Authors :
Jessica Clarrise M. Salmos; Rolaida L. Sonza
Volume/Issue :
Volume 11 - 2026, Issue 3 - March
Google Scholar :
https://tinyurl.com/bddbnecw
Scribd :
https://tinyurl.com/yuvj59hf
DOI :
https://doi.org/10.38124/ijisrt/26mar090
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
This study developed a Data-Driven Recommendation System for Improving Resource Allocation for Hospitals,
specifically designed for San Antonio District Hospital in San Antonio, Nueva Ecija. It employed a developmental research
design using the Agile Development Model to design and implement a secure, web-based system that provides real-time
monitoring of hospital equipment, supplies, room and bed availability, and maintenance schedules. The system also
generates automated alerts and structured, data-driven recommendations to support administrative and operational
decision-making without disrupting existing hospital workflows.
System evaluation was conducted using the ISO/IEC 25010 Software Quality Model. Ten (10) IT experts assessed the
system across nine quality characteristics, yielding an overall mean rating within the Highly Acceptable range (overall means
ranging from 3.70 to 3.80). Thirty (30) hospital staff evaluated the system in terms of functional suitability, performance
efficiency, and interaction capability, with results likewise interpreted as Highly Acceptable (overall means ranging from
3.20 to 3.30). These findings indicate that the system meets both technical quality standards and practical operational
requirements in a district hospital setting.
Overall, the results demonstrate that the developed system effectively addresses limitations associated with manual
resource tracking by providing centralized visibility, timely notifications, and data-supported recommendations. The study
concludes that the system is suitable for deployment in district hospitals and contributes a practical, scalable approach to
data-driven decision support for hospital resource allocation in low-resource healthcare environments.
Keywords :
Data-Driven System; Hospital Resource Allocation; Equipment Management; ISO/IEC 25010; Web-Based System.
References :
- Alpaydin, E. (2020). Introduction to machine learning (4th ed.). MIT Press.
- Bautista, R. M., Ignacio, L. R., & Lim, A. S. (2022). An evaluation of hospital information systems in Philippine provincial hospitals: Challenges and prospects. Philippine Journal of Public Health, 67(1), 45–52.
- Bekker, R., Koole, G., & Mandjes, M. (2021). Flexible capacity management for hospitals. European Journal of Operational Research, 288(3), 983–995. https://doi.org/10.1016/j.ejor.2020.06.015
- Benton, W. C. (2013). Purchasing and supply chain management (2nd ed.). McGraw-Hill Education.
- Buntin, M. B., Burke, M. F., Hoaglin, M. C., & Blumenthal, D. (2011). The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Affairs, 30(3), 464–471. https://doi.org/10.1377/hlthaff.2011.0178
- Chan, C. W., Farias, V. F., Bambos, N., & Escobar, G. J. (2019). Optimizing intensive care unit discharge decisions with patient readmissions. Operations Research, 67(5), 1300–1318. https://doi.org/10.1287/opre.2019.1889
- De Guzman, J. P., Santos, M. L., & Cruz, R. D. (2022). Hospital information systems in Philippine district hospitals: A systematic review. Philippine Journal of Health Research and Development, 26(2), 51–63.
- Department of Health (DOH). (2022). Philippine eHealth strategic framework and plan 2023–2028. https://doh.gov.ph/ehealth
- Gonzales, L. A., Ramos, E. R., & Dela Cruz, B. A. (2021). Implementing a modular inventory and bed tracking system in rural hospitals: Lessons from Mindoro. Philippine Journal of Healthcare Informatics, 12(1), 30–38.
- Guerrero, E. S., & Santos, A. G. (2023). Factors influencing hospital staff’s adoption of digital inventory systems in the Philippines. Journal of Health Informatics and Technology, 9(2), 23–31.
- Investopedia. (2022). Resource allocation. https://www.investopedia.com/terms/r/resource-allocation.asp
- Laudon, K. C., & Laudon, J. P. (2020). Management information systems: Managing the digital firm (16th ed.). Pearson.
