AccScience Publishing / DP / Volume 2 / Issue 4 / DOI: 10.36922/DP025320037
Submit to DP
Cite this article
21
Download
213
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
REVIEW ARTICLE

Attractive features of digital health care and big data analytics: A critical infrastructure for nation building and shaping the future of innovation

Moin Uddin1* ,  Shatakshi Srivastav1 ,  Sarim Moin2 ,  Sudhakar Ranjan1 ,  Vyas M. Shingateri1
Show Less
1 School of Engineering and Technology, Department of Computer Science and Engineering, Apeejay Stya University, Sohna, Haryana, India
2 Department of Computer Science and Engineering, National Institute of Technology, Delhi, India
DP 2025, 2(4), 025320037 https://doi.org/10.36922/DP025320037
Received: 6 August 2025 | Revised: 23 September 2025 | Accepted: 12 November 2025 | Published online: 16 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
XML
Cite
Abstract

Digital health care and smart health care are emerging as transformative paradigms for modernizing traditional healthcare infrastructure, recognized as a critical pillar of nation-building and aligned with the United Nations Sustainable Development Goals (SDG-17). The seamless integration of big data analytics with healthcare systems enables the development of advanced data-driven ecosystems capable of managing massive, complex, heterogeneous, and real-time patient-centric information. A wide spectrum of information and communication enabling technologies—including the Internet of Things, cloud and edge computing, artificial intelligence (AI), machine learning, 5G, smart biosensors, bioinformatics, biomarkers, and mathematical optimization techniques—are converging to deliver personalized and patient-centric healthcare solutions. Furthermore, AI-based drug discovery and multi-omics approaches (genomics, proteomics, metabolomics, and pharmacogenomics) accelerate innovation in precision medicine. The fusion of medical sciences, computer science, and AI technologies is reshaping innovation, healthcare delivery, and public policy, although the absence of unified global standards remains a challenge. This study explores the attractive features of digital health care and big data analytics, highlighting their potential to revolutionize healthcare innovation and policy reform for sustainable nation-building. Although this work highlights the potential of digital and smart health care in reshaping healthcare infrastructures, it remains largely descriptive and conceptual. In this review, an effort is made to assess the attractive features of digital health care and big data analytics and their impact on reshaping innovation in medical sciences and AI technologies for nation-building. Public policy is undergoing a changing phase, with active consideration and massive reforms.

Keywords
Digital health care
Big data analytics
MEDICAL 4.0
Sustainable Development Goal 3
E-health
M-health
Bioinformatics
Critical infrastructure
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
References
  1. Kornelia A. Personalized treatments and real-time interventions in healthcare. J Healthc Innov. 2023;17(3):45-56.

 

  1. Bella A. Optimizing healthcare systems: A focus on operational efficiencies and predictive analytics. J Health Syst Manag. 2022;31(2):112-125.

 

  1. Samantha D. Integrating data into clinical decision-making: Enhancing patient outcomes through AI-based diagnostics. Clin Inform Rev. 2021;19(1):58-72.

 

  1. Rajesh D. Real-time resource management and predictive modeling in hospital settings. Healthc Oper Res J. 2024;27(4):204-218.

 

  1. John D. Big data and predictive models in healthcare: Improving patient outcomes through AI and machine learning. J Data Sci Med. 2020;22(5):134-146.

 

  1. Fang H, Zhang Z, Wang CJ, Daneshmand M, Wang C, Wang H. A survey of big data research. IEEE Netw. 2015;29(5):6-9. doi: 10.1109/MNET.2015.7293298

 

  1. Harerimana G, Jang B, Kim JW, Park HK. Health big data analytics: A technology survey. IEEE Access. 2018;6:65661-65678. doi: 10.1109/ACCESS.2018.2878254

 

  1. Lytras MD, Papadopoulou P. Applying Big Data Analytics in Bioinformatics and Medicine. United States: IGI Global; 2017.

 

  1. Palanisamy V, Thirunavukkarasu R. Implications of big data analytics in developing healthcare frameworks-a review. J King Saud Univ Compute Inf Sci. 2017;31(4):415-425.

 

  1. Ismail A, Shehab A, El-Henawy IM. Healthcare analysis in smart big data analytics: Reviews, challenges and recommendations. In: Security in Smart Cities: Models, Applications, and Challenges. Berlin: Springer; 2019. p. 27-45.

