Author(s)

Sining Chai ¹

Affiliation(s)

¹ Northeastern University, Boston, MA, USA

Corresponding Author

Sining Chai

ABSTRACT

The balanced allocation of medical resources is a core measure to address the issues of "difficulty in accessing medical care and high medical costs" and a key proposition for advancing the Healthy China initiative. Traditional allocation models, which rely on experiential decision-making and correlation analysis, struggle to accurately identify the causal relationship between resource supply and health needs, resulting in insufficient allocation efficiency and fairness. Centering on causal inference and robust optimization theory, combined with the multi-dimensional enabling characteristics of big data technology, this paper systematically reviews the construction logic and core methods of causal optimization models for balanced medical resource allocation, as well as the implementation path of a big data-driven robust decision support system. Following the logical framework of "causal identification - model optimization - decision implementation", the study analyzes the adaptive scenarios of different causal models in resource allocation, explores the application value of big data technology in enhancing decision robustness, and finally points out the current research bottlenecks and future development directions. It aims to provide theoretical references for the scientificization and precision of medical resource allocation. 

KEYWORDS

Balanced Allocation of Medical Resources; Causal Optimization Model; Big Data; Robust Decision-Making; Health Management

CITE THIS PAPER

Sining Chai, Causal Optimization Model for Balanced Allocation of Medical Resources and Analysis of Big Data-Driven Robust Decision Support. Journal of Electronics and Information Science (2025) Vol. 10: 164-169. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2025.100220.

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