International Journal of Medical and All Body Health Research  |  e-ISSN: 2582-8940  |  Double-Blind Peer Review  |  Open Access  |  CC BY-NC-SA 4.0

Current Issues
     2026:7/4

International Journal of Medical and All Body Health Research

e-ISSN: 2582-8940 | Open Access

Machine Learning–Driven Clinical Decision Support Systems for Improving Patient Outcomes in US Healthcare

Adil Shah, Md Nurul Huda Razib, Shanzida Kabir

Pompea College of Business, University of New Haven, West Haven, Connecticut, United States; Assistant Professor, Manarat International University, Dhaka, Bangladesh; Faculty of Business & Economics, East West University, Dhaka, Bangladesh

Article Information

Volume / Issue4 / 4 (October-December 2023)
Pages75-79
AccessOpen Access

Publication Timeline

  • Received11 October 2023
  • Accepted13 November 2023
  • Published

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Abstract

Background: The exponential growth of electronic health records (EHRs) has created unprecedented opportunities for developing machine learning (ML) models to support clinical decision-making and patient risk stratification. However, translating these models into practical clinical tools remains challenging.
Objective: This review examines current applications of machine learning in clinical decision support systems (CDSS) and risk stratification, evaluates their performance across different healthcare settings, and identifies key barriers to implementation.
Methods: We conducted a systematic review of peer-reviewed literature from 2018-2022, focusing on ML models deployed in real-world clinical environments. We analyzed model architectures, performance metrics, validation approaches, and implementation outcomes across various clinical domains.
Results: We identified 127 studies meeting inclusion criteria, spanning emergency medicine, intensive care, oncology, and primary care settings. Deep learning models demonstrated superior performance for image-based diagnostics (AUC 0.89-0.96), while ensemble methods showed robust results for tabular
EHR data (AUC 0.82-0.91). Key success factors included prospective validation, clinician involvement in development, seamless EHR integration, and interpretable model outputs. Major barriers included data quality issues, algorithmic bias, regulatory uncertainty, and workflow integration challenges.
Conclusions: Machine learning models show substantial promise for enhancing clinical decision support and risk stratification. However, successful implementation requires addressing technical, ethical, and operational challenges through interdisciplinary collaboration, rigorous validation, and careful attention to clinical workflow integration.
 

Keywords

Machine LearningClinical Decision SupportRisk StratificationDeep LearningElectronic Health RecordsArtificial IntelligencePredictive Modeling

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How to Cite

Shah, A., Razib, M. N. H., & Kabir, S. (2023). Machine Learning–Driven Clinical Decision Support Systems for Improving Patient Outcomes in US Healthcare. International Journal of Medical and All Body Health Research, 4(4), 75-79. https://doi.org/10.54660/IJMBHR.2024.4.4.75-79

Shah, Adil, et al. "Machine Learning–Driven Clinical Decision Support Systems for Improving Patient Outcomes in US Healthcare." International Journal of Medical and All Body Health Research, vol. 4, no. 4, 2023, pp. 75-79. https://doi.org/10.54660/IJMBHR.2024.4.4.75-79.

Shah, A., Razib, M. N. H. and Kabir, S. (2023) 'Machine Learning–Driven Clinical Decision Support Systems for Improving Patient Outcomes in US Healthcare', International Journal of Medical and All Body Health Research, 4(4), pp. 75-79. doi: 10.54660/IJMBHR.2024.4.4.75-79.

Shah A, Razib MNH, Kabir S. Machine Learning–Driven Clinical Decision Support Systems for Improving Patient Outcomes in US Healthcare. International Journal of Medical and All Body Health Research. 2023;4(4):75-79. doi:10.54660/IJMBHR.2024.4.4.75-79

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