Working as a NLP specialist in Healthcare industry - It is a branch of AI that uses linguistics, statistics, and machine learning to give computers the ability to understand human speech. NLP-powered systems can derive meaning from what’s said or written, with all the complexities and nuances of natural narrative text. This allows machines to extract value even from unstructured data.Some of it is structured or organized into specific EHR fields. For example, a patient’s name, age, and gender, their lab values, or financial information are stored in a database according to a predefined schema. This structure allows physicians and other software systems to easily locate needed data, share it, and analyze it, basically — make use of it.But a lot of data (by different estimations, 70 or 80 percent of all clinical data) remains unstructured, kept in textual reports, clinical notes, observations, and other narrative text. Unstructured data is unavoidable, yet extremely valuable. Currently working project:Alleviate clinician burnout. Physician productivity and motivation suffer from the glut of repetitive administrative tasks that force them to spend extra hours at the computer instead of interacting with patients. This problem even has a name — the EHR burden. NLP offers several solutions to help doctors from speech-to-text transcribing technology to simplified clinical documentation management.Streamline administrative processes. Such admin tasks as prior authorization of a patient’s health plan and claims processing contribute to the aforementioned burnout and high billing and insurance-related (BIR) costs. NLP-powered systems can automate many of the steps in claim filing and reduce turnaround timeEnhance clinical decision support. Physicians have been using software for making informed care decisions for years now. Typically installed as a part of an EHR, clinical decision support systems (CDSSs) provide helpful prompts on drug selection, diagnostics, and other actions by automatically checking knowledge bases and comparing them with patient records. In some cases, the use of a CDSS is even obligatory, for example, when ordering expensive tests to check that the service provider won’t receive reimbursement for it. However useful, CDSSs are mostly limited to processing only structured data. NLP can provide way more information for a CDSS from sources that it wouldn’t use otherwise and power predictive analytics.