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

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:
1825761
负责人:
Mingyang Li
金额:
$26.11万
依托单位:
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
项目状态:
已结题
起止时间:
2018-09-01 至 2022-08-31

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中文摘要
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英文摘要
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.
期刊论文(2)
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科研奖励(0)
会议论文
DOI: --
发表时间: 2019
期刊: Proceedings of the 2019 IISE Annual Conference
影响因子: --
作者: [Sakib, Nazmus, Hyer, Kathryn, Dobbs, Debra, Peterson, Lindsay, Jester, Dylan J., Kong, N]
通讯作者: Kong, N
A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response
用于预测疗养院飓风疏散响应的 GIS 增强数据分析方法
DOI: 10.1007/s13755-022-00190-y
发表时间: 2022
期刊: Health Information Science and Systems
影响因子: 6
作者: [Sakib, Nazmus, Hyer, Kathryn, Dobbs, Debra, Peterson, Lindsay, Jester, Dylan J., Kong, Nan, Li, Mingyang]
通讯作者: Li, Mingyang
海外基金