Vol. 1 No. 1 (2025)

Articles

AI and EHR Innovation: A Smart Approach to Medical Prioritization and Patient Care

Published 2025-06-07

  • Zarif Bin Akhtar

Zarif Bin Akhtar
Department of Computing, Institute of Electrical and Electronics Engineers (IEEE), New York, NY 10016, USA

Abstract

With the rapid advancement of digital technologies, the convergence of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Cloud Computing is transforming the landscape of biomedical engineering and healthcare delivery. Among the persistent challenges in modern healthcare is the effective management of Electronic Health Records (EHRs), particularly in prioritizing patient cases, segmenting heterogeneous clinical data, and enabling timely, data-driven medical decisions. Existing EHR systems often suffer from fragmentation, inefficiency, and limited interoperability, which can delay diagnosis and treatment. This research proposes an AI-enhanced EHR framework designed to streamline healthcare workflows, improve information accessibility, and support clinical decision-making. The system integrates AI-driven algorithms for automated patient prioritization, intelligent data segmentation, and predictive analytics to enhance medical decision support. A functional prototype was developed, deployed, and tested in a simulated healthcare environment using real-world inspired datasets. The framework was implemented through a modular design, ensuring scalability and adaptability for various clinical contexts. Experimental evaluation demonstrated substantial improvements in response time, diagnostic accuracy, and system scalability compared to conventional EHR systems. The proposed solution addresses critical gaps in medical data management by enhancing efficiency, reducing clinician workload, and enabling faster, evidence-based decision-making. This study contributes to the growing body of work on intelligent healthcare systems, offering a practical, efficient, and scalable model for next-generation EHR integration. The findings underscore the transformative potential of AI-powered solutions in driving digital transformation and improving patient care outcomes in biomedical engineering.

Keywords

Artificial Intelligence (AI), Biomedical Engineering (BME), Computer Vision, Deep Learning (DL), Electronical Health Records (EHR), Machine Learning (ML), Operating Systems (OS)

