GOALI/Collaborative Research: Consistent Nursing Home Staff Planning under Heterogeneous Service Demand

GOALI/合作研究：异构服务需求下一致的疗养院人员规划

• 批准号: 1825725
• 负责人: Nan Kong
• 金额: $ 24.3万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825725&HistoricalAwards=false
• 关键词: GOALI; Collaborative; Research; Consistent; Nursing

This Grant Opportunities for Academic Liaison with Industry (GOALI) award will advance the national health by improving the delivery of health-care to the growing sector of the population served by nursing homes. Nursing homes are responsible for caring for the frail and vulnerable population of older adults who suffer from diverse chronic diseases, functional limitations and impairments. They must coordinate distinct caregivers to provide patients with round-the-clock physical and emotional care and assistance. Because of growing demand due to immutable population demographics, rapidly increasing health-care costs, and escalating nursing staff shortage, proper care for nursing home residents is at-risk. The goal of this project is to improve long-term nursing home quality of care and reduce costs using analytical methods and tools to realize proactive, resident-centered staffing plans. This project will improve workforce planning, recruitment and allocation decisions for nursing home managers, reduce stress and burn-out by better balancing workloads for nursing home staff, and enhance health outcomes for nursing home residents by better meeting their diverse care needs. The close involvement of the engineering team with a nursing home operator and national aging health policy experts will also help transform nursing home culture into delivering more resident-centered and home-like care. The research objectives will be achieved through the development of a set of innovative models, algorithms, and decision tools. A predictive data analytics model and efficient estimation algorithms will be developed to characterize heterogeneous service need trajectories and length-of-stays of nursing home residents for service demand prediction improvement at the resident level. A two-stage stochastic programming model and efficient numerical optimization algorithms will be developed for consistent nursing home staff planning under service demand fluctuation and uncertainty. To maximize the practical relevance of research deliverables, a decision support system will be also developed to evaluate and validate the work in a real-world nursing home setting through our academic-industrial partnership with Greystone Healthcare Management Corporation and industry-scale solution deployment in their facilities.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.

这一学术界与工业界联络的赠款机会（GOALI）奖将通过改善向疗养院服务的日益增长的人口提供保健服务来促进国民健康。 养老院负责照顾患有各种慢性病、功能限制和损伤的年老体弱者。 他们必须协调不同的照顾者，为病人提供24小时的身体和情感护理和帮助。 由于不可改变的人口统计数据，快速增加的医疗保健成本以及不断加剧的护理人员短缺，养老院居民的适当护理面临风险。该项目的目标是提高长期护理之家的护理质量，并使用分析方法和工具来降低成本，以实现积极主动的，以居民为中心的人员配置计划。 该项目将改善养老院管理人员的劳动力规划，招聘和分配决策，通过更好地平衡养老院工作人员的工作量来减少压力和倦怠，并通过更好地满足其多样化的护理需求来提高养老院居民的健康状况。 工程团队与养老院运营商和国家老龄化健康政策专家的密切参与也将有助于将养老院文化转变为提供更多以居民为中心的家庭式护理。研究目标将通过开发一套创新的模型，算法和决策工具来实现。一个预测性的数据分析模型和有效的估计算法将被开发，以表征异构服务需求轨迹和住院时间的养老院居民的服务需求预测改善居民水平。两阶段随机规划模型和有效的数值优化算法将开发一致的养老院工作人员规划服务需求的波动和不确定性。为了最大限度地提高研究成果的实际相关性，还将开发一个决策支持系统，通过我们与Greystone医疗保健管理公司和工业界的学术-工业合作伙伴关系，在现实世界的养老院环境中评估和验证这项工作。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

项目成果

Optimal Nursing Home Service Scheduling Under covid-19 Related Probabilistic Staff Shortage: A Two-stage Stochastic Programming Approach

covid-19相关概率性人员短缺下的最佳疗养院服务调度：两阶段随机规划方法

• 发表时间: 2021
• 期刊: 2021 Industrial and Systems Engineering Research Conference
• 作者: Jiang, S.;Kong, N.;Abrahamson, K.;Yih, Y.
• 通讯作者: Yih, Y.

A Simulation-Based Metaheuristic Approach to Integrated Scheduling of Seedling Production

• DOI: 10.1109/lra.2021.3140056
• 发表时间: 2022-04
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Jingyuan Feng;Xiangpei Hu;N. Kong

Optimal Nursing Home Shift Scheduling: A Two-Stage Stochastic Programming Approach

最佳疗养院轮班安排：两阶段随机规划方法

• 发表时间: 2020
• 期刊: CASE 2020
• 作者: Shujin Jiang, Mingyang Li

Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions

具有竞争性出院处置的疗养院急性后护理居民的异质住院时间模型

• DOI: 10.1007/s42524-022-0203-7
• 发表时间: 2022
• 期刊: Frontiers of Engineering Management
• 影响因子: 7.4
• 作者: Sakib, Nazmus;Sun, Xuxue;Kong, Nan;Masterson, Chris;Meng, Hongdao;Smith, Kelly;Li, Mingyang
• 通讯作者: Li, Mingyang

A Predictive Analytics Approach for Nursing Home Hurricane Evacuation

疗养院飓风疏散的预测分析方法

• 发表时间: 2019
• 期刊: Proceedings of the 2019 IISE Annual Conference
• 作者: Sakib, Nazmus;Hyer, Kathryn;Dobbs, Debra;Peterson, Lindsay;Jester, Dylan J.;Kong, N
• 通讯作者: Kong, N