OBJECTIVES

Timely access to appropriate levels of care is essential for improving maternal, newborn,
and child health outcomes. To address persistent service delivery fragmentation and strengthen referral systems, the Philippine Department of Health issued Administrative Order 2020-0019 to guide the design of Health Care Provider Networks (HCPNs) under the Universal Health Care Act of 2019. This study assessed the extent to which sixteen municipalities across four provinces in Eastern Visayas comply with the HCPN service delivery guidelines in the context of maternal and newborn care.

The study employed a descriptive cross-sectional mixed-methods design, utilizing structured facility checklists to assess compliance with HCPN standards. Qualitative data were gathered through key informant interviews and focus group discussions with purposively selected stakeholders—decision makers, health personnel, and mothers—to contextualize findings. A three-lever framework for integrated care (policy, operational, and cross- cutting) guided the analysis

The findings revealed significant gaps between the current capacities of study health facilities and the requirements outlined in the HCPN guidelines. Major gaps included (1) weak cooperative governance mechanisms to support network-wide coordination; (2) limited systematic linkages between facilities, including fragmented referral protocols and non-interoperable health information systems; (3) inadequate investments in infrastructure, health human resources, and medical commodities; and (4) absence of performance monitoring systems across HCPNs.

INTRODUCTION: Randomised controlled trials (RCTs) have informed guideline recommendations for the management of stable coronary artery disease (CAD). However, the real-world impact of contemporary guidelines and trials on practising physicians in the Asia-Pacific region remains uncertain. We aimed to evaluate the knowledge, attitudes and practices among cardiovascular physicians in the region regarding stable CAD management. METHOD: An anonymised cross-sectional electronic survey was administered to cardiovascular practitioners from the Asia Pacific, assessing 3 domains: 1) baseline knowledge on recent trials and society guideline, 2) attitudes towards stable CAD, and 3) case scenarios reflecting management preferences. Correlations among knowledge, attitudes and practice scores were assessed between physicians from developed and developing countries using Pearson correlation. RESULTS: Overall, 713 respondents from 21 countries completed the survey. The mean knowledge score was 2.90±1.18 (out of 4), with 37.3% of respondents answering all questions correctly, while 74.6% noted that guidelines have significant impact on their practice. Despite guidelines recommending optimal medical therapy, majority chose revascularisation (range 53.4- 90.6%) as the preferred strategy for the case scenarios. Practitioners from developed regions had higher knowledge scores and lower attitude scores compared to developing regions, while practice scores were similar in both groups. Weakly positive correlations were noted between knowledge, attitude and practice scores. CONCLUSION Variations exist in knowledge and attitudes towards guideline recommendations and correspondingly actual clinical practice in the Asia Pacific, with most practitioners choosing an upfront invasive strategy for the treatment of stable CAD. These differences reflect real-world disparities in guideline interpretation and clinical adoption.
Humans ; Coronary Artery Disease/therapy* ; Cross-Sectional Studies ; Practice Patterns, Physicians'/statistics & numerical data* ; Asia ; Health Knowledge, Attitudes, Practice ; Surveys and Questionnaires ; Male ; Practice Guidelines as Topic ; Female ; Attitude of Health Personnel ; Middle Aged ; Developing Countries

INTRODUCTION: Antimicrobial resistance (AMR) poses a critical global health threat, with millions of deaths attributed to it annually. Antibiotic stewardship to combat AMR is the responsibility of all healthcare professionals. Despite evidence suggesting that it is unnecessary, dentists routinely prescribe prophylactic antibiotics following third molar (3M) surgeries. METHOD: This mixed-methods study examined the behavioural barriers influencing antibiotic prescribing practices within the Division of Oral and Maxillofacial Surgery at the National University Centre for Oral Health Singapore. This study used the capability, opportunity and motivation for behavioural change or COM-B framework to implement interventions targeting the behavioural barriers. RESULTS: Pre- and post-intervention data over 6 months showed a significant reduction in antibiotic prescriptions from 84.45% to 20.89%, following the implementation of COM-B strategies (P<0.001). Qualitative feedback from focus group discussions highlighted a positive shift in clinicians' attitudes towards antibiotic reduction, acknowledging the minimal infection risk associated with non-prescribing practices. Notably, complication rates remained stable throughout the study period, indicating no adverse effects from reduced antibiotic usage. CONCLUSION These findings demonstrated that the COM-B model can be successfully applied to modify deeply ingrained prescription habits, and underscored the effectiveness of a structured behavioural change intervention in enhancing compliance with antibiotic stewardship guidelines. The study advocates continuation of initiatives to sustain this positive trend and mitigate AMR in clinical practice.
Humans ; Molar, Third/surgery* ; Anti-Bacterial Agents/therapeutic use* ; Singapore ; Antimicrobial Stewardship/methods* ; Practice Patterns, Dentists'/statistics & numerical data* ; Antibiotic Prophylaxis ; Female ; Attitude of Health Personnel ; Male ; Tooth Extraction ; Adult ; Focus Groups ; Practice Patterns, Physicians'

