Search Results (231)

Background/Objectives: In many parts of the world, pharmacists hold the primary responsibility for providing safe and effective pharmacotherapy. A key aspect is the availability of appropriate medicines for each individual patient. When industrially manufactured medicines are unsuitable or unavailable, pharmacists can prepare tailor-made medicines. While this principle applies globally, practices vary between countries. In the Netherlands, the preparation of medicines in pharmacies is well-established and integrated into routine healthcare. This narrative review explores the role and significance of extemporaneous compounding, pharmacy preparations and related product care in the Netherlands. Methods: Pharmacists involved in pharmacy preparations across various professional sectors, including community and hospital pharmacies, central compounding facilities, academia, and the professional pharmacists’ organisation, provided detailed and expert insights based on the literature and policy documents while also sharing their critical perspectives. Results: We present arguments supporting the need for pharmacy preparations and examine their position and role in community and hospital pharmacies in the Netherlands. Additional topics are discussed, including the regulatory and legal framework, outsourcing, quality assurance, standardisation, education, and international context. Specific pharmacy preparation topics, often with a research component and a strong focus on product care, are highlighted, including paediatric dosage forms, swallowing difficulties and feeding tubes, hospital-at-home care, reconstitution of oncolytic drugs and biologicals, total parenteral nutrition (TPN), advanced therapy medicinal products (ATMPs), radiopharmaceuticals and optical tracers, clinical trial medication, robotisation in reconstitution, and patient-centric solid oral dosage forms. Conclusions: The widespread acceptance of pharmacy preparations in the Netherlands is the result of a unique combination of strict adherence to tailored regulations that ensure quality and safety, and patient-oriented flexibility in design, formulation, and production. This approach is further reinforced by the standardisation of a broad range of formulations and procedures across primary, secondary and tertiary care, as well as by continuous research-driven innovation to develop new medicines, formulations, and production methods. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Background/Objectives: The demand for accessible and reliable digital health services has increased significantly in recent years, particularly in regions facing physician shortages. HomeDOCtor, a conversational AI platform developed in Slovenia, addresses this need with a nationally adapted architecture that combines retrieval-augmented generation (RAG) and a Redis-based vector database of curated medical guidelines. The objective of this study was to assess the performance and impact of HomeDOCtor in providing AI-powered healthcare assistance. Methods: HomeDOCtor is designed for human-centered communication and clinical relevance, supporting multilingual and multimedia citizen inputs while being available 24/7. It was tested using a set of 100 international clinical vignettes and 150 internal medicine exam questions from the University of Ljubljana to validate its clinical performance. Results: During its six-month nationwide deployment, HomeDOCtor received overwhelmingly positive user feedback with minimal criticism, and exceeded initial expectations, especially in light of widespread media narratives warning about the risks of AI. HomeDOCtor autonomously delivered localized, evidence-based guidance, including self-care instructions and referral suggestions, with average response times under three seconds. On international benchmarks, the system achieved ≥95% Top-1 diagnostic accuracy, comparable to leading medical AI platforms, and significantly outperformed stand-alone ChatGPT-4o in the national context (90.7% vs. 80.7%, p = 0.0135). Conclusions: Practically, HomeDOCtor eases the burden on healthcare professionals by providing citizens with 24/7 autonomous, personalized triage and self-care guidance for less complex medical issues, ensuring that these cases are self-managed efficiently. The system also identifies more serious cases that might otherwise be neglected, directing them to professionals for appropriate care. Theoretically, HomeDOCtor demonstrates that domain-specific, nationally adapted large language models can outperform general-purpose models. Methodologically, it offers a framework for integrating GDPR-compliant AI solutions in healthcare. These findings emphasize the value of localization in conversational AI and telemedicine solutions across diverse national contexts. Full article

Background: In this work, we examine determinants of staff departure rates in the NHS, a critical issue for workforce stability and continuity of care. High turnover, particularly among clinical staff, undermines service delivery and incurs substantial replacement costs. Methods: Here, we analyse a unique dataset derived from Electronic Staff Records at Ashford and St. Peter’s NHS Foundation Trust, using a machine learning approach to move beyond traditional survey-based methods, to assess propensity to leave. Results: In addition to established predictors such as salary and length of service, we identify drivers of increased risks of staff exits, including the distance between home and workplace and, especially for medical staff, cost centre vacancy rates. Conclusions: These findings highlight the multifactorial nature of staff retention and suggest the potential of local administrative data to improve workforce planning, for example, through hyperlocal recruitment strategies. Whilst further work will be required to assess the generalisability of our findings beyond a single Trust, our analysis offers insights for NHS managers seeking to stabilise staffing levels and reduce attrition through targeted interventions beyond pay and tenure. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