 

  1. Krumholz HM. Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system. Health Aff (Millwood). 2014;33(7):1163-1170. doi: 10.1377/hlthaff.2014.0053

 

  1. Jee K, Kim GH. Potentiality of big data in the medical sector: Focus on how to reshape the healthcare system. Healthc Inform Res. 2013;19(2):79-85. doi: 10.4258/hir.2013.19.2.79

 

  1. Mehta N, Pandit A. Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform. 2018;114:57-65. doi: 10.1016/j.ijmedinf.2018.03.013

 

  1. Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M. Deep learning for IoT big data and streaming analytics: A survey. IEEE Commun Surv Tutor. 2018;20(4):2923-2960. doi: 10.1109/COMST.2018.2844341

 

  1. Yang C, Li C, Wang Q, Chung D, Zhao H. Implications of pleiotropy: Challenges and opportunities for mining big data in biomedicine. Front Genet. 2015;6:229. doi: 10.3389/fgene.2015.00229

 

  1. Gaitanou P, Garoufallou E, Balatsoukas P. The effectiveness of big data in health care: A systematic review. In: Metadata and Semantics Research. Berlin: Springer; 2014. p. 141-153. doi: 10.1007/978-3-319-13674-5_14

 

  1. Chen H, Chiang RHL, Storey VC. Business intelligence and analytics: From big data to big impact. MIS Q. 2012;36(4):1165-1188. doi: 10.2307/41703503

 

  1. Gupta V, Rathmore N. Deriving business intelligence from unstructured data. Int J Inf Comput Technol. 2013;3(9):971-976.

 

  1. Davenport TH. Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. India: Harvard Business School Publishing; 2014.

 

  1. Gu D, Li J, Li X, Liang C. Visualizing the knowledge structure and evolution of big data research in healthcare informatics. Int J Med Inf. 2017;98:22-32. doi: 10.1016/j.ijmedinf.2016.11.006

 

  1. Castro EM, Van Regenmortel T, Vanhaecht K, Sermeus W, Van Hecke A. Patient empowerment, patient participation and patient-centeredness in hospital care: A concept analysis based on a literature review. Patient Educ Couns. 2016;99(12):1923-1939. doi: 10.1016/j.pec.2016.07.026

 

  1. Marconi K, Dobra M, Thompson C. The use of big data in healthcare. In: Liebowitz J, editor. Big Data and Business Analytics. United States: CRC Press; 2012. p. 229-248.

 

  1. Lee IK, Wang CC, Lin MC, Kung CT, Lan KC, Lee CT. Effective strategies to prevent coronavirus disease-2019 (COVID-19) outbreak in hospital. J Hosp Infect. 2020;105(1):102-103. doi: 10.1016/j.jhin.2020.02.022

 

  1. Chomiak-Orsa I, Mrozek B. Główne perspektywy wykorzystania big data w mediach społecznościowych. Inform Ekon. 2017;3(45):44-54.

 

  1. Mikalef P, Krogstie J. Big data analytics as an enabler of process innovation capabilities: A configurational approach. In: International Conference on Business Process Management. Berlin: Springer; 2018. p. 426-441.

 

  1. Bartuś K, Batko K, Lorek P. Business intelligence systems: Barriers during implementation. In: Jablonski M, editor. Strategic Performance Management New Concepts and Contemporary Trends. United States: Nova Science Publishers; 2017. p. 299-327.

 

  1. Gupta V, Singh VK, Ghose U, Mukhija P. A quantitative and text-based characterization of big data research. J Intell Fuzzy Syst. 2019;36:4659-4675. doi: 10.3233/JIFS-179016

 

  1. Michael M, Lupton D. Toward a manifesto for the‘public understanding of big data’. Public Underst Sci. 2016;25(1):104-116. doi: 10.1177/0963662515609005

 

  1. Mela A, Poniatowski ŁA, Drop B, et al. Overview and analysis of the cost of drug programs in Poland: Public payer expenditures and coverage of cancer and non-neoplastic diseases related drug therapies from 2015-2018 years. Front Pharmacol. 2020;11:1123. doi: 10.3389/fphar.2020.01123

 

  1. Olszak CM, Mach-Król MM. A conceptual framework for assessing an organization’s readiness to adopt big data. Sustainability. 2018;10(10):3734. doi: 10.3390/su10103734

 

  1. Ohlhorst F. Big Data Analytics: Turning Big Data into Big Money. United States: Wiley; 2012.

 

  1. Jordan SR. Beneficence and the expert bureaucracy. Public Integr. 2014;16(4):375-394. doi: 10.2753/PIN1099-9922160404

 

  1. Gandomi A, Haider M. Beyond the hype: Big data concepts, methods, and analytics. Int J Inf Manag. 2015;35(2):137-144. doi: 10.1016/j.ijinfomgt.2014.10.007

 

  1. Agrawal A, Choudhary A. Health Services Data: Big Data Analytics for Deriving Predictive Healthcare Insights. Berlin: Springer; 2019. doi: 10.1007/978-1-4899-7673-4_2-1

 

  1. Al Mayahi S, Al-Badi A, Tarhini A. Exploring the potential benefits of big data analytics in providing smart healthcare. In: Miraz M, Excell P, Ware A, Ali M, Soomro S, editors. Emerging Technologies in Computing-First International Conference, iCETiC 2018, Proceedings. Berlin: Springer; 2018. p. 247-258. doi: 10.1007/978-3-319-95450-9_21

 

  1. Mach-Król M. Analiza i strategia big data w organizacjach. Stud Mater Pol Stowarzyszenia Zarządzania Wiedzą. 2015;74:43-55.

