Journal of Biomedical and Sustainable Healthcare Applications

An Analysis of Artificial Intelligence Based Clinical Decision Support Systems

Journal of Biomedical and Sustainable Healthcare Applications

Received On : 15 August 2020

Revised On : 18 September 2020

Accepted On : 22 October 2020

Published On : 05 January 2021

Volume 01, Issue 01

Pages : 009-017


The growing availability of medical data has sparked fresh interests in Computerized Clinical Decision Support Systems (CDSS), thanks to recent breakthroughs in machine and deep learning. CDSS has showed a lot of promise in terms of improving healthcare, enhancing the safety of patients and minimizing treatment costs. The application of CDSS, nonetheless, is unsafe since an insufficient or defective CDSS may possibly degrade healthcare quality and place patients at potential threat. Furthermore, the deployment of a CDSS may fail when the CDSS's output is ignored by its intended users owing to a lack of confidence, relevance, or actionability. We offer literature-based advice for the various elements of CDSS adoption, with a particular emphasis on Artificial Intelligence (AI) and Machine Learning (ML) systems: quality assurance, deployment, commissioning, acceptability tests, and selection, in this research. A critical selection process will assist in the process of identifying CDSS, which effectively suits the localized sites’ needs and preferences. Acceptance testing ensures that the chosen CDSS meets the specified standards and meets the safety criteria. The CDSS will be ready for safe clinical usage at the local site once the commissioning procedure is completed. An efficient system implementation must result in a smooth rollout of the CDSS to well-trained end-users with reasonable expectations. Furthermore, quality assurance will ensure that the CDSS's levels are maintained and that any problems are discovered and resolved quickly. We conclude this research by discussing the methodical adoption process for CDSS to assist in avoiding issues, enhance the safety of patients and increasing quality of service.


Computerized Clinical Decision Support Systems (CDSS), Machine Learning (ML), Artificial Intelligence (AI)

  1. M. Tavakoli, H. Davies and R. Thomson, "Decision analysis in evidence-based decision making", Journal of Evaluation in Clinical Practice, vol. 6, no. 2, pp. 111-120, 2000. Doi : 10.1046/j.1365-2753.2000.00233.x.
  2. A. A and P. P, "Evidence Based Medicine: Key Aspects in Clinical Decision Making", Scholars Academic Journal of Pharmacy, vol. 5, no. 7, pp. 262-267, 2016. Doi : 10.21276/sajp.2016.5.7.1.
  3. A. Cohen, "Clinical Decision Making in an Era of DRG-Based Prospective Payment", Medical Decision Making, vol. 5, no. 1, pp. 3-6, 1985. Doi : 10.1177/0272989x8500500101.
  4. S. Williams, "The Impact of DRG-Based Prospective Payment on Clinical Decision Making", Medical Decision Making, vol. 5, no. 1, pp. 23-29, 1985. Doi : 10.1177/0272989x8500500106.
  5. T. Tulabandhula, "Interactions between learning and decision making", AI Matters, vol. 3, no. 1, pp. 25-26, 2017. Doi : 10.1145/3067682.3067691.
  6. S. Holm, "Handle with care: Assessing performance measures of medical AI for shared clinical decision‐making", Bioethics, 2021. Doi : 10.1111/bioe.12930.
  7. P. Falzer and M. Garman, "A conditional model of evidence-based decision making", Journal of Evaluation in Clinical Practice, vol. 15, no. 6, pp. 1142-1151, 2009. Doi : 10.1111/j.1365-2753.2009.01315.x.
  8. V. Torra, Y. Narukawa, J. Yin and J. Long, "Technologies for Decision Making and AI Applications", International Journal of Intelligent Systems, vol. 28, no. 6, pp. 523-523, 2013. Doi : 10.1002/int.21590.
  9. A. Haldorai and A. Ramu, “Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability,” Neural Processing Letters, Aug. 2020. doi:10.1007/s11063-020-10327-3
  10. D. Devikanniga, A. Ramu, and A. Haldorai, “Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm,” EAI Endorsed Transactions on Energy Web, p. 164177, Jul. 2018. doi:10.4108/eai.13-7-2018.164177
  11. L. De Panfilis, C. Peruselli, S. Tanzi and C. Botrugno, "AI-based clinical decision-making systems in palliative medicine: ethical challenges", BMJ Supportive & Palliative Care, pp. bmjspcare-2021-002948, 2021. Doi : 10.1136/bmjspcare-2021-002948.
  12. T. Noseworthy and M. Watanabe, "Health policy directions for evidence-based decision making in Canada", Journal of Evaluation in Clinical Practice, vol. 5, no. 2, pp. 227-242, 1999. Doi : 10.1046/j.1365-2753.1999.00198.x.
  13. E. Nouri, K. Georgila and D. Traum, "Culture-specific models of negotiation for virtual characters: multi-attribute decision-making based on culture-specific values", AI & SOCIETY, vol. 32, no. 1, pp. 51-63, 2014. Doi : 10.1007/s00146-014-0570-7.
  14. A. Islam and K. Chang, "Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources", Applied Sciences, vol. 11, no. 13, p. 6237, 2021. Doi : 10.3390/app11136237.
  15. A. Gillies and P. Smith, "Can AI systems meet the ethical requirements of professional decision-making in health care?", AI and Ethics, 2021. Doi : 10.1007/s43681-021-00085-w.
  16. A. Zia and C. Koliba, "The emergence of attractors under multi-level institutional designs: agent-based modeling of intergovernmental decision making for funding transportation projects", AI & SOCIETY, vol. 30, no. 3, pp. 315-331, 2013. Doi : 10.1007/s00146-013-0527-2.
  17. D. Partridge, "Human decision making & the symbolic search space paradigm in AI", AI & Society, vol. 1, no. 2, pp. 103-114, 1987. Doi : 10.1007/bf01891271.


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Cite this article

Schallig Matheus and Vaez Barzani Den, “An Analysis of Artificial Intelligence Based Clinical Decision Support Systems”, Journal of Biomedical and Sustainable Healthcare Applications, vol.1, no.1, pp. 009-017, January 2021. doi: 10.53759/0088/JBSHA202101002.


© 2021 Schallig Matheus and Vaez Barzani Den. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.