OPTIMIZING NATIONAL HEALTHCARE INFRASTRUCTURE THROUGH DATA ANALYTICS AND INFORMATION SYSTEMS
Main Article Content
Keywords
Artificial Intelligence (AI), Data Analytics, Healthcare Optimization, Health Information Systems, Policy Alignment
Abstract
In recent years, national healthcare systems globally have struggled to meet increasing demands due to aging populations, rising chronic diseases, and strained resources, exacerbated by the COVID-19 pandemic's exposure of systemic inefficiencies. While data analytics, artificial intelligence (AI), and health information systems offer transformative solutions, their adoption remains inconsistent, hindered by policy fragmentation, infrastructure limitations, and workforce skill gaps. Current research often examines these challenges in isolation, leaving a critical gap in understanding how integrated digital strategies can optimize healthcare infrastructure at scale. Addressing this gap is essential for building resilient, equitable, and cost-effective health systems, as emphasized by global organizations like the WHO and World Bank. This study aimed to (1) assess current adoption levels of digital health technologies, (2) evaluate their impact on healthcare optimization, and (3) identify key barriers and enablers for nationwide implementation. Using a mixed-methods approach, we collected quantitative survey data from 300 healthcare professionals, conducted 20 in-depth interviews with policymakers and administrators, and analyzed national policy documents. Statistical analyses included correlation tests, regression modeling, and exploratory factor analysis, complemented by thematic analysis of qualitative responses. Key findings revealed moderate adoption of AI (mean=3.15) and data analytics (mean=3.02), but significantly lagging policy support (mean=2.45). Data analytics emerged as the strongest predictor of healthcare optimization (β=0.32, p<0.001), while policy misalignment (reported by 39.3% of respondents) and system interoperability challenges (47.3%) were major barriers. The regression model explained 67% of optimization variance (R²=0.67), highlighting technology-policy integration as crucial for success. These results demonstrate that while digital technologies can significantly enhance healthcare delivery, their full potential requires coordinated policy reforms, infrastructure investments, and workforce training. This study contributes a comprehensive framework for national healthcare optimization, emphasizing the need for aligned digital transformation strategies to achieve sustainable health system improvements. The findings offer actionable insights for policymakers and healthcare leaders seeking to leverage data-driven approaches for better patient outcomes and system resilience.
References
2. Alyami, A. A. (2018). Smart e-health system for real-time tracking and monitoring of patients, staff and assets for healthcare decision support in Saudi Arabia (Doctoral dissertation, Staffordshire University).
3. Bulinski, M. A., & Prescott, J. J. (2015). Online case resolution systems: Enhancing access, fairness, accuracy, and efficiency. Mich. J. Race & L., 21, 205.
4. Bunduchi, R., Tursunbayeva, A., & Pagliari, C. (2020). Coping with institutional complexity: Intersecting logics and dissonant visions in a nation-wide healthcare IT implementation project. Information Technology & People, 33(1), 311-339.
5. Cai, Y. (2015). What contextual factors shape ‘innovation in innovation’? Integration of insights from the Triple Helix and the institutional logics perspective. Social Science Information, 54(3), 299-326.
6. Cassidy, A. (2016). A practical guide to information systems strategic planning. Auerbach Publications.
7. Choudhury, A., & Asan, O. (2020). Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR medical informatics, 8(7), e18599.
8. Cole, S. L. (2018). Social Worker Engagement of Substance Abusing Rural Young Adults: An Action Research Study (Doctoral dissertation, Walden University).
9. Colombo, F., Oderkirk, J., & Slawomirski, L. (2020). Health information systems, electronic medical records, and big data in global healthcare: progress and challenges in OECD countries. Handbook of global health, 1-31.
10. Cortellazzo, L., Bruni, E., & Zampieri, R. (2019). The role of leadership in a digitalized world: A review. Frontiers in psychology, 10, 1938.
11. Fox, A. M., Stazyk, E. C., & Feng, W. (2020). Administrative easing: Rule reduction and medicaid enrollment. Public Administration Review, 80(1), 104-117.
12. Genesis, I. O. (2018). Integrative pharmacoeconomics: redefining pharmacists’ role in formulary design and value-based healthcare systems. Int J Comput Appl Technol Res, 7(12), 435-48.
