In Indonesia, digital transformation has enabled digital routine health data collection by community health workers (CHWs). However, concerns remain about the quality and utility of these data for improving access and health outcomes. EMIS represents example of applications that highlight the potential for data integration and aligns with the national digital health transformation in Indonesia. Using the digital Elderly Management Information System (EMIS), this study evaluates the quality of routinely collected data by CHWs and examines factors influencing data quality. An explanatory sequential mixed-methods design was adopted, combining quantitative assessment of data quality with qualitative exploration of its determinants. The quantitative phase evaluated the completeness and accuracy of secondary EMIS data of older adults (2022–2024) across 57 villages in Malang City, East Java, Indonesia. Data quality was assessed by examining missing values and implausible or outlier entries, with analyses conducted in STATA-18. Findings informed the qualitative phase, which used primary data from six focus group discussions with 16 program coordinators and 17 CHWs, and two in-depth interviews with District Health Officers (DHOs) in Malang City, East Java, Indonesia. Thematic analysis using NVivo was conducted to identify organisational, behavioural, and technical determinants influencing data quality. Data coverage in EMIS increased from 13% in 2022 to 39% in 2024. Missing values remained substantial, including 31–40% for insurance numbers and 33–42% for cholesterol measurements, with some entries erroneous. Age-related outliers increased from 2% to 5% over the study period. The highest proportion of outliers was observed in total blood cholesterol levels, reaching 49% in 2022 and 42% in 2023 and 2024. Qualitative findings highlighted low demand for high-quality data, delayed feedback, unclear verification structures, limited motivation among data collectors due to high data-entry burden, inadequate skills and data literacy, and multiple technical challenges, including system design issues and limited data integration and interoperability. Improving the quality of routine health data requires coordinated interventions across organisational, behavioural, and technical determinants. Priority actions should include strengthening coordination mechanisms, implementing standardised data verification protocols, providing competency-based training for CHWs, and redesigning digital systems to incorporate automated validation features.