Background Delays in transporting sputum samples and medicines from peripheral health centers to diagnostic laboratories are a persistent barrier to timely tuberculosis (TB) diagnosis in rural India. Conventional transport methods are often constrained by poor road connectivity, long travel times, and logistical inefficiencies. Unmanned aerial vehicles/drones have shown promise in bridging such gaps in other low-resource and rural areas, yet there is limited evidence on their feasibility, acceptability, and integration within India's National TB Elimination Programme. Methods In the present qualitative study, 28 in-depth interviews and 12 focus group discussions with 101 purposively selected stakeholders were conducted to understand the feasibility across the five components: acceptability, demand, practicality, implementation, and integration. Data were thematically analyzed using a feasibility framework. Ethical approval was obtained from the Institutional Ethics Committee (IEC No: AIIMS/BBN/IEC/JULY/2022/164), and written and verbal informed consent was obtained from all participants. Results Participants highlighted that drones improved acceptability, demand, and integration by reducing travel, enabling timely sputum and medicine delivery, and building community trust. Practicality and implementation were supported by coordination and district authority support, and while stigma, limited payload, weather disruptions, and training gaps were noted, they were viewed as improvable. Peripheral workers were central to community uptake and routine adoption. Conclusion Drone-based sputum and medicine transport is operationally feasible and acceptable in rural Indian settings, and participants viewed drones as a promising way to reduce sample transport times and improve access to diagnostics, particularly in hard-to-reach areas. Addressing awareness gaps, stigma, operational barriers, and regulatory delays, while embedding drones into existing health systems, could enable sustainable scale-up and strengthen rural diagnostic networks.
Kamble et al. (Thu,) studied this question.