Abstract
In today’s digitized era, data is one of the most important entities. Every system generates a lot of data and even it has to deal with bulk data. The efficiency of the system depends on how the data is available and in which form the data is stored. With the advent of newer techniques of storing and processing the data, the medical domain has leaped the form of the electronic patient record (EPR) or electronic health record (EHR). This paper is based on a study of EHR which aims at highlighting its significance and emphasize its importance for medical reforms. In this paper, our sincere efforts were to introduce the concept of EHR with the help of literature that was already in place. Further, we have focussed on the proposed structure of EHR along with the different categories. Even though the EHR system has added a lot of benefits, there were many challenges in its adoption and implementation. We had tried enlisting the challenges for the EHR system and sincerely tried suggesting some measures to mitigate the same. Keeping aside the obstacles in a way of EHRs, numerous existing applications have been discussed further. Finally, we have given some novel and evolving usages of the EHR system which has increased its productivity to a greater extent. Overall our paper tries to give a complete overview of the EHR system which shall be useful in earning a basic understanding of the concept. It shall prove to be good literature for the novices working on the EHR related research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Seymour, T., Frantsvog, D., Graeber, T.: Electronic Health Record (EHR). Am. J. Health Sci. 3(3), 201–210 (2012)
Sun, H., Depraetere, K., Roo, J.D., Mels, G., Vloed, B.D., Twagirumukiza, M., Colaert, D.: Semantic processing of EHR data for clinical research. J. Biomed. Inform. 58, 247–259 (2015)
Gazzarata, G., Gazzarata, R., Giacomini, M.: A standardised SOA based solution to guarantee the secure access to EHR Science Direct. Procedia Comput. Sci. 64, 1124–1129 (2015)
Covvey, H.D., Zitner, D., Berry, D.M., Cowan, D.D., Shepherd, M.: Formal structure for specifying the content and quality of the electronic health record. In: 11th IEEE International Requirements Engineering Conference, pp. 162–168. IEEE (2003)
Jillian, B.D.N., Carranza, K.A.L.R., Lin, L.M.S., Ponce, A.R., Reyes, W.R.O., Bugtai, N.T., Baldovino, R.G.: Design of a web-based and electronic health record management system for medical teleconsultation. In: IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1–5. IEEE (2018)
Zhang, J., Walji, M.F.: TURF: toward a unified framework of EHR usability. Sci. Direct, J. Biomed. Inform. 44, 1056–1067 (2011)
Konnoju, P.K., Sridhar K.V., Prasad, K.S.R.: A new paradigm of electronic health record for efficient implementation of healthcare delivery. In: IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), pp. 352–354. IEEE (2010)
Kwak, Y.S.: International standards for building electronic health record (EHR). In: 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM, pp. 18–23. IEEE (2005)
Zhang, J., Kowsari, K., Harrison, J.H., Lobo, J.M., Barnes, L.E.: Patient2Vec: a personalized interpretable deep representation of the longitudinal electronic health record. IEEE Access 6, 65333–65346 (2018)
Najafabadipour, M., Zanin, M., Rodríguez-González, A., Gonzalo-Martín, C., García, B.N., Calvo, V., Bermudez, J.L.C., Provencio, M., Menasalvas, E.: Recognition of time expressions in Spanish electronic health records. In: IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 69–74. IEEE (2019)
Zhang, S., Bamakan, S.M.H., Qu, Q., Li, S.: Learning for personalised medicine: a comprehensive review from deep learning perspective. IEEE Rev. Biomed. Eng. 12, 194–208 (2019)
Janssoone, T., Bic, C., Kanoun, D., Rinder, P., Hornus, P.: Machine learning on electronic health records: models and features usages to predict medication non-adherence. https://arxiv.org/abs/1811.12234v1 [cs.LG] (2018)
Rebane, J., Karlsson, I., Papapetrou, P.: An investigation of interpretable deep learning for adverse drug event prediction. In: IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), pp. 337–342. IEEE (2019)
Che, Z., St. Sauver, J., Liu, H., Liu, Y.: Deep learning solutions for classifying patients on opioid use. In: AMIA Annual Symposium Proceedings, pp. 525–534 (2017)
Bashar, A.: Survey on evolving deep learning neural network architectures. J. Artif. Intell. Capsule Netw. 1(02), 73–82 (2019)
Acknowledgments
We are thankful to all those who helped us make this paper, for the valuable information provided by them in their respective fields. We are grateful for their co-operation during the writing of the paper.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Deolekar, R.V., Wankhade, S.B. (2021). A Study of Electronic Health Record to Unfold Its Significance for Medical Reforms. In: Chen, J.IZ., Tavares, J.M.R.S., Shakya, S., Iliyasu, A.M. (eds) Image Processing and Capsule Networks. ICIPCN 2020. Advances in Intelligent Systems and Computing, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-51859-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-51859-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51858-5
Online ISBN: 978-3-030-51859-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)