Real-Time Clinical Decision Support: Enhancing BI with Smart Analytics
DOI:
https://doi.org/10.32628/CSEIT23112557Keywords:
Artificial Intelligence, Clinical Workflow, Decision Support, Predictive Analytics, Real-Time AnalyticsAbstract
The integration of Business Intelligence with Clinical Decision Support Systems represents a transformative development in healthcare delivery, shifting from retrospective analysis to real-time decision making at the point of care. This technological evolution bridges critical temporal gaps between data collection and clinical intervention, enabling healthcare professionals to access actionable insights when they matter most. Advanced implementations demonstrate significant improvements across various clinical domains, including early sepsis detection, medication interaction tracking, and hospital readmission risk stratification. Despite promising outcomes, healthcare organizations face substantial implementation challenges, including alert fatigue, clinical accuracy concerns, workflow integration complexities, and data latency issues. Successful implementation strategies emphasize user-centered dashboard design, workflow-embedded analytics, adaptive learning systems, and approaches that augment rather than replace clinical expertise. As these technologies mature, future applications will likely include advanced predictive modeling for disease progression, increasingly personalized treatment recommendations, and integration with genomic information to enable precision medicine approaches that fundamentally transform healthcare delivery.
Downloads
References
Reed T. Sutton, et al., "An overview of clinical decision support systems: benefits, risks, and strategies for success," NPJ Digital Medicine, vol. 3, no. 1, pp. 1-10, 2020. [Online]. Available: https://www.nature.com/articles/s41746-020-0221-y
Darragh O'Reilly, et al., "Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future," Journal of Intensive Medicine, Volume 4, Issue 1, January 2024, Pages 34-45. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2667100X23000816
Santhosh Kumar Pendyala, et al., "Real-time Analytics and Clinical Decision Support Systems: Transforming Emergency Care," International Journal For Multidisciplinary Research, 2024. [Online]. Available: https://www.researchgate.net/publication/386104558_Real-time_Analytics_and_Clinical_Decision_Support_Systems_Transforming_Emergency_Care
Manasha Fernando, et al., "Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review," J Med Internet Res. 2023. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10620641/
Elena Giovanna Bignami, et al., "Artificial Intelligence in Sepsis Management: An Overview for Clinicians," J Clin Med. 2025. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11722371/#
Soha Rawas, "Transforming healthcare delivery: next-generation medication management in smart hospitals through IoMT and ML," Discover Artificial Intelligence, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s44163-024-00128-1
Selvana Awad BPharm, et al., "Development of a Human Factors–Based Guideline to Support the Design, Evaluation, and Continuous Improvement of Clinical Decision Support," Mayo Clinic Proceedings: Digital Health, Volume 3, Issue 1, March 2025, 100182. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2949761224001123
Pierre-Yves Meunier, et al., "Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review," Ann Fam Med. 2023. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC9870646/
Julian Brunner, et al., "User-centered design to improve clinical decision support in primary care," International Journal of Medical Informatics, Volume 104, August 2017, Pages 56-64. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1386505617301119
Adam Wright, et al., "Governance for clinical decision support: case studies and recommended practices from leading institutions," J Am Med Inform Assoc. 2011. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC3116253/
Jenna Wiens , et al., "Do no harm: a roadmap for responsible machine learning for health care," Nature Medicine, vol. 25, no. 9, pp. 1337-1340, 2019. [Online]. Available: https://finale.seas.harvard.edu/sites/g/files/omnuum4281/files/finale/files/do_no_harm-_a_roadmap_for_responsible_machine_learning_for_healthcare.pdf
Edward H. Shortliffe, et al., "Clinical Decision Support in the Era of Artificial Intelligence," JAMA The Journal of the American Medical Association, 2018. [Online]. Available: https://www.researchgate.net/publication/328775195_Clinical_Decision_Support_in_the_Era_of_Artificial_Intelligence
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.