Integrating EHR Data with AI Algorithms for Real-Time Patient Outcome Predictions in Hospital Settings

Abstract

EHR can be embedded into an artificial intelligence (AI) formula, and this opens a revolutionary prospect to healthcare systems of attending to patients in a superior way. The research goal is to develop and integrate AI models that will be applied to predict the outcomes of patients at their bedside, using EHRs data. This research is important because it tries to solve the issues of predictive accuracies and data use in a hospital where time is essential in making certain decisions. The goals would be to use the AI methods and, in particular, machine learning algorithms to process the EHR data and deliver a real-time assessment of the patient outcomes, which can be their disease evolution, readmission rates, responses to treatment, etc. Prior works have already indicated encouraging data when applied to similar activities through machine learning models (Smith et al., 2019; Liu et al., 2020). The given paper discusses combining a range of AI models, random forests, support vector machines, and deep learning models to find the best predictors of patient outcomes. Our findings lead to the assumption according to which the proposed multifaceted AI solution enhances the forecasting power of the EHR data and depicts a higher degree of precision and credibility as compared to the traditional methods. The results also reflect the applied way of clinical decision-making, management of patients, and operation of hospitals according to real-time prediction. The paper comes to the conclusion that additional optimization should be undertaken and more AI-driven solutions should be deployed to hospital settings, particularly related to the problem of missing data and unstructured data.