Optimization Path for Management Decision-Making of Chinese Public Hospitals Under the Background of Big Data

Abstract

This study examines how Big Data might improve Chinese public hospital management. A comprehensive study examines how data diversity, storage efficiency, analytics tools, and information system complexity affect decision-making. A carefully selected quantitative dataset from Chinese public hospitals is used in the study. Analyses use structured medical records, semi-structured billing data, and unstructured patient comments. The sample size of 115 was chosen for statistical robustness and multiple regression analysis best practices, which recommend 10-20 observations per predictor variable for estimate. Multiple linear regression analysis highlights amazing correlations and stresses data diversity, storage efficiency, analytics tools, and information system sophistication in decision efficiency. The study helps healthcare executives and regulators understand the complex relationship between regression coefficients and modified R-squared value. Also evaluated are Chinese public hospitals' strengths and weaknesses. Strengths include data integration, analytics, and advanced information systems. The report emphasizes data quality and cultural transformation, which impact Big Data and decision-making. The report emphasizes data consumption and advanced analytics to empower healthcare decision-makers. This research informs Chinese public hospital strategic reforms to improve resource allocation, patient care, and efficiency. This paper demonstrates how Big Data can impact healthcare decision-making. It enriches academic discourse and guides healthcare stakeholders through modern management with relevant insights and practical advice.