Along with the positive development in health services and facilities, hospital pharmacy practice in Nepal has also evolved. The minimum service standards (MSS) developed by the Ministry of Health and Population of Nepal act as a structured framework to assess hospitals' readiness and capacity to deliver quality health services and consist of three significant domains: governance and management standards, clinical service management standards, and support service standards. The clinical service management standards also consist of the hospital pharmacy service standards of practice checklist. This study presents a perspective review of MSS checklists and their implications for pharmacy services, where we discuss how the MSS checklist acts as a viable tool for strengthening the hospital pharmacy practice in Nepal's primary, secondary, and tertiary hospitals. Following the MSS checklist, hospital administration, hospital leaders, hospital management committee, and the hospital pharmacy and therapeutic committee can help to develop further action plans to mitigate issues, thus resulting in 100% compliance with the checklist and quality improvement. This paper can also serve as evidence or a start-up for conducting MSS-based research at the provincial and federal levels to inform hospital pharmacy-based practices among the policy-related institutions in the coming days.

Background: Drug recalls safeguard patients from potentially harmful existing pharmaceutical products in the market due to safety concerns or manufacturing issues. Objective: To analyze the drug recall patterns, frequencies, and causes based on the publicly available data from the Nepalese Drug Regulatory Authority, Department of Drug Administration (DDA). Design: A 2-year retrospective secondary analysis was conducted based on drug recalls in Nepal, which is available from the official website of DDA, Nepal. Methods: The substandard drug recalls data from April 17th, 2023 to May 14th, 2025 were included in the study from which information were extracted across various domains, including the drug name, dosage forms, manufacturing and expiry date, reasons for the recall, and recall date whereas, inclusion of falsified medicines and information regarding the “special permission,” “news & updates,” and “notices” sections within the DDA website were excluded from the studies to ensure only drug recalls were incorporated. Results: The study showed that 50 recalls were made over the 2 year period. The majority of the recalled drugs were antibiotics (16%). The most common reasons for drug recalls were assay failure (34%) and non-compliance with standards set out by the Indian Pharmacopoeia-2022 (40%), followed by United States Pharmacopoeia-2022 (28%). Oral formulations (74%) were most commonly recalled, out of which tablets (32%), suspensions (16%), and syrups (12%) were recalled in greater frequencies among all the recall notices. Most recalls were made from domestic pharmaceutical companies (84%). The majority of the drugs (34%) were recalled after 15 months of the finished product being in the market, whereas only 26% were recalled within the first 5 months. Conclusion: A robust and continuous evaluation of drug recalls by regulatory authorities can help reduce their frequency, lessen their impact on the healthcare system, and improve overall drug safety.

Background: Nepal is experiencing a rapid demographic shift toward an aging population, with concurrent increase in morbidity and medication-related problems. Despite this, the multidimensional experience of medication-related burden (MRB) and refill adherence remain under-studied, particularly through the lens of socio-demographic, clinical and medication-related predictive features. This study aimed to assess MRB and medication adherence, and utilize machine learning (ML) architectures to identify complex factors influencing both. Methods: A cross-sectional study conducted among 390 ambulatory older patients (aged≥65 years) at Bharatpur Hospital, Nepal. MRB and medication adherence was assessed using Living with Medications Questionnaire (LMQ-3) and Adherence to Refills and Medication Scale (ARMS). Six ML architectures (Ordinary Least Square, LightGBM, Random Forest, XGBoost, SVM, and Penalized linear regression) were employed to predict ARMS and LMQ scores using various socio-demographic, clinical and medication-related predictive features. Model explainability was provided through SHAP (Shapley Additive exPlanations). All the analysis were performed using R. Results: The median LMQ-3 score was 110.0 (IQR 14.0), reflecting a moderate medication-related burden, while the median ARMS score of 21.0 (IQR 6.0) indicated moderate non-adherence. Random forest was the superior predictive model for both MRB and adherence. SHAP analysis revealed requiring assistance for medication and polypharmacy as the most significant drivers of both increased burden and poor adherence. Interaction analysis revealed that while polypharmacy typically worsens adherence, the risk is partially mitigated when patients receive physical or cognitive assistance. Additional, financial factors and employment status emerged as significant predictors. Conclusion: Older patients in Nepal face a significant medication-related burden and non-adherence, driven largely by regimen complexity and the need for support. The high predictive accuracy of ML models suggests that clinical interventions should prioritize simplified regimens and patient-centered counseling for those with high dependency. These findings provide a data-driven rational for policy-level medication optimization strategies in Nepal’s evolving healthcare system. Author Summary: As ageing occurs, there is high chance of presence of the chronic conditions. For management of these conditions, older people are often prescribed with multiple medications and often are vulnerable to those medications whose risk outweighs benefits. As a result there is high chance of occurrence of adverse effects; these effects have caused substantial degradation of the health related quality of life. Although pharmacotherapy is the mainstay of the chronic disease management, older people often feel medication burden. Medication burden is practical experience arising from the practical and psychological challenge while managing medications. Research shows high burden causes the non-adherence, a significant problem among older adults, causing significant problem in pharmacotherapy. Hence, we used validated Questionnaire for assessing the medication burden and medication adherence among older ambulatory adults attending the central hospital of Nepal. We used machine learning approach for the high prediction of the predictors influencing the medication related burden and mediation adherence. Moderate burden was observed among older adults and moderate non-adherence was also observed. We found needing assistance for medication management and multiple medications were the strongest predictors for both Medication burden and non-adherence. Our Study provides new insights and area for the implementation of clinical intervention for the medication optimization.

