An assisted telemedicine model with digital diagnostics and artificial intelligence-supported clinical decision in hard-to-reach rural communities: a community-based mixed-methods study from Rajasthan, India.

OBJECTIVE

Hard-to-reach rural communities in India face persistent barriers to formal healthcare. This study evaluated an assisted telemedicine model integrating frontline health workers, portable digital diagnostics, and an artificial intelligence-supported clinical decision support system in rural Rajasthan.

METHODS

A community-based mixed-methods study was conducted in ten villages of Balotra district, Rajasthan between March 2024-Jan 2026. Five intervention villages received assisted teleconsultations with portable diagnostics, while five comparison villages underwent weekly household surveillance. Healthcare utilization was compared among 8374 consultation records (illness episodes). Qualitative evaluation included pre- and post-implementation interviews and a 10-month longitudinal assessment of frontline workers.

RESULTS

All symptomatic individuals in the intervention group (n = 4401) received teleconsultations, versus 38.5% (1528/3973) in controls accessing formal healthcare (p < 0.001). Respiratory conditions comprised 24.2% of consultations; 98.6% were managed without referral. Portable electrocardiography detected abnormalities in 21.6% of cardiac presentations. Frontline workers emerged as trusted facilitators, with growing confidence despite technical disruptions.

CONCLUSIONS

This assisted telemedicine model was associated with healthcare access in hard-to-reach areas, demonstrating the feasibility of technology-enabled primary care delivery.