Electronic Medical Records Data Quality Analysis in Supporting Smart Healthy City: A Case Study Primary Care Clinic

Abstract

The quality of Electronic Medical Records (EMR) data is a critical foundation for digital health services and Smart Healthy City implementation. Poor data quality ncluding incompleteness, inaccuracy, and lack of integration can undermine healthcare decision-making and obstruct the development of a data-driven urban health ecosystem. This study aimed to comprehensively analyze the quality of EMR data across six dimensions completeness, accuracy, consistency, timeliness, security, and integration and to assess its role in supporting Smart Healthy City implementation at Primary Care Clinic. A mixed-methods sequential explanatory design was employed. Quantitative data were collected through document audit of 150 EMR records using a structured checklist, while qualitative data were gathered through in-depth interviews with three key informants (clinic head and two healthcare staff) and document review. Results: EMR data quality demonstrated overall strong performance: timeliness reached 100%, security 91.2%, accuracy 91.1%, consistency 90.0%, completeness 87.0%, and data integration 85.7%. The overall average across all dimensions was 90.8%. Key enabling factors included management commitment (93.3%) and IT infrastructure (86.7%), while high staff workload (66.7%) and network disruptions (55.6%) were the main inhibiting factors. The EMR system demonstrated meaningful support for Smart Healthy City (85.9%), though external interoperability (73.3%) and contribution to integrated city health platforms (76.7%) remained below optimal levels. EMR data quality at Clinic is categorized as good, with timeliness as the outstanding dimension. Priority areas for improvement include strengthening end-to-end encryption, expanding interoperability using HL7-FHIR standards, and integrating the clinic's EMR with regional smart health platforms to advance the Smart Healthy City agenda.