BMC Medical Education (Feb 2025)
The essential data elements for developing an internship monitoring system in Health Information Technology
Abstract
Abstract Introduction Given the importance of the internship course in enhancing student capabilities, it seems necessary to employ appropriate techniques to manage and organize the training process. This research was conducted with the goal of identifying the essential data elements required for the development of an internship monitoring system in Health Information Technology. Methods This qualitative research was conducted in 2022 using the Delphi technique. The study population consisted of professors from the Health Information Technology departments in medical universities in Iran (n = 85), selected through a convenience sampling method. The research instrument was a researcher-developed questionnaire, designed based on the approved educational curriculum, literature review, and analysis of existing systems. Its validity was confirmed by a panel of Health Information Technology experts, and its reliability was established with a Cronbach’s alpha coefficient of 0.83. The results of the Delphi rounds were analyzed and presented using descriptive statistical methods (Percent, Average, Standard deviation) with SPSS software, version 16. Results Twenty professors from the Health Information Technology departments across the country participated in this research. The final model of the essential data elements for developing an internship monitoring system in Health Information Technology comprises five main data sections as follows: management data elements with three data axes, user data elements section with four data axes, functional data elements section with six data axes, output data elements section with two data axes, and key performance indicators section with five data axes. Conclusion In this research, a comprehensive set of essential data elements for developing an internship monitoring system in Health Information Technology was compiled. The proposed framework can facilitate the integration of data collection and provide a coherent approach to student evaluation.
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