Typically, an active TB patient is required to take six months of medication to cure the disease. However, irregular adherence or non-adherence to pills is a common occurrence. This increases the increase the risk of death, chances of relapse, and drug resistance among the patients. The Central TB Division (CTD) launched an Integrated Digital Adherence Technology Initiative (IDAT) in 2019 to deliver more patient-centric monitoring and management at scale. But an interim assessment indicated that while IDAT had achieved significant coverage of digital adherence technologies (DATs) over a brief period, several barriers at the level of the health system, health workers and patients needed to be overcome to achieve their desired impact on patient outcomes. Also, currently, the adoption and expansion of DATs is the largest in the public sector. But it is predicted that the impact would be greater if these tools are extended to privately treated TB patients.

WHP has collaborated with Indian School of Business (ISB) that received BMGF grant to conduct a multi-year, multi-faceted, comprehensive study to evaluate three adherence monitoring methods from both patient and healthcare worker’s perspectives against the standard of care in the private sector in three districts of Punjab -Ludhiana, Jalandhar and Amritsar. . This project is being implemented with guidance from the CTD Central TB Division and State TB Office of Punjab and is aimed at generating rigorous evidence to guide national decision-making on the scale-up of digital adherence management and implementation protocols along with their cost effectiveness and budget impact.

The study is structured as a multi-site randomised controlled trial embedded in mixed methods operational research and guided by economics and behavioural sciences to meet the study objectives. Qualitative studies, informed by behavioural sciences, will identify barriers and facilitators for optimal engagement with DATs among patients and healthcare workers. A Time-and-Motion study will be conducted to capture the effect of information generated by digital technologies on the workflows of frontline health workers. Advanced analytic methods such as machine learning will be used to recommend optimal use of limited health worker capacity and findings from these analyses will be integrated with estimates of detailed programmatic costs to arrive at the relative cost-effectiveness of the various monitoring methods.

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