Document Type : Original Article

Authors

1 Department of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran

2 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran

3 Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran

4 Department of Cardiology, School of Medicine, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran

Abstract

INTRODUCTION: Providing information exchange and collaboration between isolated information
systems (ISs) is essential in the health‑care environments. In this context, we aimed to develop a
communication protocol to facilitate better interoperability among electrophysiology study (EPS)‑related
ISs in order to allow exchange unified reporting in EPS ablation.
MATERIALS AND METHODS: This study was an applied‑descriptive research that was conducted
in 2019. To determine the information content of agreed cardiac EPS Minimum Data Set (MDS) in
Iran, the medical record of patients undergoing EPS ablation procedure in the Tehran Heart Center
(THC) hospital was reviewed by a checklist. Then, an information model based on Health Level
Seven, Clinical Document Architecture (HL7 CDA) standard framework for structural interoperability
has been developed. In this framework, using NPEX online browser and MindMaple software, a set
of terminology mapping rules was used for consistent transfer of data between various ISs.
RESULTS: The information content of each data field was introduced into the heading and body
sections of HL7 CDA document using Systematized Nomenclature of Medicine – Clinical Terminology
names and codes. Then, the ontology alignment was designed in the form of thesaurus mapping
routes.
CONCLUSION: The sensitive, complex, and multidimensional nature of cardiovascular conditions
requires special attention to the interoperability of ISs. Designing customized communication protocols
plays an important role in improving the interoperability, and they are compatible with the needs of
future Iranian health information exchange.

