Developing an NLP-Powered Solution to Automate The Extraction and Summarization Of Medical Information From Unstructured Clinical Notes

Expansion of unformatted clinical documentation in the healthcare environment has raised an urgent requirement for approaches to analyse and summarize significant clinical information. In light of the above, the current research creates an NLP solution to simplify the process of mining and summing up the medical data from the said note’s automatism. As applied in the current solution, Named Entity Recognition (NER), entity linking, and abstractive text summarization capabilities of the NLP technology in question enable to extract and amalgamate patient details such as diagnoses, medications, and treatment plans into summarised, actionable forms. This concept is used in the goal of eliminating various challenges that are associated with manual workflow, such as; time consuming, inaccurate and irregular. By automating this process, many healthcare practitioners can save much time, reduce the mental effort of searching the right information and retrieve highly critical data that can significantly impact the decisions made on patients hence improving patient outcomes. In addition, the solution enhances data integration and makes knowledge exchange among healthcare specialists possible. The application of Natural Language Processing in managing healthcare data is a revolutionary shift in the use of unstructured clinical data to enhance clients’ care, resources productivity and pragmatic research.