To alleviate and prevent suffering, healthcare systems require collective action through organizational design and management. Therefore, healthcare organizations are appropriate contexts for studying the epidemiology of compassion – an emerging science that seeks to understand individual and systemic factors that foster compassion. Most research on compassion in healthcare settings has been conducted in high-income countries, focused on individual attributes that facilitate the giving of compassion. Less attention has focused on compassion in low- and middle-income country settings, or on organization- and system-level conditions that often determine whether compassion flourishes or falters. Epidemiology is a quantitative science, yet qualitative research can identify characteristics that warrant further testing and quantitative assessment. We conducted qualitative case studies of two primary healthcare organizations in Uganda and Kenya to examine how compassion is cultivated and sustained at the organizational level. Using the five components of the social architecture framework (network structures, organizational culture, roles, routines, and leaders) we discuss characteristics of these organizations that may enable compassionate responses to suffering among staff. Qualitative research in healthcare settings, framed by principles of organizational science, offers a pathway to sustaining healthcare workers and improving patient care. Increased attention to organizational and system-level compassion is needed, particularly in low- and middle-income countries where suffering is profound and complex.

Health plan disenrollment may interrupt treatment for opioid use disorder (OUD) and overall care, increasing risk for serious outcomes, including overdose and death. There is limited evidence on the association of disenrollment with all-cause and overdose mortality after initiating medications for OUD (MOUD) treatment. To assess the association of health plan disenrollment with all-cause and overdose mortality in patients treated with MOUD. This cohort study included privately and publicly insured patients aged 16 years or older who initiated buprenorphine or naltrexone for OUD treatment between January 1, 2012, and December 31, 2021, at 3 integrated health insurance and care delivery systems in 2 US states. Patients were followed up to 2 years until December 31, 2022. Data were analyzed July 2024 to November 2025. Health plan disenrollment following MOUD initiation. All-cause mortality and drug-related and alcohol-related overdose mortality within 2 years of MOUD initiation ascertained from the National Death Index. Survival analyses were adjusted for patient sociodemographic and clinical characteristics. Among 20 011 patients (mean [SD] age 38.7 [15.1] years; 12 299 males [61.5%]) who were treated for OUD, 6948 (34.7%) experienced disenrollment and 586 (2.9%) died during follow-up. The crude rate was 15.3 (95% CI, 14.1-16.6) per 1000 person-years for all-cause mortality and 6.2 (95% CI, 5.4-7.0) per 1000 person-years for overdose mortality. Ever experiencing disenrollment showed elevated all-cause mortality (17.6 [95% CI, 14.9-20.8] vs 14.7 [95% CI, 13.4-16.1] per 1000 person-years) and overdose mortality (8.9 [95% CI, 7.1-11.3] vs 5.4 [95% CI, 4.7-6.3] per 1000 person-years) relative to remaining enrolled. In adjusted analyses, ever experiencing disenrollment was associated with increased hazards of all-cause (hazard ratio [HR], 1.51; 95% CI, 1.23-1.84) and overdose mortality (HR, 1.56; 95% CI, 1.17-2.09). Compared with remaining enrolled and receiving MOUD treatment, being disenrolled (HR, 4.34; 95% CI, 3.19-5.89) and being enrolled and not receiving MOUD treatment (HR, 4.19; 95% CI, 3.24-5.43) were associated with overall mortality. In this cohort study of patients who initiated MOUD, experiencing health plan disenrollment was associated with increased mortality risk compared with remaining enrolled. Strategies are needed to improve continuity of health coverage and mitigate the elevated mortality risk during insurance transitions for patients receiving medications for OUD.

