Hospitals can use NLP technology to extract discrete data from large amount of documents and expedite EHR documentation process.

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NLP to Enhance Your Hospital Documentation
Though hospitals are required to demonstrate they can capture discrete data
elements in an EHR to comply with meaningful use, they find it difficult to accomplish
this when most of the information resides in the narrative portion of the medical
record. Natural language processing or NLP handles this issue effectively and
enhances hospital documentation. While speech recognition simply translates
spoken words into digital, NLP deduces the meaning behind the spoken words.
There are several reasons for structured data to remain suboptimal in EHR such as
EHR design, lack of lab interfaces and resistance of physicians in entering data into
point and click templates. Many studies have proved that NLP can be used as an
effective tool to unlock data from EHRs. With the capability to parse medical terms in
a free text, this technology will speed up data entry and it will be easier to measure
the performance of the electronic record system. However, the implementation of
this technology in hospitals will change the role of medical transcriptionists. We will
consider that after looking at the major advantages of NLP.

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Major Advantages
 Aids EHR Content Completion – This technology can extract discrete
elements from any data source, even unstructured sources and enter that
information in the electronic record. A hospital that newly acquired an EHR
can use NLP to identify problem lists from early patient narratives and
transfer this information to the database. Without this technology, populating
data fields in real time will require manual capture using point and click
template, which may not be suitable in many clinical situations.
 Effective Abstracting and Reporting – NLP is helpful for abstracting and
reporting information for Physician Quality Reporting Initiative and other
larger quality-related initiatives. Using this technology, you can go through
huge volume of documents and extract information that specifically points to
meaningful use data elements. This would include problem list, vital signs,
allergies, social history, procedures, medications and quality measure
information.
 Real-time Patient Data – Hospitals can make use of this technology to
provide quality analysts and physicians with valuable information about
patients at the same time as they receive treatment. Once a record is
completed, it is parsed and indexed for query searches. This capability will
help hospitals to better diagnose the patient and provide quality treatment.
For example, hospitals use NLP to uncover the symptoms of sepsis
immediately rather than retrospectively, which allows physicians to provide
direct care for sepsis patients and thereby indirectly reduce readmission
rates.

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 Carry Out Sophisticated Data Queries – NLP can scan large volume
documents quickly to find terms related to quality measures and patient
safety, which is quite time-consuming when done manually. The technology
searches for explicit key terms, related terms, implicit conditions based on lab
results or other indicators.
Overall, NLP significantly improves EHR documentation. Analyzing documentation
and searches for certain conditions in real time will help to determine whether clinical
documentation improvement specialists should query physicians while the patient is
still in the hospital. Conducting concurrent queries can effectively reduce the number
of post discharge queries as well as record holds. Hospitals can use this technology
to identify records (for example, those involving one-day stays) that may be the
target of RAC audit or other third-party audits.
Changing Role of Medical Transcriptionists
With this new technology in place,
physicians can continue with their
existing dictation styles. However, it is
going to change the workflow and
required skill sets of medical
transcriptionists (MTs). Like speech
recognition, NLP also requires human
intervention to review data and ensure
that all documentation is tagged and
parsed into the correct EHR fields. MTs
will have the role of data
reviewers/validators rather than
transcribers. They will function as
knowledge-based workers, ensuring the
correctness of the results.

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They will validate the following:
 That discrete data elements and the document structure have been correctly
captured before they are uploaded to the EHR or exchanged through an HIE
(health information exchange). It is ensured that all concepts such as
medications, allergies and other details are correctly encoded so that the EHR
remains error free.
 That clinical concept is encoded correctly before the document is submitted to
a CDI (clinical documentation improvement) specialist or coder.
 That the results of population health studies are correctly captured before
they are presented to a case manager or researcher.
Projects are ongoing to create interoperable standards for the exchange and use of
healthcare information that will allow providers to have structured clinical data while
also retaining narrative information that is vital for human understanding. It may be
possible in the near future to enjoy the advantages offered by electronically
structured and encoded data and also retain the narrative created by traditional
dictation and medical transcription. Physicians can make full use of medical
transcriptionists’ knowledge and skill and thereby benefit from reduced effort,
increased job satisfaction and improved documentation quality while also realizing
maximum return on expensive investments in technology.