Business and Social Sciences
Business and social sciences | Online ISSN 3067-8919
  1. You are here:
  2. Home
  3. Journals
  4. Social Sciences
Business and Social Sciences

Volume 1 Number 1 2023

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 1 (1)

Integrating Business Analytics into Public Health Management: A Data-Driven Approach

Niladry Chowdhury1*, Md Sakib Mia1, Md Iqbal Hossain2, Sonia Khan Papia3, Sonia Nashid4

+ Author Affiliations

Business and Social Sciences 1 (1) 1-7 https://doi.org/10.25163/business.1110346

Submitted: 02 October 2023 Revised: 06 December 2023  Published: 08 December 2023 

Abstract

Background: Public health organizations worldwide implement business analytics (BA) for better decision-making and resource optimization as well as health trend prediction. The United States confronted rising influenza alongside COVID-19 and RSV case numbers in December 2023. Methods: The research executed a descriptive cross-sectional study between May and July 2025 to collect data from 110 healthcare staff members working in hospitals and public health organizations and NGOs. Researchers used structured questionnaires to record demographic information together with BA implementation status and organizational readiness alongside perceived advantages and implementation barriers. Data analysis was performed using SPSS version 27 which utilized descriptive statistics along with chi-square tests and percentage distributions. Results: The survey findings indicated that 74% of participants gained faster decision-making through Business Analytics while 68% gained better outbreak prediction capabilities and 59% achieved better resource allocation. The survey results showed 72% of respondents reported staff shortages and 64% expressed data privacy issues while 58% had budget limitations and 52% experienced limited IT infrastructure and 47% had organizational resistance to change. Statistical evaluation revealed that organizations with larger employee counts showed substantially greater adoption rates of Business Analytics (p = 0.05). The integration of Business Analytics strengthens public health management by enabling faster and accurate decision-making. Conclusion: adoption of Business Analytics requires dedicated investments in workforce training and ethical data governance together with targeted resource support for smaller organizations to achieve equitable implementation.

Keywords: Business Analytics, Public Health Management, Data-Driven Decision-Making, Predictive Analytics, Healthcare Technology

References

Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of big data’s impact on audit judgment and decision making and future research directions. Accounting Horizons, 29(2), 451–468. https://doi.org/10.2308/acch-51023

Boyce, M. B., Browne, J. P., & Greenhalgh, J. (2014). The experiences of professionals with using information from patient-reported outcome measures to improve the quality of healthcare: a systematic review of qualitative research. BMJ Quality & Safety, 23(6), 508–518. https://doi.org/10.1136/bmjqs-2013-002524

Detmer, D. E. (2003). Building the national health information infrastructure for personal health, health care services, public health, and research. BMC Medical Informatics and Decision Making, 3(1). https://doi.org/10.1186/1472-6947-3-1

Dey, P. K., Hariharan, S., & Despic, O. (2008). Managing healthcare performance in analytical framework. Benchmarking an International Journal, 15(4), 444–468. https://doi.org/10.1108/14635770810887249

Farid, S. S., Washbrook, J., & Titchener-Hooker, N. J. (2005). Decision-Support Tool for Assessing Biomanufacturing Strategies under Uncertainty: Stainless Steel versus Disposable Equipment for Clinical Trial Material Preparation. Biotechnology Progress, 21(2), 486–497. https://doi.org/10.1021/bp049692b

Forbes, D. P. (2005). The effects of strategic decision making on entrepreneurial Self–Efficacy. Entrepreneurship Theory and Practice, 29(5), 599–626. https://doi.org/10.1111/j.1540-6520.2005.00100.x

Glickman, S. W., Baggett, K. A., Krubert, C. G., Peterson, E. D., & Schulman, K. A. (2007). Promoting quality: the health-care organization from a management perspective. International Journal for Quality in Health Care, 19(6), 341–348. https://doi.org/10.1093/intqhc/mzm047

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2018). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0

Kohli, R., Piontek, F., Ellington, T., VanOsdol, T., Shepard, M., & Brazel, G. (2001). Managing customer relationships through E-business decision support applications: a case of hospital–physician collaboration. Decision Support Systems, 32(2), 171–187. https://doi.org/10.1016/s0167-9236(01)00109-9

Li, L. X., Benton, W., & Leong, G. (2002). The impact of strategic operations management decisions on community hospital performance. Journal of Operations Management, 20(4), 389–408. https://doi.org/10.1016/s0272-6963(02)00002-5

