One of the central challenges in the immunization system was data falsification by front-line vaccinators, who were responsible for both administering vaccines and recording their own performance. Supervisors (ASVs) frequently encountered inflated numbers, fake entries in registers, or discrepancies between field practices and reports. These practices undermined the accuracy of immunization coverage data, weakened trust in reporting, and ultimately obscured the extent of under-vaccination. Falsification was further complicated by systemic pressures: vaccinators were incentivized to meet ambitious numeric targets, had limited oversight, and operated within a largely paper-based system that was difficult to verify. These dynamics created what supervisors described as a “cat-and-mouse game,” where vaccinators devised ways to mask underperformance while ASVs tried to uncover inconsistencies .  

Our approach combined ethnographic fieldwork with iterative co-design. First, we conducted qualitative research with 22 ASVs and other health officials across five districts in Punjab, including interviews, focus groups, and field observations, to document falsification practices and supervisory responses . Building on these findings, we undertook an iterative design cycle for the app, creating prototypes and refining them through multiple rounds of usability testing with 12 ASVs. These sessions involved task-based scenarios (e.g., entering child vaccination data, verifying locations) and structured feedback on barriers and improvements. We also collected artifacts such as existing registers, reports, and WhatsApp records to inform design constraints. By combining insights from supervisors’ lived practices with hands-on testing of digital tools, our methods ensured that both the sociotechnical realities of falsification and the design of preventive solutions were addressed together.