Search Results (227)

Heart failure (HF) is a major global health challenge, characterized by high morbidity, mortality, and frequent hospital readmissions. Despite the advent of guideline-directed medical therapies (GDMTs), the burden of HF continues to grow, necessitating a shift toward comprehensive, multidisciplinary care models. Heart Failure Disease Management Programs (HF-DMPs) have emerged as structured frameworks that integrate evidence-based medical therapy, patient education, telemonitoring, and support for social determinants of health to optimize outcomes and reduce healthcare costs. This review outlines the key components of HF-DMPs, including patient identification and risk stratification, pharmacologic optimization, team-based care, transitional follow-up, remote monitoring, performance metrics, and social support systems. Incorporating tools such as artificial intelligence, pharmacist-led titration, and community health worker support, HF-DMPs represent a scalable approach to improving care delivery. The success of these programs depends on tailored interventions, interdisciplinary collaboration, and health equity-driven strategies. Full article

Background: Health equity can only be achieved when every individual has access to quality healthcare without fear of being discriminated against. This study analyzed the sociodemographic characteristics associated with self-reported racial discrimination when receiving medical care in the United States. Methods: This quantitative cross-sectional study utilized the 2022 National Trends Survey 6. We performed a logistic regression analysis using 6102 survey responses from study participants who answered the question about perceived discrimination. Results: Older adults aged 75 years and above had significantly lower odds of reporting perceived discrimination when receiving medical care compared to those aged 18–34 years (AOR = 0.24; 95% CI: 0.10–0.58). The odds of reporting perceived discrimination were significantly higher among non-Hispanic Blacks (AOR = 7.30; 95% CI: 4.48–11.88), Hispanics (AOR = 3.56; 95% CI: 2.45–5.17), non-Hispanic Asians (AOR = 5.95; 95% CI: 2.25–15.73), and individuals identifying as non-Hispanic Other (AOR = 10.91; 95% CI: 5.42–21.98), compared to non-Hispanic Whites. Compared to individuals from households earning less than USD 20,000, the odds of reporting perceived discrimination when receiving medical care were significantly lower among individuals from households earning between USD 50,000 and <USD 75,000 (AOR = 0.42; 95% CI: 0.23–0.78) and those earning USD 75,000 or more (AOR = 0.43; 95% CI: 0.22–0.83). Conclusions: Despite having a multicultural and ethnically diverse population, racial discrimination persists in the United States and has become a barrier to achieving health equity. Health organizations should implement policies that ensure health workers attend mandatory anti-racism training. 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

The global rise in urban-related health issues poses significant challenges to public health, particularly in cities facing socio-economic crises. In Lebanon, 70% of the population is experiencing financial hardship, and healthcare costs have surged by 172%, exacerbating the strain on medical services. Given these conditions, improving the quality and accessibility of green spaces offers a promising avenue for alleviating mental health issues in urban areas. This study investigates the psychological impact of nine urban public spaces in Beirut through a comprehensive survey methodology, involving 297 participants (locals and tourists) who rated these spaces using Likert-scale measures. The findings reveal location-specific barriers, with Saanayeh Park rated highest in quality and Martyr’s Square rated lowest. The analysis identifies facility quality as the most significant factor influencing space quality, contributing 73.6% to the overall assessment, while activity factors have a lesser impact. The study further highlights a moderate positive association (Spearman’s rho = 0.30) between public space quality and mental well-being in Beirut. This study employs a hybrid methodology combining Research for Design (RfD) and Research Through Designing (RTD). Empirical data informed spatial strategies, while iterative design served as a tool for generating context-specific knowledge. Design enhancements—such as sensory plantings, shading systems, and social nodes—aim to improve well-being through better public space quality. The proposed interventions support mental health, life satisfaction, climate resilience, and urban inclusivity. The findings offer actionable insights for cities facing public health and spatial equity challenges in crisis contexts. Full article

