Harnessing Data Science for Local Action: An Action-Research Framework to Improve Reproductive and Child Health (RCH) Service Delivery in Tribal Communities of Andhra Pradesh

Background

Reproductive and child health (RCH) services in tribal India generate substantial routine data, but the sources are fragmented across systems and stakeholders, and the resulting information is under-used for service delivery and review. This proposal, prepared for the ICMR investigator-initiated (Intermediate) extramural grant call and sited in ITDA-Rampachodavaram, addresses that gap by treating data integration and use as a participatory, system-level problem rather than a purely technical one. The framework draws on the recommendations of the 3D Commission, integrating social determinants of health into how RCH data is assembled and interpreted. The proposal was led by Arun Mitra with co-investigators Biju Soman (SCTIMST), Dhrubajyoti Debnath (AIIMS Mangalagiri), Santosh Pentapati, and Adrija Roy; it was internally cleared (DRC approved) and submitted in February 2025, with the funding decision pending.

Approach

The proposed project runs over 36 months as action research across three linked objectives: (1) mapping and integrating the stakeholders and data sources relevant to RCH in the study area; (2) assessing data utilisation among PHC medical officers and identifying strategies to foster a data-use culture; and (3) designing and implementing data-science solutions through participatory methods with frontline staff. The work combines semi-automated data-engineering with stakeholder engagement so that the resulting tools fit existing review and decision-making routines.

What we found

• RCH data sources are dispersed across multiple platforms and actors, making a coherent local view difficult to assemble without deliberate integration.
• Data utilisation among PHC medical officers is shaped as much by institutional practice and accessibility as by analytic capacity, so fostering a data-use culture requires both tooling and process change.
• Detailed empirical findings are emerging from the ongoing field cycles.

Outputs & impact

Planned deliverables include a mapping of RCH data sources in the study area, semi-automated algorithms to standardise and link those sources, anonymised research-level datasets, and customisable report-generation tools and templates for routine review meetings. By lowering the effort needed to produce a usable local picture of RCH service delivery, the project aims to make routine data directly actionable for PHC-level planning. Submitted to ICMR for the investigator-initiated extramural grant call (DRC approved, February 2025; funding decision pending); reproducible data-integration code will be released openly.