INTRODUCTION: An interdisciplinary panel, comprising professionals from medicine, AI and data science, law and ethics, and patient advocacy, convened to discuss key principles on regulation, implementation and evaluation of AI models in healthcare for Singapore. METHOD: The panel considered 14 statements split across 4 themes: "The Role and Scope of Regulatory Entities," "Regulatory Processes," "Pre-Approval Evaluation of AI Models" and "Medical AI in Practice". Moderated by a thematic representative, the panel deliberated on each statement and modified it until a majority agreement threshold is met. The roundtable meeting was convened in Singapore on 1 July 2024. While the statements reflect local perspectives, they may serve as a reference for other countries navigating similar challenges in AI governance in healthcare. RESULTS: Balanced testing approaches, differentiated regulatory standards for autonomous and assistive AI, and context-sensitive requirements are essential in regulating AI models in healthcare. A hybrid approach-integrating global standards with local needs to ensure AI comple-ments human decision-making and enhances clinical expertise-was recommended. Additionally, the need for patient involvement at multiple levels was underscored. There are active ongoing efforts towards development and refinement of AI governance guidelines and frameworks balancing between regulation and freedom. The statements defined therein provide guidance on how prevailing values and viewpoints can streamline AI implementation into healthcare. CONCLUSION This roundtable discussion is among the first in Singapore to develop a structured set of state-ments tailored for the regulation, implementation and evaluation of AI models in healthcare, drawing on interdisciplinary expertise from medicine, AI, data science, law, ethics and patient advocacy.
Singapore ; Humans ; Artificial Intelligence/standards* ; Delivery of Health Care/organization & administration*

In recent years, China has continued to face a high prevalence of oral diseases, along with uneven access to high-quality dental care. Against this backdrop, artificial intelligence (AI), as a data-driven, algorithm-supported, and model-centered technology system, has rapidly expanded its role in transforming the landscape of stomatology. This review summarizes recent advances in the application of AI in stomatology across clinical care, biomedical and materials research, education, and hospital management. In clinical settings, AI has improved diagnostic accuracy, streamlined treatment planning, and enhanced surgical precision and efficiency. In research, machine learning has accelerated the identification of disease biomarkers, deepened insights into the oral microbiome, and supported the development of novel biomaterials. In education, AI has enabled the construction of knowledge graphs, facilitated personalized learning, and powered simulation-based training, driving innovation in teaching methodologies. Meanwhile, in hospital operations, intelligent agents based on large language models (LLMs) have been widely deployed for intelligent triage, structured pre-consultations, automated clinical documentation, and quality control, contributing to more standardized and efficient healthcare delivery. Building on these foundations, a multi-agent collaborative framework centered around an AI assistant for stomatology is gradually emerging, integrating task-specific agents for imaging, treatment planning, surgical navigation, follow-up prediction, patient communication, and administrative coordination. Through shared interfaces and unified knowledge systems, these agents support seamless human-AI collaboration across the full continuum of care. Despite these achievements, the broader deployment of AI still faces challenges including data privacy, model robustness, cross-institution generalization, and interpretability. Addressing these issues will require the development of federated learning frameworks, multi-center validation, causal reasoning approaches, and strong ethical governance. With these foundations in place, AI is poised to move from a supportive tool to a trusted partner in advancing accessible, efficient, and high-quality stomatology services in China.
Artificial Intelligence ; Humans ; Oral Medicine/trends* ; China ; Delivery of Health Care ; Machine Learning