Telehealth has become an integral component of mental health care delivery worldwide. Understanding provider perceptions is essential to guiding its continued implementation. This international study used quantitative and qualitative methodologies to examine and broaden our understanding of the benefits and concerns related to telehealth for mental health care. An internet-based survey was conducted during the COVID-19 pandemic between November 11 and December 18, 2020, among mental health professionals, primarily psychiatrists and psychologists, registered with the World Health Organization's Global Clinical Practice Network. Clinicians completed the survey in 1 of 6 languages (Chinese, English, French, Japanese, Russian, or Spanish). Descriptive statistics and logistic regressions were used to analyze quantitative survey data on concerns and implementation of telehealth. Responses to an open-ended question about providers' perspectives on the benefits of telehealth were analyzed qualitatively. In total, 847 participants completed the telehealth section of the survey, and 496 provided a response to the open-ended question. Quantitative data on telehealth use and concerns revealed that clinicians' primary concerns focused on technical issues and clinical effectiveness relative to in-person services, specifically, loss of clinical information (eg, nonverbal behavior) and challenges with establishing a therapeutic alliance. Findings varied by profession, World Health Organization region, and telehealth training and experience. Qualitative data examining benefits fell into 3 major areas: accessibility and reach of mental health services, efficiency and flexibility for clinicians, and enhancement of clinical processes and outcomes. Taken together, findings revealed a trade-off between telehealth benefits and disadvantages. From the perspective of mental health professionals, telehealth practice comes with key challenges and valuable benefits. Findings offer important considerations for the implementation of telehealth systems, including the importance of training and education and balancing trade-offs to optimize care.

SUMMARYAntimicrobial resistance (AMR) is a public health threat that requires a coordinated and multi-sectoral approach. AMR is largely underexplored in Central Asia, a region shaped by the Soviet legacy. This narrative review aimed to synthesize evidence on the AMR landscape from articles published in five countries: Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. A structured search of peer-reviewed and gray literature was conducted, covering AMR prevalence, consumption patterns, knowledge, awareness, practices, stewardship interventions, and AMR governance. Key findings revealed that all five countries in the region have retained the rigidly centralized public health system, which was formerly known as the Sanitary Epidemiological Service during the Soviet era. This has led to fragmented surveillance and poor AMR monitoring. Up to 70% of Enterobacterales spp. were resistant to third-generation cephalosporins, and up to 58% of Staphylococcus species exhibited macrolide resistance. Consumption of Watch group antibiotics ranged from 53% to 68%, with a broad preference for injectables. Self-medication was found to be common, with rates ranging between 26% and 40% among the general public. High levels of patient demand coincided with medical errors, affecting up to 80% of antibiotic prescriptions. Only a limited number of antimicrobial stewardship studies were found, highlighting the insufficient integration of stewardship practices into routine healthcare delivery. Central Asian countries continue to face significant challenges because of the persistence of poor surveillance, high antibiotic consumption, and inadequate implementation of AMS because of long-standing legacies and systemic weaknesses. Addressing these gaps demands structural reforms, integrated surveillance systems, targeted education, and robust stewardship programs as urgent priorities.

The presented paper examines human resource management strategies in the context of hospital administration. This study aims to identify the key challenges and opportunities that arise in managing personnel within administrative processes. Special attention is given to strategic approaches such as staff motivation, prevention of professional burnout, and the integration of technological innovations into human resource management. These strategies contribute to improving the administrative efficiency of hospitals and enhancing the quality of patient-centered care. The study highlights the crucial role of strategic human resource management in achieving organizational objectives and provides recommendations for attracting and retaining staff, enhancing motivation, and mitigating professional burnout, all of which ultimately contribute to the operational productivity and sustainability of healthcare organizations.

