A Deep Learning Framework For Automated Encounter Reporting To The Department Of Health Services (Dhs)

Abstract

Prompt and effective reporting of contacts to the Department of Health Services (DHS) is highly critical for the monitoring of health by it, planning of resources, and decision-making in policy formulation. Manual reporting, being the conventional method, is error-prone, not reliable, and time-consuming. In this work, a deep learning architecture is presented for automated extraction, classification, and structuring of clinical encounter data from electronic health records (EHRs) for seamless submission to the DHS. Using NLP and RNNs, the system learns to identify significant encounter features such as diagnoses, procedures, providers, and timestamps from both structured fields and unstructured clinical narratives. In addition, the system applies the HL7/FHIR standard for semantic interoperability and secure data transfer. Preliminary tests on a real-world dataset attained over 92% accuracy in attribute extraction and significantly accelerated the reporting speed. The approach enhances the time, accuracy, and compliance of healthcare institution encounter reporting activities.