Systematic Review of Data Analytics Integration in Electronic Health Records: Enhancing Clinical Intelligence

Abstract

This systematic review explores the integration of data analytics into Electronic Health Records (EHRs) and its role in enhancing clinical intelligence across healthcare settings. Guided by PRISMA 2020 standards, a comprehensive literature search was conducted across five major databases, yielding 1,164 studies, of which 42 met the inclusion criteria. The selected studies, primarily from high-income countries, applied a range of analytical methods including machine learning, natural language processing, and statistical modeling. These tools were used for applications such as predictive risk modeling, early diagnosis, clinical decision support, and population health monitoring. Findings indicate that data analytics integration within EHRs improves the accuracy of clinical predictions, supports timely interventions, and enhances workflow efficiency. However, significant challenges persist, including data quality issues, lack of interoperability, clinician resistance, ethical concerns, and limited real-world validation. Furthermore, most studies lacked long-term outcome evaluation and cost-effectiveness analysis, highlighting a need for more comprehensive research. Despite these limitations, the review confirms that when thoughtfully implemented, analytics-enhanced EHRs can transform patient care by enabling proactive, data-driven clinical decisions. Future research should focus on addressing technical and ethical barriers, improving usability, and ensuring equitable adoption across diverse healthcare systems.