---
title: "从工业工程到老年护理：运筹学与AI集成综述"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.19075v1"
permalink: "https://feed.astralor.com/items/itm_e919f633e87b5081e56572bf"
published: "2026-07-21T13:07:28.000Z"
score: 72
channel: "tech"
tags: ["医疗AI","大语言模型","数字孪生","老年护理","运筹学"]
---

# 从工业工程到老年护理：运筹学与AI集成综述

## AI 点评

> 这篇综述让我印象深刻的是它明确指出了运筹学在老年护理应用中的“最后一公里”问题——优化了流程却没改善健康。对于医疗管理者、AI研究人员和政策制定者而言，它既是一份现状地图，也是一个转向临床导向研究的行动指南。

## 速览

本文对30项运筹学与老年护理交叉研究进行主题综述，涵盖家庭护理运营、多药管理及临床时间疗法。分析表明方法从静态确定性向动态随机模型演进，并与AI深度融合。但存在关键转化鸿沟：当前研究集中于过程优化（如人员调度），难以转化为可测量的临床结果。研究提出概念框架，强调通过数字孪生、大语言模型等新兴技术实现多层级智能决策，以弥合运营指标与患者健康结局之间的差距。

## 关键点

- 综述了30篇运筹学与老年护理交叉研究，分为家庭护理、多药管理、临床时间疗法三大主题
- 方法从静态确定性模型向动态随机模型演进，并与AI深度融合
- 存在关键转化鸿沟：过程优化难以转化为临床结局改善
- 提出融合数字孪生和大语言模型的概念框架，推动智能护理决策

## 全文

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.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_e919f633e87b5081e56572bf