Health care organizations have started to implement artificial intelligence-powered ambient scribe technology in clinical documentation workflows. Early outpatient studies have shown mixed results. Few studies have evaluated ambient scribes in the emergency department (ED). Due to differences in setting and patient acuity between the ED and ambulatory clinics, there remains a pressing need to research this technology in the ED. This study aimed to evaluate emergency physicians' (EPs) satisfaction, perceived documentation efficiency, after-shift documentation time, and trust in ambient scribe-generated notes compared with in-person scribes or independent documentation, and to identify ED-specific challenges. A cross-sectional survey was conducted among 16 board-certified adult and pediatric EPs who were granted access to the ambient scribe technology across 4 EDs. EPs used the ambient scribe for several months before completing a survey with multiple-choice and free-text responses. We performed a mixed methods analysis by summarizing quantitative data through descriptive statistics and performing a practical thematic analysis on free-text responses. Of the 16 EPs, 14 (87.5%) completed the survey. Among respondents, 9 (64.3%; 95% CI 38.7%-83.7%) reported being satisfied or very satisfied with the ambient scribe, while 3 (21.4%; 95% CI 7.6%-47.6%) expressed dissatisfaction. When given the option, 7 (50%; 95% CI 26.8%-73.2%) respondents preferred the ambient scribe, 4 (28.5%; 95% CI 11.7%-54.6%) preferred in-person scribes, and none (95% CI 0%-20.6%) preferred independent documentation. Among previous users of in-person scribes, 50% (4/8; 95% CI 21.5%-78.5%) favored the ambient scribe. The ambient scribe was reported to improve documentation efficiency (10/14, 71.4%; 95% CI 45.4%-88.3%) and reduce after-shift documentation time (9/14, 64.3%; 95% CI 38.7%-83.7%). However, only 42.9% (6/14; 95% CI 22.3%-69.2%) of respondents trusted the accuracy of ambient scribe-generated notes, compared with 75% (6/8; 95% CI 40.9%-92.9%) who trusted in-person scribes. Few respondents found the ambient scribe helpful for physical examinations (3/13, 23.1%; 95% CI 8.2%-50.3%) or medical decision-making documentation (5/14, 35.7%; 95% CI 16.3%-61.2%). A thematic analysis identified 5 themes: challenges due to the workplace environment, challenges due to the patient population, workflow improvement, workflow harm, and narrow usefulness. This mixed methods pilot study is among the first to evaluate ambient scribe technology in the ED. Our results add ED-specific insights to literature focused on the outpatient setting. Our findings reveal the potential for enhancing documentation efficiency and reducing administrative burden while highlighting setting-specific challenges. While most EPs preferred artificial intelligence-assisted documentation over independent charting, confidence in documentation accuracy and functionality remains limited compared with human scribes and varies by note component. As the demand for efficiency in emergency medicine continues to grow, scalable solutions such as ambient scribes could play a pivotal role if functionality, reliability, and physician trust can be further optimized.

Recent advancements in digital technologies have profoundly transformed healthcare delivery, sports analytics, and physical activity surveillance. The accelerated proliferation of wearable sensing devices and Internet of Things (IoT) infrastructures has facilitated unprecedented large-scale acquisition of multimodal physiological and kinematic data. However, accurate recognition of complex human activities remains a formidable challenge, primarily due to inadequate modeling of the spatiotemporal dependencies inherent in wearable-sensor signals. To address these fundamental limitations, this paper proposed a novel IoT-oriented activity recognition framework predicated on a Convolutional Recurrent Neural Network (CRNN) architecture, engineered to simultaneously model the spatial and temporal characteristics of multimodal wearable-sensor data streams. The proposed framework synergistically integrates Convolutional Neural Networks (CNNs) for hierarchical spatial feature extraction with Recurrent Neural Networks (RNNs) for temporal sequence modeling, thereby enabling more discriminative and robust activity classification. The methodological pipeline comprises several sequential stages: First, multidimensional wearable-sensor datasets are employed, encompassing physiological and inertial measurements including heart rate variability, triaxial accelerometer readings, gyroscopic angular velocity, magnetometer orientation data, and cutaneous temperature signals acquired from multiple anatomical locations. Second, raw sensor signals undergo preprocessing and temporal segmentation procedures to enhance data quality and optimize temporal representation. Third, spatiotemporal feature representations are learned autonomously within the hierarchical CRNN architecture. Finally, the proposed model is systematically evaluated through a comparative analysis with five representative baseline methods, encompassing both conventional machine learning algorithms and contemporary deep learning approaches. Experimental results demonstrate that the proposed CRNN framework achieves superior performance, achieving 98.2% classification accuracy, 97.2% sensitivity, 99.2% specificity, 97.4% recall, and 97.6% precision on the evaluated wearable-sensor datasets. Compared with existing methodologies, the proposed model consistently achieves higher recognition accuracy and greater generalization robustness, underscoring its efficacy for wearable-sensor-based activity recognition and its considerable potential for broader deployment in IoT-enabled monitoring paradigms and adaptable solutions in educational settings.

Abstract Background Traditional Chinese medicine (TCM) clinical decision-making involves complex integration of syndrome differentiation, constitutional assessment, and individualized treatment selection, creating challenges for standardization and quality assurance. While large language models (LLMs) demonstrate capabilities in medical knowledge integration and clinical reasoning, their application to TCM remains largely unexplored, particularly regarding syndrome differentiation principles and prescription formulation. Objective This study evaluated 5 contemporary LLMs in TCM clinical decision-making and assessed human–artificial intelligence (AI) collaboration compared with independent approaches. Specific objectives were to benchmark LLM performance in TCM knowledge assessment, evaluate clinical case analysis capabilities, identify the optimal model, and assess the quality, efficiency, and acceptability of human-AI collaboration. Methods In total, 5 mainstream LLMs were evaluated—Claude 3.7 Sonnet-Extended (Anthropic), ChatGPT 4.5 (OpenAI), Grok3-DeepSearch (xAI), Gemini 2.0 Flash Thinking Experimental (Google), and DeepSeek-R1 (DeepSeek). The evaluation consisted of four phases, (1) TCM knowledge assessment using 160 standardized questions, (2) clinical case analysis of 30 cases representing different disease systems and complexity levels, (3) optimal model selection using weighted scoring (40% knowledge and 60% clinical analysis), and (4) clinical application assessment involving 10 TCM practitioners and 2 experts comparing physician-only, AI-only, and human-AI collaboration across 5 clinical cases. Statistical analysis included descriptive statistics, reliability analysis, comparative testing, and regression analysis. Results DeepSeek-R1 demonstrated superior performance across both evaluation domains, achieving 96.7% accuracy in knowledge assessment and 17.31/20 (SD 2.65) in clinical case analysis, significantly outperforming other models ( P <.001). Human-AI collaboration achieved significant improvements compared with physician-only decision-making, with 16.1% quality enhancement (33.62 vs 28.97; P <.001) and 66.1% time reduction (162.6 s vs 479.2 s; P <.001). System usability was rated favorably (System Usability Scale score=76.8; P =.002), with high acceptance rates (74.25% adoption, 24% modification, and 1.75% rejection). AI assistance provided the greatest benefits in prescription formulation and medication selection ( P <.001). Conclusions LLMs, particularly DeepSeek-R1, demonstrate substantial capabilities in TCM knowledge assessment and clinical case analysis. Human-AI collaboration significantly enhanced clinical decision-making quality and efficiency while maintaining high physician acceptance. These findings provide compelling evidence for the clinical value of AI-assisted decision-making in TCM, suggesting potential solutions to current challenges in knowledge standardization, clinical training, and health care delivery efficiency. Strategic implementation of AI assistance could significantly enhance the quality, efficiency, and accessibility of TCM care while preserving fundamental principles of individualized treatment.