Background and Objectives: Gender disparities in pain management persist, with women frequently receiving inadequate analgesia despite reporting similar or higher pain levels compared with men. This issue is particularly evident across various medical and gynecological procedures. Materials and Methods: This integrative literature review synthesizes recent empirical studies examining gender biases in pain perception and management, focusing specifically on procedural pain in women. It includes an analysis of clinical research, patient-reported outcomes, and healthcare provider behaviors. Results: The findings indicate that unconscious biases, a lack of gender-specific clinical protocols, and prevailing cultural stereotypes contribute to the undertreatment of pain in women during procedures such as intrauterine device insertion and diagnostic hysteroscopy. Additionally, communication gaps between patients and healthcare providers exacerbate these disparities. Conclusions: Addressing gender disparities in pain management necessitates systemic reforms, including the implementation of gender-sensitive clinical guidelines, enhanced provider education, and targeted policy changes. Personalized, gender-informed approaches are essential to improving equity and quality of care in pain treatment. Full article

As diversity, equity, and inclusion (DEI) efforts in higher education face increasing political resistance, it is critical to understand how equity-centered institutional change is fostered, and who is transformed in the process. This study examines the intended and emergent outcomes of faculty professional development initiatives implemented through the Howard Hughes Medical Institute’s Inclusive Excellence (HHMI IE) program. We analyzed annual institutional reports and anonymous reflections from four public universities in a regional Peer Implementation Cluster (PIC), focusing on how change occurred at individual, community, and institutional levels. Guided by Kezar’s Shared Equity Leadership (SEL) framework, our thematic analysis revealed that while initiatives were designed to improve student outcomes through inclusive pedagogy, the most profound outcome was the development of equity consciousness among faculty. Defined as a growing awareness of systemic inequities and a sustained commitment to address them, equity consciousness emerged as the most frequently coded theme across all levels of change. These findings suggest that equity-centered faculty development can serve as a catalyst for institutional transformation, not only by shifting teaching practices but also by building distributed leadership and deeper organizational engagement with equity. This effort also emphasizes that documenting emergent outcomes is essential for recognizing the holistic impact of sustained institutional change. Full article

Indigenous students remain significantly underrepresented in medical education, contributing to persistent health inequities in their communities. Systemic barriers, including cultural isolation, inadequate resources, and biased curricula, hinder their success. But what if generative artificial intelligence (GAI) could be the game-changer? This scoping review explores the potential of generative artificial intelligence (GAI) in making medical education more inclusive and supportive for Indigenous students through a comprehensive analysis of existing literature. From AI-powered engagement platforms to personalised learning systems and immersive simulations, GAI can be harnessed to bridge the gap. While GAI holds promise, challenges like biased datasets and limited access to technology must be addressed. To unlock GAI’s potential, we recommend faculty development, expansion of digital infrastructure, and Indigenous-led AI design. By carefully harnessing GAI, medical schools can take a crucial step towards creating a more diverse and equitable healthcare workforce, ultimately improving health outcomes for Indigenous communities. Full article

Background/Objectives: Adult vaccination remains suboptimal, particularly among older adults and individuals with chronic conditions. Hospitals represent a strategic setting for improving vaccination coverage among these high-risk populations. This systematic review and meta-analysis evaluated hospital-based interventions aimed at enhancing vaccine uptake in adults aged ≥60 years or 18–64 years with at-risk medical conditions. Methods: We conducted a systematic review and meta-analysis following PRISMA and MOOSE guidelines. Searches in PubMed, EMBASE, and Scopus identified studies published in the last 10 years evaluating hospital-based interventions reporting vaccination uptake. The risk of bias was assessed using validated tools (NOS, RoB 2, ROBINS-I, QI-MQCS). A meta-analysis was conducted for categories with ≥3 eligible studies reporting pre- and post-intervention vaccination coverage in the same population. Results: We included 44 studies. Multi-component strategies (n = 21) showed the most consistent results (e.g., pneumococcal uptake from 2.2% to 43.4%, p < 0.001). Reminder-based interventions (n = 4) achieved influenza coverage increases from 31.0% to 68.0% and a COVID-19 booster uptake boost of +38% after SMS reminders. Educational strategies (n = 11) varied in effectiveness, with one study reporting influenza coverage rising from 1.6% to 12.2% (+662.5%, OR 8.86, p < 0.01). Standing order protocols increased pneumococcal vaccination from 10% to 60% in high-risk adults. Hospital-based catch-up programs improved DTaP-IPV uptake from 56.2% to 80.8% (p < 0.001). For patient education, the pooled OR was 2.11 (95% CI: 1.96–2.27; p < 0.001, I² = 97.2%) under a fixed-effects model, and 2.47 (95% CI: 1.53–3.98; p < 0.001) under a random-effects model. For multi-component strategies, the OR was 2.39 (95% CI: 2.33–2.44; p < 0.001, I² = 98.0%) with fixed effects, and 3.12 (95% CI: 2.49–3.92; p < 0.001) with random effects. No publication bias was detected. Conclusions: Hospital-based interventions, particularly those using multi-component approaches, effectively improve vaccine coverage in older and high-risk adults. Embedding vaccination into routine hospital care offers a scalable opportunity to reduce disparities and enhance population-level protection. Future policies should prioritize the institutional integration of such strategies to support healthy aging and vaccine equity. Full article

