Search Results (373)

To enhance PharmD student leadership and advocacy skills, combat the paucity of trained pre-health advisors for pharmacy admissions, augment community relationships, and increase pharmacy admissions volume, we designed, implemented, and assessed PAALS, a Pre-health Academic Advising and Leadership System. PAALS was grounded in Astin’s Theory of Student Involvement and evaluated using the RE-AIM implementation science framework. RE-AIM measured outcomes across Reach, Effectiveness, Adoption, Implementation, and Maintenance as indicators of PAALS’s scale, fidelity, sustainability, and institutional embedding. Analysis of PAALS using the RE-AIM framework demonstrated the following outcomes: (1) Reach: 42 P1-P3 PharmD students participated as mentors; external partnerships expanded from 2 to 8 regional high schools and community programs; and more than 25 mentored learners successfully matriculated into the PharmD program. (2) Effectiveness: students enacted sustained leadership, advocacy, and mentoring roles. (3) Adoption: voluntary uptake of mentoring and governance roles by PharmD students occurred with repeated engagement by external partner institutions. (4) Implementation: Core program components were delivered consistently using existing institutional resources. (5) Maintenance: PAALS remained operational across five academic years despite student turnover, with leadership succession and institutional embedding sustained across cohorts. Our findings demonstrate that student-led advising and advocacy ecosystems address critical gaps in pharmacy-specific pre-health advising models. Full article

This study evaluated the accuracy and educational potential of three generative AI models, ChatGPT 3.5, ChatGPT 4o, and Gemini 2.5, by addressing pharmacy-related content across three key areas: biostatistics, pharmaceutical calculations, and therapeutics. A total of 120 exam-style questions, categorized by Bloom’s Taxonomy levels (Remember, Understand, Apply, and Analyze), were administered to each model. Overall, the AI models achieved a combined accuracy rate of 77.5%, with ChatGPT 4o consistently outperforming ChatGPT 3.5 and Gemini 2.5. The highest accuracy was observed in therapeutics (83.3%), followed by biostatistics (81.7%) and calculations (67.5%). Performance was strongest at lower Bloom levels, reflecting proficiency in recall and conceptual understanding, but declined at higher levels requiring analytical reasoning. These findings suggest that generative AI tools can serve as effective supplementary aids for pharmacy education, particularly for conceptual learning and review. However, their limitations in quantitative and higher-order reasoning highlight the need for guided use and faculty oversight. Future research should expand to additional subject areas and assess longitudinal learning outcomes to better understand AI’s role in improving critical thinking and professional competence among pharmacy students. Full article

Background and Objectives: Osteoporosis risk in real-world, outpatient settings is shaped by intersecting socio-demographic, environmental, and clinical factors. We evaluated predictors of fracture risk status among adults seeking care in community pharmacies in Lahore, Pakistan. Materials and Methods: We conducted a cross-sectional study across urban and suburban pharmacies using a validated questionnaire aligned with international guidelines. Participants were classified as lower risk (osteopenia/osteoporosis without fragility fracture) or high risk (≥1 fragility fracture with clinical osteoporosis). Associations between candidate factors and risk status were examined using univariate and multivariable logistic regression analyses. Results: Of 286 participants, 53.1% were classified as lower risk. After adjustment, most sociodemographic characteristics were not independently associated with fracture risk status, except monthly income. Strong associations were observed for diabetes (AOR = 0.005, 95% CI 0.0007–0.040; p < 0.001), short-term glucocorticoid use (AOR = 32.33; p = 0.004), current smoking (AOR = 14.23; p = 0.002), ex-smoking (AOR = 4.95; p = 0.042), and lack of sunlight exposure (AOR = 7.09; p = 0.019). CKD, rheumatoid arthritis, and vitamin D insufficiency demonstrated borderline non-significant trends. Multivariable modeling did not include “not tested” categories or sparse variables. Conclusions: In Lahore’s community pharmacies, diabetes, CKD, RA, glucocorticoid exposure, smoking, and sunlight/vitamin D-related factors were the dominant correlates of osteoporosis fracture risk status, whereas most socio-demographic factors exerted limited independent effects. Pharmacy-anchored screening and counseling focused on these high-yield clinical indicators alongside timely BMD referral and guideline-concordant therapy may help identify individuals at elevated fracture risk. Full article

