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.
“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.
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.