SBIR Phase I: Predictive Analytics and Machine Learning Modeling for New Patient Cancer Referrals

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是减少患者转诊等待时间。转诊等待时间通常很长，因为在医生看病人之前，办公室需要检索大量关于病人的医疗信息。不幸的是，病历往往没有存储在一个地方，这使得收集所需的病历变得困难。快速、完整的病历检索对癌症患者尤其重要，因为他们的病情可能很快就会发生变化。危重病人需要医生及时就诊，才能开始治疗。该公司正在开发一种技术，可以帮助快速检索医疗信息，以缩短从转诊到预约的时间。该公司预计这些算法将加快文档对账速度7天，从而将从转诊到新患者预约的时间缩短1周。通过促进更快和更有意义的记录检索，这些算法预计将使治疗开始时间缩短7-14天。该公司计划将其技术商业化，用于大型学术医疗系统，首先专注于那些拥有大量癌症中心的系统。这个小型企业创新研究(SBIR)第一阶段项目将为癌症中心推进一个新的患者转介预测分析软件平台。该平台将简化转诊，提高资源利用率，并优化护理路径。该公司将开发深度学习算法，以简化新患者预约的记录检索，并识别关键医疗条件、资源能力、当地转诊模式和面临风险的社会经济因素。这种干预措施可以使每个患者每周的死亡风险降低3.2-6.4%。为了实现这些目标，该软件将包含两个主要组件：基于云的医疗信息交换平台和基于机器学习(ML)的分析平台。一旦完全开发和推出，预计来自交换平台的真实世界识别和聚合的临床数据将用于进一步培训和改进ML模型。在此阶段之前，来自大型公开数据库和多机构数据库的数据将用于为该模型提供培训数据点。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to decrease patient referral wait times. Referral wait times are often long since offices need to retrieve a large amount of medical information on a patient before they are seen by a doctor. Unfortunately, medical records are often not stored in one place, making it difficult to gather the needed medical histories. Quick and complete medical record retrieval is especially important for cancer patients, whose conditions can quickly change. Critical patients need to be seen by doctors in a timely manner to begin treatment. The company is creating a technology that could help quickly retrieve medical information to decrease the time from referral to appointment. The company expects these algorithms to expedite document reconciliation by 7 days, thereby reducing the time from referral for the new patient appointment by 1 week. By facilitating quicker and more meaningful record retrieval, the algorithms are expected to improve treatment initiation by 7-14 days. The company plans to commercialize its technology for use in large academic healthcare systems, first focusing on those with high-volume cancer centers. This Small Business Innovation Research (SBIR) Phase I project will advance a new patient referral predictive analytics software platform for cancer centers. This platform will streamline referrals, increase resource utilization, and optimize care pathways. The company’s deep learning algorithms will be developed to streamline record retrieval for new patient appointments and recognize critical medical conditions, resource capacity, local referral patterns, and at-risk socioeconomic factors. This intervention may reduce the mortality risk by 3.2-6.4% per week per patient. To achieve these objectives, the software will contain two major components a cloud-based platform for medical information exchange and an machine learning (ML)-based analytics platform. Once fully developed and launched, it is anticipated that real-world deidentified and aggregated clinical data from the exchange platform will be used to further train and refine the ML model. Prior to this stage, data from large publicly available and multi-institutional databases will be used to provide training data points for the model.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.