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Maxim Terekhov

Maxim Terekhov

Information Systems and Operations Management at University of Florida

Max Terekhov is a PhD candidate in Information Systems and Operations Management at University of Florida, Warrington College of Business. He also holds a M.S. degree in Biostatistics and an MBA. His research interests span across enterprise information, decision support systems, and include data science and machine learning applications in healthcare and financial technologies. Teaching experience includes undergraduate-level courses such as decision analysis, queueing, and project management. His work is motivated by emerging practice and research issues in the industry, where Dr. Terekhov advanced as a performance-focused leader with a distinguished career driving the design and implementation of advanced analytical tools, including data capture, integration, analysis, and reporting solutions that generate actionable insights to enhance decision-making. Dr. Terekhov is skilled in collaborating with business, leadership, teams and other stakeholders to identify needs, define strategy, and guide the delivery of tools, systems, interfaces and analytics to measure performance against goals, identify trends, and respond immediately to new opportunities or business issues. His research has been presented at Informs and POMS, published in Production and Operations Management journal, with multiple healthcare-related papers circulated in the official journal of the American Society of Anesthesiologists
Track: Life Science & Healthcare

Emerging Practice and Research Issues in the Health Insurance Industry

This presentation summarizes emerging practice and research issues in the health insurance industry. I provide an industry overview, epitomize business analytics applications, and outline current and emerging problems of interest to key stakeholders and researchers in information systems, operations management, and healthcare management. Specifically, I focus on three areas of research that are of importance to the health insurance industry as well as the healthcare system: (1) management and evaluation of intervention programs, (2) creation of effective provider networks, and (3) emerging  issues  such as the impact of a pandemic, patient communication and steering, and technological innovations in healthcare. I lay out brief details of how each of these problems can be formulated, where the data can be acquired (public and/or proprietary data sets), and provide guidance for solving them. For several of these problems, I provide models that span causal modeling, predictive modeling, and prescriptive modeling.