AI-driven insights: Exploring barriers and facilitators in Long COVID follow-up study on patients discharged alive from ICUs in Pakistan.

<bold>Background:</bold> "Long COVID" describes patients who exhibit clinical characteristics of the disease for up to three weeks after the initial onset of symptoms. There is limited data on Long COVID in lower-middle-income countries. Objectives: The study aims to utilize AI technology to delve into barriers, facilitators, and experiences of Long COVID patients post-hospitalization with SARS-CoV-2 infection, with individuals discharged from ICU/HDUs across Pakistan. Methodology: An observational, multicenter study was conducted involving adult patients discharged from collaborating ICUs across Pakistan from August 1, 2021, to January 31, 2022. Harnessing AI algorithms, 357 patients were interviewed using the ISARIC COVID-19 Follow-up case report form via telephone. <bold>Results:</bold> Several themes emerged highlighting barriers such as COVID-19 denial, emotional distress, and communication challenges, including unanswered calls and power-off devices and incorrect numbers. Facilitators included patient willingness to participate and satisfaction with contact, alongside the effectiveness of post-ICU counseling in addressing non-COVID health concerns. <bold>Conclusion:</bold> The findings highlight the challenges of long-term follow-up in resource-limited settings, emphasizing the pivotal role of AI-supported methodologies. AI's rapid data processing capabilities facilitate efficient analysis, enabling swift identification of trends and insights from vast datasets. As we move forward, AI promises to revolutionize respiratory research by expediting data analysis, synthesizing research materials, and providing instant access to the latest pulmonology COVID-related findings. Its integration holds immense potential in advancing respiratory research, enhancing patient care, and ultimately shaping the future of healthcare.