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Comparison of openEHR open-source servers

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Medical information systems could benefit from electronic health records management using openEHR. On the other hand, such a standard adds an additional software layer to the system, which might impact performance. In this article, we present an in-depth comparison of open-source openEHR servers and propose tools for testing them. Load tests for selected opensource servers were prepared using Apache JMeter. Statistics of elapsed time of requests and throughput of each solution were calculated. Results show that open-source openEHR servers significantly differ in performance and stability and prove that load testing should be a crucial part of a development process.
Rocznik
Strony
161--167
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
  • Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Radioelectronics and Multimedia Technology
  • Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Radioelectronics and Multimedia Technology
autor
  • Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Radioelectronics and Multimedia Technology
  • Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Radioelectronics and Multimedia Technology
  • Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Radioelectronics and Multimedia Technology
Bibliografia
  • [1] “openEHR Home.” https://www.openehr.org/ (accessed Dec. 07, 2022).
  • [2] I. D. Pǎun et al., “Local EHR management based on openEHR and EN13606,” J Med Syst, vol. 35, no. 4, pp. 585-590, Aug. 2011, doi:10.1007/s10916-009-9395-1
  • [3] B. Christensen and G. Ellingsen, “Evaluating Model-Driven Development for large-scale EHRs through the openEHR approach,” Int J Med Inform, vol. 89, pp. 43-54, May 2016, doi:10.1016/j.ijmedinf.2016.02.004
  • [4] L. Min, Q. Tian, X. Lu, and H. Duan, “Modeling EHR with the openEHR approach: An exploratory study in China Philip Payne,” BMC Med Inform Decis Mak, vol. 18, no. 1, pp. 1-15, Aug. 2018, doi:10.1186/s12911-018-0650-6
  • [5] F. Hak, D. Oliveira, N. Abreu, P. Leuschner, A. Abelha, and M. Santos, “An OpenEHR Adoption in a Portuguese Healthcare Facility,” in Procedia Computer Science, Elsevier B.V., Jan. 2020, pp. 1047-1052. doi:10.1016/j.procs.2020.03.075
  • [6] G. M. Bacelar-Silva, H. César, P. Braga, and R. Guimarães, “OpenEHR-based pervasive health information system for primary care: First Brazilian experience for public care,” Proceedings of CBMS 2013 - 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 572-573, 2013, doi:10.1109/CBMS.2013.6627881
  • [7] J. Buck, S. Garde, C. D. Kohl, and P. Knaup-Gregori, “Towards a comprehensive electronic patient record to support an innovative individual care concept for premature infants using the openEHR approach,” Int J Med Inform, vol. 78, no. 8, pp. 521-531, Aug. 2009, doi:10.1016/j.ijmedinf.2009.03.001
  • [8] C. Pahl et al., “Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics,” J Biomed Inform, vol. 55, pp. 174-187, Jun. 2015, doi: https://doi.org/10.1016/j.jbi.2015.04.004
  • [9] R. Chen, P. Georg-Hemming, and H. Åhlfeldt, “Representing a chemotherapy guideline using openEHR and rules,” in Studies in Health Technology and Informatics, IOS Press, 2009, pp. 653-657. doi: https://doi.org/10.3233/978-1-60750-044-5-653
  • [10] M. Li et al., “Development of an openEHR Template for COVID-19 Based on Clinical Guidelines,” J Med Internet Res, vol. 22, no. 6, p. e20239, Jun. 2020, doi: https://doi.org/10.2196/20239
  • [11] L. Min, Q. Tian, X. Lu, J. An, and H. Duan, “An openEHR based approach to improve the semantic interoperability of clinical data registry,” BMC Med Inform Decis Mak, vol. 18, no. 1, p. 15, Mar. 2018, doi: 10.1186/s12911-018-0596-8
  • [12] S. Garde, E. Hovenga, J. Buck, and P. Knaup, “Expressing clinical data sets with openEHR archetypes: A solid basis for ubiquitous computing,” Int J Med Inform, vol. 76, no. SUPPL. 3, pp. S334-S341, Dec. 2007, doi:10.1016/j.ijmedinf.2007.02.004
  • [13] C. D. Kohl, S. Garde, and P. Knaup, “Facilitating secondary use of medical data by using openEHR archetypes,” in Studies in Health Technology and Informatics, IOS Press, 2010, pp. 1117-1121. doi:10.3233/978-1-60750-588-4-1117
  • [14] A. Wulff, B. Haarbrandt, E. Tute, M. Marschollek, P. Beerbaum, and T. Jack, “An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR,” Artif Intell Med, vol. 89, pp. 10-23, Jul. 2018, doi:10.1016/j.artmed.2018.04.012
  • [15] F. Khennou, Y. I. Khamlichi, and N. E. H. Chaoui, “Improving the use of big data analytics within electronic health records: A case study based OpenEHR,” in Procedia Computer Science, Elsevier B.V., Jan. 2018, pp. 60-68. doi:10.1016/j.procs.2018.01.098
  • [16] J. N. S. Rubí and P. R. L. Gondim, “IoMT platform for pervasive healthcare data aggregation, processing, and sharing based on oneM2M and openEHR,” Sensors (Switzerland), vol. 19, no. 19, p. 4283, Oct. 2019, doi:10.3390/s19194283
  • [17] Y. Yang, H. Xu, B. Qi, X. Niu, M. Li, and D. Zhao, “Stroke screening data modeling based on openEHR and NINDS Stroke CDE,” Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, pp. 2147-2152, Dec. 2020, doi:10.1109/BIBM49941.2020.9313127
  • [18] J. Kryszyn, K. Cywoniuk, W. T. Smolik, D. Wanta, P. Wróblewski, and M. Midura, “Performance of an openEHR based hospital information system,” Int J Med Inform, vol. 162, p. 104757, Jun. 2022, doi:10.1016/J.IJMEDINF.2022.104757
  • [19] “GitHub - ppazos/cabolabs-ehrserver: Open platform to manage and share standardized clinical data, designed by @ppazos at CaboLabs Health Informatics.” https://github.com/ppazos/cabolabs-ehrserver (accessed May 13, 2023).
  • [20] “ehrbase/ehrbase: An open source openEHR server.” https://github.com/ehrbase/ehrbase (accessed May 13, 2023).
  • [21] “GitHub - ethercis/ethercis.” https://github.com/ethercis/ethercis (accessed May 13, 2023).
  • [22] “Clinical Knowledge Manager.” https://ckm.openehr.org/ckm/templates/1013.26.80 (accessed May 26, 2023).
  • [23] “ppazos/openEHR-OPT: Java/Groovy Support of openEHR Operational Templates, Reference Model, Data Generators and other tools for www.CaboLabs.com projects.” https://github.com/ppazos/openEHR-OPT (accessed May 26, 2023).
  • [24] “jkryszyn/openehr-test-suite: JMeter test suite for openEHR servers: EHRServer and EHRbase.” https://github.com/jkryszyn/openehr-test-suite (accessed May 26, 2023).
Uwagi
1. Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
2. This work was financed thanks to an internal grant for employees of the Warsaw University of Technology supporting scientific activity in the discipline of Biomedical Engineering [grant number 504/04763/1034/43.052202].
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e770236f-ddea-42aa-a24c-017e7b7d8dd2
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