AI-Powered Predictive Models for Patient Referral Resolution and Case Outcome Forecasting

Patient referral is an important aspect of contemporary healthcare, and it needs to be addressed in a timely manner and be highly predictive of the outcome to enhance patient care and process efficiency. To regulate this, a predictive framework that incorporates several machine learning models is suggested, based on AI. The data used includes structured data captured in multi-institutional EHRs, such as demographics and medical histories, as well as unstructured data, including referral notes, among others. The preprocessing task will address missing values, numeric normalization, and NLP-based cleaning of referral notes. Recursive feature elimination and mutual information scores from feature selection techniques aid in identifying large clinical and administrative variables. In the case of unstructured text, relevant patterns are extracted using advanced word representation techniques, such as TF-IDF. It utilizes XGBoost and a BiLSTM-based neural network, integrating them into an ensemble model that combines structured data and trends in textual data sequences. This integrated practice foretells the urgency of referral and case outcome. The framework, evaluated using an accuracy of 91.2%, a precision of 89.6%, a recall of 88.4%, and an F1-score of 89.0%, showed better prediction and significantly eliminated delays in administration, thereby facilitating better management of patient referrals.