- Martinez, D. A., & Pines, J. M. (2020). Applying predictive analytics to hospital operations: A strategic framework. Health Systems, 9(4), 312–322. https://doi.org/10.1057/s41306-020-00086-z
- Mohan, R., Sharma, S., & Jha, V. (2020). Feasibility of open-source hospital information systems in low-resource settings: A case from India. International Journal of Medical Informatics, 137, 104108. https://doi.org/10.1016/j.ijmedinf.2020.104108
- Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care. JAMA, 309(13), 1351–1352. https://doi.org/10.1001/jama.2013.393
- Parimbelli, E., Bottalico, B., Losiouk, E., Tomasi, M., Santosuosso, A., Lanzola, G., & Quaglini, S. (2018). Trusting telemedicine: A discussion on risks, safety, legal implications, and regulatory needs. Telemedicine and e-Health, 24(11), 924–929. https://doi.org/10.1089/tmj.2017.0253
- Parker, F., Martínez, D. A., Scheulen, J. J., & Ghobadi, K. (2024). An interactive decision-support dashboard for optimal hospital capacity management. Decision Support Systems, 182, 113500. https://doi.org/10.1016/j.dss.2024.113500
- Ramos, R. M., & Del Rosario, J. C. (2023). Real-time systems and the operational efficiency of rural hospitals in the Philippines. Asian Journal of Health Systems Research, 8(2), 62–71.
- Shelly, G. B., & Rosenblatt, H. J. (2011). Systems analysis and design (9th ed.). Course Technology.
- Stallings, W. (2018). Data and computer communications (10th ed.). Pearson.
- Toerper, M., Shermock, K. M., Martinez, D. A., & Pines, J. M. (2021). Capacity planning and optimization for hospital systems. Healthcare Management Review, 46(4), 288–297. https://doi.org/10.1097/HMR.0000000000000291
- U.S. Food and Drug Administration. (2022). Calibration standards for medical devices. https://www.fda.gov/medical-devices/calibration-standards-medical-devices
- Weissman, G. E., Crane-Droesch, A., Chivers, C., et al. (2020). Locally informed simulation to predict hospital capacity needs during the COVID-19 pandemic. Annals of Internal Medicine, 173(1), 21–28. https://doi.org/10.7326/M20-1260
- World Health Organization. (2011). Medical device technical series: Medical equipment maintenance programme overview. https://apps.who.int/iris/handle/10665/44574
- World Health Organization. (2020). Strengthening the health system response to COVID-19: Technical guidance. https://apps.who.int/iris/handle/10665/336240
This study developed a Data-Driven Recommendation System for Improving Resource Allocation for Hospitals,
specifically designed for San Antonio District Hospital in San Antonio, Nueva Ecija. It employed a developmental research
design using the Agile Development Model to design and implement a secure, web-based system that provides real-time
monitoring of hospital equipment, supplies, room and bed availability, and maintenance schedules. The system also
generates automated alerts and structured, data-driven recommendations to support administrative and operational
decision-making without disrupting existing hospital workflows.
System evaluation was conducted using the ISO/IEC 25010 Software Quality Model. Ten (10) IT experts assessed the
system across nine quality characteristics, yielding an overall mean rating within the Highly Acceptable range (overall means
ranging from 3.70 to 3.80). Thirty (30) hospital staff evaluated the system in terms of functional suitability, performance
efficiency, and interaction capability, with results likewise interpreted as Highly Acceptable (overall means ranging from
3.20 to 3.30). These findings indicate that the system meets both technical quality standards and practical operational
requirements in a district hospital setting.
Overall, the results demonstrate that the developed system effectively addresses limitations associated with manual
resource tracking by providing centralized visibility, timely notifications, and data-supported recommendations. The study
concludes that the system is suitable for deployment in district hospitals and contributes a practical, scalable approach to
data-driven decision support for hospital resource allocation in low-resource healthcare environments.
Keywords :
Data-Driven System; Hospital Resource Allocation; Equipment Management; ISO/IEC 25010; Web-Based System.