🤖 AI Summary
This study addresses a critical gap in geriatric care research, which has predominantly emphasized operational efficiency while lacking robust connections to clinical health outcomes. Through a systematic review of 30 interdisciplinary studies at the intersection of industrial engineering and operations research applied to elderly care, the work categorizes existing literature into three thematic domains: home-based medical care, polypharmacy management, and chronotherapeutic clinical scheduling. It proposes a novel conceptual framework that explicitly links operational optimization with clinical outcomes, advocating a paradigm shift from isolated task-level improvements toward integrated, multi-layer decision-making. By incorporating human-centered design, systems analysis, and emerging technologies such as digital twins and large language models, the study highlights the field’s current overemphasis on workforce scheduling at the expense of health impact, thereby laying a theoretical and technical foundation for intelligent care decision systems spanning hospital and community settings.
📝 Abstract
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.