Can NLP be helpful in health care industry ?

Health care is one of the industries which is way behind in successfully transforming health-related big data into meaningful data through which analytics can be performed.The main reason for this lagging behind is the availability of unstructured data. There is an increasing amount of usage of electronic health records for patients for current few years. Electronic health records consist of a variety of unstructured data like radiology images, pathology reports, patient feedback, physician notes, admission and discharge notes and other clinical reports. All of these unstructured data are used to perform analytics which will help healthcare payers and health systems.

Natural Language Processing is the best way to extract useful information from these unstructured data. For example, the clinician-generated narrative consists of patient's most accurate and complete picture of medical history which contains valuable information that can be missed if these narratives are in structured format.

Other tasks which can be performed by NLP in Healthcare industries are:-

Summarization of long narrative texts such as identifying key concepts and phrases present in a clinical note or academic journal.
Transferring data elements present in unstructured text to structured electronic health record.
Converting data that is readable by machine into natural language for reporting and other purposes.

Currently, the major focus in health-care industry is to use NLP for clinical decision support. IBM also started the investigation on how NLP and machine learning can help to tell whether a patient is suffering from heart disease or not.They realized that it's not only clinical data relevant to a disease that is sufficient for predicting heart failure but also the patient's social and behavioral factors that are recorded in clinical notes which are not in structural data of electronic health record.

Other uses of NLP in clinical decision support are as follows:-

In 2013, the Department of Veterans Affairs used NLP techniques to review around 2 billion Electronic health records documents for indications of Post-traumatic stress disorder, depression, and potential self-harm in veteran patients.
At the University of California Los Angeles, researchers analyzed electronic free text to flag patients with liver damage caused by cirrhosis.
Researchers from the University of Alabama found that NLP identification of reportable cancer cases was 22.6 percent more accurate and precise than a manual review of medical records.

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