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

该SBIR第一阶段项目旨在优化急诊科人员配备决策。直接的病人护理人员成本消耗了一家普通医院运营收入的近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，以提高运营能力。这些成本削减举措带来了显著的风险敞口，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。人员不足会导致员工感到工作过度，并导致工作倦怠，这在一定程度上是因为人员流失，这让医院每年花费超过90亿美元。优化临床工作人员配置在急诊科尤为重要，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，而不会有明显的延误。随着美国近一半的医疗保健发生在急诊科，急诊科就诊人数在过去十年中增加了44%，美国公共卫生部门必须优化急诊科人员配置，以确保高质量的患者护理和临床结果，同时提供更好的财务业绩。目前还没有全面的技术来优化急诊科的人员配备决策。该SBIR第一阶段项目将用于：i)开发一个自动化的急诊科患者需求预测引擎，并创建一个自动化预测模型重新校准过程的软件平台；ii)开发一个工作人员灵活推荐和决策支持引擎，它允许用户在做出最终的人员配备决策之前输入和进行临床判断；iii)将该推荐引擎与实时患者数量反馈集成；以及iv)开发适合急诊科动态临床设置的决策支持引擎的以人为中心的设计界面。该引擎及其用户界面将与三个测试版网站合作开发。该项目成功完成后，该引擎将用于对拟议的测试版站点的数据驱动的人员配备决策进行成本效益分析。该奖项反映了NSF的法定使命，并已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。

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.