Pursuing Equilibrium of Medical Resources via Data Empowerment in Parallel Healthcare System
Yi Yu, Shengyue Yao, Kexin Wang, Yan Chen, Fei-Yue Wang, Yilun Lin

TL;DR
This paper proposes a novel parallel healthcare system leveraging data empowerment through MOOS, MOSE, and MOLMs to balance medical resource supply and demand, significantly improving accessibility.
Contribution
It introduces a comprehensive framework combining digital, scenario engineering, and large models to optimize healthcare resource allocation.
Findings
Up to 300% improvement in facility accessibility.
Effective balancing of supply and demand through data-driven methods.
Demonstrated potential for addressing global healthcare resource imbalance.
Abstract
The imbalance between the supply and demand of healthcare resources is a global challenge, which is particularly severe in developing countries. Governments and academic communities have made various efforts to increase healthcare supply and improve resource allocation. However, these efforts often remain passive and inflexible. Alongside these issues, the emergence of the parallel healthcare system has the potential to solve these problems by unlocking the data value. The parallel healthcare system comprises Medicine-Oriented Operating Systems (MOOS), Medicine-Oriented Scenario Engineering (MOSE), and Medicine-Oriented Large Models (MOLMs), which could collect, circulate, and empower data. In this paper, we propose that achieving equilibrium in medical resource allocation is possible through parallel healthcare systems via data empowerment. The supply-demand relationship can be…
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Taxonomy
TopicsHealthcare Systems and Reforms · Healthcare Operations and Scheduling Optimization · Global Health Care Issues
