Design and Implementation of an EHR-Driven, Web-Based Workflow for Real-Time Analytics in Acute Myocardial Infarction Care (Preprint)

BACKGROUND In technically constrained clinical settings, conventional electronic health record (EHR) storage and data extraction methods often limit the implementation of real-time analytics for monitoring clinical performance measures. These limitations pose challenges for interoperability between EHR systems and analytics platforms, particularly in contexts with restricted time, resources, and specialized technical personnel. OBJECTIVE To describe the design and implementation of an EHR-driven, web-based data collection workflow for real-time analytics of acute myocardial infarction care, and to assess its feasibility, data completeness, and ability to generate actionable clinical and managerial indicators in routine practice within a high-complexity healthcare institution. METHODS A descriptive implementation study was conducted integrating the institutional EHR with REDCap to support structured data collection, automated data extraction, and longitudinal follow-up. The workflow incorporated rule-based patient identification, manual validation by clinical staff, and interoperability with a business intelligence platform to enable real-time visualization of predefined clinical performance measures. RESULTS The implemented workflow enabled automated identification and daily updating of patients with suspected acute myocardial infarction, structured longitudinal data capture, and real-time visualization of clinical and operational indicators through interactive dashboards. The system demonstrated feasibility in routine practice, acceptable data completeness, and adaptability to clinical and managerial information needs without requiring additional data warehousing infrastructure. CONCLUSIONS An EHR-driven, web-based data collection workflow integrated into routine acute myocardial infarction care is feasible and supports the generation of real-time clinical and managerial indicators. This approach provides a practical framework for data-driven monitoring and quality improvement in high-complexity healthcare settings with limited technical resources.