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

这项小企业创新研究（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，因为办公室需要在医生看到病人之前检索大量的医疗信息。不幸的是，医疗记录通常不是存储在一个地方，因此很难收集所需的病史。快速完整的医疗记录检索对于病情变化很快的癌症患者尤为重要。危重患者需要及时得到医生的诊治。该公司正在开发一种技术，可以帮助快速检索医疗信息，减少从转诊到预约的时间。该公司预计这些算法将加快文件核对7天，从而减少新患者预约转诊的时间1周。通过促进更快、更有意义的记录检索，该算法有望将治疗起始时间缩短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，首先关注那些拥有大量癌症中心的医疗系统。这个小企业创新研究（SBIR）第一阶段项目将为癌症中心推进一个新的患者转诊预测分析软件平台。该平台将简化转诊，提高资源利用率，并优化护理途径。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、资源容量、当地转诊模式和风险社会经济因素。这种干预措施可使每名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。一旦完全开发和启动，预计来自交换平台的真实世界的去识别和汇总临床数据将用于进一步训练和完善ML模型。在此阶段之前，来自大型公开可用和多机构数据库的数据将用于为模型提供训练数据点。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.