using published checklists (score 0–9). Symptom control was assessed by asthma control test. Patients were randomized into active (ACCa; THa) and control (ACCc; THc) groups. All patients received a “Show-and-Tell” inhaler technique counseling service. Active patients also received inhaler labels highlighting their initial errors. Baseline data were available for 95 patients, 68% females, mean age 44.9 (SD 15.2) years. Mean inhaler scores were ACCa:5.3 ± 1.0; THa:4.7 ± 0.9, ACCc:5.5 ± 1.1; THc:4.2 ± 1.0. Asthma was poorly controlled (mean ACT scores ACCa:13.9 ± 4.3; THa:12.1 ± 3.9; ACCc:12.7 ± 3.3; THc:14.3 ± 3.7). After training, all patients had correct technique (score 9/9). After 3 months, there was significantly less decline in inhaler technique scores for active than control groups (mean difference: Accuhaler −1.04 (95% confidence interval −1.92, −0.16, P  = 0.022); Turbuhaler −1.61 (−2.63, −0.59, P  = 0.003). Symptom control improved significantly, with no significant difference between active and control patients, but active patients used less reliever medication (active 2.19 (SD 1.78) vs. control 3.42 (1.83) puffs/day, P  = 0.002). After inhaler training, novel inhaler technique labels improve retention of correct inhaler technique skills with dry powder inhalers. Inhaler technique labels represent a simple, scalable intervention that has the potential to extend the benefit of inhaler training on asthma outcomes. Asthma: Reminder labels improve inhaler technique Personalized labels on asthma inhalers remind patients of correct technique and help improve symptoms over time. Iman Basheti at the Applied Science Private University in Jordan and co-workers trialed the approach of placing patient-specific reminder labels on dry-powder asthma inhalers to improve long-term technique. Poor asthma control is often exacerbated by patients making mistakes when using their inhalers. During the trial, 95 patients received inhaler training before being split into two groups: the control group received no further help, while the other group received individualized labels on their inhalers reminding them of their initial errors. After three months, 67% of patients with reminder labels retained correct technique compared to only 12% of controls. They also required less reliever medication and reported improved symptoms. This represents a simple, cheap way of tackling inhaler technique errors.

Drug-drug interaction (DDI) is an important topic for public health and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html .

The counterfeiting of vaccines is an increasing problem globally with the safety of persons vaccinated, the trust in vaccines generally and the associated reputation of vaccine manufacturers and regulatory agencies at risk. This risk is especially critical with the on-going development of COVID-19 vaccines. The ability to track and trace vaccines through the vaccine supply chain down to persons vaccinated has to be enhanced. In this context of traceability, the global immunization community has recently set the barcoding of the primary packaging of vaccines, specifically vaccine vials and pre-filled syringes, as a top priority. Emerging vaccine manufacturers are already engaged in investigating ways to incorporate barcoding in their labelling and packaging using GS1 international standards. A specific pilot taking place in Indonesia by the national vaccine manufacturer, Bio Farma, shows the innovation of barcoding on primary packaging already underway with a relatively modest level of investment and success at this stage. This article highlights the efforts of industry and governments on the value of traceability and introduction to 2D barcodes. Access to financial resources and support from the international immunization community would accelerate such innovations leading to enhanced security of the vaccine supply chain.

Communication about prescription drugs ought to be a paragon of public science communication. Unfortunately, it is not. Consumers see $4 billion of direct-to-consumer advertising annually, which typically fails to present data about how well drugs work. The professional label—the Food and Drug Administration's (FDA) mechanism to get physicians information needed for appropriate prescribing—may also fail to present benefit data. FDA labeling guidance, in fact, suggests that industry omit benefit data for new drugs in an existing class and for drugs approved on the basis of unfamiliar outcomes (such as depression rating scales). The medical literature is also problematic: there is selective reporting of favorable trials, favorable outcomes within trials, and “spinning” unfavorable results to maximize benefit and minimize harm. In contrast, publicly available FDA reviews always include the phase 3 trial data on benefit and harm, which are the basis of drug approval. However, these reviews are practically inaccessible: lengthy, poorly organized, and weakly summarized. To improve accessibility, we developed the Drug Facts Box: a one-page summary of benefit and harm data for each indication of a drug. A series of studies—including national randomized trials—demonstrates that most consumers understand the Drug Facts Box and that it improves decision-making. Despite calls from their own Risk Communication Advisory Committee and Congress (in the Affordable Care Act) to consider implementing boxes, the FDA announced it needs at least 3–5 y more to make a decision. Given its potential public health impact, physicians and the public should not have to wait that long for better drug information.