Background and Aims: Gastroesophageal reflux disease (GERD) is the most common esophageal disorder worldwide and a progressive condition leading to Barrett’s esophagus and adenocarcinoma. Continuous medical therapy with proton pump inhibitors fails to restore the antireflux barrier and is unable to relieve symptoms in up to 40% of patients. A tailored and standardized antireflux surgical procedure may increase cure rates and meet patient expectations. Methods and Results: Antireflux surgery aims to reestablish the natural antireflux barrier, which includes the diaphragmatic crura, the lower esophageal sphincter (LES), and the angle of His along with the gastroesophageal flap valve. For decades, the Nissen total fundoplication has been the primary procedure and remains the gold standard for surgical treatment. Alternatives such as Toupet partial fundoplication, Dor partial fundoplication, and the magnetic sphincter augmentation (LINX™) procedure have been developed to mitigate side effects like dysphagia, gas-bloat syndrome, and the inability to belch or vomit. Recent clinical findings regarding a novel procedure, RefluxStop™, indicate that restoring the gastroesophageal flap valve, in conjunction with anterior fundoplication and a silicone device for stabilizing the LES beneath the diaphragm, can achieve lasting reflux control and enhance patient-reported outcomes. Conclusions: The planning of healthcare services and actionable strategies to improve equity and quality of treatment is critical to address the global burden of GERD. Modern laparoscopic surgery for GERD is safe and effective and should be performed in centers offering a complete diagnostic pathway and specific surgical techniques tailored to the individual GERD phenotype. Shared decision-making between the surgeon and the patient is essential for the choice of operation. A personalized approach can offer clinical benefits over total fundoplication and improve patient-reported outcomes. Full article

The equitable allocation of healthcare resources reflects social equity. Previous studies of healthcare accessibility have overlooked the impact of inter-city patient mobility on local residents’ and local residents’ multi-mode travel choices, distorting accessibility calculation outcomes. Taking the area within Beijing’s Sixth Ring Road as an example, this study established a Multi-Mode Accessibility Model for Local Residents (MMALR) to tertiary hospitals, using the proportion of non-local patients to adjust hospital supply capacity and considering the various travel mode shares from residential communities to hospitals to calculate the number of potential patients. We compared the changes in geospatial accessibility under different travel modes and employed the Gini coefficient to evaluate the geospatial equity of accessibility for different regions when using different accessibility methods. The results indicate that the spatial distribution of healthcare accessibility via different methods is similar, and it gradually decreases along subway lines from the urban center to the periphery. We found that the equities in access to high-level healthcare for Dongcheng District, Xicheng District, the area between the Third and Fourth Ring Road, and the area between the Fourth and Fifth Ring Road, display different ranking results across different methods, revealing that an unreasonable analysis framework could mislead the placement decisions for new hospitals or the allocation of medical resources. These findings emphasize the impact of inter-city patient mobility and the diversity of travel mode choices on accessibility. Our model can assist stakeholders in more accurately evaluating the accessibility and equity of local residents in terms of tertiary hospitals, which is crucial for cities with abundant medical resources and superior conditions. Our analytical findings provide a scientific basis for the location decisions of tertiary hospitals. Full article

