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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Human ; Universal Health Care ; Rural Health ; Delivery Of Health Care ; Health Services Needs And Demand ; Health Facilities ; Algorithms ; Back

Background and Objective: The adoption of electronic medical records (EMRs) in the Philippines has been initiated and adjusted since the last decade through the Philippine eHealth Agenda framework. EMRs are known to improve clinical management and have been widely adopted in advanced economies. However, empirical research on EMR implementation remains limited. This study aims to determine how public primary health care facilities in the country interacted with EMRs before and during the COVID-19 pandemic to understand EMR adoption. Methods: More than 270,000 records generated from EMR usage logs in six rural primary health facilities in Western Visayas were analyzed. Average time of EMR use during work hours was estimated and compared before and during the pandemic. EMR adoption based on specific EMR features used was also determined. Results: In 2020, EMR use ranged from less than one hour to more than eight hours in selected rural health units (RHUs). There was a statistical increase and decrease in use of features during the pandemic. Some EMR users had efficient use indicated by complete adoption of EMR features although such features were not as frequently used as those pertaining to basic adoption. Conclusion This study demonstrates that for EMR use in rural settings, progressive use from basic to complete may vary among users. Public health emergencies such as a pandemic may also affect EMR use. Future research directions should explore other mechanisms which affect user behavior and encourage full adoption of technology such as use of games or non-monetary incentives.
Adoption

BACKGROUND AND OBJECTIVES

The adoption of electronic medical records (EMRs) in the Philippines has been initiated and adjusted since the last decade through the Philippine eHealth Agenda framework. EMRs are known to improve clinical management and have been widely adopted in advanced economies. However, empirical research on EMR implementation remains limited. This study aims to determine how public primary health care facilities in the country interacted with EMRs before and during the COVID-19 pandemic to understand EMR adoption.

METHODS

More than 270,000 records generated from EMR usage logs in six rural primary health facilities in Western Visayas were analyzed. Average time of EMR use during work hours was estimated and compared before and during the pandemic. EMR adoption based on specific EMR features used was also determined.

RESULTS

In 2020, EMR use ranged from less than one hour to more than eight hours in selected rural health units (RHUs). There was a statistical increase and decrease in use of features during the pandemic. Some EMR users had efficient use indicated by complete adoption of EMR features although such features were not as frequently used as those pertaining to basic adoption.

CONCLUSION

This study demonstrates that for EMR use in rural settings, progressive use from basic to complete may vary among users. Public health emergencies such as a pandemic may also affect EMR use. Future research directions should explore other mechanisms which affect user behavior and encourage full adoption of technology such as use of games or non-monetary incentives.

Adoption ; Health Facilities ; Electronic Health Records ; Delivery Of Health Care