Abstract
Objectives
Due to the diversity, volume, and distribution of ingested data, the majority of current healthcare entities operate independently, increasing the problem of data processing and interchange. The goal of this research is to design, implement, and evaluate an electronic health record (EHR) interoperability solution – prototype – among healthcare organizations, whether these organizations do not have systems that are prepared for data sharing, or organizations that have such systems.
Methods
We established an EHR interoperability prototype model named interoperability smart lane for electronic health record (islEHR), which comprises of three modules: 1) a data fetching APIs for external sharing of patients’ information from participant hospitals; 2) a data integration service, which is the heart of the islEHR that is responsible for extracting, standardizing, and normalizing EHRs data leveraging the fast healthcare interoperability resources (FHIR) and artificial intelligence techniques; 3) a RESTful API that represents the gateway sits between clients and the data integration services.
Results
The prototype of the islEHR was evaluated on a set of unstructured discharge reports. The performance achieved a total time of execution ranging from 0.04 to 84.49 s. While the accuracy reached an F-Score ranging from 1.0 to 0.89.
Conclusions
According to the results achieved, the islEHR prototype can be implemented among different heterogeneous systems regardless of their ability to share data. The prototype was built based on international standards and machine learning techniques that are adopted worldwide. Performance and correctness results showed that islEHR outperforms existing models in its diversity as well as correctness and performance.
Acknowledgments
We are grateful to the Palestine Red Crescent Society (PRCS) hospital for providing us with the data we needed to finish our project. We also appreciate Dr. Alaa Najjar’s help in establishing the list of medical abbreviations, as well as her participation in the WE assessment process.
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Research funding: None declared.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Ethical approval: The local Institutional Review Board deemed the study exempt from review.
References
1. Adel, E, El-Sappagh, S, Barakat, S, Elmogy, M. A unified fuzzy ontology for distributed electronic health record semantic interoperability. In: U-healthcare monitoring systems [Internet]. Egypt: Elsevier; 2019:353–95 pp. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780128153703000141. [Accessed 15 Nov 2020].10.1016/B978-0-12-815370-3.00014-1Search in Google Scholar
2. ISO. International standard 2382: information technology - vocabulary [Internet]; 2015. Available from: https://www.iso.org/obp/ui/#iso:std:iso-iec:2382:ed-1:v1:en.Search in Google Scholar
3. Adel, E, El-Sappagh, S, Barakat, S, Elmogy, M. Ontology-based electronic health record semantic interoperability: a survey. In: U-healthcare monitoring systems [Internet]. Egypt: Elsevier; 2019:315–52 pp. Available from: https://linkinghub.elsevier.com/retrieve/pii/B978012815370300013X. [Accessed 15 Nov 2020].10.1016/B978-0-12-815370-3.00013-XSearch in Google Scholar
4. Reisman, M. EHRs: the challenge of making electronic data usable and interoperable. P & T : a peer-reviewed journal for formulary management; 2017, vol. 4.Search in Google Scholar
5. do Espírito Santo, JM, Medeiros, CB. Semantic interoperability of clinical data. In: Da Silveira, M, Pruski, C, Schneider, R, editors. Data integration in the life sciences [Internet]. (Lecture Notes in Computer Science). Cham: Springer International Publishing; 2017, vol. 10649:29–37 pp. Available from: http://link.springer.com/10.1007/978-3-319-69751-2_4. [Accessed 15 Nov 2020].10.1007/978-3-319-69751-2_4Search in Google Scholar
