UNDERSTANDING THE APPLICATIONS OF NLP IN HEALTHCARE

1. Automate Data Import

   
   

DrAid™'s Speech2Text feature

The automatic input of documents using voice saves time for doctors and therefore has more time for patients. Nuance and M*Modal are products that can extract information from patient-physician conversations in the standardized language. The two products above use speech recognition and natural language understanding (NLU) technology to summarize information and include it in medical documents.

2. Speech Recognition

Speech recognition technology allows physicians to enter text into electronic health databases (EHRs). The front-end technology eliminates typing, while the back-end technology automatically corrects errors in the output text before passing it onto a human to double-check. In developed countries, applications for speech recognition are almost mature and saturated.

3. Medical coding assistant 

Medical coding is the representation of medical diagnoses, procedures, services, and devices in the form of alphanumeric characters. Diagnostic and procedural codes are extracted from medical documents such as doctor's notes, test results, diagnostic imaging, etc. Medical coding assistant is a popular NLP application. However, the application rate is still not high, the problem lies in the accuracy.

4. Data mining

Data mining in health systems allows agencies and organizations to reduce the level of subjectivity in the decision-making process and provide useful information. When applied, data mining technologies can become useful tools in knowledge discovery, helping health managers come up with effective strategies, deliver quality healthcare services for the patient.

5. Registry reporting

Clinical data registry agencies use observational research methods to collect and aggregate data on treatment, outcomes according to the health status of patients. Periodic medical data reporting is laborious because the information to be reported is scattered in many documents. Data reporting automation enables information to be extracted from documents and saved in a structured form that can be used by an organization's analytics platforms for automated reporting. 

6. Medical decision support

Healthcare providers always need to stay up-to-date with the latest knowledge in order to provide the best treatments for their patients. With the rapid development of biomedical research, it is becoming increasingly difficult to keep up to date with the best information for each case. With the help of NLP technology, physicians can quickly access large document libraries, with reliable and up-to-date information. The query is done through natural questions, like asking a colleague. Some of the top providers of this service are M*Modal and IBM Watson Health.

7. Searching for clinical trial candidates

   

Inspirata uses NLP in finding clinical trial candidates for cancer patients

Manual search of candidates for clinical trials is extremely inefficient. Determining relevance to clinical trials took an average of 120 minutes per cancer patient. NLP technology can analyze the medical reports of each patient and the criteria for each trial so that potential candidates can be identified automatically. IBM Watson Health and Inspirata are pioneers in this field. 

8. Prior authorization

Prior authorization is the process insurance companies use to determine whether a specified procedure, service, or drug is acceptable for payment. This process creates an additional burden on medical staff, instead of focusing time on patients. IBM Watson and Anthem are the companies that have produced NLP application products that are used by insurance companies to quickly determine pre-authorization.

9. Encoding the stratification state

Stratified Condition Coding (HCC) is a risk assessment model built to predict a patient's healthcare costs. With the popularity of the pay-for-value model, coding the state of stratification is becoming increasingly important. Insurance companies may use this code to estimate costs. NLP can automate the assignment of HCC codes to each patient.

10. Virtual assistant 

    

AI SmartCare application with the goal of becoming a healthcare assistant for everyone

Popular virtual assistants like Amazon's Alexa or Google Assistant can help users with everyday tasks. Similar assistants could be developed to provide health information. Values ​​that virtual assistants in healthcare can bring include anonymity (especially for sensitive or psychological health issues), surveillance (monitoring a patient's condition), personalization, and personalization. personalization (some applications can use parameters from sensors, tracking behavior through facial analysis), and of course the ability to interact in real-time, anytime, anywhere, with digital Unlimited servings. Some health care virtual assistant applications such as Woebot, Babylon, Ada Health, Bouy Health.