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.

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.

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

Costs And Cost Analysis ; Delivery Of Health Care

Palliative care has emerged as a crucial component of healthcare, particularly in the context of an aging population and the increasing prevalence of chronic and life-limiting illnesses. In the Philippines, however, access to palliative care remains significantly limited, especially in rural and underserved areas. This disparity is primarily driven by systemic challenges such as inadequate healthcare infrastructure, a shortage of trained professionals, and insufficient public awareness. While the inclusion of palliative care in the Universal Health Care (UHC) Act of 2019 (Republic Act No. 11223) reflects a progressive step toward addressing these needs, the implementation of comprehensive palliative services continues to face considerable hurdles. This paper advocated for the stronger integration of palliative care into primary health care systems at the barangay level, emphasizing the need to strengthen policy frameworks, ensure adequate resource allocation, and actively engage communities in this endeavor. Such efforts are essential to guaranteeing equitable, compassionate, and dignified care for all individuals, regardless of their stage of life or even socioeconomic status.

BACKGROUND

Lesbian, gay, bisexual, and transgender (LGBT) individuals continue to experience health disparities related to discrimination and health professional inadequacies despite increasing social acceptance of the LGBT community in society. Understanding the knowledge, attitudes, and practices concerning the healthcare of LGBT individuals among healthcare professionals can enable gender-affirming healthcare for the LGBT community. However, there is a need for a culturally relevant and updated tool to describe healthcare professionals' interactions with the LGBT community.

The objective of this study is to describe the development of the Knowledge, Attitudes, and Practices towards LGBT in Healthcare Questionnaire (KAP-LHQ) and demonstrate its content and face validity.

The KAP-LHQ was developed using a two-phase process. Phase 1 involved the generation of items through a literature review and discussions among the authors Phase two included the evaluation of its content and face validity. Content validity was determined by six experts using the Content Validity Index, while ten healthcare professionals evaluated the tool for face validity.

The KAP-LHQ demonstrated good content and face validity. Content validity indices were high. Item-Level CVI (I-CVI) ranges from 0.83 to 1.00 for the items under the Knowledge section and has a Scale-Level CVI (S-CVI) of 0.97. The Attitude section items scored an I-CVI of 0.83 to 1.00 and a S-CVI of 0.99, while most of the Practices section items scored an I-CVI of 1.00 with an overall S-CVI of 0.94. One item was omitted on the final version for having an I-CVI of 0.67. Face validity of the final questionnaire across parameters has a median of 4 out of 4, reflecting the appropriateness, comprehensibility, and relevance of the questionnaire.

The KAP-LHQ appears to be a valid tool that has the potential for assessing the KAP of health professions to improve training and increasese healthcare outcomes for the LGBT community. Using the tool on a larger population can firmly establish its efficacy, further validity, and even reliability.

BACKGROUND

PhilHealth serves as the Philippines’ national health insurance provider and is central to implementing the Universal Health Care (UHC) Law. Despite this, existing gaps and ongoing challenges in claims and financial management systems continue to affect public healthcare facilities’ operations and sustainability.

This article examined the effectiveness and challenges of PhilHealth’s claims and financial management systems in public healthcare facilities, focusing on accreditation, claims processing, reimbursements, and financial governance.

A qualitative multiple-case study design was employed in Quezon City and the provinces of La Union, Sorsogon, Leyte, and Bukidnon, with data collected from 2022 to 2023. Prior to data collection, a certificate of exemption was granted by the Department of Health – Single Joint Research Ethics Board (DOH-SJREB). Data were collected through key informant interviews with healthcare facility heads, claims processors, and PhilHealth personnel, supplemented by document reviews and facility observations. Thematic analysis was employed to examine the implementation of national health insurance policies at the facility level.

Accreditation standards are uniformly defined, yet compliance varies widely, directly influencing reimbursement outcomes. Facilities with compliance gaps often face provisional accreditation, downgrades, or suspension, resulting in reduced revenue. Although the Universal Health Care (UHC) Law guarantees patient access to PhilHealth benefits, the efficiency of claims processing remains uneven and highly dependent on administrative capacity, staffing adequacy, and digital infrastructure. Systemic inefficiencies at both PhilHealth and facility levels contribute to delays and claim denials. Reimbursements are further constrained by outdated case rate ceilings, inconsistent financial practices, inadequate recordkeeping, weak information systems, and poor storage conditions—particularly in lower-level hospitals and rural health units.

Reforms in claims processing workflows, information system integration, and financial management capacities are crucial to enhance reimbursement efficiency. Strengthening these systems is fundamental for supporting sustainable, equitable, and high-quality healthcare delivery in the public sector within the Universal Health Care (UHC) framework.