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Exploring the Signifi cance of Syntactic and Semantic Interoperability in Healthcare: An Interpretive Literature Review

Weinan Shen

Abstract


In the digital healthcare era, syntactic and semantic interoperability are essential for effi cient data exchange, improved clinical decisions, and medical research. This study reviews recent research on their signifi cance, technological requirements, and roles in data linkage
and interpretation. Syntactic interoperability ensures standardized data transmission, while semantic interoperability focuses on accurate data
interpretation. Despite their benefi ts, challenges like high costs and complex technology remain. Overcoming these obstacles and fostering
collaboration is crucial for achieving full interoperability in healthcare systems.

Keywords


Syntactic Interoperability; Semantic Interoperability; Healthcare Data Exchange; Clinical Information Systems; Standardization

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References


[1] Gansel, X., Mary, M., & van Belkum, A. (2019). Semantic data interoperability, digital medicine, and e-health in infectious disease management: A review. European Journal of Clinical Microbiology & Infectious Diseases, 38(6), 10231034.

[2] Sachdeva, S., & Bhalla, S. (2012). Semantic interoperability in standardized electronic health record databases. Journal of Data and Information Quality, 3(1), 137.

[3] Daclin, N., & Mallek, S. (2014). Capturing and Structuring Interoperability Requirements: A Framework for Interoperability Requirements. In K. Mertins, F. Bnaben, R. Poler, & J.-P. Bourrires (Eds.), Enterprise Interoperability VI (pp. 239249). Springer International Publishing.

[4] Kalogiannis, S., Deltouzos, K., Zacharaki, E. I., Vasilakis, A., Moustakas, K., Ellul, J., & Megalooikonomou, V. (2019). Integrating an

openEHR-based personalized virtual model for the ageing population within HBase. BMC Medical Informatics and Decision Making,

19(1), 25.

[5] Matney, S., Heale, B., Hasley, S., Decker, E., Frederiksen, B., Davis, N., Langford, P., Ramey, N., & Huff, S. (2019). Lessons Learned

in Creating Interoperable Fast Healthcare Interoperability Resources Profiles for Large-Scale Public Health Programs. Applied Clinical

Informatics, 10(01), 087095.

[6] De Lusignan, S., Mold, F., Sheikh, A., Majeed, A., Wyatt, J. C., Quinn, T., Cavill, M., Gronlund, T. A., Franco, C., Chauhan, U., Blakey,

H., Kataria, N., Barker, F., Ellis, B., Koczan, P., Arvanitis, T. N., McCarthy, M., Jones, S., & Rafi, I. (2014). Patients online access to

their electronic health records and linked online services: A systematic interpretative review. BMJ Open, 4(9), e006021e006021.

[7] Keshav, S. (2007). How to read a paper. ACM SIGCOMM Computer Communication Review, 37(3), 8384.

[8] Ogunyemi, O. I., Meeker, D., Kim, H.-E., Ashish, N., Farzaneh, S., & Boxwala, A. (2013). Identifying Appropriate Reference Data

Models for Comparative Effectiveness Research (CER) Studies Based on Data from Clinical Information Systems. Medical Care,

51(Supplement 8Suppl 3), S45S52.

[9] Nijeweme-dHollosy, W. O., van Velsen, L., Huygens, M., & Hermens, H. (2015). Requirements for and Barriers towards Interoperable

eHealth Technology in Primary Care. IEEE Internet Computing, 19(4), 1019.

[10] Boussadi, A., & Zapletal, E. (2017). A fast healthcare interoperability resources (FHIR) layer implemented over i2b2. BMC Medical Informatics and Decision Making, 17, 112.

[11] Gazzarata, R., Giannini, B., & Giacomini, M. (2017). A SOA?Based Platform to Support Clinical Data Sharing. Journal of Healthcare

Engineering, 2017(1), 2190679.

[12] Soguero-Ruiz, C., Mora-Jimnez, I., Ramos-Lpez, J., Quintanilla Fernndez, T., Garca-Garca, A., Dez-Mazuela, D., Garca-Alberola,

A., & Rojo-lvarez, J. (2018). An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring. International Journal

of Environmental Research and Public Health, 15(3), 428.

[13] Min, L., Tian, Q., Lu, X., An, J., & Duan, H. (2018). An openEHR based approach to improve the semantic interoperability of clinical

data registry. BMC Medical Informatics and Decision Making, 18(S1), 15.

[14] Zhao, X., Li, X., Yang, W., Feng, Q., Zhou, Y., & Wang, Q. (2018). Primary health information standard system based on semantic interoperability. BMC Medical Informatics and Decision Making, 18(S5), 112.

[15] Sun, H., Depraetere, K., Meesseman, L., De Roo, J., Vanbiervliet, M., De Baerdemaeker, J., Muys, H., von Dossow, V., Hulde, N., &

Szymanowsky, R. (2021). A scalable approach for developing clinical risk prediction applications in different hospitals. Journal of Biomedical Informatics, 118, 103783.

[16] Kiourtis, A., Nifakos, S., Mavrogiorgou, A., & Kyriazis, D. (2019). Aggregating the syntactic and semantic similarity of healthcare data

towards their transformation to HL7 FHIR through ontology matching. International Journal of Medical Informatics, 132, 104002.

[17] Sass, J., Lehne, M., & Thun, S. (2019). Comparing Two Standardized Value Sets of Infectious Agents: Implications for Semantic Interoperability. Stud Health Technol Inform, 264, 15741575.

[18] Kush, R. D., Warzel, D., Kush, M. A., Sherman, A., Navarro, E. A., Fitzmartin, R., Ptavy, F., Galvez, J., Becnel, L. B., Zhou, F. L.,

Harmon, N., Jauregui, B., Jackson, T., & Hudson, L. (2020). FAIR data sharing: The roles of common data elements and harmonization.

Journal of Biomedical Informatics, 107, 103421.

[19] Mishra, N. K., Duke, J., Lenert, L., & Karki, S. (2020). Public health reporting and outbreak response: Synergies with evolving clinical

standards for interoperability. Journal of the American Medical Informatics Association, 27(7), 11361138.

[20] Klopfenstein, S. A. I., Vorisek, C. N., Shutsko, A., Lehne, M., Sass, J., Lbe, M., Schmidt, C. O., & Thun, S. (n.d.). Fast Healthcare Interoperability Resources (FHIR) in a FAIR Metadata Registry for COVID-19 Research. 5.

[21] Mller, A., Haneke, H., Kirchberger, V., Mastella, G., Dommasch, M., Merle, U., Heinze, O., Siegmann, A., Spinner, C., Buiatti, A.,

Laugwitz, K.-L., Schmidt, G., & Martens, E. (2021). Integration of mobile sensors in a telemedicine hospital system: Remote-monitoring in COVID-19 patients. Journal of Public Health.

[22] Ng, M. S. Y., Charu, V., Johnson, D. W., OShaughnessy, M. M., & Mallett, A. J. (2021). National and international kidney failure registries: Characteristics, commonalities, and contrasts. Kidney International, S0085253821010206.

[23] Rikhotso, M., Kalema, B., & Seaba, T. (2024). Data Interoperability Assessment Model for Health Information System in South African

Public Healthcare.




DOI: http://dx.doi.org/10.70711/pmr.v2i4.6113

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