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

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是开发自然语言处理软件，以简化生育诊所患者与其提供者之间的沟通。生育诊所是专门的医疗设施，提供临床诊断和治疗，以帮助怀孕。他们的患者在整个怀孕过程中经历了强烈的焦虑和大量的自付费用，生育诊所多达三分之二的患者将停止他们的治疗方案，情绪压力被认为是主要原因。患者满意度，这是更复杂的不仅仅是实现预期的结果，是相关的护理团队如何有效地导航大的信息差距和沟通的差距。该项目推进技术，以促进这种沟通。这个小型企业创新研究（SBIR）第一阶段项目将解决与医疗保健（特别是生育护理）中自然语言处理相关的主要技术障碍。这些挑战包括会话代理必须理解特定于生育相关问题的词汇，自然语言平台必须克服在整个连续多人对话中保持上下文意识的困难，以及在更广泛的会话上下文中理解模糊的问题，并且算法必须回复针对特定患者个性化的临床准确响应。为了实现这些目标，将根据生育利益相关者（患者，提供者和诊所工作人员）的反馈开发生育特定语料库或字典，并将开发机器学习算法，分析患者的意图消息，并提供个性化的响应。最后，将进行可用性研究，以完善平台的用户界面、语气和信息内容。拟议研究的预期结果是一个平台，提高病人护理团队沟通的质量和效率。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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 platform’s 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.