The task of repackaging resident’s medication into medication organizers is increasingly outsourced from nursing homes to pharmacies, presenting an opportunity to redefine the interaction between nursing and pharmaceutical staff. This study investigated whether outsourcing medication repackaging changes the quality and subjects of collaboration between the two professions. A cross-sectional survey was developed targeting heads of nursing in German nursing homes. A simple random sample of 1415 nursing homes was contacted by phone. Respondents participated either by phone or by online survey. Quality of collaboration was measured using Kenaszchuk’s Interprofessional Collaboration Scale (ICS) with its subscales Communication, Accommodation and Isolation. Topics of interaction were ascertained using items along a medication management phase model. Differences in response frequencies were analyzed using Fisher’s exact test. A total of 268 nursing homes participated (response: 18.9%). Of these, 132 (49.3%) had outsourced repackaging. Respondents at nursing homes with in-house medication repackaging rated the subscale Accommodation more favorably (p = 0.008), while Communication and Isolation showed no difference. Of the 13 individual ICS items, “passing on information” (Communication) was rated better by respondents at homes with outsourced repackaging (p = 0.019) and “consideration of convenience” (Accommodation) more favorably by respondents at homes with in-house repackaging (p = 0.042). Nursing staff at homes with outsourced medication repackaging interacted with pharmaceutical staff more frequently on medication changes (p < 0.001), but less frequently on tablet splitting (p = 0.035). In conclusion, outsourcing medication repackaging has a limited impact on the quality of interprofessional collaboration between the two professions but may have the potential to reduce ambiguities regarding splitting tablets. Full article

Large language models (LLMs) are rapidly being explored as tools to support learning and assessment in health science education, yet their performance across discipline-specific evaluations remains underexamined. This study evaluated the accuracy of two prominent LLMs on university-level pharmacognosy examinations and compared their performance to that of pharmacy students. Authentic exam papers comprising a range of question formats and content categories were administered to ChatGPT and DeepSeek using a structured prompting approach. Student data were anonymized and LLM responses were graded using the same marking criteria applied to student cohorts, and a Monte Carlo simulation was conducted to determine whether observed performance differences were statistically meaningful. Facility Index (FI) values were calculated to contextualize item difficulty and identify where LLM performance aligned or diverged from student outcomes. The models demonstrated variable accuracy across question types, with a stronger performance in recall-based and definition-style items and comparatively weaker outputs for applied or interpretive questions. Simulated comparisons showed that LLM performance did not uniformly exceed or fall below that of students, indicating dimension-specific strengths and constraints. These findings suggest that while LLM-resistant examination design is contingent on question structure and content, further research should refine their integration into pharmacy education. Full article

Introduction: During pharmaceutical care, community pharmacists play a crucial role by carrying out interventions aimed at preventing, detecting, and resolving drug-related problems (DRPs) and negative outcomes associated with medication (NOM), simultaneously enhancing patients’ knowledge about their treatments. The chronic use of Benzodiazepines (BZDs) is known to be associated with risks such as tolerance, dependence, and cognitive impairment. Furthermore, the combined use of BZDs with other medications or alcohol may expose patients to significant drug interactions. Objectives: This study aimed to characterize and describe the clinical profile of patients using BZDs, to evaluate the extent of polypharmacy and potential drug interactions, to investigate their level of knowledge regarding BZD treatment, and ultimately, to propose evidence-based interventions from the community pharmacy to contribute to improving patient safety and minimizing risks associated with BZD use. Method: A cross-sectional, descriptive study was conducted in a single community pharmacy in Gran Canaria (Canary Islands, Spain). The study population comprised 125 adult patients with active BZD prescriptions. Data collection was performed through pharmacist–patient structured interviews using a questionnaire that included sociodemographic, clinical, and BZD knowledge variables. Results: Lormetazepam and alprazolam were the BZDs most frequently prescribed and dispensed. Potential drug interactions with other medications were detected in 38.4% of BZD users. Notably, 61.5% of patients using BZDs also reported the concurrent use of opioid analgesics, with tramadol being the most common opioid (48.1% of BZD users were also treated with tramadol). Statistically significant differences were observed between patients with and without BZD and other drug interactions in several adverse outcome variables, including the risk of falls (p = 0.003), cognitive impairment (p = 0.047), and urinary incontinence (p = 0.016). Existing BZD dependence is detected in 25% and 22.1% of cases, respectively. Patients’ knowledge of their BZD treatment revealed critical gaps, which are identified as a challenge and a clear opportunity for intervention through pharmaceutical care services. Conclusions: The findings underscore the essential and proactive role of community pharmacists in identifying and managing drug interactions, as well as in supporting deprescribing strategies through collaborative and interprofessional care models. Full article