Background Drug adverse events (AEs) represent a significant public health concern. US Food and Drug Administration (FDA) drug labeling documents are an essential resource for studying drug safety such as assessing a drug's likelihood to cause certain organ toxicities; however, the manual extraction of AEs is labor-intensive, requires specialized expertise, and is challenging to maintain, due to frequent updates of the labeling documents. Objective To automate the extraction of AE data from FDA drug labeling documents, we developed a workflow based on AskFDALabel, a large language model (LLM)-powered framework, and its demonstration in drug safety studies. Methods This framework incorporates a retrieval-augmented generation (RAG) component based on FDALabel to enhance standard LLM inference. Key steps include (1) selection of a task-specific template, (2) FDALabel database querying, and (3) content preparation for LLM processing. We evaluated the performance of the framework in three benchmark experiments, including drug-induced liver injury (DILI) classification, drug-induced cardiotoxicity (DICT) classification, and AE term recognition. Results AskFDALabel achieved F1-scores of 0.978 for DILI, 0.931 for DICT, and 0.911 for AE annotation, outperforming other traditional methods. It also provided cited labeling content and detailed explanations, facilitating manual verification. Conclusion AskFDALabel exhibited high consistency with human AE annotation, particularly in classifying and profiling DILI and DICT. Thus, it can significantly enhance the efficiency and accuracy of AE annotation, with promising potential for advanced AE surveillance and drug safety research.

Background Most people do not apply sunscreen effectively. The Australian and New Zealand standard for sunscreen specifies labels must provide clear and adequate directions for use but does not prescribe specific wording or positioning. Additionally, water-resistant sunscreens must declare the duration of laboratory-tested water resistance, up to 4 h maximum. Formative research found consumers are confused by reapplication directions and water resistance claims. This study aimed to explore whether enhanced sunscreen labelling information can improve sunscreen reapplication. Methods Adult sunscreen users ( n  = 3,363) were randomised to view one of ten mock sunscreen labels in a 2 × 5 online experiment. Labels differed according to front-of-pack (FOP) water resistance claim (standard: tested for 4 h water resistance vs. alternative: water resistant) and reapplication information (none vs. any; with four message variations: simple text, simple icon, extended text, extended icon). We used multivariate logistic regression to examine the effect of FOP labelling on knowledge and intention to reapply sunscreen every 2 h and after swimming, sweating and towel drying (henceforth: activity), considering: (i) water resistance and reapplication information and (ii) reapplication message type. Results Compared to no information, FOP reapplication information increased knowledge (48% vs. 70%) and intention to reapply within 2 h (41% vs. 54%), but not after activity. Compared to the standard claim, the alternative water resistant claim increased knowledge (60% vs. 72%) and intention to reapply within 2 h (47% vs. 56%), but not after activity. Although there was no clear pattern of effects for reapplication message type, only the extended icon (with directions to reapply every 2 h or after activity) increased knowledge to reapply after activity, irrespective of the water resistance claim (52% standard and 57% alternative). Conclusions Under the current standard, sunscreen labels do not provide clear directions for use, which leaves consumers vulnerable to UV damage. Mandating FOP reapplication directions and adopting an alternative ‘water resistant’ claim could improve consumer understanding of how often to reapply sunscreen. Due to common misperceptions about the limits of water resistance, further user-centred label design and public education is needed to improve reapplication after swimming, sweating and towel drying.

Medication error is an important cause of patient morbidity and mortality, yet it can be a confusing and underappreciated concept. This article provides a review for practicing physicians that focuses on medication error (1) terminology and definitions, (2) incidence, (3) risk factors, (4) avoidance strategies, and (5) disclosure and legal consequences. A medication error is any error that occurs at any point in the medication use process. It has been estimated by the Institute of Medicine that medication errors cause 1 of 131 outpatient and 1 of 854 inpatient deaths. Medication factors (eg, similar sounding names, low therapeutic index), patient factors (eg, poor renal or hepatic function, impaired cognition, polypharmacy), and health care professional factors (eg, use of abbreviations in prescriptions and other communications, cognitive biases) can precipitate medication errors. Consequences faced by physicians after medication errors can include loss of patient trust, civil actions, criminal charges, and medical board discipline. Methods to prevent medication errors from occurring (eg, use of information technology, better drug labeling, and medication reconciliation) have been used with varying success. When an error is discovered, patients expect disclosure that is timely, given in person, and accompanied with an apology and communication of efforts to prevent future errors. Learning more about medication errors may enhance health care professionals' ability to provide safe care to their patients.