---
title: "From Industrial Engineering to Elderly Care: A Thematic Review of OR and AI Integration"
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: ["Medical AI","Large Language Models","Digital twin","Elderly Care","Operations Research"]
---

# From Industrial Engineering to Elderly Care: A Thematic Review of OR and AI Integration

## AI commentary

> What impresses me is this review's clear identification of the 'last mile' problem in OR for elderly care—optimizing processes without improving health. For healthcare managers, AI researchers, and policymakers, it serves as both a landscape map and a call to action for clinically oriented research.

## At a glance

This thematic review of 30 seminal studies at the intersection of operations research and elderly care covers home healthcare operations, polypharmacy management, and clinical chronotherapy. The analysis reveals a methodological evolution from static deterministic models to dynamic stochastic frameworks integrated with AI. However, a critical translational gap persists: current research focuses on process-level optimizations like staff routing, struggling to translate efficiencies into measurable clinical outcomes. The study proposes a conceptual framework leveraging digital twins and large language models for multi-level intelligent decision-making to bridge the gap between operational metrics and patient-level health outcomes.

## Key points

- Reviews 30 studies at OR-elderly care intersection across home healthcare, polypharmacy, and chronotherapy
- Methodological shift from static deterministic to dynamic stochastic models integrated with AI
- Critical translation gap: process optimizations fail to yield measurable clinical outcomes
- Proposes framework using digital twins and LLMs for smart, multi-level care decisions

## Full text

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.

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