Enhancing Clinical Decision Support through Intelligent Integration of Electronic Health Records

Authors

  • J Ananya Mehta Senior Researcher, United Kingdom Author

Keywords:

Electronic Health Records, Clinical Decision Support, Machine Learning, Healthcare Informatics, Predictive Analytics, AI in Healthcare 

Abstract

The integration of Electronic Health Records (EHR) with Clinical Decision Support Systems (CDSS) has transformed modern healthcare by enhancing diagnostic accuracy, reducing medical errors, and improving patient outcomes. This study investigates the potential of intelligent integration strategies using structured and unstructured data from EHRs, bolstered by artificial intelligence (AI) methods. It presents a detailed literature review, a proposed framework, and real-world data insights into system effectiveness.

References

[1] Bates, D. W., et al. (2003). The impact of computerized physician order entry on medication error prevention. Journal of the American Medical Informatics Association, 10(2), 199–206. https://doi.org/10.1197/jamia.M1042

[2] Bajjuru, R., Kacheru, G., & Arthan, N. (2020). Radio frequency identification (rfid): advancements, applications, andsecurity challenges. International journal of computer engineering and technology, 11(3).

[3] Kawamoto, K., et al. (2005). Improving clinical practice using clinical decision support systems. BMJ, 330(7494), 765. https://doi.org/10.1136/bmj.38398.500764.8F

[4] Miotto, R., et al. (2016). Deep Patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6, 26094. https://doi.org/10.1038/srep26094

[5] Goldstein, B. A., et al. (2017). Opportunities and challenges in developing risk prediction models with electronic health records data. Medical Decision Making, 37(1), 70–80. https://doi.org/10.1177/0272989X16668202

[6] Bajjuru, R., Kacheru, G., & Arthan, N. (2019). AI and Sales Automation: Revolutionizing Lead Generation and Conversion in Salesforce. International Journal of Communication Networks and Information Security (IJCNIS), 11(3), 491–506.

[7] National Academy of Medicine. (2011). Learning What Works: Infrastructure Required for Comparative Effectiveness Research. National Academies Press.

[8] Wright, A., & Sittig, D. F. (2008). A four-phase model of the evolution of clinical decision support architectures. International Journal of Medical Informatics, 77(10), 641–649.

[9] Shortliffe, E. H., & Sepúlveda, M. J. (2018). Clinical Decision Support in the Era of Artificial Intelligence. JAMA, 320(21), 2199–2200. https://doi.org/10.1001/jama.2018.17163

[10] Arthan, N., Kacheru, G., & Bajjuru, R. (2019). Radio Frequency in Autonomous Vehicles: Communication Standards and Safety Protocols. Revista de Inteligencia Artificial en Medicina, 10(1), 449478.

[11] Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine—beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507–2509. https://doi.org/10.1056/NEJMp1702071

[12] Khairat, S., et al. (2018). Reasons For Physicians Not Adopting Clinical Decision Support Systems. JMIR Medical Informatics, 6(2), e24. https://doi.org/10.2196/medinform.8919

[13] Saria, S., Butte, A., & Sheikh, A. (2018). Better medicine through machine learning. PLoS Medicine, 15(11), e1002702. https://doi.org/10.1371/journal.pmed.1002702

Downloads

Published

2022-09-10

How to Cite

Enhancing Clinical Decision Support through Intelligent Integration of Electronic Health Records. (2022). ICMERD-International Journal of Medical Science (ICMERD-IJMS), 3(1), 1-4. https://icmerd.org/index.php/ICMERD-IJMS/article/view/ICMERD-IJMS_03_01_002