Big Data and Machine Learning in Healthcare: A Business Intelligence Approach for Cost Optimization and Service Improvement

Mohammad Tonmoy Jubaear Mehedy; Muhammad Saqib Jalil; Maham Saeed; Abdullah al mamun; Esrat Zahan Snigdha; MD Nadil khan; Nahid Khan; MD Mohaiminul Hasan

doi:10.37547/tajmspr/Volume07Issue03-14

Articles | Open Access | DOI: https://doi.org/10.37547/tajmspr/Volume07Issue03-14

Big Data and Machine Learning in Healthcare: A Business Intelligence Approach for Cost Optimization and Service Improvement

Mohammad Tonmoy Jubaear Mehedy , Department of Information Technology, Washington University of Science and Technology (wust Eisenhower Ave, Alexandria VA 22314, USA
Muhammad Saqib Jalil , Management and Information Technology, St. Francis College, Brooklyn, New York, USA
Maham Saeed , Master of science in management Healthcare, St. Francis College, Brooklyn, New York, USA
Abdullah al mamun , Department of Business Analytics, St. Francis College, Brooklyn, New York, USA
Esrat Zahan Snigdha , Master’s of Business Administration, Health Care Management, Washington University of Science and Technology (wust), Eisenhower Ave, Alexandria VA 22314, USA
MD Nadil khan , Department of Information Technology, Washington University of Science and Technology (wust Eisenhower Ave, Alexandria VA 22314, USA.
Nahid Khan , East West University, Dhaka, Bangladesh
MD Mohaiminul Hasan , Department master’s in project information technology, St Francis College, Brooklyn, New York, USA

Download PDF

Published Date 2025-03-24

Pages 115-135

Abstract

Healthcare business intelligence advances through the combination of Big Data and Machine Learning (ML) technology which improves both cost reduction and service quality. Healthcare organizations employ predictive analysis together with AI-driven choices and real-time processing to minimize costs as global healthcare fees continue increasing while improving patient care efficiency. This paper investigates the transformation of resource distribution and predictive equipment maintenance and individual medical approaches through Big Data and ML models along with supervised learning and deep learning and anomaly detection algorithms. The research follows a quantitative approach to study both actual case examples and statistical models which predict hospital admissions while optimizing resource management to lower operational flaws. AI predictive analytics produces a 30% deduction in healthcare bills supported by studies with results showing also a 25% increase in medical service delivery efficiency. Real-time data integration allows medical professionals to detect diseases earlier and develop precise treatment plans for each patient which increases patient satisfaction rates. The study adds to existing AI-driven healthcare business intelligence research by delivering practical guidelines which healthcare administrators and policymakers and technology leaders can use. The paper requirement of data governance frameworks together with ethical AI implementation methods and scalable decision systems based on ML is necessary for achieving the complete benefits of Big Data in healthcare.

Keywords

Big Data, Machine Learning, Healthcare Business Intelligence, Cost Optimization

References

Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3. https://doi.org/10.1186/2047-2501-2-3

Wang Y, Kung L, Byrd TA. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Change. 2018;126:3-13. https://doi.org/10.1016/j.techfore.2015.12.019

Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data. 2019;6:54. https://doi.org/10.1186/s40537-019-0217-0

Kruse CS, Goswamy R, Raval Y, Marawi S. Challenges and opportunities of big data in health care: a systematic review. JMIR Med Inform. 2016;4(4):e38 . https://doi.org/10.2196/medinform.5359

Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018;22(5):1589-1604. https://doi.org/10.1109/JBHI.2017.2767063

Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88. https://doi.org/10.1016/j.media.2017.07.005

Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM Comput Surv. 2009;41(3):15. https://doi.org/10.1145/1541880.1541882 Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood). 2014;33(7):1123-1131. https://doi.org/10.1377/hlthaff.2014.0041

Hossain S, Ahmed A, Khadka U, Sarkar S, Khan N. AI-driven Predictive Analytics, Healthcare Outcomes, Cost Reduction, Machine Learning, Patient Monitoring. AIJMR. 2024;2(5):1104. https://doi.org/10.62127/aijmr.2024.v02i05.1104

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. https://doi.org/10.1038/s41591-018-0300-7

Davenport TH, Ronanki R. Artificial intelligence for the real world. Harv Bus Rev. 2018;96(1):108-116.

PwC. AI in healthcare: Transforming the industry. 2020. https://www.pwc.com/gx/en/industries/healthcare/publications/ai-in-healthcare.html

Chen M, Mao S, Liu Y. Big data: A survey. Mob Netw Appl. 2014;19(2):171-209. https://doi.org/10.1007/s11036-013-0489-0

Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: Mapping the debate. Big Data Soc. 2016;3(2):2053951716679679. https://doi.org/10.1177/2053951716679679

Voigt P, Von dem Bussche A. The EU General Data Protection Regulation (GDPR). Springer; 2017. https://doi.org/10.1007/978-3-319-57959-7

Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. J R Soc Med. 2019;112(1):22-28. https://doi.org/10.1177/0141076818815510

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. https://doi.org/10.1126/science.aax2342

Samek W, Wiegand T, Müller KR. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296. 2017.

Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood). 2014;33(7):1123-1131. https://doi.org/10.1377/hlthaff.2014.0041