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Updated: Apr 17, 2026
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Advancing the use of evidence-based decision-making in local health departments with systems science methodologies
Yan Li1, Nan Kong, Mark Lawley
1Yan Li and José A. Pagán are with the Center for Health Innovation, The New York Academy of Medicine, New York. Nan Kong is with the Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN. Yan Li and Mark Lawley are with the Department of Industrial and Systems Engineering, Dwight Look College of Engineering, Texas A&M University, College Station. Linda Weiss is with the Center for Evaluation and Applied Research, The New York Academy of Medicine. José A. Pagán is also with the Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York.
Objectives:
We assessed how systems science methodologies might be used to bridge resource gaps at local health departments (LHDs) so that they might better implement evidence-based decision-making (EBDM) to address population health challenges.
Methods:
We used the New York Academy of Medicine Cardiovascular Health Simulation Model to evaluate the results of a hypothetical program that would reduce the proportion of people smoking, eating fewer than 5 fruits and vegetables per day, being physically active less than 150 minutes per week, and who had a body mass index (BMI) of 25 kg/m(2) or greater. We used survey data from the Behavioral Risk Factor Surveillance System to evaluate health outcomes and validate simulation results.
Results:
Smoking rates and the proportion of the population with a BMI of 25 kg/m(2) or greater would have decreased significantly with implementation of the hypothetical program (P < .001). Two areas would have experienced a statistically significant reduction in the local population with diabetes between 2007 and 2027 (P < .05).
Conclusions:
The use of systems science methodologies might be a novel and efficient way to systematically address a number of EBDM adoption barriers at LHDs.
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