Luo, J., Wu, M., Gopukumar, D., & Zhao, Y. (2016). Big data application in Biomedical Research and Health Care: A Literature review. Biomedical Informatics Insights, 8, BII.S31559. https://doi.org/10.4137/bii.s31559

McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D., Lagerquist, R., Smith, T., & Williams, J. K. (2017). Using artificial intelligence to improve Real-Time Decision-Making for High-Impact weather. Bulletin of the American Meteorological Society, 98(10), 2073–2090. https://doi.org/10.1175/bams-d-16-0123.1

Mosa, A. S. M., Yoo, I., & Sheets, L. (2012). A Systematic review of healthcare applications for smartphones. BMC Medical Informatics and Decision Making, 12(1). https://doi.org/10.1186/1472-6947-12-67

Musen, M. A., Middleton, B., & Greenes, R. A. (2021). Clinical Decision-Support Systems. In Biomedical informatics. (pp. 795–840). https://doi.org/10.1007/978-3-030-58721-5_24

Orwat, C., Graefe, A., & Faulwasser, T. (2008). Towards pervasive computing in health care – A literature review. BMC Medical Informatics and Decision Making, 8(1). https://doi.org/10.1186/1472-6947-8-26

Politi, M. C., Han, P. K. J., & Col, N. F. (2007). Communicating the uncertainty of harms and benefits of medical interventions. Medical Decision Making, 27(5), 681–695. https://doi.org/10.1177/0272989x07307270

Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866. https://doi.org/10.7326/m18-1990

Ranjan, J. (2008). Hurdles and opportunities for Indian firms adopting business intelligence. Journal of Advances in Management Research, 5(1), 56–62. https://doi.org/10.1108/97279810880001267

Ratia, M., Myllärniemi, J., & Helander, N. (2019). The potential beyond IC 4.0: the evolution of business intelligence towards advanced business analytics. Measuring Business Excellence, 23(4), 396–410. https://doi.org/10.1108/mbe-12-2018-0103

Rehman, A., Naz, S., & Razzak, I. (2021). Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems, 28(4), 1339–1371. https://doi.org/10.1007/s00530-020-00736-8

Rew, L. (2000). Acknowledging intuition in clinical decision making. Journal of Holistic Nursing, 18(2), 94–108. https://doi.org/10.1177/089801010001800202

Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), 433–441. https://doi.org/10.1057/ejis.2014.17

Simpao, A. F., Ahumada, L. M., Gálvez, J. A., & Rehman, M. A. (2014). A review of analytics and clinical informatics in health care. Journal of Medical Systems, 38(4). https://doi.org/10.1007/s10916-014-0045-x

Spring, B. (2007). Evidence-based practice in clinical psychology: What it is, why it matters; what you need to know. Journal of Clinical Psychology, 63(7), 611–631. https://doi.org/10.1002/jclp.20373

Tzeng, S., Chen, W., & Pai, F. (2007). Evaluating the business value of RFID: Evidence from five case studies. International Journal of Production Economics, 112(2), 601–613. https://doi.org/10.1016/j.ijpe.2007.05.009

Varda, D. M. (2011). Data-Driven management strategies in public health collaboratives. Journal of Public Health Management and Practice, 17(2), 122–132. https://doi.org/10.1097/phh.0b013e3181ede995

Wang, Y., & Byrd, T. A. (2017). Business analytics-enabled decision-making effectiveness through knowledge absorptive capacity in health care. Journal of Knowledge Management, 21(3), 517–539. https://doi.org/10.1108/jkm-08-2015-0301

Wang, Z., Wang, N., Su, X., & Ge, S. (2019). An empirical study on business analytics affordances enhancing the management of cloud computing data security. International Journal of Information Management, 50, 387–394. https://doi.org/10.1016/j.ijinfomgt.2019.09.002

Ward, M. J., Marsolo, K. A., & Froehle, C. M. (2014). Applications of business analytics in healthcare. Business Horizons, 57(5), 571–582. https://doi.org/10.1016/j.bushor.2014.06.003

Wickramasinghe, N., & Schaffer, J. L. (2018). Enhancing healthcare value by applying proactive measures: the role for business analytics and intelligence. International Journal of Healthcare Technology and Management, 17(2/3), 128. https://doi.org/10.1504/ijhtm.2018.098376


View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
218
View
0
Share

Stay connected