SBIR Phase I: Improving Fertility Patient-Provider Communication with a Personalized Natural Language Processing Platform

Award ID: 2124872

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop natural language processing software that will streamline communication between fertility clinic patients and their providers. Fertility clinics are specialized medical facilities that offer clinical diagnoses and treatments to assist with the conception of a child. Their patients undergo intense anxiety and large out-of-pocket expenses throughout their journey to conceive, and up to two-thirds of patients at fertility clinics will discontinue their treatment regimen with emotional stress being cited as the primary reason. Patient satisfaction, which is more complex than just achieving desired outcomes, is correlated with how effectively care teams can navigate the large information disparity and bridge the communication gap. This project advances technologies to facilitate this communication. This Small Business Innovation Research (SBIR) Phase I project will address major technical hurdles associated with natural language processing in healthcare, specifically fertility care. These challenges include that a conversational agent must understand the vocabulary particular to fertility-related questions, the natural language platform must overcome the difficulty in maintaining contextual-awareness throughout a continuous multi-person dialogue as well as understand ambiguous questions within a broader conversational context, and that the algorithm must reply with clinically accurate responses personalized to a specific patient. To meet these objectives, a fertility-specific corpus, or dictionary, will be developed from fertility stakeholders (patients, providers, and clinic staff) feedback, and a machine-learning algorithm will be developed that analyzes patient messages for intent and provides personalized responses drawing from the developed corpus in addition to external sources. Finally, a usability study will be performed to refine the platforms user-interface, tone, and informational content. The anticipated result of the proposed research is a platform that improves both the quality and efficiency of patient-care team communication. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Alastair Monk