SCH: INT: Collaborative Research: Smart Alarms 2.0: Foundations for Caregiver-in-the-loop Suppression of Non-Informative Alarms

SCH：INT：协作研究：智能警报 2.0：护理人员实时抑制非信息性警报的基础

• 批准号: 1915398
• 负责人: Insup Lee
• 金额: $ 98万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1915398&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Smart

Systems used to continuously monitor vital signs in hospitals and home settings generate alarms intended to warn caregivers, clinicians in the hospital, or parents at home of conditions that warrant their immediate attention. However, less than 1% of these alarms are considered actionable or informative, which has led to clinical alarm fatigue being ranged as one of the Top 10 Health Technology Hazards. While there have been advances in human factors, operational factors, and technical factors independently, their interdependencies have yet to be collectively considered. Consequently, current alarm suppression systems aimed at addressing clinical alarm fatigue are generally not extensible, suffer from low adoption rates, and/or have poor performance in practice.This proposal aims to develop foundations for the design of next generation smart alarms. Smart Alarms 2.0 will utilize caregiver-in-the-loop alarm suppression systems to reduce the frequency of non-informative alarms in-clinic and at home. Realizing an effective caregiver-in-the-loop alarm suppression system requires fundamental advances in robust alarm suppression design methodologies and caregiver-in-the-loop modeling and analysis. The potential impact of the proposed design and analysis techniques is evaluated in medical scenarios for alarm suppression applications, spanning pediatric wards and at-home monitoring of Bronchopulmonary dysplasia (BPD) patients. This project will lay the foundations for designing caregiver-in-the-loop alarm systems in which physicians, nurses, and parents monitor the right children, at the right times, using optimal alarm settings to maximize informativeness.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.

用于在医院和家庭环境中持续监测生命体征的系统会生成警报，旨在警告护理人员、医院的临床医生或家中的父母需要立即注意的情况。然而，这些警报中只有不到1%被认为是可操作的或提供信息的，这导致临床警报疲劳被列为十大健康技术危害之一。虽然在人的因素、操作因素和技术因素方面取得了独立的进展，但它们的相互依存关系尚未得到集体考虑。因此，目前的报警抑制系统，旨在解决临床报警疲劳一般是不可扩展的，从低采用率，和/或在practice.This建议的性能差，旨在开发下一代智能报警器的设计基础。智能报警2.0将利用闭环报警抑制系统来减少诊所和家中非信息报警的频率。实现一个有效的闭环报警抑制系统需要在鲁棒的报警抑制设计方法和闭环建模和分析的根本进步。在警报抑制应用的医疗场景中，评估了拟议设计和分析技术的潜在影响，涵盖儿科病房和支气管肺发育不良（BPD）患者的家庭监测。该项目将为设计一个医生、护士和家长在正确的时间使用最佳报警设置来最大化信息量，从而监控正确的儿童的循环报警系统奠定基础。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Evaluating Alarm Classifiers with High-confidence Data Programming

使用高置信度数据编程评估警报分类器

• DOI: 10.1145/3549942
• 发表时间: 2022
• 期刊: ACM Transactions on Computing for Healthcare
• 作者: Pugh, Sydney;Ruchkin, Ivan;Bonafide, Christopher;Demauro, Sara;Sokolsky, Oleg;Lee, Insup;Weimer, James
• 通讯作者: Weimer, James

Raproto: an open source platform for rapid prototyping of wearable medical devices

Raproto：用于可穿戴医疗设备快速原型设计的开源平台

• DOI: 10.1145/3446913.3460315
• 发表时间: 2021
• 期刊: Medical Cyber-Physical Systems Workshop
• 作者: Watson, Amanda;Choi, Hyonyoung;Lee, Insup;Weimer, James
• 通讯作者: Weimer, James

Improving Classifier Confidence using Lossy Label-Invariant Transformations

• 发表时间: 2020-11
• 作者: Sooyong Jang;Insup Lee;James Weimer