Question

1. Investigate three ways in which NLP could be used in healthcare.

2. Describe how NLP are being used, or if not, how they might be used in the future.

3.  What benefits might NLP technology offer?

4. Are there any drawbacks in NLP?

5.What if any are the potential ethical concerns if any?

Answer 1

1) Clinical documentation improvement

    Automated registry reporting

    Clinical trial matching

    Prior Authorisation

    Clinical decision support

     Risk adjustment and hierarchical condition categories

     Computational phenotyping and biomarker discovery

      Population surveillance

2)" NLP is currently used for first two things in the above list extensively.It reduces the manual work for hospital. Healthcare      providers are taking the opportunity to beef up their big data defenses and develop the technological infrastructure required to meet the impending challenges of value-based reimbursement, population health management, and the unstoppable tide of chronic disease. "

"Clinical analytics and population health management have been a trickier mountain to climb. Dissatisfaction with electronic health records remains at a fever pitch, and is unlikely to cool off as developers and regulators try to stuff more and more patient safety features, quality measures, and reporting requirements into the same old software. "

3) "Natural language processing has many potential applications. NLP can enhance the completeness and accuracy of electronic health records by translating free text into standardized data. It can fill data warehouses and semantic data lakes with meaningful information accessed by free-text query interfaces. It may be able to make documentation requirements easier by allowing providers to dictate their notes, or generate tailored educational materials for patients ready for discharge.

Computer-assisted coding with an NLP foundation received a great deal of attention during the drawn-out ICD-10 conversation process, when it was viewed as a possible silver bullet for the problems of adding sufficient detail and specificity to clinical documentation.

But perhaps of greatest interest right now, especially to providers in desperate need of point-of-care solutions for incredibly complex patient problems, NLP can be – and is being – used for clinical decision support.

The most famous example of a machine learning NLP whiz-kid in the healthcare industry is IBM Watson, which has dominated headlines in recent months due to its voracious appetite for academic literature and its growing expertise in clinical decision support (CDS) for precision medicine and cancer care."

4) " Providers often lack access to the socioeconomic, behavioral, and environmental data that would help to create truly actionable analytics at the point of care, and consumer excitement over Internet of Things devices and patient-generated health data is only further complicating the question of how to bring meaningful results to end-users without hopelessly cluttering the computer screen. "