SBIR Phase I: Nursing Workforce Optimization Algorithm and Software

SBIR 第一阶段：护理人员优化算法和软件

• 批准号: 2052208
• 负责人: Colin Plover
• 金额: $ 25.6万
• 依托单位: 1442 S. FALLON STREET, LLC
• 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: 2021-05-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052208&HistoricalAwards=false
• 关键词: SBIR; Phase; Nursing; Workforce; Optimization

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project improves nursing operations in hospitals for better patient outcomes. This will be achieved through the analysis of a health system’s data regarding nurse staffing, scheduling, and nurse-patient matching. This research will analyze data on nurses, patients, and inpatient clinical environments and their relationship to outcomes to develop unique algorithms, software, and datasets in care facilities. This is significant because the approach to nursing workforce management decisions influences care outcomes and the cost of delivery of quality care. This Small Business Innovation Research (SBIR) Phase I project involves advanced research techniques that aim to optimize nurse staffing, scheduling, and nurse-patient matching. Relationships will be examined between 1. independent variables associated with nursing operations and 2. dependent variables that include patient safety indicator variables developed by the Agency for Healthcare Quality and Research. The exploration of these relationships will help answer questions including 1) how many nurses to employ and deploy day-to-day (i.e. staffing), 2) how many and in what complement to deploy nurses on shifts (i.e. scheduling), and 3) how to match nurses to patients on each unit each shift (i.e. nurse-patient assignments) to optimize outcomes. The proposed optimization process enables a data- driven approach to address staffing, scheduling, and nurse-patient matching challenges. The methods involve multivariate regression analyses and machine learning techniques including autoregressive integrated moving average (ARIMA). The goals of this research involve the development of algorithms and software that empower hospital administrators with the insight and technology to improve nursing care and patient outcomes.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）I阶段项目的更广泛的影响 /商业潜力改善了医院的护士操作，以获得更好的患者结果。这将通过分析有关卫生系统有关护士人员配备，调度和护士患者匹配的数据来实现。这项研究将分析有关护士，患者和住院临床环境的数据，及其与在护理设施中开发独特算法，软件和数据集开发独特算法的结果的关系。这很重要，因为护士劳动力管理决策的方法会影响护理结果和质量护理的交付成本。这项小型企业创新研究（SBIR）I阶段项目涉及高级研究技术，旨在优化护士人员配备，调度和护士患者匹配。将在1个。与护士操作相关的自变量和2个。的关系变量，其中包括患者安全指标变量由医疗保健质量和研究机构开发的。对这些关系的探索将有助于回答问题，包括1）每天要使用和部署多少护士（即人员），2）2）在轮班上部署护士（即安排）以及如何将护士部署给每个单位的患者（即护士分配）的患者（即护士分配）（以培训护士分配）的互补者（即，护士分配）以最佳匹配。提出的优化过程可以采用数据驱动的方法来解决人员调整，调度和护士与患者的匹配挑战。这些方法涉及多元回归分析和机器学习技术，包括自回归综合运动平均值（ARIMA）。这项研究的目标涉及算法和软件的开发，使医院管理员拥有洞察力和技术，以改善护士护理和患者的结果。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响标准来评估NSF的法定任务。