6. Matney, SA. Semantic interoperability: the good, the bad, and the ugly. Nursing 2016;46:23–4. https://doi.org/10.1097/01.nurse.0000490225.92179.69.Search in Google Scholar PubMed
7. González Bernaldo de Quirós, F, Otero, C, Luna, D. Terminology services: standard terminologies to control health vocabulary: experience at the Hospital Italiano de Buenos Aires. Yearb Med Inf 2018;27:227–33.10.1055/s-0038-1641200Search in Google Scholar PubMed PubMed Central
8. Liyanage, H, Krause, P, De Lusignan, S. Using ontologies to improve semantic interoperability in health data. J Innovat Health Inf 2015;22:309–15. https://doi.org/10.14236/jhi.v22i2.159.Search in Google Scholar PubMed
9. Legaz-García, MDC, Martínez-Costa, C, Menárguez-Tortosa, M, Fernández-Breis, JT. A semantic web based framework for the interoperability and exploitation of clinical models and EHR data. Knowl-Based Syst 2016;105:175–89.10.1016/j.knosys.2016.05.016Search in Google Scholar
10. Puttini, RS, Toffanello, AA, Chaim, RM, Alves, G, Rotzsch, JMP, Carvalho, EO, et al.. Semantic framework for electronic health records. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC) [Internet]. San Diego, CA, USA: IEEE; 2017:334–7 pp. Available from: http://ieeexplore.ieee.org/document/7889558. [Accessed 15 Nov 2020].10.1109/ICSC.2017.98Search in Google Scholar
11. Blackman-Lees, SM. Towards a conceptual framework for persistent use: a technical plan to achieve semantic interoperability within electronic health record systems. In: Proceedings of the 51st Hawaii International Conference on System Sciences; 2018.10.24251/HICSS.2018.551Search in Google Scholar
12. Oliveira, D, Coimbra, A, Miranda, F, Abreu, N, Leuschner, P, Machado, J, et al.. New approach to an openEHR introduction in a Portuguese healthcare facility. In: Rocha, Á, Adeli, H, Reis, LP, Costanzo, S, editors. Trends and advances in information systems and technologies [Internet]. (Advances in Intelligent Systems and Computing). Cham: Springer International Publishing; 2018, vol 747:205–11 pp. Available from: http://link.springer.com/10.1007/978-3-319-77700-9_21. [Accessed 15 Nov 2020].10.1007/978-3-319-77700-9_21Search in Google Scholar
13. Hong, N, Wen, A, Shen, F, Sohn, S, Wang, C, Liu, H, et al.. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA Open 2019;2:570–9. https://doi.org/10.1093/jamiaopen/ooz056.Search in Google Scholar PubMed PubMed Central
14. Gomes, F, Freitas, R, Ribeiro, M, Moura, C, Andrade, O, Oliveira, M. GIRLS, a gateway for interoperability of electronic health record in low-cost system: interoperability between FHIR and OpenEHR standards. In: 2019 IEEE International Conference on E-health Networking, Application & Services (HealthCom) [Internet]. Bogota, Colombia: IEEE; 2019:1–6 pp. Available from: https://ieeexplore.ieee.org/document/9009602. [Accessed 3 Jun 2021].10.1109/HealthCom46333.2019.9009602Search in Google Scholar
15. Kiourtis, A, Mavrogiorgou, A, Menychtas, A, Maglogiannis, I, Kyriazis, D. Structurally mapping healthcare data to HL7 FHIR through ontology alignment. J Med Syst 2019;43:62. https://doi.org/10.1007/s10916-019-1183-y.Search in Google Scholar PubMed
16. Meng, M, Steinhardt, S, Schubert, A. Application programming interface documentation: what do software developers want? J Tech Writ Commun 2018;48:295–330. https://doi.org/10.1177/0047281617721853.Search in Google Scholar
17. HL7 International. HL7 FHIR [Internet]. Available from: http://hl7.org/fhir/.Search in Google Scholar
18. Lange, K. The little book on REST services. Copenhagen: RESTful Web Services; 2019, vol. 31. https://www. kennethlange. com/books/The-Little-Book-on-REST-Services. pdf.Search in Google Scholar
