Transforming Healthcare Delivery: AI-Powered Clinical Decision Support Systems
DOI:
https://doi.org/10.32628/CSEIT25111233Keywords:
Clinical Decision Support Systems, Artificial Intelligence in Healthcare, Medical Informatics, Healthcare Innovation, Machine Learning ApplicationsAbstract
Integrating Artificial Intelligence in Clinical Decision Support Systems (CDSS) has fundamentally transformed healthcare delivery by enhancing diagnostic accuracy, improving treatment outcomes, and streamlining clinical workflows. This comprehensive article explores the key features, benefits, implementation challenges, and future innovations in AI-powered CDSS. Through examination of real-world implementations across multiple healthcare institutions, this article demonstrates how advanced algorithms, multimodal data integration, and automated analysis capabilities are revolutionizing clinical decision-making. The article highlights significant improvements in diagnostic accuracy, reduced medical errors, and enhanced patient outcomes while addressing critical challenges in data quality, workflow integration, regulatory compliance, and clinician acceptance. Furthermore, the article explores emerging technologies, including federated learning, ambient clinical intelligence, and extended reality integration, providing insights into the future evolution of healthcare decision support systems.
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Michelle Knees et al., "Cognitive Load Theory and Its Impact on Diagnostic Accuracy," Agency for Healthcare Research and Quality, May 2024. Available: https://www.ahrq.gov/sites/default/files/wysiwyg/diagnostic/resources/issue-briefs/dxsafety-cognitive-load-theory.pdf
Serdar Bozyel et al., "Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases," Anatol J Cardiol. 2024 Feb 1;28(2):74–86. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10837676/
Mohamed Khalifa et al., "Advancing clinical decision support: The role of artificial intelligence across six domains," Computer Methods and Programs in Biomedicine Update, Volume 5, 2024, 100142. Available: https://www.sciencedirect.com/science/article/pii/S2666990024000090
Hans Eguia et al., "Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review," J Med Internet Res. 2024 Sep 30;26:e55315. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11474138/
Cesar A. Gomez-Cabello et al., "Artificial-Intelligence-Based Clinical Decision Support Systems in Primary Care: A Scoping Review of Current Clinical Implementations," Eur. J. Investig. Health Psychol. Educ. 2024, 14(3), 685-698, 13 March 2024. Available: https://www.mdpi.com/2254-9625/14/3/45
Abdul Sajid Mohammed et al., "Understanding the Impact of AI-driven Clinical Decision Support Systems," 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 04 November 2024. Available: https://ieeexplore.ieee.org/document/10726136
Samantha Tyler et al., "Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review," Cureus. 2024 May 8;16(5):e59906. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11158416/
Zhao Chen et al., "Harnessing the power of clinical decision support systems: challenges and opportunities," Open Heart. 2023 Nov 28;10(2):e002432. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10685930/
Julia Schaefer et al., "The use of machine learning in rare diseases: a scoping review," Orphanet J Rare Dis. 2020 Jun 9;15:145. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC7285453/
Mah Laka et al., "Challenges and opportunities in implementing clinical decision support systems (CDSS) at scale: Interviews with Australian policymakers," Health Policy and Technology, Volume 11, Issue 3, September 2022, 100652. Available: https://www.sciencedirect.com/science/article/pii/S2211883722000600
Krishna Juluru et al., "Integrating Al Algorithms into the Clinical Workflow," Radiol Artif Intell. 2021 Aug 4;3(6):e210013. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC8637237/
Oussama Laraichi et al., "Technology readiness assessment: Case of clinical decision support systems in healthcare," Technology in Society, Volume 79, December 2024, 102736. Available: https://www.sciencedirect.com/science/article/abs/pii/S0160791X24002847
Jasmine Balloch et al., "Use of an ambient artificial intelligence tool to improve quality of clinical documentation," Future Healthcare Journal, Volume 11, Issue 3, September 2024, 100157. Available: https://www.sciencedirect.com/science/article/pii/S2514664524015479
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