Development of Minimum Data Set and Data Model in Electronic Health Record System: Literature Review Article

Abstract

Electronic Health Record Systems (EHRs) require well-defined Minimum Data Sets (MDS) and robust data models to ensure data quality, interoperability, and effective system implementation. However, existing studies on EHR-related MDS and data modeling remain fragmented, often focusing on disease-specific or domain-specific contexts and stopping at conceptual or architectural levels. This systematic literature review aims to analyze and synthesize existing research on the development of MDS and data models for EHR systems, identifying prevailing approaches, limitations, and research gaps. A Systematic Literature Review (SLR) was conducted using the PICOC framework and a structured search strategy applied to academic databases, focusing on studies published within the last ten years. The review examined research related to EHR data sets, data elements, and data models, including conceptual, logical, and physical modeling approaches. The findings indicate that most MDS studies successfully standardize administrative and clinical data elements through expert consensus methods but lack comprehensive metadata specifications and integration with complete data modeling processes. Similarly, many data modeling studies emphasize conceptual or reference models (e.g., openEHR, HL7 RIM, ISO-based frameworks) without sufficient progression to logical and physical implementations or empirical validation in real clinical environments. In addition, limited adoption of newer interoperability standards, such as HL7 FHIR, and insufficient centralized metadata governance further hinder large-scale interoperability and reuse. This review highlights the need for a holistic, workflow-based MDS integrated with complete conceptual, logical, and physical data models to bridge the gap between theory and practice in EHR development. The study provides a consolidated reference for researchers, system developers, and policymakers in designing standardized, interoperable, and scalable EHR systems, particularly in contexts lacking national EHR data standards.

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