Paradise
Paradise | Life Science Engineering Business Natural Science
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
Paradise

Volume 1 Number 1 2025

Article Contents

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

Advancing Healthcare Through Data Analytics Transitioning from Descriptive Insights to Predictive and Prescriptive Solutions

Fahim Rahman1*, Ariful Islam2, Sonia Nashid3, Al Akhir2, Sonia Khan Papia4

+ Author Affiliations

Paradise 1 (1) 1-8 https://doi.org/10.25163/paradise.1110377

Submitted: 29 April 2025 Revised: 23 July 2025  Published: 25 July 2025 

Abstract

Background: The healthcare field stores large digital datasets through its clinical information systems and patient observation systems and diagnostic examination systems and administrative workflows. Healthcare service delivery needs data transformation to create operational knowledge which improves service delivery processes. The transition from traditional reporting systems to predictive and prescriptive reporting systems enables advanced analytics to deliver proactive patient-centered care.

Methods: Researchers studied hospital records from 115 healthcare facilities throughout the 24-month duration that began in January 2024 and ended in December 2025. A three-phase analytical framework was applied. Researchers used descriptive analytics during the initial phase to investigate patterns inpatient admissions and readmissions together with treatment outcomes. Predictive analytics developed readmission prediction models through logistic regression and decision trees while producing patient outcome probability estimations.

Results: The analysis of admission data shows chronic diseases make up 36% of hospital admissions and 42% of these patients return to the hospital within thirty days. The analysis found that 28% of hospital admissions were due to acute infections while surgical complications made up 22% of admissions with 19% of patients returning to the hospital within thirty days. The predictive modeling system showed high predictive ability since logistic regression delivered 83% accuracy but decision trees achieved 87% accuracy together with 82% sensitivity and 84% specificity levels.

Conclusion: The study demonstrates that descriptive analytics explains previous performance while predictive analytics forecasts risks and prescriptive analytics delivers practical solutions.

Keywords: Healthcare analytics, Descriptive insights, Predictive modeling, Prescriptive solutions, Patient outcomes

References

Alghamdi, A., Alsubait, T., Baz, A., & Alhakami, H. (2021). Healthcare analytics: A comprehensive review. Engineering Technology & Applied Science Research, 11(1), 6650–6655. https://doi.org/10.48084/etasr.3965

Alharthi, H. (2018). Healthcare predictive analytics: An overview with a focus on Saudi Arabia. Journal of Infection and Public Health, 11(6), 749–756. https://doi.org/10.1016/j.jiph.2018.02.005

Bauskar, S. R., Madhavaram, C. R., Galla, E. P., Sunkara, J. R., & Gollangi, H. K. (2022). Predicting disease outbreaks using AI and big data: A new frontier in healthcare analytics. European Chemical Bulletin. https://doi.org/10.53555/ecb.v11i12.17745

Berros, N., Mendili, F. E., Filaly, Y., & Idrissi, Y. E. B. E. (2023). Enhancing digital health services with big data analytics. Big Data and Cognitive Computing, 7(2), 64. https://doi.org/10.3390/bdcc7020064

Cano-Marin, E., Mora-Cantallops, M., & Sanchez-Alonso, S. (2023). Prescriptive graph analytics on the digital transformation in healthcare through user-generated content. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05495-z

Chowdhury, A. K., & Hossain, M. M. (2025). Exploring the role of renewable energy in enhancing rural livelihoods. Energy Environment and Economy, 3(1), 1–7. https://doi.org/10.10328

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0

Diamant, A. (2023). Introducing prescriptive and predictive analytics to MBA students with Microsoft Excel. INFORMS Transactions on Education, 24(2), 152–174. https://doi.org/10.1287/ited.2023.0286

Epizitone, A., Moyane, S. P., & Agbehadji, I. E. (2023). A data-driven paradigm for a resilient and sustainable integrated health information system for healthcare applications. Journal of Multidisciplinary Healthcare, 16, 4015–4025. https://doi.org/10.2147/jmdh.s433299

Fernandes, D. (2024). Prescriptive analytics in healthcare: Advanced decision making for optimal treatment. Theseus. https://urn.fi/URN:NBN:fi:amk-2024060621539

Forbes, A., & Griffiths, P. (2002). Methodological strategies for the identification and synthesis of ‘evidence’ to support decision-making in relation to complex healthcare systems and practices. Nursing Inquiry, 9(3), 141–155. https://doi.org/10.1046/j.1440-1800.2002.00146.x

Frazzetto, D., Nielsen, T. D., Pedersen, T. B., & Šikšnys, L. (2019). Prescriptive analytics: A survey of emerging trends and technologies. The VLDB Journal, 28(4), 575–595. https://doi.org/10.1007/s00778-019-00539-y

