从运营到老年护理结果：工业工程与决策支持方法的主题综述
From Operations to Elderly Care Outcomes: A Thematic Review of Industrial Engineering and Decision-Support Approaches
- Isfahan University of Technology（伊斯法罕理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
全球老龄化给医疗带来挑战，本文对运筹学与老年护理交叉研究综述，指出方法从静态向动态随机演变，存在转化差距，提出概念框架，强调利用新兴技术弥合理论与患者健康结果差距。
AI中文摘要：
全球老龄化人口的快速增长给医疗系统带来严峻挑战，需要高效、公平且以患者为中心的护理模式。工业工程和运筹学提供了强大工具，但当前应用零散。本文对30项运筹学与老年护理交叉的开创性研究进行主题综述，分为家庭医疗运营、多重用药管理和临床时间疗法。分析表明方法从静态、确定性模型向与人工智能集成的动态、随机框架演变。然而，存在关键转化差距，当前文献多为流程层面优化，难以转化为可衡量临床结果，且医院与社区护理过渡的整体模型未充分探索。本研究提出概念框架，强调通过新兴数字技术弥合理论运营指标与患者层面健康结果差距的必要性。
英文摘要：
The rapid growth of the global aging population presents severe challenges to healthcare systems, necessitating efficient, equitable, and patient-centered care models. While Industrial Engineering and Operations Research (OR) provide robust optimization and decision-support tools to address these multidimensional complexities, current applications often remain fragmented. This paper presents a thematic review of 30 seminal studies at the intersection of OR and elderly care, categorizing the literature into home healthcare operations, polypharmacy management, and clinical chronotherapy. Our analysis highlights a significant methodological evolution from static, deterministic models toward dynamic and stochastic frameworks integrated with artificial intelligence (AI). Despite these advancements, a critical translational gap persists: the current OR literature is heavily dominated by process-level optimizations, such as staff routing, and struggles to translate these operational efficiencies into measurable clinical outcomes. Furthermore, holistic models bridging the transition between hospital and community care remain critically underexplored. To develop resilient and smart healthcare systems, this study proposes a conceptual framework that shifts the research focus from isolated operational tasks to integrated, multi-level decision-making. We emphasize the critical need for robust systems analysis, human-inclusive design, and the smartification of care through emerging digital technologies - including digital twins and large language models - to successfully bridge the gap between theoretical operational metrics and tangible patient-level health outcomes.
