ANALYSIS OF THE QUALITY OF MATERNAL AND CHILD HEALTH (MCH) DATA IN THE HEALTH INFORMATION SYSTEM AT COMMUNITY HEALTH CENTERS IN THE SOUTHERN REGION OF KEDIRI CITY

Abstract

Maternal and Child Health (MCH) data quality is essential for planning, monitoring, evaluation, and decision-making in health programs. Problems related to incomplete, inaccurate, untimely, and inconsistent data remain challenges in health information systems at primary healthcare facilities. This study aimed to analyze MCH data quality based on completeness, timeliness, accuracy, and consistency dimensions and to identify factors influencing data quality at a public health center in the Southern Region of Kediri City. This study employed a mixed-methods approach with a convergent parallel design. Quantitative data were collected through audits and verification of MCH documents, including cohort registers, SIPIA reports, e-Kohort records, and monthly MCH reports. Qualitative data were obtained through in-depth interviews with 10 purposively selected informants. Quantitative data were analyzed descriptively using percentages, while qualitative data were analyzed using the Miles and Huberman model. The results showed that the completeness of maternal identity data reached 96.5% (good), clinical variables 72.8% (moderate), and geospatial data 58.4% (poor). Timeliness of reporting reached 83.3%, while data accuracy was 86.4%. Consistency analysis revealed discrepancies between register records and health information systems. Factors affecting data quality included staff workload, limited human resources, information technology infrastructure constraints, and suboptimal system integration. In conclusion, the quality of MCH data was generally adequate; however, improvements are still needed in data completeness, accuracy, and consistency through strengthening human resources, improving data validation, and enhancing health information system integration.