The NHS accounts for approximately 4-5% of England's total carbon footprint and was the first healthcare organisation to commit to a net-zero target. Reducing the inappropriate use of diagnostic tests could play a meaningful role in reaching this goal. In 2024, the microbiology laboratory at Lancashire Teaching Hospitals NHS Foundation Trust received >90,000 urine and >15,000 wound samples. Local audit data highlights samples are sent for testing in the absence of clinical signs and symptoms of infection. Furthermore, 25 % of superficial swabs and 10% of urines grew mixed-faecal organisms. The aim was to implement a diagnostic stewardship intervention to reduce inappropriate urine and wound swab submissions from primary care and estimate associated carbon savings. A pre-analytical stage diagnostic stewardship intervention was implemented consisting of a computerised clinical decision support tool (CCDS). The tool prompts clinicians, using evidence-based guidance, on when to obtain samples for testing. The UK Government 2024 greenhouse gas conversion factors were used to calculate the total CO2e associated with testing urine and wound samples. 3-month intervention period data was compared with two 3-month pre-intervention periods (I and II). Comparing number of samples received during the intervention period with pre-intervention II, urine samples decreased by 10.2%, saving 190.5kg CO₂e. Similarly, wound samples decreased by 12.9%, saving 80kg CO₂e. The CCDS tool effectively reduced unnecessary testing and associated carbon emissions, supporting the NHS's net-zero ambitions. Similar tools can be employed in other areas of pathology to reduce the impact of inappropriate testing whilst supporting sustainable healthcare.

Analytical capacities at the heart of learning health systems: Conceptual framework based on a developmental literature review.

686 Background: Over the past 3 decades, treatment of mUC has been transformed by 3 landmark backbone therapies: platinum-based chemotherapy (PBC), immune-checkpoint inhibitors (ICIs), and antibody–drug conjugates (ADCs), either alone or in combination therapies. Temporal trends of OS improvement in patients (pts) with mUC are instrumental to identify research gaps and unmet needs. Methods: We used the TriNetX research database to conduct a retrospective, large-scale outcome analysis of pts with mUC who received ≥1 line of treatment across worldwide healthcare institutions. Pts were stratified into 3 therapeutic temporal periods: PBC era (1999–2015), ICIs era (2016–2018), and ADC era (2019–2025). Baseline clinical and demographic characteristics were compared using standard statistics. Kaplan–Meier analysis estimated OS, and propensity score matching (PSM) adjusted for sex, age, stage at diagnosis, lines of treatment and comorbidities. Results: Among 4,720 pts with mUC, 2,383 received 1st-line therapy between 1999 and 2025 and were included in the analysis. Overall, 783, 663, and 937 pts were treated in the PBC, ICI, and ADC eras. Median age was 70 years across PBC, ICI, and ADC eras, respectively; the proportion of male pts was 72.9%, 69.4%, and 73.1%. Across treatment eras, 51.7% of pts in PBC era received PBC (49.3% non-PBC), 32% in the ICI era received ICIs (35.3% only PBC, 32.6% non-PBC), and 20% in the ADC era received ADCs (20.9% only PBC, 30.2% only non-PBC, 28.9% ICIs). Regarding subsequent therapies, in the PBC, ICI, and ADC eras, 51.4%, 52.6%, and 53.2% of pts received 2nd-line and 25.4%, 25.9%, and 26.7% 3rd-line therapy, respectively. The three cohorts yielded no significant differences in sex, race, comorbidities, lines of treatment, or stage at diagnosis (all p > 0.05). Median OS was 13.5 months (mo) in the PBC era, 17.5 mo in the ICIs era, and 21.1 mo in the ADC era with a significant difference between the ADC vs PBC eras (p = 0.0017). After PSM, median OS was 13.4, 17.3, and 22.3 mo in the PBC, ICI, and ADC eras, respectively, with the difference between the PBC vs ADC eras remaining significant (p = 0.005). Notably, a subgroup analysis of pts who received only chemotherapy (PBC or non-PBC) in their respective temporal period showed a median of OS 13.5, 14.6, and 17.1 mo in the PBC, ICI, and ADC eras, respectively, with a trend toward significance for the PBC vs ADC eras comparison (p = 0.006). Conclusions: OS in mUC has significantly improved over the past 3 decades, with the greatest gains observed in the ADC era during which an over 7.5 month improvement in median OS was observed (vs PBC era). Notably, improvements were seen even among many pts not receiving era-specific systemic therapies, suggesting that advances in diagnostics, staging, supportive care, and healthcare delivery —should be considered when interpreting real-world survival gains.

