Background: The use of electronic systems supported by text-mining software applications that support the End TB strategy' needs to be explored. This study aimed to address this knowledge gap, and synthesis of evidence.
Methods: The PubMed database was searched for structured review articles published in English since 2012 on interventions to control and manage TB. Nine hundred twenty-five articles met the inclusion criteria. The included articles were synthesized using the text and content analysis software Leximancer. The themes were chosen based on the hit words that emerged in the frequency and heat maps. After the themes were chosen, the concept built the themes based on likelihood.
Results: The framework resulting in the study focuses on early detection and treatment to minimize the chance of TB transmission in the population, especially for highly susceptable populations. The main area highlighted is the appropriate screening and treatment domains. The framework generated in this study is somewhat in line with the WHO Final TB Strategy. This study highlights the importance of improving TB prevention through a patient-centered approach and protecting susceptible populations.
Conclusion: Our findings will be helpful in guiding TB practice, policy development and future research. Future research can elaborate the framework and elicit feedback from TB management stakeholdesr to assess its utility.
Keywords: Automatic knowledge; Control; Framework; Management; Tuberculosis.
Copyright© 2023 Handayani et al. Published by Tehran University of Medical Sciences.