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

这项“与工业界学术联络机会资助”（GOALI）奖将通过改善向养老院服务的人口中日益增长的群体提供保健服务，促进国家健康。养老院负责照顾患有各种慢性疾病、功能限制和损伤的体弱多病和脆弱的老年人。他们必须协调不同的护理人员，为患者提供全天候的身体和情感护理和帮助。由于不可改变的人口统计数据、迅速增加的医疗保健成本和日益严重的护理人员短缺，对养老院居民的适当护理面临风险。该项目的目标是通过分析方法和工具来实现主动的、以居民为中心的人员配备计划，提高养老院的长期护理质量，降低成本。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轻压力和倦怠，并通过更好地满足养老院居民的各种护理需求来改善他们的健康状况。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合作，也将有助于将养老院文化转变为提供更多以居民为中心和家庭式的护理。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。本文将开发预测数据分析模型和有效的估计算法，以表征养老院居民的异质服务需求轨迹和住院时间，以改善居民层面的服务需求预测。针对服务需求波动和不确定性下的养老院人员一致性规划问题，提出了两阶段随机规划模型和高效的数值优化算法。为了最大限度地提高研究成果的实际相关性，我们还将开发一个决策支持系统，通过我们与Greystone医疗保健管理公司的学术-工业合作伙伴关系和在其设施中部署工业规模的解决方案，在现实世界的养老院环境中评估和验证工作。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.