Preventable medication harm in oncology is often driven by drug-related adverse events (AEs) that trigger order changes such as holds, dose reductions, delays, rechallenges, and enhanced monitoring. Much of the evidence needed to make these decisions lives in unstructured clinical texts, where large language models (LLMs), a type of artificial intelligence (AI), now offer extraction and reasoning capabilities. In this narrative review, we synthesize empirical studies evaluating LLMs and related NLP systems applied to clinical text for oncology AEs, focusing on three decision-linked tasks: (i) AE detection from clinical documentation, (ii) Common Terminology Criteria for Adverse Events (CTCAE) grade assignment, and (iii) grade-aligned actions. We also consider how these findings can inform pharmacist-facing recommendations for order-level safety. We conducted a narrative review of English-language studies indexed in PubMed, Ovid MEDLINE, and Embase. Eligible studies used LLMs on clinical narratives and/or authoritative guidance as model inputs or reference standards; non-text modalities and non-empirical articles were excluded. Nineteen studies met inclusion criteria. LLMs showed the potential to detect oncology AEs from routine notes and often outperformed diagnosis codes for surveillance and cohort construction. CTCAE grading was feasible but less stable than detection; performance improved when outputs were constrained to CTCAE terms/grades, temporally anchored, and aggregated at the patient level. Direct evaluation of grade-aligned actions was uncommon; most studies reported proxies (e.g., steroid initiation or drug discontinuation) rather than formal grade-to-action correctness. While prospective, real-world impact reporting remained sparse, several studies quantified scale advantages and time savings, supporting an initial role as high-recall triage with pharmacist adjudication. Overall, the evidence supports near-term, pharmacist-in-the-loop use of AI for AE surveillance and review, with CTCAE-structured, citation-backed outputs delivered into the pharmacist’s electronic health record order-verification workspace as reviewable artifacts. Future work must standardize reporting and CTCAE/version usage, and measure grade-to-action correctness prospectively, to advance toward order-level decision support. Full article

Background: Community pharmacies are increasingly delivering structured services to support chronic disease management, such as Multidose Drug Dispensing (MDD). This strategy can improve adherence and safety, but evidence of its economic feasibility in Portuguese pharmacies remains limited. Objective: To estimate the cost of implementing and operating an MDD system in a community pharmacy, informing reimbursement models and policy. Methods: A micro-costing approach assessed fixed and variable expenses for serving polymedicated elderly patients. Costs were calculated in euros (2024/2025) and expressed per working day based on 253 annual preparation days. Results: First-year costs totaled €70,985.68, including €8184.00 for setup, €21,579.00 for supplies, and €41,222.68 for staff salaries. The daily operating cost was €280.58, with labour representing the major expense. A break-even analysis indicated sustainability with around 700 users at €10/month. Conclusion: Although requiring significant initial investment, MDD can become financially viable through scaling, workflow efficiency, and supportive reimbursement strategies. Full article

Sustaining a well-designed healthcare intervention justifies the resources allocated during its conceptualization and implementation and maximizes its clinical benefits, but staff influences on sustainment have been studied insufficiently. This study evaluates the effects of pharmacy staff (i.e., pharmacists/technicians) perceptions about the sustainability of Senior Safe^TM, a U.S. pharmacy-based intervention to reduce older adult over-the-counter (OTC) medication misuse. Three months after introducing Senior Safe into 67 pharmacies in a large Midwestern health-system, all pharmacy staff (N = 279) received a survey invitation. Fifty-nine pharmacists and 94 technicians completed the survey. Using logistic regression modeling for the 14 belief-based survey items, and staff roles (pharmacist or technician), the final factors significantly predicting staff views that Senior Safe was sustainable were as follows: perceiving Senior Safe as well-integrated into leadership operations (OR = 5.606, p < 0.001) and believing the intervention reduced OTC misuse (OR = 8.217, p < 0.001). Also, technicians were more confident than pharmacists about Senior Safe’s sustainment and its OTC misuse reduction success. Overall, an intervention’s sustainability relies on those using it. Since the principal predictor of maintaining Senior Safe was its perceived effectiveness, increasing staff buy-in and awareness of an intervention’s benefits may be central to its long-term viability. With an aging U.S. population, sustainable solutions to older adult medication misuse remain critical. Full article