CYP2C9 and CYP2C19 are major CYP450 enzymes that heavily influence the hepatic metabolism and bioactivation of many medications, including over-the-counter and narrow therapeutic index drugs. Compared to the wild-type alleles, genetic variants in either gene could potentially alter the pharmacokinetics of widely used medications, affect the desired therapeutic outcomes of a drug therapy, or increase the risk of undesired adverse events. The frequency of genetic polymorphisms associated with CYP450 enzymes can widely differ across and between racial and ethnic groups. This narrative review highlights the differences in CYP2C9 and CYP2C19 allele frequencies among European and Asian population subgroups, using published literature. Identifying the substantial differences across European and Asian populations, as well as within Asian subgroups, indicates the need to further scrutinize general population data. Clinical scientists and healthcare providers should advocate for more inclusive clinical pharmacogenomic data and racially and ethnically diverse pharmacogenomic databases. Clinical trials of limited racial and geographical diversity may not necessarily have strong external generalizability for all populations. Furthermore, clinical trials that designate an all-inclusive Asian population consisting of multiple ethnicities may not be adequate due to the perceived genetic differences among Asian subgroups. Gravitating towards a more comprehensive approach to utilizing pharmacogenomic data necessitates granular population-level genetic information which can be leveraged to improve how drug therapies are prescribed, achieve health equity, and advance the future of precision medicine. Full article

Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cyber-secure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets—including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers—and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning. Full article

Background: During the COVID-19 pandemic, a high-volume field clinic was rapidly established in Taichung, Taiwan, to manage patients with mild symptoms and reduce hospital burden. To streamline workflow and support timely care, a tailored medical informatics system was developed and implemented midway through clinic operations. Methods: We conducted a retrospective observational study analyzing data from 8287 patients who visited the clinic between 20 May and 4 June 2022. Patients were divided into two groups based on whether they received care before or after the informatics system was introduced (28 May). The primary outcomes included consultation volume, physician workload distribution, and operational efficiency during peak hours. A secondary analysis examined the subgroup receiving antiviral prescriptions. Results: After system implementation, the total number of consultations during peak hours increased significantly (from 138.6 to 199.0, a 43.5% increase; p = 0.001), along with the average number of consultations per physician (from 12.3 to 22.5, an 83% increase; p = 0.003). Similar trends were observed in the subgroup receiving antiviral therapy, despite the complexity of prescribing decisions. These prescribing trends suggest improved identification of high-risk patients and more timely antiviral initiation, which are critical for reducing disease progression and preventing hospitalization. Conclusions: The integration of a targeted medical informatics system significantly improved consultation efficiency and workload equity in a field clinic setting. This experience highlights a scalable model for digitally enhanced, rapid-response outpatient care during public health emergencies. Full article

Healthcare disparities in cancer care remain pervasive, driven by intersecting socioeconomic, racial, and insurance-related inequities. These disparities manifest in various forms such as limited access to medical resources, underrepresentation in clinical trials, and worse cancer outcomes for marginalized groups, including low-income individuals, racial minorities, and those with inadequate insurance coverage, who face significant barriers in accessing comprehensive cancer care. This manuscript explores the multifaceted nature of these disparities, examining the roles of socioeconomic status, race, ethnicity, and insurance status in influencing cancer care access and outcomes. Historical and contemporary data highlight that minority racial status correlates with reduced clinical trial participation and increased cancer-related mortality. Barriers such as insurance coverage, health literacy, and language further hinder access to cancer treatments. Addressing these disparities requires a systemic approach that includes regulatory reforms, policy changes, educational initiatives, and innovative trial and treatment designs. This manuscript emphasizes the need for comprehensive interventions targeting biomedicine, socio-demographics, and social characteristics to mitigate these inequities. By understanding the underlying causes and implementing targeted strategies, we can work towards a more equitable healthcare system. This involves improving access to high-quality care, increasing participation in research, and addressing social determinants of health. This manuscript concludes with policy recommendations and future directions to achieve health equity in cancer care, ensuring optimal outcomes for all patients. Full article

This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through advanced natural language processing capabilities. Current implementations demonstrate LLMs’ promising applications across clinical decision support, medical education, diagnostics, and patient care, while highlighting critical challenges in privacy, ethical deployment, and factual accuracy that require resolution for responsible integration into healthcare systems. This paper provides a comprehensive understanding of the background of healthcare LLMs, the evolution and architectural foundation, and the multimodal capabilities. Key methodological aspects—such as domain-specific data acquisition, large-scale pre-training, supervised fine-tuning, prompt engineering, and in-context learning—are explored in the context of healthcare use cases. The paper highlights the trends and categorizes prominent application areas in medicine. Additionally, it critically examines the prevailing technical and social challenges of healthcare LLMs, including issues of model bias, interpretability, ethics, governance, fairness, equity, data privacy, and regulatory compliance. The survey concludes with an outlook on emerging research directions and strategic recommendations for the development and deployment of healthcare LLMs. Full article