SBIR Phase I: Nursing Workforce Optimization Algorithm and Software

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力改善了医院的护理操作，以改善患者的结果。这将通过分析卫生系统关于护士人员配置、日程安排和护患匹配的数据来实现。这项研究将分析护士、患者和住院患者临床环境的数据及其与结果的关系，以开发护理机构中独特的算法、软件和数据集。这一点很重要，因为护理人力管理决策的方法影响护理结果和提供高质量护理的成本。这个小型企业创新研究(SBIR)第一阶段项目涉及先进的研究技术，旨在优化护士人员配置、日程安排和护患匹配。将检查1.与护理操作相关的自变量和2.因变量之间的关系，这些因变量包括由医疗质量和研究机构开发的患者安全指标变量。对这些关系的探索将有助于回答以下问题：1)雇用和部署多少护士日常工作(即人员配备)；2)部署多少护士轮班(即排班)；3)如何在每个轮班单位匹配护士和病人(即护患分配)，以优化结果。拟议的优化流程支持数据驱动的方法，以解决人员配备、日程安排和护士与患者匹配的挑战。这些方法涉及多元回归分析和机器学习技术，包括自回归积分移动平均(ARIMA)。这项研究的目标包括开发算法和软件，使医院管理人员具有改善护理和患者结果的洞察力和技术。该奖项反映了NSF的法定使命，并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。

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.