Longitudinal Analysis of Patient Outcomes through AI-Augmented Electronic Health Record Mining in Population Health Management Systems
Keywords:
AI-Augmented EHR, Longitudinal Analysis, Population Health Management, Predictive Analytics, Healthcare InformaticsAbstract
Artificial Intelligence (AI) in healthcare has revolutionized the potential of Electronic Health Records (EHR) by offering new avenues for longitudinal patient monitoring and outcomes prediction. This paper explores AI-augmented EHR mining within the scope of population health management systems. We assess recent developments, identify methodological innovations, and compare outcome-based improvements across multiple studies. The findings emphasize that the integration of AI into EHR systems not only enables predictive modeling but also enhances proactive interventions in population health.
References
Allam, A., Feuerriegel, S., & Rebhan, M. (2021). Analyzing patient trajectories with artificial intelligence. Journal of Medical Internet Research, 23(12), e29812. https://www.jmir.org/2021/12/e29812/
Borna, S., Maniaci, M.J., Haider, C.R., & Maita, K.C. (2023). Artificial intelligence models in health information exchange: a systematic review of clinical implications. Healthcare, 11(18), 2584. https://www.mdpi.com/2227-9032/11/18/2584
Relton, S., Ruddle, R.A., Shantsila, E., et al. (2022). The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity. Journal of Evaluation in Clinical Practice, 28(4), 567–575.
Dambha-Miller, H., & Farmer, A. (2023). Artificial Intelligence for Multiple Long-term conditions (AIM): A consensus statement from the NIHR AIM consortia. NIHR Open Research, 3, 21. https://eprints.soton.ac.uk/477692
Johnson, M., Patel, M., & Phipps, A. (2023). The potential and pitfalls of artificial intelligence in clinical pharmacology. Clinical Pharmacology & Therapeutics, 112(2), 279–288. https://pmc.ncbi.nlm.nih.gov/articles/PMC10014043/
Khoury, P., Srinivasan, R., Kakumanu, S., & Ochoa, S. (2022). A framework for augmented intelligence in allergy and immunology practice and research. The Journal of Allergy and Clinical Immunology: In Practice, 10(3), 423–433. https://www.sciencedirect.com/science/article/pii/S221321982200143X
Esmaeilzadeh, P. (2024). Challenges and strategies for wide-scale artificial intelligence deployment in healthcare practices. Artificial Intelligence in Medicine, 140, 103939. https://www.sciencedirect.com/science/article/pii/S0933365724001039
Mintz, Y., & Brodie, R. (2019). Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies, 28(2), 73–81.
Sun, T.Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector. Government Information Quarterly, 36(2), 368–383. https://www.sciencedirect.com/science/article/pii/S0740624X17304781
Oikonomou, E.K., & Khera, R. (2024). Artificial intelligence-enhanced patient evaluation. European Heart Journal, 45(35), 3204–3213. https://academic.oup.com/eurheartj/article-abstract/45/35/3204/7709329
