How Natural Language Processing (NLP) is a Blessing for Healthcare Organisations

by August 27, 2018 0 comments

Natural Language Processing (NLP) has proven valuable to the healthcare industry, with the potential to simplify clinical documentation and improve the overall healthcare experience. With improvements in technology, the contribution of NLP is all set to witness new heights. A recent report from points out that the NLP market is poised to grow at a CAGR of 16.1 % until 2021, which translates to a $16 billion market opportunity. Across industries, the healthcare domain has been an early adopter of NLP to deliver more customized and solution-oriented care to its users.

 

Improving Patient-Provider Interactions Through EHR

Patients call for undivided and customized attention from their healthcare providers which leaves many clinicians feeling drained and dissatisfied as they have to manage the burdensome documentation including medical billing services along with patient care. A recent survey found that 83% of clinicians see physician burnout rampant within their workplace. By deploying the NLP tools including voice recognition, clinicians may reach out to a viable solution to EHR (Electronic Health Records) distress.

Integrating NLP for EHR also improves the quality of the documentation, making it easier for analytical analysis. The future may see voice recognition tools going beyond clinical dictation to receive and carry out directions from healthcare providers. A beginning is being made with virtual assistants like Alexa, Siri, and Cortana having already made their way into healthcare organizations as administrative aids, assisting with customer service duties and help desk tasks.

As the healthcare industry redefines its capabilities, virtual assistants powered by voice recognition techniques may soon enter the clinical side of the healthcare industry as medical scribes and ordering assistants. Google has recently begun to hire individuals to develop voice recognition tools that will record clinical documentation, making virtual assistants a soon to be a reality in the future.

 

Empowering Patients with Health Literacy

In addition to easing EHR difficulties for healthcare providers, NLP tools will empower patients to understand their health symptoms access their health data, make more informed medical decisions, and keep their health on track through customized chatbot interactions.  This will be possible when NLP is integrated to EHR data to drive the results into the patient portal, thus making patients more informed about their health conditions.

In the past, researchers have used NLP tools to match medical terms from clinical documents with their lay-language counterparts to improve patient EHR understanding and the patient portal experience.  As more NLP application tools are tested, there lies a huge potential for NLP algorithms to boost patient EHR understanding in the future.

 

Addressing to Higher Quality of Patient Care

NLP tools will be the frontrunners to evaluate and improve care quality by measuring physician performance and identifying gaps in medical care delivery.

Researchers have shown how NLP can simplify the assessment of a physician’s professional skills, automating patient diagnosis and reducing the amount of time and human effort typically required for routine tasks like patient diagnosis.

NLP algorithms could also be used to identify and mitigate potential errors in healthcare delivery. Additionally, a study conducted showed that NLP could help healthcare providers to measure the quality of patient care delivered and monitor adherence to clinical guidelines. Researchers have developed an NLP system to extract relevant EHR data and identify whether heart failure patients were prescribed clinically relevant medications upon hospital discharge.

 

Locate Patients Who Need Improved Care

Machine learning and NLP tools have the potential to detect complex patients who have a mental health history and substance abuse disorders to benefit from enhanced care coordination.

Non-clinical factors including housing instability and food insecurity can prove to be a deterrent to adhere to treatment protocols thereby making these patients incur more care costs in their lifetimes.

Data detailing patients’ social status and the demographic pattern is often harder to access than their clinical information and is usually in an unstructured format, where NLP algorithms can offer a solution. NLP algorithms can filter meaningful information from large datasets, providing clinicians with all the relevant information they need to detect complex patients who need improved healthcare.

In addition, NLP will prove to be a blessing in improving care coordination for patients with behavioral health issues. Machine learning and NLP tools can be deployed to mine unstructured patient data and identify those in danger of falling through gaps in the healthcare system.

The healthcare industry generates both structured and unstructured healthcare data silos that must be refined before NLP tools are widely deployed within clinical organizations. NLP techniques have a significant potential to improve healthcare delivery and streamline clinical and healthcare provider workflows. In the future, NLP and other machine learning tools will be the key driving better clinical decision support and patient health outcomes.

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