Background: he surge in the use of Pre-hospital Emergency Medical Systems (EMS) and Emergency Departments (ED) has become a pressing issue worldwide after the COVID-19 pandemic. To address this challenge, we developed an experimental and innovative care pathway supported by telemedicine. The aim of this study is to describe the activity of the Integrated Medical Center (CMI): a new telemedicine-based care model for patients referring to the Emergency Medical System. Methods: A prospective observational study was conducted from January 2022 to December 2022. The CMI was established to manage patients referring to the Emergency Medical System. Results: From January to December 2022, a total of 8680 calls were managed by CMI, with an average of 24 calls per day. 6243 patients (71.9%) were managed without ED access of whom 4884 patients (78.2%) were managed through telemedicine evaluation only, and 1359 (21.8%) with telemedicine evaluation and dispatch of the Home Rapid Response Team (HRRT). The population treated by the HRRT exhibited a higher age. The mean satisfaction score was 9.1/10. Conclusions: Telemedicine evaluation allowed for remote assessments, treatment prescriptions, and teleconsultation for HRRT and was associated with high patient satisfaction. This model could be useful in future pandemics for managing patients with non-urgent illnesses at home, preventing hospital admissions for potentially infectious patients, and thereby reducing in-hospital transmission. Full article

Maternal mortality and morbidity are critical health challenges in the U.S., and building the perinatal workforce is a key to providing high-quality maternal medical care and services. Texas Southern University (TSU), home to a Doctor of Pharmacy program, launched the first Maternal Health Education and Research Training (MHERT) program to educate a cohort of high school, pharmacy, and health sciences students. Aiming to raise awareness of maternal health issues, build research skills, and promote action-based solutions. MHERT integrated online self-paced interactive lessons with hands-on research or community projects. Topics included maternal health epidemiology, causes of morbidity and mortality, research methods, literature reviews, and the development of action plans addressing maternal health challenges. Assessment tools included quizzes, open-ended reflection responses, training surveys, and course evaluations. Running from 3 June to 26 July 2024, the program enrolled 22 students. All participants completed both course components. Course evaluations showed strong and consistent satisfaction with the program, with teaching effectiveness rated at 95% and 96% for mid-program and final evaluations, respectively. MHERT enhanced participants’ understanding of maternal health, improved research skills, and encouraged community engagement and interdisciplinary collaboration. It offers a scalable model to strengthen public health education among high school, pharmacy, and health sciences students. Full article

Background: China’s rapidly aging population has increased the need for effective community-based eldercare services. However, the lack of standardized, culturally relevant evaluation frameworks hinders consistent service quality assessment and improvement. Objective: This systematic review aims to identify, synthesize, and critically evaluate the existing quality indicators (QIs) currently utilized for home-based community elderly care HCEC in China. It also aims to highlight gaps to inform the development of a more comprehensive and context-appropriate quality framework. Methods: Following PRISMA guidelines, systematic searches were conducted across Web of Science, PubMed, Wiley, and CNKI databases for studies published in English and Chinese from 2008 onward. Extracted QIs from eligible studies were categorized using Donabedian’s structure–process–outcome (SPO) model. Results: Fifteen studies met the inclusion criteria, with QI sets ranging from 5 to 64 indicators. Most studies emphasized structural and procedural aspects, while outcome measures were limited. Key gaps include inconsistent terminology, insufficient medical care integration, narrow stakeholder engagement, and limited cultural adaptation of Western theoretical frameworks. Furthermore, subjective weighting methods predominated, impacting indicator reliability. Conclusions: Currently, there is no formal quality framework to guide service providers in HCEC, and therefore, quality indicators can be described as fragmented and lack cultural specificity, medical integration, and methodological robustness. Future research should prioritize developing culturally anchored and medically comprehensive QI frameworks, standardize indicator terminology, actively involve diverse stakeholders through participatory methods, and adopt hybrid methodological approaches combining subjective expert insights and objective, data-driven techniques. Alignment with established international standards, such as the OECD long-term care quality indicators, is essential to enhance eldercare quality and support evidence-based policymaking. Full article

The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users’ daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. Therefore, utilizing reusable and cost-effective flexible bioelectrodes (with a signal-to-noise ratio of 33 dB), a miniaturized wearable system (MWS) is proposed for unconstrained ECG monitoring, which holds a size of 65 × 52 × 12 mm³ and a weight of 69 g. Given these compelling features, this system enables reliable and high-quality ECG signal monitoring in individuals’ daily activities, including sitting, walking, and cycling, with the acquired signals exhibiting distinguishable QRS characteristics. Furthermore, an exercise intensity classification model was developed based on ECG characteristics and a fully connected neural network (FCNN) algorithm, with an evaluation accuracy of 98%. These results exhibit the promising potential of the MWS in tracking individuals’ physiological signals and assessing exercise intensity. Full article