19. Kruse, CS, Smith, B, Vanderlinden, H, Nealand, A. Security techniques for the electronic health records. J Med Syst 2017;41:127. https://doi.org/10.1007/s10916-017-0778-4.Search in Google Scholar PubMed PubMed Central
20. Brail, G, Ramji, S. OAuth - the big picture. Apigee ebook; 2012, vol. 19.Search in Google Scholar
21. Abie, H. An overview of firewall technologies. Oslo, Norway: Norwegian Computing Center; 2000, vol. 10:47–52 pp.Search in Google Scholar
22. Chawla, BK, Gupta, OP, Sawhney, BK. A review on IPsec and SSL VPN. Int J Sci Eng Res 2014;5:4.Search in Google Scholar
23. Outen, JL. VPN security and methodology, vol 9. 2014.Search in Google Scholar
24. Khattak, FK, Jeblee, S, Pou-Prom, C, Abdalla, M, Meaney, C, Rudzicz, F. A survey of word embeddings for clinical text. J Biomed Inf 2019;4:100057. https://doi.org/10.1016/j.yjbinx.2019.100057.Search in Google Scholar PubMed
25. Babic, K, Martinčic-Ipšic, S, Meštrovic, A, Guerra, F. Short texts semantic similarity based on word embeddings. In: Central European Conference on Information and Intelligent Systems; 2019, vol. 7:1411–20 https://doi.org/10.1145/2806416.2806475.Search in Google Scholar
26. Mikolov, T, Chen, K, Corrado, G, Dean, J. Efficient estimation of word representations in vector space. ArXiv13013781 Cs [Internet]; 2013. Available from: http://arxiv.org/abs/1301.3781. [Accessed 11 Sep 2020].Search in Google Scholar
27. Le, QV, Mikolov, T. Distributed representations of sentences and documents. ArXiv14054053 Cs [Internet]; 2014. Available from: http://arxiv.org/abs/1405.4053. [Accessed 15 Jun 2021].Search in Google Scholar
28. Bojanowski, P, Grave, E, Joulin, A, Mikolov, T. Enriching word vectors with subword information. ArXiv160704606 Cs [Internet]; 2017. Available from: http://arxiv.org/abs/1607.04606. [Accessed 15 Jun 2021].10.1162/tacl_a_00051Search in Google Scholar
29. Torregrossa, F, Claveau, V, Kooli, N, Gravier, G, Allesiardo, R. On the correlation of word embedding evaluation metrics. In: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020); 2020, vol. 9.Search in Google Scholar
30. Borah, A, Barman, MP, Awekar, A. Are word embedding methods stable and should we care about it? ArXiv210408433 Cs [Internet]; 2021. Available from: http://arxiv.org/abs/2104.08433. [Accessed 21 Jun 2021].10.1145/3465336.3475098Search in Google Scholar
31. Zitouni, I, editor. Natural language processing of semitic languages [Internet]. (Theory and Applications of Natural Language Processing). Berlin, Heidelberg: Springer; 2014. Available from: http://link.springer.com/10.1007/978-3-642-45358-8. [Accessed 8 Jun 2021].10.1007/978-3-642-45358-8Search in Google Scholar
32. Calders, T, Daelemans, W. A formal framework for evaluation of information extraction. n.p.; 2004, vol. 13. Available from: http://www. cnts. ua. ac. be/Publications/2004/DCD04.Search in Google Scholar
33. Dong, X, Chowdhury, S, Qian, L, Li, X, Guan, Y, Yang, J, et al.. Deep learning for named entity recognition on Chinese electronic medical records: combining deep transfer learning with multitask bi-directional LSTM RNN. PLoS One 2019;14:e0216046. https://doi.org/10.1371/journal.pone.0216046.Search in Google Scholar PubMed PubMed Central
34. Gligic, L, Kormilitzin, A, Goldberg, P, Nevado-Holgado, A. Named entity recognition in electronic health records using transfer learning bootstrapped neural networks. Neural Network 2020;121:132–9. https://doi.org/10.1016/j.neunet.2019.08.032.Search in Google Scholar PubMed
35. Singh, S. Natural language processing for information extraction. ArXiv180702383 Cs [Internet]; 2018. Available from: http://arxiv.org/abs/1807.02383. [Accessed 25 Jun 2021].Search in Google Scholar
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