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References

  1. [1] Akhtar, Z.B., 2025. Artificial intelligence within medical diagnostics: A multi-disease perspective. Artificial Intelligence in Health. 2(3), 44–62. DOI: https://doi.org/10.36922/aih.5173
  2. [2] Akhtar, Z.B., 2025. Exploring AI for pain research management: A deep dive investigative exploration. Journal of Pain Research and Management. 1(1), 28–42. DOI: https://doi.org/10.46439/Painresearch.1.004
  3. [3] Akhtar, Z.B., Rozario, V.S., 2025. AI Perspectives within Computational Neuroscience: EEG Integrations and the Human Brain. Artificial Intelligence and Applications. 3(2), 145–160. DOI: https://doi.org/10.47852/bonviewaia52024174
  4. [4] Akhtar, Z.B., 2025. Voices in the night: Sleep paralysis & The intersection of brain, trauma, traditions. Journal of Pain Research and Management. 1(1), 62–74. DOI: https://doi.org/10.46439/Painresearch.1.007
  5. [5] Akhtar, Z.B., Rawol, A.T., 2025. Advanced Methods Towards Functional Genomics & Medical Informatics: The Artificial Intelligence (AI) and Integrated Computing Perspectives. Trends Telemed E-Health. 5(5).
  6. [6] Akhtar, Z.B., 2025. Next-Generation Healthcare: Exploring GAI, Computational Medicine & Tissue Engineering, within Biomedical Engineering. Iris Online Journal of Sciences. 1(5), IOJS.MS.ID.000523.
  7. [7] Akhtar, Z., Rawol, A., 2025. Harnessing artificial intelligence (AI) towards the landscape of big earth data: Methods, challenges, opportunities, future directions. Journal of Geography and Cartography. 8(1), 10224. DOI: http://dx.doi.org/10.24294/jgc10224
  8. [8] Shen, Y., Yu, J., Zhou, J., et al., 2025. Twenty-five years of evolution and hurdles in electronic health records and interoperability in medical research: comprehensive review. Journal of Medical Internet Research. 27, e59024. DOI: https://doi.org/10.2196/59024
  9. [9] Finnegan, H., Mountford, N., 2025. 25 years of electronic health record implementation processes: scoping review. Journal of Medical Internet Research. 27, e60077. DOI: https://doi.org/10.2196/60077
  10. [10] Apathy, N.C., Holmgren, A.J., Nong, P., et al., 2025. Trending in the right direction: critical access hospitals increased adoption of advanced electronic health record functions from 2018 to 2023. Journal of the American Medical Informatics Association. 32(1), 71–78. DOI: https://doi.org/10.1093/jamia/ocae267
  11. [11] Alzyoud, M., Hunitie, M.F.A., Alka'awneh, S.M.N., et al., 2025. Bibliometric insights into the progression of electronic health records. In: Hannoon, A., Mahmood, A. (eds.). Intelligence-Driven Circular Economy: Studies in Computational Intelligence. Springer Nature: Cham, Switzerland. pp. 49–62.
  12. [12] Raymond, L., Motulsky, A., Vial, G., et al., 2025. Navigating large-scale EHR implementations in public health systems: Lessons learned and recommendations from a rapid review. Health Informatics Journal. 31(2), 14604582251347120. DOI: https://doi.org/10.1177/14604582251347120
  13. [13] Costa, R.T., Adib, K., Salama, N., et al., 2025. Electronic health records and data exchange in the WHO European region: A subregional analysis of achievements, challenges, and prospects. International Journal of Medical Informatics. 194, 105687. DOI: https://doi.org/10.1016/j.ijmedinf.2024.105687
  14. [14] Gulkesen, K.H., Sonuvar, E.T., 2025. Data Privacy in Medical Informatics and Electronic Health Records: A Bibliometric Analysis. Health Care Analysis. 1–13. DOI: https://doi.org/10.1007/s10728-025-00519-0
  15. [15] Ismail, M.R., Singh, P.J., Al Shehri, M., et al., 2025. The Adoption of Electronic Health Records Among Nurses in Saudi Arabia. Frontiers in Health Informatics. 14(2), 158–167.
  16. [16] Aggarwal, T., 2025. Harnessing AI Algorithms for Accurate Medical Diagnosis from Electronic Health Record. In Proceedings of 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), Gwalior, India, 6–8 March 2025; pp. 1–5.
  17. [17] Karaferis, D., Balaska, D., Pollalis, Y., 2025. Design and Development of Data-Driven AI to Reduce the Discrepancies in Healthcare EHR Utilization. American Journal of Clinical and Medical Research. 5(1), 100184. DOI: https://doi.org/10.71010/AJCMR.2025-e184
  18. [18] Mataraso, S.J., Espinosa, C.A., Seong, D., et al., 2025. A machine learning approach to leveraging electronic health records for enhanced omics analysis. Nature Machine Intelligence. 7(2), 293–306. DOI: https://doi.org/10.1038/s42256-024-00974-9
  19. [19] Jonnagaddala, J., Wong, Z.S.Y., 2025. Privacy preserving strategies for electronic health records in the era of large language models. NPJ Digital Medicine. 8(1), 34. DOI: https://doi.org/10.1038/s41746-025-01429-0
  20. [20] Al-Momani, A.A., Ramayah, T., 2025. The UTAUT model in understanding EHR adoption: a systematic review. In: Hannoon, A., Mahmood, A. (eds.). Intelligence-Driven Circular Economy. Studies in Computational Intelligence. Springer: Cham, Switzerland. pp. 179–194.