Primary Care and Geography: Global Populations.

Impact of prospective payment systems: An umbrella review of systematic reviews.

Risk differences allow decision makers to easily estimate the excess safety risk associated with a medical product relative to the potential benefits. However, in post-market observational surveillance studies that actively monitor (e.g., sequentially over time) for safety risk of new medical products, available methods target a relative measure (e.g., odds ratio and relative risk), which can be especially unstable in the rare event setting. These studies are typically conducted within distributed healthcare networks (e.g., Food and Drug Administration [FDA] Sentinel and Centers for Disease Control [CDC] Vaccine Safety Datalink) with patient-level data protected behind firewalls, but sharing of aggregate, deidentified data for centralized analyses. We propose an inverse probability of treatment weighting (IPTW) method that uses site-specific propensity scores to estimate site-specific risk differences that are combined to create an overall stratified risk difference estimate. This method is tailored to the rare event setting and requires minimal data sharing. The stratified IPTW approach is then extended to the active post-market surveillance setting by incorporating group sequential monitoring boundaries using a novel permutation approach. A simulation study is conducted to evaluate the performance of the new methods relative to two centralized analysis approaches, and the methods are applied to a safety surveillance study comparing the risk of febrile seizure between two vaccines using FDA Sentinel Data from three healthcare organizations.

Healthcare utilization patterns among adolescents with dual diagnoses of mental health and substance use disorders.

To evaluate the risk of diabetic retinopathy (DR) progression and associated ophthalmic complications after YAG laser capsulotomy in patients with nonproliferative DR (NPDR) after cataract surgery. Multicenter study using deidentified electronic health records from 69 U.S. healthcare organizations across outpatient and academic ophthalmology settings. Retrospective cohort study using propensity score matching to balance baseline characteristics. Patients aged 18 years or older with type 1 or 2 diabetes and NPDR who underwent cataract surgery with or without subsequent YAG laser capsulotomy were identified. Patients were followed for 1-year postcataract surgery. Exclusion criteria included less than 6 months of follow-up. Primary outcomes included development of proliferative DR (PDR), vitreous hemorrhage (VH), tractional retinal detachment (TRD), neovascular glaucoma, and need for panretinal photocoagulation (PRP) or pars plana vitrectomy. 10 750 patients (10 750 eyes) were included after matching: 5375 YAG-treated and 5375 control eyes. At 1 year, YAG-treated patients had higher risk of PDR (hazard ratio [HR], 1.91; 95% CI, 1.67-2.18), VH (HR, 1.40; 95% CI, 1.15-1.72), TRD (HR, 2.04; 95% CI, 1.32-3.13), and PRP (HR, 1.48; 95% CI, 1.14-1.91). A secondary analysis of patients with ≥5 years of NPDR showed similar elevated risks. YAG laser capsulotomy in patients with NPDR is associated with increased risk of DR progression and vision-threatening complications. Ophthalmic screening and close follow-up are recommended in this population after YAG treatment.

Durability of sotatercept response in pulmonary hypertension: Insights from extended real-world follow-up.

Implementation and evaluation of a community health workers-led digital integrated diseases screening system to provide healthcare for patients at community level in Rwanda.

In this insightful conversation, Susan Baker, a healthcare executive and patient experience expert, outlines actionable strategies for building patient loyalty and increasing willingness to recommend healthcare providers and organizations. From the importance of consistent behaviors to identifying individual patient preferences, Baker emphasizes the role of personalized care in fostering trust and confidence. She highlights how social media and online reviews can amplify patient satisfaction and why asking patients for feedback is critical. Practical techniques — such as creating visible expectations, addressing service failures, and engaging team members — are explored to enhance patient experiences. The discussion also underscores the value of maintaining connections with retired associates and leveraging testimonials effectively to strengthen patient-provider relationships.

Putting It Out There: An Overview of Public Reports of Perinatal and Women's Health Indicators in the United States.