Introduction: The digital divide means economic and social inequality for access to and use of ICT. Information and communication technology, despite the spatial and temporal barriers, can provide a valuable opportunity for patients to access health information. The aim of this study was to identify factors affecting the digital divide in patients and ways to reduce it. Materials and Methods: This study was a systematic review by searching online databases, reputable journals and Google Scholar search engine and related books using keywords digital gap, health information technology, patients from 2010 to 2018. 110 articles were found out of which 70 were included in the study and then the articles were evaluated for title, abstract and full text and after excluding duplicate and unrelated cases, about 20 research related articles were selected. Results: Significant differences in patients' ability to find the health information needed on the Internet are among the most important issues in the information gap with the use of health information technology. Accordingly, the most important factors affecting the information gap are geographic location, skill level, computer use, race and ethnicity, education level, age, income, and gender. It also offers solutions such as computer training, introducing reputable health websites, encouraging and educating the elderly, investing and providing government assistance for home computers, providing free internet access, and providing the infrastructure needed for High-speed Internet can help digital divide the use of information technology. Conclusion: Reducing the digital divide in patients will increase the readiness and motivation of patients to use information and communication technology and improve the quality of health care.
Figure 2. A, complex decision tree using four parameters of DWI restriction, central necrosis, T2 map, and TMCE ratio; B, confusion matrix for the number of lesions on true and false predicted classes; C, scatter-plot for distribution of lesions based on the complex model. B, Benign; M, malignant; Y, yes; N, no.
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