SBIR Phase I: Optimal Clinical Workforce Staffing and Scheduling using an Advanced Predictive Modeling System

SBIR 第一阶段：使用先进的预测建模系统优化临床劳动力人员配置和调度

• 批准号: 1914040
• 负责人: Stephanie Gravenor
• 金额: $ 22.5万
• 依托单位: Medecipher, Inc.
• 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: 2019-06-01 至 2020-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914040&HistoricalAwards=false
• 关键词: SBIR; Phase; Optimal; Clinical; Workforce

This SBIR Phase I project aims at optimizing emergency department staffing decisions. Direct patient care staffing costs consume nearly 50% of an average hospital's operating revenues. As hospitals adapt to a rapidly changing healthcare market, hospital management often seek to reduce staffing costs to increase operational viability. These cost-cutting initiatives introduce significant risk exposure, with studies showing that staffing a unit below the target level is associated with increased mortality and other adverse patient events. Inadequate staffing causes staff to feel overworked and leads to burnout, which costs hospitals over $9 billion annually, in part because of turnover. Optimizing clinical workforce staffing is especially critical in the emergency department, which must provide on-demand availability of staff to meet the needs of rapidly changing patient populations without significant delay. With nearly half of all medical care in the United States occurring in emergency departments and visits to the emergency department increasing 44% over the past decade, it is essential for U.S. public health to optimize emergency department staffing to ensure quality patient care and clinical outcomes, while also delivering better financial performance. No comprehensive technology exists for optimizing emergency department staffing decisions. This SBIR Phase I project will be used to: i) develop an automated emergency department patient demand predictive engine and create a software platform that automates the predictive model recalibration process; ii) develop a staff flexing recommender and decision support engine, which allows for user input and clinical judgment before a final staffing decisions is made, iii) integrate the recommender engine with real-time patient volume feed, and iv) develop human-centered design interface for the decision support engine that is appropriate for the dynamic clinical setting of the emergency department. The engine and its user interface will be developed in collaboration with three beta sites. Upon successful completion of the project, the engine will be used for cost-benefit analysis of data driven staffing decision making at the proposed beta sites.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.

该 SBIR 第一阶段项目旨在优化急诊科人员配置决策。直接患者护理人员成本占医院平均营业收入的近 50%。随着医院适应快速变化的医疗保健市场，医院管理层经常寻求降低人员成本以提高运营活力。这些削减成本的举措带来了巨大的风险，研究表明，单位人员配备低于目标水平与死亡率和其他不良患者事件的增加有关。人员配备不足会导致员工感到过度劳累并导致倦怠，这导致医院每年损失超过 90 亿美元，部分原因是人员流动。优化临床人员配置对于急诊科尤为重要，急诊科必须按需提供工作人员，以满足快速变化的患者群体的需求，而不会造成明显延误。美国近一半的医疗护理发生在急诊科，过去十年来急诊科就诊人数增加了 44%，因此美国公共卫生部门必须优化急诊科人员配置，以确保优质的患者护理和临床结果，同时还提供更好的财务业绩。目前尚不存在用于优化急诊科人员配置决策的综合技术。该 SBIR 第一阶段项目将用于： i) 开发自动化急诊科患者需求预测引擎，并创建一个自动执行预测模型重新校准过程的软件平台； ii) 开发灵活的人员推荐和决策支持引擎，允许在做出最终人员配置决策之前进行用户输入和临床判断，iii) 将推荐引擎与实时患者量反馈相集成，以及 iv) 开发以人为本的设计适用于急诊科动态临床环境的决策支持引擎界面。该引擎及其用户界面将与三个测试站点合作开发。该项目成功完成后，该引擎将用于在拟议的测试站点上对数据驱动的人员配置决策进行成本效益分析。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和技术进行评估，被认为值得支持。更广泛的影响审查标准。