This project will address the referral process between primary care physicians (PCPs) and specialists. Currently, there is a lack of effective information transfer between the two parties, leading to duplicated tests, higher patient costs, and diagnostic errors. Dr. Peng and his team plan to use deep learning and natural language processing (NLP) methods to develop a novel framework that will collect and synthesize electronic health record (EHR) data to automatically summarize information and generate a referral form. Deep learning refers to the method in artificial intelligence of using models that mimic the workings of the human brain. NLP refers to the technology in machine learning that allows computers to analyze natural language data. The framework will focus on headache symptoms in primary care and the referrals between PCPs and neurologists.
The first aim of the study is to design a standardized referral form. With the second aim, researchers will build machine learning models that extract necessary EHR data to automatically fill the referral form. The third aim targets longitudinal EHR data and the narrative component of clinical notes. Dr. Peng explains that summarizing a given problem in a referral often relies on a series of clinical notes. Additionally, information regarding social determinants of health is not always recorded in the structured EHR, but in the unstructured clinician notes taken during a patient visit. Dr. Peng’s goal is to apply NLP techniques to extract necessary information from those notes and ensure that the referral forms generate text from both structured and unstructured EHR data.
Finally, researchers will validate the headache referral system with a user-centered design. The goal is to assist clinicians with a timely and accurate triaging decision.