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

SBIR 第一阶段：针对新癌症患者转诊的预测分析和机器学习建模

• 批准号: 2304498
• 负责人: Max Jiam
• 金额: $ 27.5万
• 依托单位: IIAM CORPORATION
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304498&HistoricalAwards=false
• 关键词: SBIR; Phase; Predictive; Analytics; Machine; SBIR; Phase; Predictive; Analytics; Machine

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.

这项小型企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是减少患者的转诊等待时间。转诊等待时间通常很长，因为办公室需要在医生看到患者之前检索大量医疗信息。不幸的是，病历通常不会存储在一个地方，因此很难收集所需的病史。快速完整的病历检索对于癌症患者尤为重要，癌症患者的病情可能会迅速改变。医生需要及时观察关键患者才能开始治疗。该公司正在创建一项可以帮助快速检索医疗信息的技术，以减少从转诊到约会的时间。该公司预计这些算法将在7天之前加快文件对帐，从而将新患者预约的转诊时间减少1周。通过支持更快，更有意义的记录检索，该算法有望在7-14天之前改善治疗计划。该公司计划将其技术商业化用于大型学术医疗保健系统，首先关注那些具有大量癌症中心的人。这项小型企业创新研究（SBIR）I期项目将推动癌症中心的新患者推荐预测分析软件平台。该平台将简化推荐，增加资源利用并优化护理路径。该公司的深度学习算法将被开发，以简化新的患者任命的记录检索，并确认关键的医疗状况，资源能力，本地推荐模式和处于危险的社会经济因素。这种干预措施可能会使每位患者每周的死亡率风险降低3.2-6.4％。为了实现这些目标，该软件将包含两个主要组件，该组件是基于云的医疗信息交换和基于机器学习（ML）的分析平台的平台。一旦充分开发和启动，可以预见，来自Exchange Platform的现实世界被识别和汇总的临床数据将用于进一步训练和完善ML模型。在此阶段之前，将使用来自大型公共可用和多机构数据库的数据为模型提供培训数据点。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响评估标准来评估NSF的法定任务。