How Will Natural Language Processing Support Your Practice?

by The DermEngine Team on Jan 22, 2019

Natural Language Processing (NLP) is a $16 billion market opportunity positioned for great innovations in the healthcare sector.1 Designed to create comprehensive documentation solutions, this niche artificial intelligence (AI)-based solution is already revolutionizing how data is utilized.

What is Natural Language Processing?
NLP is the term given to a form of artificial intelligence (AI)-powered programs that analyze unstructured data and derive meaning from the information. This means that these algorithms can quickly and accurately document data, thus freeing up valuable time for a physician to distribute a greater amount of time to their patients rather than administrative tasks.

Big Data Processing for Patient Security and Connectivity on DermEngine

The Need For NLP
Studies demonstrate that on average healthcare providers need to complete 20,000 forms a year, translating to ~49% of their time. 2,3  With nearly half of a physician’s time dedicated to desk work, there is a desperate need for assisted information storage tools to allow for a more patient-focused approach. Perfect for a wide variety of tasks, NLP can be used for:

  • Speech recognition/voice-based diction

  • Documentation improvement

  • Data mining research

  • Error identification

  • Identifying complex patient cases

  • Computer-assisted coding

  • Automated registry reporting

  • Term/word conversion

Applying NLP To Healthcare
Building on this basic knowledge of what NLP is and what it can be used for, how can this support medical professionals and their patients?

1. Convert Handwritten Notes & PDFs
Current research estimates that the average healthcare organization spends approximately $20 in labor to file each physical document.2 It is important to recognize that many of these forms do not have a digital counterpart, or if they do it is simply a scanned version. Since 15% of patient case histories are identified as “illegible” and another 37% as “poor” NLPs can be used to transcribe these documents into accessible, clean data that can be processed by machines.4 As a result, physicians and nurses can avoid the laborious repetition associated with writing down basic information on consecutive visits.

Physical Patient Record Documentation-2

2. Medical Terminology Conversion
At least 15% of patients who have access to their EMR records experience challenges understanding their information, resulting in 78% failing to use this healthcare data towards making medical decisions.1 NLP programs can identify shorthand or medically ambiguous terms and replace them with more user-friendly language, allowing patients to better understand their results towards better informed care choices.

3. Identifying Potential Errors In Care Delivery
Over three quarters (83%) of clinicians identify physician burnout as a primary challenge within their practice, resulting in an increased risk of documentation errors in patients’ medical records.5 A huge risk to patient health, NLP programs possess the unique opportunity to save lives by locating perceived errors within medical records and care delivery. One study with such a purpose found that the program was able to identify mistakes with a 92.7% accuracy, demonstrating the potential relieve it could provide on ensuring the continued safety and care of patients.1

Conclusion
Able to support medical professionals in nearly every step of their workflow, natural language processing programs can overturn inefficiencies, from transcribing handwritten notes to helping patients better understand their care plan or results. Due to the undeniable liability associated with scattered or unstructured data, NLP presents itself as only one viable solution for comprehensive, accessible medical information.


-The MetaOptima Team

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Sources:
  1. https://healthitanalytics.com 
  2. https://getreferralmd.com
  3. https://www.jwatch.org 
  4. https://www.ncbi.nlm.nih.gov 
  5. https://ehrintelligence.com 

     

 

Topics: Artificial Intelligence Big Data Artificial Intelligence in Dermatology Intelligent Dermatology Software Big Data in Healthcare Medical Records EMR Solutions Dermatology EMR System EMR System