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

In 2018, the Philippine Congress passed the Universal Healthcare (UHC) Law and its implementing rules which mandated the enrollment of all Filipinos to PhilHealth, the national social health insurance corporation. The Department of Health (DOH) and PhilHealth will leverage their strategic purchasing power by affiliating Health Care Provider Networks (HCPNs), established within the geopolitical boundaries of a province or a highly urbanized city, through service level agreements. This study aims to shed light on what is expected from providers, payers, and regulators to implement UHC successfully.

The researchers conducted an inductive, content analytic qualitative study guided by the World Health Organization’s (WHO) Building Blocks Framework to determine the understanding and acceptance of the implementing rules of the UHC Law and the perceived barriers and enablers from the provider, payer, and regulator stakeholders in three provinces in the Philippines. Purposive sampling was utilized to provide the best representation across different economic and physical settings. A content analysis was done through an inductive process of coding concepts, which was the basis for categories grouped and matched deductively with the WHO framework. This formed the broader sub-themes and were used for the final data interpretation.

A total of 16 focus group discussions (FGDs) and nine in-depth interviews (IDIs) were performed with 84 participants. Inductive thematic analysis of categories and subcategories showed that the participants support the goals and objectives of the UHC Law. Still, perceived barriers refer to the lack of and improper use of funds, the need to clarify the implementing guidelines, and the role of politics. The participants indicated that solidarity and social connectedness with health system adaptability and resilience are enablers for the success of UHC reforms.

Proposals to mitigate the barriers include expanding the funding source, clarifying rules on the f inancial management system, and providing guidelines on health delivery integration to ensure access to patient care. Decentralization with autonomy will allow the stakeholders to align health programs with local needs. Proper representation in decision-making bodies is desirable to establish strong community involvement and solidarity. Resilience and adaptability based on a feedback loop are imperative.

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.

BACKGROUND AND OBJECTIVE

Proper documentation of patient safety events is important to be able to provide changes that can prevent events from occurring again. The Philippine General Hospital launched an online platform for reporting patient safety events in 2017. This paper aimed to describe the patient safety events, initial response to the event, and preventive actions done in the institution.

This is a retrospective descriptive study of patient safety event records from August 2017 to April 2022. General data of the patients, details surrounding the events, response to the event, and preventive measures done after the event were documented. Descriptive analysis was performed.

There was a total of 625 events reported with 525 total unique reports. There was an increased rate of patient safety event reports from 2021 to 2022. The average rate was 23.8 and 25.7 reports per month, respectively. Most reports were for in-patient cases and were type 3 preventable adverse events. The general initial response of healthcare personnel to the adverse events is to provide the appropriate clinical care. Preventive measures include re-orientation and event specific actions.

Documentation is crucial for patient safety events to provide solutions and prevent reoccurrence of these events that can cause harm to patients.

BACKGROUND AND OBJECTIVE

In 2018, the Philippine Congress passed the Universal Healthcare (UHC) Law and its implementing rules which mandated the enrollment of all Filipinos to PhilHealth, the national social health insurance corporation. The Department of Health (DOH) and PhilHealth will leverage their strategic purchasing power by affiliating Health Care Provider Networks (HCPNs), established within the geopolitical boundaries of a province or a highly urbanized city, through service level agreements. This study aims to shed light on what is expected from providers, payers, and regulators to implement UHC successfully.

The researchers conducted an inductive, content analytic qualitative study guided by the World Health Organization’s (WHO) Building Blocks Framework to determine the understanding and acceptance of the implementing rules of the UHC Law and the perceived barriers and enablers from the provider, payer, and regulator stakeholders in three provinces in the Philippines. Purposive sampling was utilized to provide the best representation across different economic and physical settings. A content analysis was done through an inductive process of coding concepts, which was the basis for categories grouped and matched deductively with the WHO framework. This formed the broader sub-themes and were used for the final data interpretation.

A total of 16 focus group discussions (FGDs) and nine in-depth interviews (IDIs) were performed with 84 participants. Inductive thematic analysis of categories and subcategories showed that the participants support the goals and objectives of the UHC Law. Still, perceived barriers refer to the lack of and improper use of funds, the need to clarify the implementing guidelines, and the role of politics. The participants indicated that solidarity and social connectedness with health system adaptability and resilience are enablers for the success of UHC reforms.

