STTR Phase I: Advancing Health Equity using Interactive Condition Assessment and Monitoring

这项小型企业技术转移（STTR）第一阶段项目的更广泛影响/商业潜力是建立一个疾病评估和监测平台，利用人工智能（AI）进行智能决策支持，以加快诊断和促进个性化治疗解决方案。目前，患者病史数据是口头获得的，临床医生和患者之间的健康沟通不良可能导致78%的误诊，导致8万例本可避免的医院死亡，每年给美国经济造成7500亿美元的损失。语言障碍大大加剧了这种情况，使严重和频繁的不良后果的风险增加了49%。文化、语言和健康素养低的患者受到的影响是其两倍。拟议的工作有三个主要的技术目标：(1)开发一个交互式的可视化支持的医疗数据捕获用户界面（UI），以克服沟通和语言障碍；(2)开发一个基于人工智能的风险评估引擎，将症状传达给提供者，以进行分类决策；(3)利用预测分析来推荐适当的治疗方案，并开发一个基于可视化的状态监测和管理工具。拟议的数据收集和人工智能驱动的提供者决策支持平台将通过加强沟通、改善健康结果和降低医疗成本来增强患者和提供者的能力。这个小型企业技术转移（STTR）第一阶段项目是为所有读写水平的人设计的，与语言无关。该平台的数字原生、基于云的方法允许其可扩展，以实现更广泛和更快的影响。前端应用程序在患者检查前捕获重要的患者信息和症状，并以易于阅读的格式为临床医生总结患者自我报告。临床医生可能会更有准备，通过避免误诊、错误和患者早期的并发症，促进个性化护理的提供和改善结果。基于人工智能的模型从患者自我报告中提取结构化数据，如人口统计信息和症状，并使用历史和现实世界的结果数据对数据集进行监督学习。将评估多个人工智能模型，包括分类和神经网络模型，那些表现足够准确的模型将被视为集成学习的输入，这是本项目的最终预测输出。示例输出包括患者评估器，如患者自我报告中的一致性、估计的风险评估评分和近似的诊断准确性评分。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 Technology Transfer (STTR) Phase I project is to build a condition assessment and monitoring platform that leverages artificial intelligence (AI) for intelligent decision support to expedite diagnosis and facilitate personalized therapy solutions. Currently, patient history data is obtained verbally, and poor health communication between clinician and patient potentially causes 78% of misdiagnoses, resulting in 80,000 avoidable hospital deaths and $750 billion in costs to the US economy each year. Language barriers exacerbate this situation significantly, increasing the risk of severe and frequent adverse outcomes by 49%. Patients with cultural, language, and low health literacy are twice as affected. The proposed work has three major technical objectives: (1) develop an interactive visually-supported healthcare data capture user interface (UI) to overcome communication and language barriers, (2) develop an AI-based risk assessment engine to communicate symptoms to the provider for triage decisions, and (3) leverage predictive analytics to recommend appropriate treatment options and develop a visualization-based condition monitoring and management tool. The proposed data collection and AI-powered provider decision support platform will empower patients and providers by enhancing communication, improving health outcomes, and reducing healthcare costs.This Small Business Technology Transfer (STTR) Phase I project is designed for accessibility across all levels of literacy and is language-agnostic. The platform’s digitally-native, cloud-based approach allows it to be scalable for widespread impact and faster impact. The frontend app captures important patient information and symptoms before the patient exam and summarizes the patient self-report in an easily readable format for the clinician. The clinician will potentially be more prepared, facilitating the delivery of personalized care and improved outcomes by avoiding misdiagnosis, errors, and complications earlier in a patient’s journey. The AI-based model pulls structured data, such as demographic information and symptoms from the patient self-report, and the dataset undergoes supervised learning using historical, real-world outcomes data. Multiple AI models will be evaluated, including classification and neural network models, and those with sufficiently accurate performance will be considered as inputs for ensemble learning, the final prediction output for this project. Example outputs include patient evaluators such as consistencies within the patient’s self-report, an estimated risk assessment score, and an approximate diagnosis accuracy score.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.