Date
5-1-2025
Department
School of Health Sciences
Degree
Doctor of Philosophy in Health Sciences (PhD)
Chair
Lolo Chinyere Fidelia Nnajiofor
Keywords
artificial intelligence (AI), health information management (HIM), medical coders, perceptions, experiences, automated coding, natural language processing, deep learning
Disciplines
Health Information Technology
Recommended Citation
Atkinson, LaTonya M., "Evaluating the Integration of Artificial Intelligence from the Perspective and Experiences of Medical Coders: A Qualitative Study" (2025). Doctoral Dissertations and Projects. 6859.
https://digitalcommons.liberty.edu/doctoral/6859
Abstract
This study investigated the perceptions and experiences of medical coders and other relevant industry stakeholders regarding the integration of artificial intelligence (AI) technologies and tools for automating clinical coding within health information management (HIM). Using a qualitative research methodology, data was collected through twelve semi-structured interviews with participants from diverse employment settings, each possessing distinct levels of experience in medical coding. Among the participants, four individuals also took part in a focus group discussion. A purposive sampling strategy was exercised to obtain an intentionally diverse balance of perceptions and experiences from a varied group of participants. The qualitative data gathered from the semi-structured individual interviews and the focus group session were systemically analyzed to identify key themes and insights. The thematic analysis of the data unearths the idea that healthy communication among healthcare organization stakeholders can augment financial reimbursement, optimize patient health outcomes, and provide critical organizational insights. By investigating the perspectives and lived experiences of medical coders, this study presents a thorough analysis of the human factors and illustrates complexities critical for the successful adoption of AI in HIM. Through this exploration, a fundamental gap is identified in existing qualitative literature that has focused on the quantitative and technical aspects of AI technology and tools. Existing literature has often overlooked the human dimension in the implementation of these advancements.