Gates, J. D., Yulianti, Y., & Pangilinan, G. A. (2024). Big data analytics for predictive insights in healthcare. International Transactions on Artificial Intelligence (ITALIC), 3(1), 54–63. https://doi.org/10.33050/italic.v3i1.622

Houtmeyers, K. C., Jaspers, A., & Figueiredo, P. (2021). Managing the training process in elite sports: From descriptive to prescriptive data analytics. International Journal of Sports Physiology and Performance, 16(11), 1719–1723. https://doi.org/10.1123/ijspp.2020-0958

Islam, M. R., & Chowdhury, A. K. (2025). The socio-economic effects of transitioning from conventional energy sources to renewable energy systems. Energy Environment and Economy, 3(1), 1–8. https://doi.org/10.10320

Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018). A systematic review on healthcare analytics: Application and theoretical perspective of data mining. Healthcare, 6(2), 54. https://doi.org/10.3390/healthcare6020054

Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2019). Prescriptive analytics: A survey of approaches and methods. In Lecture Notes in Business Information Processing (pp. 449–460). https://doi.org/10.1007/978-3-030-04849-5_39

El Khatib, M., Hamidi, S., Al Ameeri, I., Al Zaabi, H., & Al Marqab, R. (2022). Digital disruption and big data in healthcare: Opportunities and challenges. ClinicoEconomics and Outcomes Research, 563–574. https://doi.org/10.2147/ceor.sXXXX

Malleswari, T. Y. J. N., Ushasukhanya, S., Karthikeyan, M., Cherian, A. K., & Vaidhehi, M. (2024). Role of predictive analytics for enhanced decision making in business applications. In Advances in Business Information Systems and Analytics (pp. 313–326). https://doi.org/10.4018/979-8-3693-3234-4.ch023

Morr, C. E., & Ali-Hassan, H. (2019). Descriptive, predictive, and prescriptive analytics. In SpringerBriefs in Health Care Management and Economics (pp. 31–55). https://doi.org/10.1007/978-3-030-04506-7_3

Mosavi, N. S., & Santos, M. F. (2020). How prescriptive analytics influences decision making in precision medicine. Procedia Computer Science, 177, 528–533. https://doi.org/10.1016/j.procs.2020.10.073

Muneeswaran, V., Nagaraj, P., Dhannushree, U., Lakshmi, S. I., Aishwarya, R., & Sunethra, B. (2021). A framework for data analytics-based healthcare systems. In Lecture Notes on Data Engineering and Communications Technologies (pp. 83–96). https://doi.org/10.1007/978-981-15-9651-3_7

Palanisamy, V., & Thirunavukarasu, R. (2017). Implications of big data analytics in developing healthcare frameworks: A review. Journal of King Saud University – Computer and Information Sciences, 31(4), 415–425. https://doi.org/10.1016/j.jksuci.2017.12.007

Patnaik, M., & Mishra, S. (2022). Indoor positioning system assisted big data analytics in smart healthcare. In Studies in Computational Intelligence (pp. 393–415). https://doi.org/10.1007/978-3-030-97929-4_18

Pramanik, P. K. D., Pal, S., & Mukhopadhyay, M. (2021). Healthcare big data. In IGI Global eBooks (pp. 119–147). https://doi.org/10.4018/978-1-6684-3662-2.ch006

Roy, D., Srivastava, R., Jat, M., & Karaca, M. S. (2022). A complete overview of analytics techniques: Descriptive, predictive, and prescriptive. In EAI/Springer Innovations in Communication and Computing (pp. 15–30). https://doi.org/10.1007/978-3-030-82763-2_2

Santos, R., Piqueiro, H., Dias, R., & Rocha, C. D. (2024). Transitioning trends into action: A simulation-based digital twin architecture for enhanced strategic and operational decision-making. Computers & Industrial Engineering, 198, 110616. https://doi.org/10.1016/j.cie.2024.110616

Sarker, I. H. (2021). Data science and analytics: An overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5). https://doi.org/10.1007/s42979-021-00765-8

Sharma, A. K., Sharma, D. M., Purohit, N., Rout, S. K., & Sharma, S. A. (2021). Analytics techniques: Descriptive analytics, predictive analytics, and prescriptive analytics. In EAI/Springer Innovations in Communication and Computing (pp. 1–14). https://doi.org/10.1007/978-3-030-82763-2_1

Shi-Nash, A., & Hardoon, D. R. (2016). Data analytics and predictive analytics in the era of big data. In Handbook of Big Data (pp. 329–345). https://doi.org/10.1002/9781119173601.ch19


View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
217
View
0
Share

Stay connected