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

In the context of continuously deepening medical and health system reforms and comprehensively promoting the "Healthy China" strategy, Henan Provincial People's Hospital has established a regional collaborative and vertically integrated critical care service structure and network. This initiative aims to enhance information empowerment, strengthen regional collaboration, improve the insufficient primary medical services, and ensure timely and effective treatment for critically ill patients. By establishing a comprehensive dispatch service platform for regional collaborative critical care, building a "top-down" remote medical collaboration network, and forming a cross-regional specialty alliance for critical care, the hospital has improved the efficiency of medical services and enhanced regional capabilities for treating critically ill patients. Simultaneously, for critically serious patients and those with complex diseases at primary medical institutions, a one-stop consultation and referral service has been implemented. This service adopts a "three specialists" approach and a multidisciplinary consultation mechanism within the hospital, constructs a multi-dimensional critical care transfer mode integrating air, ground, and the internet, creates a regional collaborative rescue mode, and implements full-cycle treatment for critically serious patients. The comprehensive, flexible, and efficient service pathway for regional collaborative critical care established by this system ensures timely and safe treatment for critically ill patients, promotes the distribution of high-quality medical resources, and effectively addresses issues such as uneven distribution of high-quality medical resources and varying levels of critical care capabilities. It has facilitated the formation of a new tiered diagnosis and treatment order characterized by "first diagnosis at the primary level, two-way referral, separate treatment for acute and chronic diseases, and vertical integration". This approach has enhanced the diagnostic and comprehensive service capabilities of primary medical institutions. Currently, by strengthening information empowerment and sharing, creating a full-process critical care diagnosis and treatment model, providing medical assistance and cultivating primary-level critical care talent, and promoting appropriate technologies, the hospital has gradually overcome challenges such as barriers to information exchange and sharing between hospitals, overloaded critical care teams, high pressure on patient reception and transfer, and limited critical care capabilities at primary medical institutions. This article summarizes the construction and practical application of this regionally coordinated critical care system, aiming to provide a reference for the management of critical care treatment.
Humans ; China ; Critical Care/organization & administration* ; Delivery of Health Care/organization & administration*

OBJECTIVE: Traditional Chinese medicine (TCM) incorporates traditional diagnostic methods and several major treatment modalities including Chinese herbal medicine, Chinese patent medicine, and non-pharmacological methods such as acupuncture and tuina. Even though TCM is used daily by more than 70,000 healthcare facilities and over 700,000 clinical practitioners in China, there is a poor understanding of the extent to which TCM diagnostic methods are used, how different treatment modalities are deployed in general, and what major factors may affect the integration of TCM and Western medicine. This study aimed to fill this void in the literature. METHODS: In the 2021 National Healthcare Improvement Evaluation Survey, we included three questions gauging the perception and practices of TCM amongst physicians working in TCM-related facilities, investigating the frequency of their deployment of TCM diagnostic methods, and predominant TCM treatment methods. Our empirical analysis included descriptive statistics, intergroup chi-square analysis, and binary logistic regression to examine the association between different types of facilities and individual characteristics and TCM utilization patterns. RESULTS: A total of 7618 clinical physicians comprised our study sample. Among them, 84.27% have integrated TCM and Western medicine in their clinical practice, and 80.77% of TCM practitioners used the 4 diagnostic methods as a tool in their clinical practice. Chinese herbal medicine was the most widely utilized modality by Chinese TCM physicians (used by 88.49% of respondents), compared with the Chinese patent medicine and non-pharmacological TCM methods, which were used by 73.14%, and 69.39%, respectively. Herbal tea as an out-of-pocket health-maintenance intervention is also a notable practice, recommended by 29.43% of physicians. Significant variations exist across certain institutions, departments, and individual practitioners. CONCLUSION Given that most of the surveyed physicians integrated TCM with Western medicine in their clinical practices, the practice of "pure TCM" appears to be obsolete in China's tertiary healthcare institutions. Notably, remarkable variation exists in the use of different TCM modalities across institutions and among individuals, which might be related to and thus limited by the practitioners' experience. Future research focusing on the efficacy and safety of TCM interventions for specific diseases, the development of standardized clinical guidelines, and the enhancement of TCM education and training are called for to optimize TCM-Western medicine integration. Please cite this article as: Guo R, Zeng D, Zhao Q, Zhang XY, Zhang XK, Liu YL. How are different traditional Chinese medicine modalities deployed by clinical practitioners in China? Findings from a national survey. J Integr Med. 2025; 23(1): 36-45.
Medicine, Chinese Traditional/statistics & numerical data* ; Humans ; China ; Surveys and Questionnaires ; Female ; Male ; Physicians/statistics & numerical data* ; Practice Patterns, Physicians'/statistics & numerical data* ; Adult ; Middle Aged

Human ; Delivery Of Health Care ; Primary Health Care

Human ; Delivery Of Health Care ; Primary Health Care