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SWASTHYA HARYANA-Transforming Active Case Finding (ACF) through AI-Powered Thoracic & Non-Communicable Disease (NCD) Screening in Haryana

JSI Research & Training Institute, Inc. (JSI) implements the Tuberculosis Implementation Framework Agreement (TIFA) managed by the United States Department of State (DOS). The eight-year (2019– 2027) project builds on the United States government’s (USG) investments in tuberculosis (TB) and other global health priorities. Through direct engagement with local governments and their partners, TIFA co-designs fixed amount subawards, which may include Health Commitment Grants (HCGs), that accelerate countries’ progress toward national health targets, support country ownership, and foster sustainability while advancing the DOS goals of making America safer, stronger, and more prosperous.

The state of Haryana has recently procured 90 AI-enabled digital hand held X-ray (HHX) machines and has set an ambitious target to screen 18.7 Lakh people for TB in the next 12 months. JSI seeks an organisation to demonstrate a scalable, technology-driven screening model to assist Haryana state to establish active TB and non-communicable diseases (NCDs) screening linked to its acceleration of active case finding (ACF) with hand held X-ray machines (HHX) through a rapid, 10- month intervention. The successful applicant will support the Haryana State NTEP to operationalize 90 new AI-enabled highly portable HHX by hiring 25 radiological technicians, will deploy an application to facilitate the rapid scale up pre-registration for screening events to improve efficient deployment of HHX where they are most needed, ensure the state is able to manage TB testing surges by contracting 5 laboratory technicians to staff selected high-volume laboratories, and support integrated TB-NCD screening in the state. At the end of this intervention, the state TB control program will assume operations and take the activity forward; therefore, all interventions will be designed and implemented in close coordination with the State TB Cell. This activity aims to ensure high-yield detection through a self-registration platform for screening, AI-integrated workflows, standardized field operation protocols, and full optimization of human resources via automated data entry. The expected outcome is to accelerate the early detection and management of TB and NCDs in vulnerable populations by implementing high-efficiency, AI-driven screening and standardized scheduling systems that optimize healthcare resources.

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