In order to better understand the natural language processing system, it is important to examine the basics.  Natural language processing or NLP incorporates processing algorithms that provide certain rules and guidelines of speech and language in order for examination and analysis.

Too many times do physicians require careful analysis of a word, but the search and retrieval capabilities of their software program limits the number of retrievals.  For example, if the word ‘whooping cough’ is keyed, natural language processing would take into careful consideration all of the modifiers of the word.  This means that ‘signs of’ or ‘no signs of’ whooping cough would be retrieved along with the exact wording.  Physicians are able to yield a more accurate count this way.

This is especially important in data retrieval throughout a network of medical centers.  Perhaps data is needed to know how many patients were treated for pneumonia.  Through NLP, data is retrieved that pulls up all related information about pneumonia mentioned for each and every patient.  The data may include pneumonia cases that were then ruled out as non-pneumonia related incidences.  Data retrieval is more accurate, and as a result, more effective in compiling research and information about the medical center or hospital.

Benefits of NLP

One of the most significant benefits of natural language processing is the flexibility involved with the system.  Since it is able to be implemented with electronic health records, there is a wide range of flexibility into the customization of the program.

Clinical decision tools are among the most sought after components of NLP and EHRs working together.  These support tools may offer physicians detailed information on how to treat patients based on data collected and extracted from clinical notes.  This display is extremely powerful as it can provide a better quality of care should the physician take the recommendations and then apply them to the treatment plan.

Other Important Information

Aside from flexibility, accuracy is another key to implementing NLP into EHRs.  Physicians are finding the using a natural language processing tool is uniform and provides heightened accuracy across the network.  This means that the algorithms set forth in the program pick up the chosen language by many physicians at a time.  The components are then analyzed, processed and can later be retrieved to deliver highly accurate results.

For example, in searching for ‘signs of pneumonia,’ the program may also include issues associated with the problem.  Perhaps pulmonary embolism would be extracted to offer further insight and information on particular patients.  This is extremely helpful for physicians who want to see the ‘big picture’ rather than bits and pieces of a puzzle that then needs to be put together.

The technology is still growing and continuing to make advancements in the development of natural language processing.  As the demands are made, vendors of NLP are putting their research and development teams to work to create the perfect technological model for physicians.  Improvements are ongoing and development will not stop until the technology is far superior than the medical community could even imagine.