Objective: Given concerning statistics regarding recurring errors in the healthcare sector and existing challenges, this study aims to validate a telemedicine knowledge logistics model in hospitals affiliated with the Ministry of Health and Medical Education of Iran. The goal is to confirm or reject the proposed components of this model, ultimately leading to the identification of a final model endorsed by experts and specialists.
Methodology: This applied research represents the second phase of a broader qualitative study, utilizing a quantitative approach to perform confirmatory factor analysis on telemedicine knowledge logistics indicators. The research instrument was a researcher-developed questionnaire derived from categories, concepts, and relationships discovered in the initial qualitative phase, which employed a grounded theory methodology. The study population included all individuals working in or studying at hospitals across the country, as well as those who had utilized hospital services at least once. Sampling was conducted using a probabilistic cluster method. Data analysis was performed using SPSS software, while first and second-order confirmatory factor analyses were carried out using LISREL software to assess model fit and verify the accurate measurement of latent variables.
Findings: The results of first and second order confirmatory factor analysis with LISREL software confirmed the optimal fit of the final knowledge logistics model. All goodness-of-fit indices (CFI=0.97, GFI=0.91, RMSEA=0.065) were within the acceptable range. Also, all factor loading coefficients between the main constructs (including knowledge flow, influential elements, implementation strategies, and outcomes) and their observable variables are not less than 0.4. Therefore, it can be concluded that the reliability of the first order measurement model of the variable components of the factors affecting the creation of knowledge flow in telemedicine is acceptable and approved. Their significance coefficients were greater than 1.96, indicating the significance of the relationships in the measurement model of the research variables. These results fully confirm the validity and reliability of the research conceptual model.
Conclusion: The findings demonstrate that the proposed knowledge logistics model possesses a favorable fit and statistical validity. It can serve as a systematic framework to ensure the optimal flow of knowledge from identification to application within the telemedicine system. Although the presented model has scientific validity, challenges related to the existence of the technical, cultural, legal, and human resources infrastructure necessary for the full deployment of knowledge logistics in telemedicine in the country, cultural issues, the challenge of remote examination, and the variability of the digital environment still exist.