  21. [21] Tsai, M.L., Chen, K.F., Chen, P.C., 2025. Harnessing electronic health records and artificial intelligence for enhanced cardiovascular risk prediction: A comprehensive review. Journal of the American Heart Association. 14(6), e036946. DOI: https://doi.org/10.1161/JAHA.124.036946
  22. [22] Khashan, M.A., Alasker, T.H., Ghonim, M.A., et al., 2025. Understanding physicians' adoption intentions to use Electronic Health Record (EHR) systems in developing countries: an extended TRAM approach. Marketing Intelligence & Planning. 43(1), 1–27. DOI: https://doi.org/10.1108/MIP-05-2023-0225
  23. [23] Zhang, B., Thacker, D., Zhou, T., et al., 2025. Cardiovascular post-acute sequelae of SARS-CoV-2 in children and adolescents: cohort study using electronic health records. Nature communications. 16(1), 3445. DOI: https://doi.org/10.1038/s41467-025-56284-0
  24. [24] Meduri, K., Nadella, G.S., Yadulla, A.R., et al., 2025. Leveraging federated learning for privacy-preserving analysis of multi-institutional electronic health records in rare disease research. Journal of Economy and Technology. 3, 177–189. DOI: https://doi.org/10.1016/j.ject.2024.11.001
  25. [25] Abend, A.H., He, I., Bahroos, N., et al., 2025. Estimation of prevalence of autoimmune diseases in the United States using electronic health record data. The Journal of Clinical Investigation. 135(4), e178722. DOI: https://doi.org/10.1172/JCI178722
  26. [26] Natarajan, M., Bharathi, A., Varun, C.S., et al., 2025. Quantum secure patient login credential system using blockchain for electronic health record sharing framework. Scientific Reports. 15(1), 4023. DOI: https://doi.org/10.1038/s41598-025-86658-9
  27. [27] Alghamdi, H., Mostafa, A., 2025. Advancing EHR analysis: Predictive medication modeling using LLMs. Information Systems. 131, 102528. DOI: https://doi.org/10.1016/j.is.2025.102528
  28. [28] Cahill, M., Cleary, B.J., Cullinan, S., 2025. The influence of electronic health record design on usability and medication safety: systematic review. BMC health services research. 25(1), 31. DOI: https://doi.org/10.1186/s12913-024-12060-2
  29. [29] Hama, T., Alsaleh, M.M., Allery, F., et al., 2025. Enhancing patient outcome prediction through deep learning with sequential diagnosis codes from structured electronic health record data: Systematic review. Journal of Medical Internet Research. 27, e57358. DOI: https://doi.org/10.2196/57358
  30. [30] Dhinakaran, D., Ramani, R., Edwin Raja, S., et al., 2025. Enhancing security in electronic health records using an adaptive feature-centric polynomial data security model with blockchain integration. Peer-to-Peer Networking and Applications. 18(2), 7. DOI: https://doi.org/10.1007/s12083-024-01883-9
  31. [31] Cui, H., Shen, Z., Zhang, J., et al., 2025. Llms-based few-shot disease predictions using ehr: A novel approach combining predictive agent reasoning and critical agent instruction. AMIA Annual Symposium Proceedings. 2024, 319–328.
  32. [32] Wang, Z., Zhu, Y., Zhao, H., et al., 2025. Colacare: Enhancing electronic health record modeling through large language model-driven multi-agent collaboration. Available from: extension://ngbkcglbmlglgldjfcnhaijeecaccgfi/https://openreview.net/pdf?id=rb3wgt85in (cited 15 April 2025).
  33. [33] Sadler, M.C., Apostolov, A., Cevallos, C., et al., (2025). Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications. Nature communications. 16(1), 2913. DOI: https://doi.org/10.1038/s41467-025-58152-3
  34. [34] Trocin, C., Lee, G., Bernardi, R., et al., 2025. How do unintended consequences emerge from EHR implementation? An affordance perspective. Information Systems Journal. 35(1), 39–70. DOI: https://doi.org/10.1111/isj.12526
  35. [35] Gu, B., Shao, V., Liao, Z., et al., 2025. Scalable information extraction from free text electronic health records using large language models. BMC Medical Research Methodology. 25(1), 23. DOI: https://doi.org/10.1186/s12874-025-02470-z
  36. [36] Song, J., Deng, Y., Yang, Y., et al., 2025. Change in address in electronic health records as an early marker of homelessness. PloS one. 20(3), e0318552. DOI: https://doi.org/10.1371/journal.pone.0318552
  37. [37] Liu, T., Krentz, A., Lu, L., et al., 2025. Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis. European Heart Journal-Digital Health. 6(1), 7–22. DOI: https://doi.org/10.1093/ehjdh/ztae080
  38. [38] Singh, A.P., Balogh, E.P., Carlson, R.W., et al., 2025. Re-envisioning electronic health records to optimize patient-centered cancer care, quality, surveillance, and research. JCO Oncology Practice. 21(2), 128–135. DOI: https://doi.org/10.1200/OP.24.00260
  39. [39] Ramakrishnaiah, Y., Macesic, N., Webb, G.I., et al., 2025. EHR-ML: A data-driven framework for designing machine learning applications with electronic health records. International Journal of Medical Informatics. 196, 105816. DOI: https://doi.org/10.1016/j.ijmedinf.2025.105816
  40. [40] Mwogosi, A., Kibusi, S., 2025. Effectiveness of EHR systems on decision support in primary healthcare: a technology acceptance model 3 perspective. Journal of Health Organization and Management. 39(3), 310–333. DOI: https://doi.org/10.1108/JHOM-07-2024-0296