Keywords

  1. Aydar M. Developing a Semantic Framework for Healthcare
    Information Interoperability: Kent State University; 2015. DOI:
    http://orcid.org/0000‑0002‑5578‑758X.
  2. 2. Souza AC, de Medeiros AP, Martins CB. Technical interoperability
    among EHR systems in Brazilian public health organizations.
    Rev Brasil Comput Aplicada 2019;11:42‑55. DOI: 10.5335/rbca.
    v11i2.8651.
    3. Hammami R, Bellaaj H, Kacem AH. Interoperability for medical
    information systems: An overview. Health Technol 2014;4:261‑72.
    DOI: 10.1007/s12553‑014‑0085‑8.
    4. Ranade‑Kharkar P, Narus SP, Anderson GL, Conway T, Del Fiol G.
    Data standards for interoperability of care team information to
    support care coordination of complex pediatric patients. J Biomed
    Inform 2018;85:1‑9. DOI: 10.1016/j.jbi.2018.07.009.
    5. Mirani N, Ayatollahi H, Haghani H. A survey on barriers to the
    development and adoption of electronic health records in Iran.
    J Health Adm 2013;15(50).
    6. Sharifi M, Ayat M, Jahanbakhsh M, Tavakoli N, Mokhtari H,
    Wan Ismail WK. E‑health implementation challenges in Iranian
    medical centers: A qualitative study in Iran. Telemed J E Health
    2013;19:122‑8. DOI: 10.1089/tmj.2012.0071.
    7. Park H, Hardiker N. Clinical terminologies: A solution for
    semantic interoperability. J Korean Soc Med Informat 2009;15:1‑11.
    DOI: https://doi.org/10.4258/jksmi.2009.15.1.1.
    8. Gøeg KR, Chen R, Højen AR, Elberg P. Content analysis of
    physical examination templates in electronic health records using
    SNOMED CT. Int J Med Inform 2014;83:736‑49. DOI: 10.1016/j.
    ijmedinf.
    9. Slotwiner DJ, Abraham RL, Al‑Khatib SM, Anderson HV,
    Bunch TJ, Ferrara MG, et al. HRS White Paper on interoperability
    of data from cardiac implantable electronic devices (CIEDs). Heart
    Rhythm 2019;16:e107‑27. DOI: 10.1016/j.hrthm.2019.05.002.
    10. Shanbehzadeh M, AbdiJ, Ahmadi M. Designing a communication
    protocol for acquired immunodeficiency syndrome information
    exchange. J Educ Health Promot 2019;8:99. DOI: 10.4103/jehp.
    jehp_2_19.
    11. Quinn TA, Granite S, Allessie MA, Antzelevitch C, Bollensdorff C,
    Bub G, et al. Minimum Information about a Cardiac
    Electrophysiology Experiment (MICEE): Standardised reporting
    for model reproducibility, interoperability, and data sharing. Prog
    Biophys Mol Biol 2011;107:4‑10. DOI: 10.1016/j.pbiomolbio.
    12. Dixon BE, Vreeman DJ, Grannis SJ. The long road to semantic
    interoperability in support of public health: Experiences from
    two states. J Biomed Informat 2014;49:3‑8. DOI: https://doi.
    org/10.1016/j.jbi.2014.03.011.
    13. Lee G, Sanders P, Kalman JM. Catheter ablation of atrial
    arrhythmias: State of the art. Lancet (London, England)
    2012;380:1509‑19. DOI: https://doi.org/10.1016/S0140‑6736(12)
    61463‑9.
    14. Burstein B, Barbosa RS, Kalfon E, Joza J, Bernier M, Essebag V.
    Venous thrombosis after electrophysiology procedures:
    A systematic review. Chest 2017;152:574‑86. DOI: 10.1016/j.chest.
    2017.05.040.
    15. Desjardins B. Imaging for cardiac electrophysiology. SA J Radiol
    2016;20:1‑8. DOI: http://dx.doi.org/10.4102/sajr.v20i2.1048
    16. Neuberger HR, Tilz RR, Bonnemeier H, Deneke T, Estner HL,
    Kriatselis C, et al. A survey of German centres performing invasive
    electrophysiology: Structure, procedures, and training positions.
    Europace 2013;15:1741‑6. DOI: 10.1093/europace/eut149.
    17. Vasheghani‑Farahani A, Shafiee A, Akbarzadeh M,
    Bahrololoumi‑Bafruee N, Alizadeh‑Diz A, Emkanjoo Z, et al.
    Acute complications in cardiac electrophysiology procedures:
    A prospective study in a high‑volume tertiary heart center. Res
    Cardiovasc Med 2018;7:20. DOI: 10.4103/rcm.rcm_34_17.
    18. Kazemi‑Arpanahi H, Vasheghani‑Farahani A, Baradaran A,
    Ghazisaeedi M, Mohammadzadeh N, Bostan H. Development
    of a minimum data set for cardiac electrophysiology study
    ablation. J Educ Health Promot 2019;8:101. DOI: 10.1016/j.
    jacep.2018.11.013.
    19. Sagita AA, Nurlaela L, Widodo W, editors. Mind Maple Lite
    Software: Improve Student’s Learning Outcomes and Stimulating
    Metacognition in Nutrition Science Subject. 1st International
    Conference on Social, Applied Science and Technology in Home
    Economics (ICONHOMECS 2017). New York: Atlantis Press;
    2017. DOI: https://doi.org/10.2991/iconhomecs‑17.2018.1.
    20. Marshall S, Harrison J, Flanagan B. The teaching of a structured
    tool improves the clarity and content of interprofessional clinical
    communication. Qual Saf Health Care 2009;18:137‑40. DOI:
    10.1136/qshc.2007.025247.
    21. Ciubotaru B, Muntean GM. Network Communications Protocols
    and Services. Advanced Network Programming–Principles and
    Techniques. London: Springer; 2013. p. 29‑52.
    22. Shade SB, Chakravarty D, Koester KA, Steward WT, Myers JJ.
    Health information exchange interventions can enhance quality
    and continuity of HIV care. Int J Med Informat 2012;81:e1‑9. DOI:
    https://doi.org/10.1016/j.ijmedinf.2012.07.003.
    23. Kohl P, Mirams G, Quinn TA, Wang K. Minimum information
    about a cardiac electrophysiology experiment (MICEE):
    Standardised reporting for model reproducibility‚ interoperability‚
    and data sharing. Prog Biophys Mol Biol 2011 Oct 1;107(1):4‑10.
    DOI: https://doi.org/10.1016/j.pbiomolbio.2011.07.001.
    24. Gansel X, Mary M, van Belkum A. Semantic data interoperability,
    digital medicine, and e‑health in infectious disease management:
    A review. Eur J Clin Microbiol Infect Dis 2019;38:1023‑34. DOI:
    10.1016/j.ijmedinf.2012.06.004.
    25. Rahimi A, Liaw ST, Taggart J, Ray P, Yu H. Validating an
    ontology‑based algorithm to identify patients with type 2
    diabetes mellitus in electronic health records. Int J Med Inform
    2014;83:768‑78. DOI: 10.1007/s10096‑019‑03501‑6.
    26. Jabbar S, Ullah F, Khalid S, Khan M, Han K. Semantic
    interoperability in heterogeneous IoT infrastructure for
    healthcare. Wirel Comm Mobile Comput 2017; 2017. DOI:
    10.1007/s10096‑019‑03501‑6.
    27. Adel E, El‑Sappagh S, Barakat S, Elmogy M. Ontology‑Based
    Electronic Health Record Semantic Interoperability:
    A Survey. U‑Healthcare Monitoring Systems. Amsterdam:
    Elsevier; 2019. p. 315‑52. DOI: https://doi.org/10.1016/
    B978‑0‑12‑815370‑3.00013‑X.
    28. Janaswamy S, Kent RD, editors. Semantic Interoperability and
    Data Mapping in EHR Systems. 2016 IEEE 6th International
    Conference on Advanced Computing (IACC). IEEE; 2016. DOI:
    10.1109/IACC.2016.31.
    29. Yang M, Loeb DF, Sprowell AJ, Trinkley KE. Design and
    implementation of a depression registry for primary care. Am J Med
    Q 2019;34:59‑66. DOI: https://doi.org/10.1177/1062860618787056.
    30. Hurvitz EA, Gross PH, Gannotti ME, Bailes AF, Horn SD.
    Registry‑based research in cerebral palsy: The cerebral palsy
    research network. Phys Med Rehabil Clin N Am 2020;31:185‑94.
    DOI: 10.1016/j.pmr.2019.09.005.
    31. Duftschmid G, Wrba T, Rinner C. Extraction of standardized
    archetyped data from electronic health record systems based on
    the entity‑attribute‑value model. Int J Med Inform 2010;79:585‑97.
    DOI: 10.1016/j.ijmedinf.2010.04.007.
    32. Ahmadi M, Foozonkhah S, Shahmoradi L, Mahmodabadi AD.
    Messaging standard requirements for electronic health records
    in Islamic Republic of Iran: A Delphi study. East Mediterr Health
    J 2017;22:794‑801. DOI: 10.26719/2016.22.11.794.
    33. Van der Velde ET, Foeken H, Witteman TA, van Erven L,
    Schalij MJ. Integration of data from remote monitoring systems
    and programmers into the hospital electronic health record system
    based on international standards. Neth Heart J 2012;20:66‑70. DOI:
    10.1007/s12471‑011‑0234‑x.