Introduction: The National Action Plan on antimicrobial resistance (AMR) strategic priority of Nepal focuses on strengthening the surveillance system for the containment of AMR within the country, as such, this study aims to report the prevalence of bacterial isolate and antimicrobial susceptibility pattern. Method: A hospital-based retrospective cross-sectional study was conducted using routine urine culture and antimicrobial susceptibility records from Hetauda Hospital, Nepal, from May 28, 2023, to September 8, 2023. Clinically significant uropathogens were identified using standard microbiological procedures, and antimicrobial susceptibility testing was performed using the modified Kirby–Bauer disk diffusion method. Susceptibility results were interpreted according to the CLSI M100 criteria used by the hospital laboratory during the study period. Data were analysed descriptively using frequencies, percentages, and relevant denominators. Result: Out of 3893 patient samples, 1162 (29.84%) bacterial isolates were detected. Gram-negative bacteria accounted for 67.81% of the isolates, with Escherichia coli (47.16%) being the most common, followed by Klebsiella sps. (14.54%). AST has revealed varying patterns across different antibiotics and bacterial species. Escherichia coli ( E. coli ) emerged as the predominant pathogen showing variable susceptibility across antibiotic classes, with highest sensitivity to amikacin (83.90%) and notable resistance to fluoroquinolones and cephalosporins, highlighting significant challenges in empirical therapy. Notably, 20.40% of the isolates were classified as MDR, with Proteus vulgaris showing the highest MDR rate (46.15%). Conclusion: This study highlights the prevalence of urinary bacterial isolates ( E. coli) and multidrug-resistant strains ( Proteus vulgaris ) at Hetauda Hospital, with considerable resistance to cephalosporins and fluoroquinolones. These findings support AST-guided prescribing, local antimicrobial stewardship, and development of a hospital antibiogram. The results are contextually relevant to Nepal’s AMR policy agenda, but they should be interpreted as hospital-level data rather than as provincial or national surveillance estimates.

Coronavirus is a large family of viruses that cause illnesses ranging from the common cold to severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Coronavirus related to the RNA virus is mainly transmitted through droplet nuclei from infected persons cough, sneeze, etc. It aims to assess the socio-psychological behaviors of COVID-19 patients from neighbors during home isolation. A cross-sectional study in Kathmandu Valley assessed the socio-psychological behavior of neighbors toward COVID-19 patients in home isolation. Validated questionnaires collected data from randomly selected respondents. Descriptive and inferential analyses were conducted, with P < 0.05 considered significant. The study included 422 respondents (54% male, 46% female), with most aged 36-45 years (38.6%), and followed by 46-55 years (21.1%). The most belonged to the middle class (39.8%) or poor economic status (38.6%). Regarding COVID-19, 66.4% of respondents' neighbors were known of their infection, with 49.8% avoiding them, 42.4% ignoring them, and 36.6% maintaining social distance. Furthermore, 60.2% of respondents faced protests to leave home isolation and go to a government isolation center, and 69.2% reported had not received support from neighbors. Among the 30.8% who received support, primarily emotional (55.4%), followed by financial and medical support (18.5% each). Neighbor behavior significantly impacted respondents' psychological well-being, with 27% experiencing depression, 21.3% anxiety, and 19.5% stress. A statistically significant association was observed between neighbor behavior and psychological effects, with a p-value of 0.023 (p < 0.05). The majority of respondents experienced depression, anxiety, and stress due to their neighbor's ignorance, social distancing, and avoidance behavior. The psychological effects were significantly associated with neighbor's behaviors and home isolation strategy.