ezNLP

What is ezNLP Platform ?

  • ezNLP Platform is a natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text.
  • Using ezNLP Platform, you can quickly and accurately extract information, such as procedures, medication, devices, problems, and PHI from a variety of sources like EHRs, doctors’ notes, clinical and lab reports, and financial docs.
  • One of the important ways to improve patient care and accelerate clinical research is by understanding and analyzing the insights and relationships that are “trapped” in free-form medical text, including hospital admission notes and a patient’s medical history.

How is it done today?

  • Today this is achieved either manually or by writing a set of customized rules which are complicated to build, time-consuming to maintain, and fragile.
  • A change to a single classification code name, for example, can impact dozens of hard-coded rules and failing to update a single one of them can result in missed or incorrect data.
  • Machine learning can change all that with models that can reliably understand the medical information in unstructured text, identify meaningful relationships, and improve over-time.

How do we do it better?

  • ezNLP Platform has access to millions of patient records and dictations that we have used to train our AI engine.  We use advanced machine learning models to accurately and quickly identify medical information and determine their relationship to each other.
  • No other company has access to such a large database to provide such a high accuracy. We guarantee a 90%+ accuracy.
  • You access ezNLP Platform through a simple API call, no machine learning expertise is required, no complicated rules to write, and no models to train.
  • You can use the extracted medical information and their relationships to build applications for use cases like computer assisted coding (CAC), clinical documentation improvement (CDI), and several others.
  • Because ezNLP Platform is HIPAA compliant and can quickly identify protected health information (PHI), such as name, age, and medical record number, you can also use it to create applications that securely process, maintain, and transmit PHI.
  • To prove how superior our platform is we have developed our own applications and deployed it in several large hospitals across the US
Use Case

For a hospital, the process of finding the right diagnosis in the patient notes that should be mapped to the correct code in the International Classification of Diseases (ICD) can be time-consuming and tedious. It is particularly challenging to extract diagnoses that can be represented in different ways.

For example:
“atrial fibrillation” is sometimes written as “AF.” ezNLP Platform can accurately identify abbreviations, misspellings, and typos in medical text. The platform also suggests the correct codes. This reduces the time a medical coder must spend analyzing unstructured notes and helps save millions of dollars.

Accurate and timely clinical documentation by physicians has always been the cornerstone of quality patient care and reimbursement.

There are also a myriad of other reasons that a comprehensive medical record, that includes a detailed history and physical, progress notes, and consults, is vital. For a hospital, the process of improving clinical documentation involve too many manual processes to identify the gaps. ezDI -based CDI solution is helping hospitals identify those gaps and improve the clinical documentation which results into improvement in overall patient care.

We extract medical information from patient data return structured results with analytics for you to access.

For example:
We can help you build an early warning system to help identify individuals at risk of multiple sclerosis by extracting diagnosis, sign, and symptoms from more than a million clinical notes. By providing a “single lens” into the patient’s medical history, clinical teams can make decisions that are more informed.

ezNLP Platform understands and identifies complex medical information found in unstructured text to help make indexing and searching easier.

For example: In oncology, it is critical that the right selection criteria are quickly discovered to recruit patients for clinical trials. You can use these insights to identify recruit patients to the appropriate clinical trial in a fraction of the time and cost from manual selection processes.

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