To enable fully automated medicine warehousing in intelligent pharmacy systems, accurately detecting disordered, stacked pillboxes is essential. This paper proposes a high-precision detection algorithm for such scenarios based on an improved YOLOv8 framework. The proposed method integrates a novel convolutional module that replaces traditional stride convolutions and pooling layers, enhancing the detection of small, low-resolution targets in computer vision tasks. To further enhance detection accuracy, the Bi-Level Routing Attention (BiFormer) Vision Transformer is incorporated as a Cognitive Computing module. Additionally, the circular Smooth Label (CSL) technique is employed to mitigate boundary discontinuities and periodic anomalies in angle prediction, which often arise in the detection of rotated objects. The experimental results demonstrate that the proposed method achieves a precision of 94.24%, a recall of 90.39%, and a mean average precision (mAP) of 94.16%—improvements of 3.34%, 2.53%, and 3.35%, respectively, over the baseline YOLOv8 model. Moreover, the enhanced detection model outperforms existing rotated-object detection methods while maintaining real-time inference speed. To facilitate reproducibility and future benchmarking, the full dataset and source code used in this study have been released publicly. Although no standardized benchmark currently exists for pillbox detection, our self-constructed dataset reflects key industrial variations in pillbox size, orientation, and stacking, thereby providing a foundation for future cross-domain validation. Full article

Accurate, non-invasive glucose monitoring remains a major challenge in biomedical sensing. We present a high-sensitivity planar microwave biosensor that progresses from a 2-cell hexagonal array to an 8-cell hexagonal array, and finally to a 16-cell double-honeycomb (DHC-CSRR) architecture to enhance field confinement and resonance strength. Full-wave simulations using Debye-modeled glucose phantoms demonstrate that the optimized 16-cell array on a Rogers RO3210 substrate substantially increases the electric field intensity and transmission response |S₂₁| sensitivity compared with FR-4 and previous multi-CSRR designs. In vitro measurements using pharmacy-grade glucose solutions (5–25%) and saline mixtures with added glucose, delivered through an acrylic channel aligned to the sensing region, confirm the simulated trends. In vivo, vector network analyzer (VNA) tests were conducted on four human subjects (60–150 mg/dL), comparing single- and dual-finger placements. The FR-4 substrate (${\mathsf{\epsilon }}_{\mathrm{r}}$ = 4.4) provided higher frequency sensitivity (2.005 MHz/(mg/dL)), whereas the Rogers RO3210 substrate (${\mathsf{\epsilon }}_{\mathrm{r}}$ = 10.2) achieved greater amplitude sensitivity (9.35 × 10⁻² dB/(mg/dL)); dual-finger contact outperformed single-finger placement for both substrates. Repeated intra-day VNA measurements yielded narrow 95% confidence intervals on |S₂₁|, with an overall uncertainty of approximately ±0.5 dB across the tested glucose levels. Motivated by the larger |S₂₁| response on Rogers, we adopted amplitude resolution as the primary metric and built a compact prototype using the AD8302-EVALZ with a custom 3D-printed enclosure to enhance measurement precision. In a cohort of 31 participants, capillary blood glucose was obtained using a commercial glucometer, after which two fingers were placed on the sensing region; quadratic voltage-to-glucose calibration yielded R² = 0.980, root–mean–square error (RMSE) = 2.316 mg/dL, overall accuracy = 97.833%, and local sensitivity = 1.099 mg/dL per mV, with anthropometric variables (weight, height, age) showing no meaningful correlation. Clarke Error Grid Analysis placed 100% of paired measurements in Zone A, indicating clinically acceptable agreement with the reference meter. Benchmarking against commercial continuous glucose monitoring systems highlights substrate selection as a dominant lever for amplitude sensitivity and positions the proposed fully non-invasive, consumable-free architecture as a promising route toward portable RF-based glucose monitors, while underscoring the need for larger cohorts, implementation on flexible biocompatible substrates, and future regulatory pathways. Full article

