The Oretical Exploration of Dual Value Correlation Functions in Benchmarking Rural Health Resources and Epistemic Uncertainty in Clinical Information Behavior

Abstract

The study will provide solutions to the major issues of rural healthcare systems, such as resource constraints, insufficient infrastructure, and unstable data collection by creating a framework to standardize the healthcare resources and simulate the effects of the epistemic uncertainty on clinical decisions. The research employs dual-value correlation functions to analyze the relationship between the availability of healthcare resources (e.g., staffing, medical equipment, transport access) and the behavior of clinical decision-making. Also, fuzzy logic and Bayesian networks can be applied to measure epistemic uncertainty arising from incomplete or unreliable data. The methodology includes benchmarking rural health resources, using dual-value correlation functions to assess its impact on decision-making, and treating epistemic uncertainty as a moderating variable. The findings indicate strong associations between available resources and clinical decision-making, and that the doctor’s adequacy and access to transportation positively affect decision-making confidence (r = 0.30). High quality of decision-making is a result of the use of digital tools, as indicated by a high value of R2 (0.75). The results of ANOVA indicate that the epistemic uncertainty has a significant influence on the decision-making (???? = ????.????????, ???? = ????.????????????). The null assumptions of Objectives 1, 2, and 3 are rejected. This validates the idea that clinical decision-making in rural healthcare facilities is affected by both resource availability and epistemic uncertainty. The study identifies the role of digital tools and resource optimization in reducing uncertainty and improving clinical outcomes in rural healthcare settings, suggesting that combining these factors in rural healthcare systems can lead to better decision-making and greater resilience. These results can support better resource allocation and decision-making in underserved healthcare settings.