I-Corps: Software platform to predict patient no-shows using machine learning algorithms

I-Corps：使用机器学习算法预测患者缺席的软件平台

• 批准号: 2146853
• 负责人: Janani Narumanchi
• 金额: $ 5万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146853&HistoricalAwards=false
• 关键词: Corps; Software; platform; predict; patient

The broader impact/commercial potential of this I-Corps project is the development of an AI-based healthcare patient scheduling system. Patient no-shows to medical appointments are a major logistical and economic challenge for clinics and hospitals, with an estimated average yearly no-show rate for primary care/specialty medical appointments of 20 - 35% leading to significant revenue loss. In addition, patients who failed to keep an appointment were 70% more likely not to return within 18 months, and older patients experiencing chronic illnesses are likely not to return to their physicians’ offices after missing just one appointment. Missing an appointment has been shown to create a significant negative impact on disease management leading to increased morbidity and increased costs. Factors that cause patients to miss their appointments have been identified, and the proposed system offers a proactive solution that may result in higher appointment adherence. Reduced missed appointments may decrease economic costs in medical services such as imaging studies, and downstream services resulting such as pharmacy services and medical equipment services. The proposed technology may increase operational efficiency by minimizing empty timeslots, increasing revenue, and also may lead to a significant boost in patient and clinician satisfaction and improved patient outcomes.This I-Corps project is based on the development of software tool that employs AI-based predictive models that has the potential to more accurately predict patient no-show and cancellation rates. Initial testing of the proposed AI-based algorithm resulted in no-show prediction rates of up to 90% as compared to other software applications using least-squares predictive techniques, which realized a 68% prediction rate. In addition, the use of a patient’s historical demographic data allows the system to learn/improve, increasing prediction accuracy over time. Currently available solutions are not dynamically adaptive and are dependent on individual physicians for protocols, which increases variability in scheduling and hence difficult to implement at a system level. The proposed algorithm provides the likelihood of appointment adherence based on patient’s historic behavior, and clinics and hospitals as well as patients may benefit by scheduling appointments more efficiently.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.

这个i-Corps项目的更广泛的影响/商业潜力是开发一个基于人工智能的医疗患者调度系统。对诊所和医院来说，病人预约就诊是一项重大的后勤和经济挑战，据估计，初级保健/专科医疗预约的年均缺勤率为20%-35%，导致重大收入损失。此外，未能遵守预约的患者在18个月内不回来的可能性要高出70%，患有慢性病的老年患者很可能在错过一次预约后不会回到医生的办公室。事实证明，错过预约会对疾病管理造成严重的负面影响，导致发病率增加和费用增加。导致患者错过预约的因素已经确定，拟议的系统提供了一个主动解决方案，可能会导致更高的预约依从性。减少错过预约可能会降低医疗服务(如成像研究)以及由此产生的下游服务(如药房服务和医疗设备服务)的经济成本。拟议的技术可能会通过最大限度地减少空闲时间段来提高运营效率，增加收入，还可能导致患者和临床医生满意度的显著提高以及患者结果的改善。该i-Corps项目基于软件工具的开发，该工具采用基于人工智能的预测模型，有可能更准确地预测患者的缺勤率和取消率。基于人工智能的算法的初步测试结果表明，与使用最小二乘预测技术的其他软件应用程序相比，未显示的预测率高达90%，实现了68%的预测率。此外，使用患者的历史人口统计数据使系统能够学习/改进，随着时间的推移提高预测精度。当前可用的解决方案不是动态自适应的，并且依赖于单个医生的协议，这增加了调度的可变性，因此难以在系统级别实施。建议的算法根据患者的历史行为提供遵守预约的可能性，诊所和医院以及患者可能通过更有效地安排预约而受益。这一奖项反映了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。