Pediatric palliative care (PPC) is an evolving field that focuses on supporting children with life-limiting conditions, where the quality of care is vital. This study is a retrospective observational investigation that examines the experiences of caregivers to inform health and social service planning and enhance PPC quality. Methods: Data of pediatric patients aged 3 months to 18 years admitted to a PPC inpatient unit over two years were retrospectively reviewed. Sociodemographic characteristics of primary caregivers, including age, gender, number of siblings, education, income, occupation, and marital status, were recorded. Caregiver burden and burnout were assessed using the Zarit Burden Interview and the Maslach Burnout Inventory, respectively. Associations between caregiver characteristics and these measures were analyzed. Results: A total of 118 patients and caregivers were evaluated; 54.2% of patients were male. The most common diagnoses were neurological diseases (44.9%), followed by syndromic–genetic disorders (28.8%). About 34% of patients required more than three medical devices. Most caregivers were female (91.5%), mainly mothers and 53% had only primary education. No significant differences in care burden or burnout were found based on caregiver gender, marital status, or child’s diagnosis. However, the use of nasogastric tubes and multiple medical devices was associated with higher burnout. Lower income was significantly linked to higher care burden, while longer caregiving duration correlated with both increased burden and burnout. A moderate positive correlation was found between Zarit and Maslach scores. Conclusions: The complexity of PPC patients’ care increases caregiver burden and burnout. Expanding specialized PPC services is crucial to support caregivers and sustain home-based care. Full article

The transition from hospital-based palliative care to home care is a critical phase often marked by logistical, medical, and emotional challenges. Effective discharge planning is essential to ensure continuity of care, yet gaps in communication, interdisciplinary coordination, and access to resources frequently hinder this process. This qualitative study explored key barriers, related support needs, and strategies for optimising palliative care discharge through semi-structured interviews with 28 participants, including healthcare professionals, recently discharged palliative care patients, and primary caregivers. Reflexive thematic analysis revealed five main themes: (1) discharge planning and coordination; (2) symptom management and medication; (3) psychosocial support; (4) communication and information; (5) the role of assistive devices and home care services. Discharge processes were often late or unstructured. Poor interdisciplinary collaboration and a lack of caregiver preparation also contributed to hospital readmissions and emotional distress. By focusing on needs, our analysis identifies not only what was lacking but also what is required to overcome these barriers. Our findings suggest that standardised discharge protocols and checklists, earlier planning, structured communication tools, and improved integration of home care services could enhance patient outcomes and reduce caregiver burden. Addressing psychosocial needs and ensuring timely access to assistive devices are also crucial. Strengthening interdisciplinary collaboration and refining discharge practices can facilitate smoother transitions and improve the quality of palliative care at home. Full article

Background/Objectives: This study aims to explore how gender influences the role of informal caregivers aged 65 years, considering the increasing involvement of men in caregiving due to longer life expectancy and societal norms. Methods: A two-year cross-sectional study was conducted in the Valencian Community, Spain, involving informal caregivers of 65 years of age and older who provided home-based care for dependent individuals with chronic conditions. The participants were recruited through public health schools, carers’ associations, and clinical consultations. The caregivers completed a comprehensive semi-structured interview, which included items from the Zarit Brief Scale (seven items) to assess caregiver burden and questions about their caregiving responsibilities, training, and experience, as well as the self-perceived frequency of medication errors. Results: A sample of 80 caregivers over 65 years old was analyzed, including 23 men (28.8%) and 57 women (71.2%). Male caregivers were significantly less experienced (mean = 3.1 years, SD = 5.9) compared to female caregivers (mean = 10.1 years, SD = 13.0; p = 0.004). Men reported lower emotional and physical burdens than women (p-value = 0.003), as reflected in the Zarit scores. Caregiving performance, measured by self-reported errors, was comparable between genders. Conclusions: This study explores the growing role of older male caregivers, highlighting their lower experience and training compared to those of women but similar caregiving performance and lower burden. Additionally, trained caregivers demonstrated significantly lower odds of experiencing burden, underscoring the importance of training as a modifiable factor. The findings emphasize the need for gender-sensitive support and tailored training programs to address disparities, reduce caregiver burden, and enhance caregiving quality and equity. Full article

