<![CDATA[Chronic and acute respiratory diseases such as asthma, COPD, COVID-19, ARDS, and acute respiratory infections (ARI) pose a substantial global health burden. Conventional surveillance systems are reactive and limited in early detection. Electronic Health Records (EHRs) offer great potential as a longitudinal data source for more effective respiratory disease monitoring and management. This systematic review aims to evaluate the impact and effectiveness of EHR use in monitoring disease trends, early detection, and optimizing clinical management of patients with various respiratory conditions. This systematic review followed the PRISMA 2020 guidelines. Literature searches were conducted in PubMed, Scopus, Web of Science, and Google Scholar (2016--2025). The research question was formulated using the PICO framework. A total of 40 articles meeting the inclusion criteria were analyzed thematically. The majority of studies originated from high-income countries (USA, UK, Denmark, Netherlands, Spain, Australia), with diverse study designs (retrospective cohort 35%, model development 25%, cluster RCT 5%). Types of EHR interventions included real-time surveillance systems, NLP/AI-based prediction models, CDSS, and data quality evaluations. EHRs were shown to detect SARI case surges up to 8 days earlier, predict ARDS with an AUROC of 0.84, reduce unnecessary antibiotic prescribing by 12--16%, and extract asthma symptoms with an F1-score >0.95. However, significant challenges remain regarding data quality (completeness <50% for some variables) and geographic gaps (no studies from Indonesia). EHRs, particularly when integrated with NLP and artificial intelligence, are effective for surveillance, early detection, and clinical management of respiratory diseases. However, data quality challenges and the lack of evidence from developing countries still need to be addressed. Data standardization, interoperability investments, and technology adaptation for local contexts such as Indonesia are required.]]>
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