Developing Data Model for Electronic Health Record System
Downloads
The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.
S. Daneshvari Berry, P. J. Kroth, H. J. H. Edgar, and T. D. Warner, “Developing the Minimum Dataset for the New Mexico Decedent Image Database,” Appl. Clin. Inform., vol. 12, no. 3, pp. 518–527, 2021, doi: 10.1055/s-0041-1730999.
R. Sherman, “Foundational Data Modeling,” Bus. Intell. Guideb., pp. 173–195, 2015, doi: 10.1016/b978-0-12-411461-6.00008-3.
M. Ahmadi, T. Madani, and J. Alipour, “Development a national minimum data set (MDS) of the information management system for disability in Iran,” Disabil. Health J., vol. 12, no. 4, pp. 641–648, 2019, doi: 10.1016/j.dhjo.2019.05.008.
Z. Karbasi et al., “Better monitoring of abused children by designing a child abuse surveillance system: Determining national child abuse minimum data set,” Int. J. Health Plann. Manage., vol. 35, no. 4, pp. 843–851, 2020, doi: 10.1002/hpm.2935.
J. Zarei, A. Mohammadi, M. R. Akrami, and A. Jeihooni Kalhori, “Designing a minimum data set for the information management system (registry) of spinal canal stenosis: An applied-descriptive study,” Heal. Sci. Reports, vol. 6, no. 11, 2023, doi: 10.1002/hsr2.1671.
F. A. Bernardi et al., “The Minimum Data Set for Rare Diseases: Systematic Review,” J. Med. Internet Res., vol. 25, pp. 1–13, 2023, doi: 10.2196/44641.
Z. Rampisheh, M. E. Kameli, J. Zarei, A. V. Barzaki, M. Meraji, and A. Mohammadi, “Developing a national minimum data set for hospital information systems in the Islamic Republic of Iran,” East. Mediterr. Heal. J., vol. 26, no. 4, pp. 400–409, 2020, doi: 10.26719/emhj.19.046.
J. Owusu-marfo, Z. Lulin, H. A. Antwi, and M. O. Antwi, “Electronic Health Records Adoption in China ’ s Hospitals : A Narrative Review,” Eur. J. Contemp. Res., vol. 8, no. 1, pp. 409–418, 2019, [Online]. Available: https://www.researchgate.net/publication/337293396_Electronic_Health_Records_Adoption_in_China’s_Hospitals_A_Narrative_Review
R. Abbasi, R. Khajouei, and M. Mirzaee, “Evaluating the demographic and clinical minimum data sets of Iranian National Electronic Health Record,” BMC Health Serv. Res., vol. 19, no. 1, pp. 1–10, 2019, doi: 10.1186/s12913-019-4284-x.
V. Ehrenstein, H. Kharrazi, H. Lehmann, and C. O. Taylor, “Obtaining Data From Electronic Health Records,” Tools Technol. Regist. Interoperability, Regist. Eval. Patient Outcomes A User’s Guid., pp. 1–92, 2019.
P. Bruland et al., “Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting,” BMC Med. Res. Methodol., vol. 16, no. 1, pp. 1–10, 2016, doi: 10.1186/s12874-016-0259-3.
D. Xiao, C. Song, N. Nakamura, and M. Nakayama, “Development of an application concerning fast healthcare interoperability resources based on standardized structured medical information exchange version 2 data,” Comput. Methods Programs Biomed., vol. 208, p. 106232, 2021, doi: 10.1016/j.cmpb.2021.106232.
G. Jiang et al., “Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution,” J. Biomed. Inform., vol. 62, pp. 232 – 242, 2016, doi: 10.1016/j.jbi.2016.07.008.
L. Lang, R. P. Moser, J. Odenkirchen, and D. Reeves, “Common Data Elements ( CDEs ),” vol. 13, no. 6, pp. 671–676, 2017, doi: 10.1177/1740774516653238.Improving.
C. J. Date, H. Darwen, and N. A. Lorentzos, “Database Design I: Structure,” Time Relational Theory, pp. 197–226, 2014, doi: 10.1016/b978-0-12-800631-3.50012-5.
M. G. Kahn, D. Batson, and L. M. Schilling, “Data model considerations for clinical effectiveness researchers,” Med. Care, vol. 50, no. SUPPL. 1, 2012, doi: 10.1097/MLR.0b013e318259bff4.
A. M. C. de Araújo, V. C. Times, and S. C. B. Soares, “A Conceptual Data Model for Health Information Systems,” Steer. Comm. World Congr. Comput. Sci. Comput. Eng. Appl. Comput., no. July, pp. 236–242, 2016.
M. West, “Some Types and Uses of Data Models,” Dev. High Qual. Data Model., pp. 23–36, 2011, doi: 10.1016/b978-0-12-375106-5.00003-8.
S. H. El-sappagh, S. El-masri, A. M. Riad, and M. Elmogy, “Electronic Health Record Data Model Optimized for Knowledge Discovery,” Int. J. Comput. Sci. Issues, vol. 9, no. 5, pp. 329–338, 2012.
Z. Yang et al., “Defining health data elements under the HL7 development framework for metadata management,” J. Biomed. Semantics, vol. 13, no. 1, pp. 1–15, 2022, doi: 10.1186/s13326-022-00265-5.
A. I. Paganelli et al., “A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home,” Internet of Things (Netherlands), vol. 18, p. 100399, 2022, doi: 10.1016/j.iot.2021.100399.
V. L. Tiase, K. A. Sward, and J. C. Facelli, “A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation,” JMIR Nurs., vol. 7, 2024, doi: 10.2196/55793.