With the growing number of patients living with chronic conditions, there is an increasing need for efficient systems that can automatically capture and convert medical device readings into digital data, particularly in home-based care settings. However, most home-based medical devices are closed systems that do not support straightforward automatic data export and often require complex connections to access or transmit patient information. Since most of these devices display clinical information on a screen, this research explores how a standard smartphone camera, combined with artificial intelligence, can be used to automatically extract the displayed data in a simple and non-intrusive way. In particular, this study provides a comparative analysis of several You Only Look Once (YOLO) and Single Shot MultiBox Detector (SSD) models to evaluate their effectiveness in detecting and recognizing the readings on medical device displays. In addition to these comparisons, we also explore a hybrid approach that combines the YOLOv8l model for object detection with a Convolutional Neural Network (CNN) for classification. Several iterations of the aforementioned models were tested, using image resolutions of 320 × 320 and 640 × 640. The performance was assessed using metrics such as precision, recall, mean average precision at 0.5 Intersection over Union (mAP@50), and frames per second (FPS). The results show that YOLOv8l (640) achieved the highest mAP@50 of 0.979, but at a lower inference speed (13.20 FPS), while YOLOv8n (320) offered the fastest inference (129.79 FPS) with a reduction in mean average precision (0.786). Combining YOLOv8l with a CNN classifier resulted in a slight reduction in overall accuracy (0.96) when compared to the standalone model (0.98). While the results are promising, the study acknowledges certain limitations, including dataset-specific biases, controlled acquisition settings, and challenges in adapting to real-world scenarios. Nevertheless, the comparative analysis offers valuable insights into the trade-off between inference time and accuracy, helping guide the selection of the most suitable model based on the specific demands of the intended scanning application. Full article

Background: Social isolation, national turmoil, and an adolescent mental health crisis in the wake of the COVID-19 pandemic have resulted in a significant uptick in inpatient admissions and re-admissions for high-risk patients. This trend persists even as the pandemic wanes. Intensive outpatient programs (IOPs) serve as a critical steppingstone between the community and inpatient mental health services, providing comprehensive psychiatric care for at-risk youth. Significant research has identified family engagement as a key element of successful collaborative care in adolescents. Objectives: This article provides models of family-centered care in the adolescent IOP through a case study detailing the six-week course of care of an adolescent struggling with increased emotionality and distress intolerance in the context of family conflicts. Methods: This case highlights five family engagement components, including (1) family-centered psychiatric medication management, (2) individualized case management, parental education, and peer support, (3) Measurement Based Care (MBC) family assessment and feedback sessions, (4) Dialectical Behavior Therapy (DBT) multi-family skill groups, and (5) Compassionate Home Action Together (CHATogether) family intervention to address teen–parent relational health and communication. Results: This case showed improvement in depressive and anxiety symptoms, family conflict behaviors, self-reported suicide risk, and help-seeking attitudes towards parents/adults. The case family, along with others (n = 26), endorsed the parent peer support groups’ acceptability and feasibility implemented in the adolescent IOP. Conclusions: This article emphasizes the importance of family engagement during clinical care and provides a practical guide to implement collaborative family-centered therapeutic interventions in intensive outpatient services. Full article

Background/Objectives: Despite documented benefits across multiple outcome areas, referral and uptake into pulmonary rehabilitation (PR) following discharge after an exacerbation of chronic obstructive pulmonary disease (COPD) is low in many health care systems. Surveys documenting this underutilization may ignore the fact of disease severity or comorbidity severe enough to make many patients ineligible based on accepted selection criteria for the intervention. The aim of this study was to evaluate the magnitude of non-eligibility for PR following discharge after a COPD exacerbation. Methods: Medical records of COPD patients discharged over a one-year period in two hospitals were reviewed. Records from 353 patients discharged home were reviewed by six clinicians with experience in respiratory medicine and/or PR, three at each hospital. Results: The mean age of the total sample was 71 ± 12 years; 53% were female. Full concordance (all three reviewers agreed on the eligibility or non-eligibility of each patient) was 73%. Our eligibility criterion (two of three reviewers agreed) for PR was 39%. Categories (%) of non-eligibility criteria included the severity of medical condition(s) (44%), cognitive problems, psychiatric disease or substance abuse (24%), incorrect diagnosis (18%), institutionalized post-discharge (9%), and language barriers (4%) (patients may have been placed into more than one criteria category). Conclusions: Our study indicates that a majority of patients with clinical diagnoses of COPD discharged following exacerbations may not be appropriate referrals to PR based on accepted inclusion and/or exclusion criteria for the intervention. However, even after taking this into account, PR uptake is still critically underutilized. Full article