Proposals to mitigate the barriers include expanding the funding source, clarifying rules on the f inancial management system, and providing guidelines on health delivery integration to ensure access to patient care. Decentralization with autonomy will allow the stakeholders to align health programs with local needs. Proper representation in decision-making bodies is desirable to establish strong community involvement and solidarity. Resilience and adaptability based on a feedback loop are imperative.

OBJECTIVE: To identify early potential risk factors for hospital-acquired pneumonia (HAP) in patients with mild traumatic brain injury (mTBI), construct a risk prediction model, and evaluate its predictive efficacy. METHODS: A case-control study was conducted using clinical data from mTBI patients admitted to the neurosurgery department of Changzhou Second People's Hospital from September 2021 to September 2023. The patients were divided into two groups based on whether they developed HAP. Clinical data within 48 hours of admission were statistically analyzed to identify factors influencing HAP occurrence through univariate analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection to identify the most influential variables. The dataset was divided into training and validation sets in a 7:3 ratio. A multivariate Logistic regression analysis was then performed using the training set to construct the prediction model, exploring the risk factors for HAP in mTBI patients and conducting internal validation in the validation set. Receiver operator characteristic curve (ROC curve), decision curve analysis (DCA), and calibration curve were utilized to assess the sensitivity, specificity, decision value, and predictive accuracy of the prediction model. RESULTS: A total of 677 mTBI patients were included, with 257 in the HAP group and 420 in the non-HAP group. The significant differences were found between the two groups in terms of age, maximum body temperature (MaxT), maximum heart rate (MaxHR), maximum systolic blood pressure (MaxSBP), minimum systolic blood pressure (MinSBP), maximum respiratory rate (MaxRR), cause of injury, and laboratory indicators [C-reactive protein (CRP), procalcitonin (PCT), neutrophil count (NEUT), erythrocyte sedimentation rate (ESR), fibrinogen (FBG), fibrinogen equivalent units (FEU), prothrombin time (PT), activated partial thromboplastin time (APTT), total cholesterol (TC), lactate dehydrogenase (LDH), prealbumin (PAB), albumin (Alb), blood urea nitrogen (BUN), serum creatinine (SCr), hematocrit (HCT), hemoglobin (Hb), platelet count (PLT), glucose (Glu), K⁺, Na⁺], suggesting they could be potential risk factors for HAP in mTBI patients. After LASSO regression analysis, the key risk factors were enrolled in the multivariate Logistic regression analysis. The results revealed that the cause of injury being a traffic accident [odds ratio (OR) = 2.199, 95% confidence interval (95%CI) was 1.124-4.398, P = 0.023], NEUT (OR = 1.330, 95%CI was 1.214-1.469, P < 0.001), ESR (OR = 1.053, 95%CI was 1.019-1.090, P = 0.003), FBG (OR = 0.272, 95%CI was 0.158-0.445, P < 0.001), PT (OR = 0.253, 95%CI was 0.144-0.422, P < 0.001), APTT (OR = 0.689, 95%CI was 0.578-0.811, P < 0.001), Alb (OR = 0.734, 95%CI was 0.654-0.815, P < 0.001), BUN (OR = 0.720, 95%CI was 0.547-0.934, P = 0.016), and Na⁺ (OR = 0.756, 95%CI was 0.670-0.843, P < 0.001) could serve as main risk factors for constructing the prediction model. Calibration curves demonstrated good calibration of the prediction model in both training and validation sets with no evident over fitting. ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model in the training set was 0.943 (95%CI was 0.921-0.965, P < 0.001), with a sensitivity of 83.6% and a specificity of 91.5%. In the validation set, the AUC was 0.917 (95%CI was 0.878-0.957, P < 0.001), with a sensitivity of 90.1% and a specificity of 85.0%. DCA indicated that the prediction model had a high net benefit, suggesting practical clinical applicability. CONCLUSIONS The cause of injury being a traffic accident, NEUT, ESR, FBG, PT, APTT, Alb, BUN, and Na⁺ are identified as major risk factors influencing the occurrence of HAP in mTBI patients. The prediction model constructed using these parameters effectively assesses the likelihood of HAP in mTBI patients.
Humans ; Risk Factors ; Case-Control Studies ; Logistic Models ; Healthcare-Associated Pneumonia/epidemiology* ; Brain Injuries, Traumatic/complications* ; Male ; Female ; ROC Curve ; Pneumonia/etiology* ; Middle Aged ; Adult

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