Co-creating data science solutions for maternal and child health decision-making in tribal primary health centres: an action research using the Three Co's Framework

Background: Digital health tools are increasingly promoted for strengthening health information systems in low- and middle-income countries, yet routine maternal and child health (MCH) data in tribal primary health centres (PHCs) in India remains underutilised for local decision-making. Top-down digital tools often fail in low-resource settings because they are designed without meaningful input from end-users. Co-creation approaches for digital health in tribal and indigenous settings are largely unexplored. Methods: We conducted an action research study in three tribal PHCs under the Integrated Tribal Development Agency (ITDA), Rampachodavaram, Andhra Pradesh, India. We applied the Three Co's Framework (Co-Define, Co-Design, Co-Refine) to co-create data science solutions for MCH decision-making with five medical officers, 24 auxiliary nurse midwives, and 36 accredited social health activists across two action research cycles (August 2023 to August 2024). Co-creation involved collaborative indicator definition, data modelling, data quality validation, health facility catchment area construction, spatial analysis, and interactive dashboard development. Keller's Data Science Framework was employed using R to structure the analytical pipeline, and Data.org's Data Maturity Assessment (DMA) was used to assess organisational data maturity pre- and post-intervention. Findings: During Co-Define, co-creators identified a fundamental mismatch between system outputs (aggregate statistics for upward reporting) and their operational need for individual-level, geographically disaggregated, prospective information. Co-Design produced five interconnected data science solutions: (1) 42 co-defined MCH indicators grounded in clinical workflows; (2) a data model linking individuals, health services, providers, and facilities; (3) a data quality framework using the pointblank R package; (4) health facility catchment area boundaries constructed from scratch using medical officers' local knowledge, enabling spatial analysis that revealed significant clustering of ANC coverage and anaemia prevalence; and (5) an R Shiny dashboard integrating these solutions into an offline-capable interface with lifecycle-organised views and village-level navigation. The DMA showed moderate improvement in organisational data maturity from 5.04 to 5.75 out of 10, with the largest gain in Analysis (+1.90). Co-Refine continued beyond the formal study period, with two transferred medical officers maintaining analytical engagement from new postings. Interpretation: The Three Co's Framework, combined with a data science approach, provided a structured yet flexible method for co-creating locally relevant data science solutions in a tribal setting. The framework's explicit separation of problem definition from solution design was particularly valuable in a context where "the problem" is typically defined externally. Co-creation in tribal digital health settings is feasible and produces solutions that address locally articulated needs.