OPTIMIZATION OF MEDICAL RECORD DATA FOR CLINICAL DECISION-MAKING IN TUBERCULOSIS PATIENTS: A LITERATURE REVIEW

Abstract

Tuberculosis (TB) remains a major global health problem with a high disease burden, particularly in developing countries such as Indonesia. The complexity of TB management requires accurate and comprehensive data to support clinical decision-making. Medical records and health information systems play a crucial role in providing relevant patient data, yet their utilization is often not optimal due to issues related to data quality, system integration, and limited interoperability. This study aims to analyze the optimization of medical record data in supporting clinical decision-making for TB patients through a literature review approach.

This study employed a systematic literature review (SLR) method by analyzing scientific articles published between 2022 and 2025 from databases including Scopus, PubMed, ScienceDirect, and Google Scholar. The selection process followed the PRISMA approach, with inclusion criteria focusing on studies related to medical records, health information systems, and clinical decision-making in tuberculosis cases.

The results indicate that electronic medical records (EMR) and integrated health information systems significantly contribute to improving the accuracy of diagnosis, treatment monitoring, and clinical decision-making. The use of technologies such as clinical decision support systems (CDSS), big data, and artificial intelligence enhances data utilization and supports evidence-based clinical practices. However, several challenges remain, including incomplete data, inconsistent documentation, lack of system integration, and variability in human resource capacity, particularly in Indonesia. The implementation of systems such as the Tuberculosis Information System (SITB) has improved reporting and case management, although its integration with electronic medical records is still limited.

In conclusion, optimizing medical record data is essential to enhance clinical decision-making in TB management. Strengthening data quality, improving system interoperability, and maximizing the use of digital technologies are necessary to support more effective and evidence-based healthcare services, particularly in high TB burden settings.