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.

BACKGROUND AND OBJECTIVE

Access to healthcare remains a challenge in most areas in the Philippines. Fifty-three percent (53%) of the Philippine population do not have access to a rural health unit (RHU) within a 30-minute travel t ime. As a response, the Department of Health (DOH) needs to construct an additional 2400 RHUs by 2025. This paper uses the Philippine Health Facility Development Plan 2020-2040 (PHFDP) as a reference to present a solution for locating sites for RHU placement in under-served areas using neural networks to meet the 30-minute travel time by maximizing population accessibility.

RHU accessibility was measured using geographic attributes as inputs to a back propagation neural network (BPNN) and a recurrent neural network (RNN): (1) land coverage and hazard data, representing geographical limitations; (2) population density and distribution, indicating demand for healthcare services; and (3) infrastructure-related features, such as road networks, points of interest, and the locations of existing RHUs, which influence healthcare accessibility. The models were trained to identify underserved areas and were implemented on a nationwide scale, excluding NCR, to locate candidate areas to increase population access to the new RHUs. The models were validated using a healthcare facility accessibility index (HCFAI) to assess RHU coverage improvement.

The BPNN showed stronger generalization across regions, achieving 79.1% average accuracy in distinguishing low from high accessible areas on Region 1 and identifying 1668 out of 3305 locations in the region as candidate sites. The RNN, better capturing unique regional characteristics, required separate training: 77.2% average accuracy on Region 1, identifying 1593 candidate sites. Our findings suggest expanding the use of land improves population access to healthcare facilities. Both models found more than the needed number of RHUs by 2040. The BPNN was more consistent than RNN to improve a region’s overall accessibility by increasing the HCFAI. The BPNN can increase population access to an RHU from 2.5-98.5% from its original population with access to an RHU.

The study demonstrates the usage of geographic attributes and neural networks to improve healthcare accessibility. The BPNN and RNN are adequate algorithms to find under-served areas and candidate sites for RHU construction to maximize population accessibility. The HCFAI metric validates the locations to highlight which neural network maximizes more of the region’s populat ion. The study contributes to ongoing efforts to improve healthcare infrastructure and accessibility, offering datadriven recommendations for RHU locations.

BACKGROUND

Access to prison healthcare and healthcare equity remains poorly understood in the Philippines. With Philippine jail health systems subjected to budgetary restrictions, optimization of efficient mechanisms in healthcare delivery is warranted. This makes understanding how PDLs’ utilization to healthcare services more relavant and of high importance.

This study sought to determine healthcare accessibility and explored what influences healthcare access by Persons Deprived of Liberty (PDLs). Additionally, the study looked into whether healthcare was equally accessible to different PDL subgroups.

A descriptive-correlational design was employed in recruiting 261 PDLs utilizing stratified random sampling in two jail facilities in Dumaguete City from May to June 2023. Data on healthcare accessibility and availability from self-report survey questionnaires were analyzed using SPSS version 25.

Hierarchical linear regression analysis shows that, collectively, predisposing factors (age, sex) F=200.82, enabling resources (availability of health services) F=52.52, and perceived needs (physical activities, sleep, diet, and mental health) F=30.24 significantly predict healthcare accessibility, having the availability of healthcare services as the strongest predictor with an R2 change of 43.7% followed by percieved needs (3.9%) and predisposing factor (1.4%), respectively. Furthermore, ordinal logistic regression analysis shows that healthcare by age groups 18-34 (OR=0.379) and 35-54 (OR=0.449) are less likely to be available and accessed than those aged 55 and above. Additionally, availability and accessibility of healthcare are less likely for males (OR=0.24) than females while PDLs with average physical activities (OR=0.87), good (OR=50.7) to average sleep (OR=27.4), and average mental health (OR=0.35) have higher odds of availing and accessing healthcare than their poor counterparts.

These findings indicate that PDLs tend to access healthcare services based predominantly on availability rather than their desired needs warranting strategies that allow catering to a wide range of health needs in PDL subgroups which ultimately lead to better prison health outcomes.

Workforce ; Delivery Of Health Care ; Healthcare

BACKGROUND AND OBJECTIVE

The Philippine Package of Essential Noncommunicable Disease Interventions (PhilPEN) was introduced by the Department of Health through AO 2012-0029. This is anchored to WHO PEN, a prioritized set of cost-effective interventions that can be carried out to provide an acceptable standard of care at the primary health care level, even in low-resource settings. The study aims to evaluate the availability and adequacy of primary health care facilities in providing the PhilPEN package of interventions using the WHO assessment tool.

A cross-sectional survey was conducted in 25 randomly selected primary health care facilities in Metro Manila. Data were collected through structured interviews with facility staff and direct observation using a standardized questionnaire aligned with PhilPEN and WHO PEN guidelines. The tool assessed PhilPEN inputs (infrastructure, human resources, basic tools and equipment, essential medicines, record-keeping, financing) and services (risk assessment and screening, early diagnosis and monitoring, treatment and follow-up, counseling, referral of patients).

All facilities met the basic standards for infrastructure, human resources, record keeping, and financing. However, only 40% had all essential medicines, and just 16% had complete tools, including urine ketone/protein test strips. Risk assessment and patient counseling were consistently implemented, but early diagnosis and follow-up services were inconsistent due to training and supply gaps.

Primary health care centers in Metro Manila demonstrate partial readiness for PhilPEN implementation. Gaps in tools, medicines, and protocol availability should be addressed to optimize NCD service delivery.

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