Drug–drug interactions (DDIs) are one of the most common problems related to drug administration which represent a risk for patient safety. Considering their position in the healthcare system, pharmacists should be more proactively involved in DDI management. The paper shows representation of DDI intervention models in each DDI category. This research enrolled outpatients prescribed pharmacotherapies from 40 randomly selected community pharmacies. DDIs were analyzed using Lexicomp^® Lexi-InteractTM Online (Lexi-Comp, Inc., Hudson, NY, USA) software. Clinical pharmacists’ panel, according to the necessary interventions, determined an independent model of pharmacist interventions (category 1) and models that require cooperation with physicians (category 2) for DDI management. In total, 4107 patients were enrolled in the study. Mean patient age was 67.5; they were mostly women (56.5%) and had on average of 3.4 diagnosis and 5.5 prescription drugs. Overall, 14,175 potential clinically significant DDIs were identified: 83.3% of C, 15.4% of D, and 1.3% of X category. At least one potential DDI was found in 78.6% of patients. Models of pharmacist DDI interventions in collaboration with a physician (category 2) were more prevalent than independent models (category 1): 57.5% vs. 42.5% in C category DDIs, 97.8% vs. 2.2% in D category, and 100% vs. 0% in category X DDIs. This research aimed to gain an insight into the distribution of interventions in DDI management models between physicians and pharmacists, which can contribute to more efficient pharmaceutical care and visibility. Full article

Audits of medication charts conducted by Royal Hobart Hospital Pharmacy revealed that dose omission was the most common medication error experienced by patients. Investigation of these errors also found that nurses spend significant time organising medication for inpatients. To address the issues contributing to these problems, an alternative model of medication management was implemented and tested. This model of bedside medication management involves medication supply managed by ward pharmacy technicians who review charts daily for changes to medicines and obtain the medicines needed for each patient. Outcomes on two intervention wards showed that the model, combined with technician involvement in controlled medicines stock management, resulted in 29.78 h of nursing time released to patient care per 20-bed ward per week, for an investment of 22.28 h of ward pharmacy technician time; a 75% reduction in delayed doses; a 44% reduction in missed doses; and an average decrease of two hours in the turnaround time for supply of inpatient medication. Introducing bedside medication management and controlled medicines stock management activities can release 1.34 h of nursing time to patient care for every hour of ward pharmacy technician time (at a lower hourly salary cost), decrease dose delays and omissions, and improve patient safety. Full article

In order to reach and maintain hepatitis C virus (HCV) elimination goals, it is imperative to reach marginalized people who do not engage with traditional testing and treatment. Peer-led interventions are effective in engaging such individuals. Studies have demonstrated community pharmacy as a low-threshold setting for HCV testing, but pharmacy teams’ competing demands are a barrier to maximizing potential. This pilot project aimed to assess whether a pharmacy-based peer-led model of HCV testing was implementable, deliverable, able to engage marginalized people, and overcome pharmacy staff constraints. We implemented a peer-led HCV testing service in one community pharmacy in the Midlands, England, providing four focused phases of testing, totalling 198 h, over two years. In total, 591 tests for antibodies or RNA were undertaken, identifying 24 active infections. Subsequent phases retested 20%, 16%, and 11% of those tested in combined preceding phases. In response to feedback, phases 3 and 4 included health screening (blood pressure, cholesterol, diabetes, and nurse-performed Fibroscans^®). We demonstrate engagement and the ability to identify and refer those with abnormal results to appropriate healthcare. This pilot shows that peer-led testing in the pharmacy setting can be implemented and warrants further scale up and evaluation. Full article

Background: Supply chains in pharmaceutical industry encounter constant challenges in balancing the availability of medicine with cost efficiency, particularly in developing regions with limited storage capacity, as regulatory constraints increase operational complexity. Methods: This research focuses on developing a multi-product, multi-period inventory planning model designed to optimize the supply process for a pharmacy located in Barranquilla, Colombia. The methodology involves conducting field studies within the pharmaceutical sector, which includes regular visits to pharmacies, interaction with employees, and analysis of historical data collected over a 16-month period. Results: The primary goal is to minimize costs while ensuring that products remain available to customers, considering various internal and external factors. Several scenarios will be examined to evaluate different alternatives for enhancing the supply process. Initial findings suggest that the proposed model could reduce inventory planning costs by approximately 15.78% by classifying antibiotics, which in turn leads to better resource utilization and improved order management. Conclusions: The proposed model minimizes the inventory planning costs associated with antibiotic management, ultimately leading to improved resource utilization and more accurate order management. Full article