Hi. We are scarethestats.

And this is our project, Analyzing the Availability of Emergency and Specialty Services Provided by Health sites in the Philippines, This project aims to analyze the distribution and accessibility of healthcare facilities across different regions in the Philippines using machine learning and statistical methods..

Access to healthcare is essential in ensuring that individuals receive timely and appropriate medical care. In 2025, the Department of Health (DOH) was allocated a ₱35.37-billion budget—a ₱6.78-billion increase from 2024—to enhance healthcare facilities and upgrade medical equipment nationwide. To effectively allocate these resources, it is crucial to identify areas lacking healthcare facilities with adequate services.

Analyzing the clustering of healthcare facilities provides valuable insights into the distribution of medical resources and helps pinpoint regions with potential gaps in access. By studying these clusters, policymakers and healthcare planners can develop data-driven strategies to improve healthcare infrastructure, optimize resource allocation, and reduce disparities in service availability.

Data Science Team

  • Ishan Balao, CS 132 WFV
  • Rafael Jimenez, CS 132 WFV
  • Pio Lorenzo Perez, CS 132 WFV

Our research question:

What are the different clusters of healthcare accessibility across different regions?

Null Hypothesis

There are no significant clusters of healthcare accessibility across regions (healthcare facilities are evenly distributed)

Alternative Hypothesis

There are significant clusters of healtcare accessibility, with some regions having more access than others

Proposed Solution

To answer this question, we utilize the use of data science and machine learning to uncover possible clusters of healthcare across the region, basing off of their operators, emergency service availability, as well as specializations.




What was the data used for the project?

Our team used 2 datasets one was acquired from National Health Facility Registry and the other was aquired from PSA Population Density. The first dataset contains information about the healthcare facilities in the Philippines, including their locations, types, and services. It includes geographic, operational, and service-related details of various medical establishments such as hospitals, clinics, pharmacies, and specialty centers. The second dataset contains the population density of the Philippines in the different cities and municipalities in the Philippines. Combined these datasets provides insights into the distribution of healthcare facilities and their accessibility to the population.

Google Sheets was used to preprocess the data. First, we removed the unnecessary columns and rows from the dataset like landline numbers, fax numbers and other information that was not relevant for our analysis. Our team also had to manully match cities in both dataset so we can properly merge them together. There were also inconsistencies when trying to match the 2 datasets, as regional conflicts occurred during the time in between the recording of the datasets, resulting in some mismatches in geographical features These were handled by updating the PSA dataset to match the Negros Islands Region in the NHFR, and changing the regions of the Special Geographical Area in the NHFR dataset to match the PSA dataset. Highly Urbanized Cities were updated to be part of the provinces they are geographically in, instead of being a separate province altogether (as listed in datasets).

Data Credits: National Health Facility Registry

Link to our dataset

Project Plots.

RQ1

What percentage of healthcare facilities provide emergency services?

RQ2

What is the distribution of public vs. private healthcare facilities?

RQ3

How many facilities provide specialized healthcare services, and what are the most common specializations?

Hypothesis Testing:

From the chi square goodness-of-fit test, P-value obtained is significantly higher than 0.05 (0.589), so we fail to reject the null hypotheses

Null Hypothesis

There is a non equal distribution of Private and Public owned healthcare facilities

Alternative Hypothesis

There is an equal, 50/50, distribution of Private and Public owned healthcare facilities

Link to our hypothesis testing



Discussion and Conclusion

This section provides a reflective interpretation of the findings and summarizes the key takeaways.

Discussion

Our findings reveal a strong reliance on public health care across the Philippines, as most of the facilities are publicly owned or regulated. In contrast, highly urbanized areas show a different pattern. These regions tend to have a greater concentration of privately owned healthcare facilities. To illustrate, public healthcare facilities account for 31,527 out of the total, compared to 9,475 private one roughly 77% public and 23% private in overall distribution.

Conclusion

Since many Filipinos rely more on public healthcare, there needs to be a stronger focus on maintaining and improving public facilities. Future research can focus more on individual communities, as our current analysis used a broader overview of national data. This would provide deeper insights into the specific conditions and needs of local healthcare facilities.