HCI 655 Describe three challenges associated with migration of data from one EHR product to another
Create an integrated testing strategy.
The term “electronic health record” (EHR) refers to a system designed to improve the administration of patient data. EHRs are routinely used to collect and store patient data. The electronic health record (EHR) is one of the technologies used by many healthcare organizations in the twenty-first century to improve the delivery of high-quality treatment to patients. To improve the security of patient information, the system is usually connected with databases (Shickel et al., 2017). Healthcare practitioners can share information and facilitate the delivery of high-quality care using EHR systems. The EHR system, in most cases, has security mechanisms in place to protect data and other information related to healthcare delivery procedures. Data migration or transfer from one EHR to another is frequently performed during maintenance. However, the entire procedure is usually coupled with a variety of difficulties (Fragidis & Chatzoglou, 2018).
A blind spot in planning and scheduling is one of the issues connected with data migration from one EHR to the other. Due to a lack of good preparation, there may not be enough time to carry out effective data transfer activities. During the migration of data from one EHR to the other, a lack of awareness of both the old and new IT platforms may operate as a roadblock (Gold et al., 2017). Finally, when migrating data from one EHR system to another, the bulk, complexity, and condition of the data may pose a barrier or obstacle. Individuals participating in data transfer operations are frequently dealing with massive volumes of data, which can be difficult to manage.
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There are various dangers associated with sloppy data migration planning. The loss of information or data is the first and most common risk. The second scenario could involve security breaches, in which individuals engaged meddle with database security requirements.
B. Shickel, P. J. Tighe, A. Bihorac, and P. Rashidi (2017). A summary of current improvements in deep learning approaches for electronic health record (EHR) analysis is presented in Deep EHR. 1589-1604 in IEEE journal of biomedical and health informatics. https://ieeexplore.ieee.org/abstract/document/8086133/
R. Gold, E. Cottrell, A. Bunce, M. Middendorf, C. Hollombe, S. Cowburn, G. Melgar, R. Gold, E. Cottrell, A. Bunce, M. Middendorf, M. Middendorf, M. Middendorf, M. Middendorf, M. Middendorf, M. Middendorf, M. Middendorf, M. Middendorf, M (2017). Developing electronic health record (EHR) solutions that address the social determinants of health of health center patients. 30(4), 428-447, Journal of the American Board of Family Medicine. https://www.jabfm.org/content/30/4/428.short
L. L. Fragidis and P. D. Chatzoglou (2018). The international experience of implementing a nationwide electronic health record (EHR) in 13 nations. Quality assurance in health care is a peer-reviewed international publication. https://www.emerald.com/insight/content/doi/10.1108/IJHCQA-09-2016-0136/full/html?fullSc=1&mbSc=1&fullSc=1&fullSc=1