SBIR Phase II: Optimizing emergency department nurse scheduling via a novel operational intelligence platform

SBIR 第二阶段：通过新颖的运营智能平台优化急诊科护士调度

• 批准号: 2112491
• 负责人: Stephanie Gravenor
• 金额: $ 99.97万
• 依托单位: Medecipher, Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112491&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Optimizing; emergency

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve emergency clinical care. Emergency Departments are facing complex nursing utilization problems, fueled in part by scheduling systems built on flawed assumptions. Escalating nursing shortages and burnout must be addressed as fatigue is rampant. With hospitals facing significant revenue losses, there is no room for inefficient use of nurses. Nurse shifts must be intelligently sequenced in a way that optimizes available assets and balances complex sets of tradeoffs. The proposed solution uses artificial intelligence (AI) and operations research to predictively right-size clinical resources. The platform will be packaged as a cloud-based solution that reduces nursing staff churn, decreases patient wait times, reduces healthcare delivery costs, and improves revenue. Sophisticated mathematical approaches beyond what is available in the calendar and an Excel spreadsheet – specifically this project’s constraint-based algorithms, simulation methods and machine learning – will be the turning point in optimizing emergency department operational performance. This will improve the health system’s patient care, operational, and financial outcomes while reducing effort, waste, cost, and nurse burnout. This proposed project addresses the multi-stage emergency department nurse staffing problem by increasing scalability and usability. The proposed solution scales a set of multi-objective optimization algorithms based on operations research, sophisticated data science and queueing theory models to create an end-to-end decision support platform. The platform will provide longitudinal decision support for emergency department nurse staffing: nurse managers will use it daily for flexing decisions, monthly for schedule planning and assignment, and annually for budgeting. Objectives include: (1) Data science: refine and automate the forecasting, adapt the optimization model, generate and maintain input files, automate output files; (2) Front-end: create UI components, create service layer/API, wire service layer and UI, develop integration and component testing; (3) Back-end: implement container orchestration system, upgrade security, implement data ingest protocols, support development operations; and (4) Validate recommendation and create client ROI calculator.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）II期项目的更广泛的影响/商业潜力是改善紧急临床护理。急诊部门面临着复杂的护理利用问题，部分原因是安排基于射击假设的系统。由于疲劳猖ramp，必须解决不断升级的护士短缺和倦怠。由于医院面临巨大的收入损失，因此没有效率低下的护士的空间。护士轮班必须以优化可用资产和平衡复杂折衷集的方式进行智能测序。拟议的解决方案使用人工智能（AI）和操作研究来预测右尺寸的临床资源。该平台将被包装为基于云的解决方案，可减少护士工作人员流失，增加患者的等待时间，降低医疗保健服务成本并提高收入。超越日历中可用的数学方法和Excel电子表格（特别是该项目的基于约束的算法，仿真方法和机器学习）将是优化急诊部门运营绩效的转折点。这将改善卫生系统的患者护理，运营和财务成果，同时减少努力，浪费，成本和护士倦怠。该建议的项目通过提高可伸缩性和可用性来解决多阶段急诊室护士人员配备问题。提出的解决方案根据操作研究，复杂的数据科学和排队理论模型来缩放一组多目标优化算法，以创建端到端的决策支持平台。该平台将为急诊部护士人员配备提供纵向决策支持：护士经理每天将其用于弹性决策，每月进行计划计划和分配以及每年进行预算。目标包括：（1）数据科学：完善和自动化预测，调整优化模型，生成和维护输入文件，自动输出文件； （2）前端：创建UI组件，创建服务层/API，电线服务层和UI，开发集成和组件测试； （3）后端：实施集装箱编排系统，升级安全性，实施数据摄入协议，支持开发操作； （4）验证建议并创建客户ROI计算器。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，我们是否通过评估来诚实地支持我们的支持。