University of Pittsburgh Schools of the Health Sciences Launches Realyze Intelligence

The University of Pittsburgh Schools of the Health Sciences has recently launched Realyze Intelligence, which will use natural language processing and artificial intelligence to pinpoint optimal treatments for chronic disease and cancer patients. The platform will read EHR data to pick out cohorts of patients with detailed conditions who may be at a higher risk for negative health outcomes.

The analysis done by the company will allow physicians to prioritize individuals who need the most serious medical attention. Realyze Intelligence was founded in partnership with UPMC Enterprises, the solutions branch of UPMC. The platform can get a holistic view of the entire patient and their history, not just the chronic condition they are being treated for at the moment.

Gilan El Saadawi, MD, PhD, chief medical officer at Realyze Intelligence said that the patients aren’t defined by their preliminary diagnosis. They are not dealing with that one condition, but also many other factors that make them complex. Currently, this is a manual, time-consuming effort to extract and use the relevant information from the EMR to ensure effective care. Realyze facilitates this procedure and rapidly decodes the patient’s ‘story’ so they get the best care.

While EHR systems enable simplified data storage, it can be hard for physicians to analyze large quantities of data to comprehend the complexities of each patient. The platform is aimed at making it easier for physicians to shortly gain important insights. The platform may be helpful for various chronic conditions, including inflammatory bowel disease, chronic kidney disease, and cancer.

In the US healthcare system nearly $8.7 billion is spent per year on employing workers to read clinical notes and analyse the data.Natural language processing encompasses various machine learning and artificial intelligence disciplines to extricate valuable insights from unstructured data, making it useful for clinical data. Its functions can be applied to clinical decision support and can help to streamline data from EHR systems.Physicians can predict and analyse chronic conditions and risk factors using artificial intelligence, machine learning, and natural language processing. A recent study applied machine learning to wearable device data to predict clinical laboratory measurements, without patients having to go to the doctor’s office.