Photo of Min Jung  Kim, Ph.D.

Min Jung Kim, Ph.D.

Assistant Professor of Medicine
Academic Office Location:
Pat and Jim Calhoun Cardiology Center
UConn Health
263 Farmington Avenue
Farmington, CT 06030
Education
DegreeInstitutionMajor
B.S.Chung-Ang UniversityMathematics and Statistics
M.Sc.Pennsylvania State UniversityIndustrial Engineering with Option in Operation Research
Ph.D.Pennsylvania State UniversityIndustrial Engineering
PostdocUniversity of MarylandMechanical Engineering

Awards
Name of Award/HonorAwarding Organization
Center for Integrated Healthcare Delivery System Research FellowshipPennsylvania State University
Center for Integrated Healthcare Delivery System ScholarshipPennsylvania State University
Excellent Honors ScholarshipChung-Ang University

My research interests lie in health care system development, specifically applying statistical engineering methodologies to improve the syndromic surveillance, chronic disease monitoring, and clinical information system. Throughout my research and academic coursework, I have maintained a strong interest in how statistical engineering approaches can address challenges in the complex health care system and promote continuous improvement in the health care system. Prior research has proved that a strong background in operation research techniques (optimization, decision making, stochastic modeling, process/quality control and multivariate statistics) and system engineering (system development, system dynamics, and risk management) could be essential keys to unravel complexities in the health care system.

In particular, I currently focus on identifying the main components and interpreting the dynamics in the chronic care management system. Using data in healthcare system, it can define key variables and functions explaining between all the parts of a system. Using system analytics, it can account for how those relationships influence the behavior of the system over time. Thereby, when the system engineering is adopted in chronic disease management system, it can contribute to 1) better understanding how changes in one area will affect the function in another area within the system, 2) better predicting secondary effects stemming from the complicatedly interrelated nature of the system elements, and 3) better decision making by projecting how alternative course of changes can impact the system over time.