Forhad Hossain

Abstract The Global South faces significant healthcare challenges, making the achievement of Sustainable Development Goal 3 (SDG 3): Good Health and Wellbeing difficult by 2030. This study aims to identify the key challenges associated with each SDG 3 target. Major barriers include poor healthcare infrastructure, financial constraints, workforce shortages, high out-of-pocket healthcare expenses and the lack of universal health coverage. To address these issues, the study explores the feasibility of Micro-Healthcare Entrepreneurship (MHE) as an innovative and sustainable model for improving healthcare accessibility and affordability. MHE integrates local entrepreneurship, digital health technologies, mentorship and social business funding to create an affordable and easily accessible, community-driven preventive healthcare solution. The study identifies critical implementation challenges, including financial sustainability, regulatory barriers, limited ICT infrastructure, workforce shortages and cultural resistance. The findings suggest that digital health innovations, public–private partnerships and social business models can significantly enhance healthcare service delivery in resource-limited settings. Comparing MHE with existing healthcare delivery models, this research highlights its potential to bridge healthcare gaps by reducing dependency on centralised hospitals and limited resources. By leveraging technology, entrepreneurship and social impact, MHE can become one of the sustainable solutions for accessible, affordable and quality healthcare service delivery approach in Global South countries, contributing to the achievement of SDG 3 and beyond.

This study investigates awareness, use, perceptions, and future intentions regarding telemedicine among rural and urban populations in Bangladesh. It further explores how these findings can inform sustainable social business strategies to expand access to telemedicine. A survey of 76 individuals from Dhaka (Urban) and Gopalganj (Rural) was conducted to assess key behaviors and attitudes. The results show that urban respondents are more familiar with telemedicine, whereas rural participants perceive it as more effective and are more likely to use it in the future. A notable gap exists between awareness and actual usage across age groups, with only weak correlations observed between perceived quality, awareness, and future intention. This suggests that people’s interest in telemedicine is driven more by their healthcare needs than by familiarity with the technology. A social business model that focuses on keeping costs low, engaging the community, and reinvesting profits could help overcome barriers such as low awareness, lack of trust, and limited access. The study concludes that telemedicine, when supported by sustainable social business practices, has the potential to improve healthcare access in underserved areas of Bangladesh and other developing countries.

Automatic speech recognition has advanced rapidly for high-resource languages, yet performance remains limited for low-resource languages such as Bangla, particularly in telehealth settings. Most systems rely on a standardized 16 kHz sampling rate, a design choice despite evidence that Bangla contains sibilant fricatives and other phonetic cues with substantial high-frequency energy that may be suppressed under bandwidth and latency constraints. This study evaluates audio sampling rate as a controllable signal-level parameter for Bangla telehealth ASR to identify an empirically grounded operating range balancing transcription accuracy, execution time, and network bandwidth. Twenty real-world Bangla doctor–patient consultations were deterministically resampled to 55 configurations between 8 kHz and 32 kHz and transcribed using a fixed cloud-based ASR system. Session-level Word Error Rate, execution latency, payload bandwidth, and high-frequency phonetic content were analyzed using a composite sibilant-likelihood score. WER decreased from 0.338 at 8 kHz to a local minimum of 0.232 at 18.75 kHz, with gains plateauing beyond this range despite substantial bandwidth increases. Elbow-point, Pareto frontier, weighted scoring, and Minimum Acceptable Trade-off analyses converged on an optimal region between 17.25 and 18.75 kHz, demonstrating that sampling rate optimization improves ASR accuracy without proportional resource costs in telehealth settings.