ASSESSING THE IMPACT OF KNOWLEDGE-SEEKING PRACTICES, COLLABORATION, AND AI TOOLS ON TICKET ESCALATION IN HEALTHCARE IT SUPPORT: A LOGISTIC REGRESSION ANALYSIS

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Lahus_ASSESSING IMPACT.pdf (597.26 KB)

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2026-05

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Lahus, Valeriia

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Abstract

This study investigates the factors influencing the escalation of support tickets to programmers in a healthcare IT environment. It focuses on three factors: knowledge-seeking practices (use of requirements and documentation), cross-team consultation, and the use of an artificial intelligence (AI) chatbot. Besides escalation frequency, the study examines valid escalation, defined as cases where escalation is justified and confirmed as a system defect, deficiency, or enhancement. A quantitative research approach was applied using logistic regression on a dataset of 150 support tickets to evaluate the main and interaction effects of these factors on escalation outcomes. The results show that knowledge-seeking practices have a statistically significant impact on both escalation and valid escalation. The use of requirements increases the likelihood of escalation while improving the accuracy of escalation decisions. Cross-team consultation and AI chatbot use do not have statistically significant independent effects. However, the interaction between requirements and AI chatbot is significant, indicating that AI can reduce escalation when combined with structured documentation. The findings suggest that effective technical support depends not only on reducing escalations but also on improving their accuracy and justification. The study recommends strengthening knowledge management, improving documentation quality and accessibility, and integrating AI tools with system requirements to optimize support operations in healthcare IT systems.

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healthcare IT support, support ticket resolution, knowledge-seeking practices, cross-team collaboration, AI chatbot, artificial intelligence, logistic regression, effective support

Citation

Lahus, Valeriia. (2026). ASSESSING THE IMPACT OF KNOWLEDGE-SEEKING PRACTICES, COLLABORATION, AND AI TOOLS ON TICKET ESCALATION IN HEALTHCARE IT SUPPORT: A LOGISTIC REGRESSION ANALYSIS . Kyiv: American University Kyiv. URI: https://er.auk.edu.ua/handle/234907866/195

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https://er.auk.edu.ua/handle/234907866/195

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School of Management (capstones)