13. Gualandi, R., Masella, C., & Tartaglini, D. (2020). Improving hospital patient flow: a systematic review. Business process management journal, 26(6), 1541-1575.
14. Hussmann, K., & Kirya, M. (2020). Health sector corruption. Practical recommendations for donors. CHR. Michelsen Institute. U4, (2020), 10.Renukappa, S., Mudiyi, P., Suresh, S., Abdalla, W., & Subbarao, C. (2022). Evaluation of challenges for adoption of smart healthcare strategies. Smart health, 26, 100330.
15. Johnson, J. D. (2017). Symbolic Innovations: Lessons from Health Services and Higher Education Organizations. Universal-Publishers.
16. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC medicine, 17, 1-9.
17. Khisro, J., Lindroth, T., & Magnusson, J. (2022). Mechanisms of constraint: a clinical inquiry of digital infrastructuring in municipalities. Transforming Government: People, Process and Policy, 16(1), 81-96.
18. Kruk, M. E., Nigenda, G., & Knaul, F. M. (2015). Redesigning primary care to tackle the global epidemic of noncommunicable disease. American journal of public health, 105(3), 431-437.
19. Kushnir, D., Hansen, T., Vogl, V., & Åhman, M. (2020). Adopting hydrogen direct reduction for the Swedish steel industry: A technological innovation system (TIS) study. Journal of Cleaner Production, 242, 118185.
20. Lee, G. (2019). What roles should the government play in fostering the advancement of the Internet of Things?. Telecommunications Policy, 43(5), 434-444.
21. Martinez, D. A., Kane, E. M., Jalalpour, M., Scheulen, J., Rupani, H., Toteja, R., ... & Levin, S. R. (2018). An electronic dashboard to monitor patient flow at the Johns Hopkins Hospital: communication of key performance indicators using the Donabedian model. Journal of medical systems, 42, 1-8.
22. Mountford, N. (2019). Managing by proxy: Organizational networks as institutional levers in evolving public good markets. Journal of Business Research, 98, 92-104.
23. Murray, C., Guha, S., Reed, D., Herrera, G., Kleese van Dam, K., Salahuddin, S., ... & Skonicki, V. (2018). Basic Research Needs for Microelectronics: Report of the Office of Science Workshop on Basic Research Needs for Microelectronics, October 23–25, 2018. USDOE Office of Science (SC)(United States).
24. Palacholla, R. S., Fischer, N., Coleman, A., Agboola, S., Kirley, K., Felsted, J., ... & Jethwani, K. (2019). Provider-and patient-related barriers to and facilitators of digital health technology adoption for hypertension management: scoping review. JMIR cardio, 3(1), e11951.
25. Palinkas, L. A., Mendon, S. J., & Hamilton, A. B. (2019). Innovations in mixed methods evaluations. Annual review of public health, 40(1), 423-442.
26. Potempa, K., Rajataramya, B., Singha-Dong, N., Furspan, P., Kahle, E., & Stephenson, R. (2022). Thailand’s challenges of achieving health equity in the era of non-communicable disease. Pacific Rim international journal of nursing research, 26(2), 187.
27. Seery, E., Marriott, A., Malouf Bous, K., & Shadwick, R. (2020). From Catastrophe to Catalyst: Can the World Bank make COVID-19 a turning point for building universal and fair public healthcare systems?.
28. Turner, K. V. (2018). The augmented rural reality: How rural high school students' decisions to pursue university study in digital media are'augmented'by the role of life history and cultural capital (Doctoral dissertation, Queensland University of Technology).
29. Tzamaria, M., Petaniti, E., Liakou, C., & Plytas, M. (2020). Leveraging artificial intelligence, data analysis, and computer science in primary care: Enhancing electronic health records for improved patient outcomes. International Journal of Systems and Applications Technology, 16(1), 1-15.
30. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, 3-13.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Md Yousuf Ahmad
College of Graduate and Professional Studies, Trine University, Angola, Indiana, USA
Ismoth Zerine
Faculty of Arts, National University Bangladesh
Md Mainul Islam
School of Business, Primeasia University, Bangladesh
Younis Ali Biswas
School of Hospitality and Tourism, Lincoln University College, Malaysia
Md Saiful Islam
Financial Analyst, Hajj Finance Company Limited. Bangladesh