 

  1. Manyika J, Chui M, Brown B, et al. Big Data: The Next Frontier for Innovation, Competition, and Productivity. New York: McKinsey Global Institute; 2011.

 

  1. Bainbridge M. Big data challenges for clinical and precision medicine. In: Househ M, Kushniruk A, Borycki E, editors. Big Data, Big Challenges: A Healthcare Perspective: Background, Issues, Solutions and Research Directions. Berlin: Springer; 2019. p. 17-31.

 

  1. Bollier D, Firestone CM. The Promise and Peril of Big Data. Heidelberg: Aspen Institute, Communications and Society Program; 2010. p. 1-66.

 

  1. El-Gayar O, Timsina P. Opportunities for business intelligence and big data analytics in evidence-based medicine. In: 2014 47th Hawaii International Conference on System Sciences. United States: IEEE; 2014. p. 749-757.

 

  1. Wu PY, Cheng CW, Kadia CD, Venugopalan J, Hoffman R, Wang MD. Omic and electronic health record big data analytics for precision medicine. IEEE Trans Biomed Eng. 2017;64:263-273. doi: 10.1109/TBME.2016.2573285

 

  1. Islam MS, Hasan MM, Wang X, Germack HD, Noor-E-Alam M. A systematic review on healthcare analytics: Application and theoretical perspective of data mining. Healthc (Basel). 2018;6(2):54. doi: 10.3390/healthcare6020054

 

  1. Kankan Halli A, Hahn J, Tan S, Gao G. Big data and analytics in healthcare: Introduction to the special section. Inf Syst Front. 2016;18:223-225. doi: 10.1007/s10796-016-9641-2

 

  1. Archana J, Anita EA. A survey of big data analytics in healthcare and government. Procedia Comput Sci. 2015;50:408-413. doi: 10.1016/j.procs.2015.04.021

 

  1. Bose R. Competitive intelligence process and tools for intelligence analysis. Ind Manag Data Syst. 2008;108(4):510-528. doi: 10.1108/02635570810868362

 

  1. Costa FF. Big data in biomedicine. Drug Discov Today. 2014;19:433-440. doi: 10.1016/j.drudis.2013.10.012

 

  1. Dinov ID, Heavner B, Tang M, et al. Preditive big data analytics: A study for Parkinson’s disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations. PLoS One. 2016;11:e0157077. doi: 10.1371/journal.pone.0157077

 

  1. Yao Q, Tian Y, Li PF, Tian LL, Qian YM, Li JS. Design and development of a medical big data processing system based on Hadoop. J Med Syst. 2015;39:23. doi: 10.1007/s10916-015-0220-

 

  1. Kambale K, Kollias G, Kumar V, Grama A. Trends in big data analytics. J Parallel Disturb Comput. 2014;74:2561-2573. doi: 10.1016/j.jpdc.2014.01.003

 

  1. Mukhopadhyay SC. Internet of Things - Challenges and Opportunities. Berlin: Springer; 2014.

 

  1. Misra S, Roy C, Mukherjee AR. Introduction to Industrial Internet of Things and Industry 4.0. United States: CRC Press; 2016.

 

  1. Bannister J, Mather P, Coop S. Convergence Technologies for 3 G Networks. United States: John Wiley and Sons Ltd.; 2004.

 

  1. Maria D. Clinical informatics: Improving patient outcomes through EHR integration and decision support systems. J Clin Inform. 2023;29(2):130-142.

 

  1. Chen CP, Zhang CY. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Inf Sci. 2014;275:314-347. doi: 10.1016/j.ins.2014.01.015

 

  1. Bagha A, Madi Setti V. Big Data Science and Analytics: A Hands -on Approach. Sri Lanka: Walnutpublication; 2016.

 

  1. Stepanian RSH, Wood B. M- Health Fundamentals and Applications. United States: Wiley; 2021.

 

  1. De Marin HF, Massad E, Gutierrez MA, Rodrigues RJ, Sigulem D. Global Health Informatics. Netherland: Elsever; 2017.

 

  1. Dean S, Foureur M, Zaslawski C, Newton-John T, Yu N, Pappas E. The effects of a structured mindfulness program on the development of empathy in healthcare students. Nurs Plus Open. 2017;3:1-5. doi: 10.1016/j.npls.2017.02.001

 

  1. Trifonova OP, Il’in VA, Kolker EV, Lisitsa AV. Big data in biology and medicine. Acta Nat. 2013;5:13-16.

 

  1. Buyya RK, Desjardin AV. Internet of Things Principles and Paradigms. Netherlands: Elsevier; 2016.
Share
Back to top
Design+, Electronic ISSN: 3060-8953 Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept