Next Article in Journal
Research Methods and Ethics in Health Emergency and Disaster Risk Management: The Result of the Kobe Expert Meeting
Next Article in Special Issue
Public Perception on Healthcare Services: Evidence from Social Media Platforms in China
Previous Article in Journal
Basic Life Support Training Methods for Health Science Students: A Systematic Review
Article

Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

1
Logistics, Molde University College, Molde, NO-6410 Molde, Norway
2
DEI, Instituto Superior Técnico, 1049-001 Lisboa, Portugal
3
Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, 1649-026 Lisbon, Portugal
4
Instituto Universitário de Lisboa (ISCTE-IUL), BRU-IUL, 1649-026 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(5), 769; https://doi.org/10.3390/ijerph16050769
Received: 30 December 2018 / Revised: 4 February 2019 / Accepted: 24 February 2019 / Published: 3 March 2019
(This article belongs to the Special Issue Analytics in Digital Health)
Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources. View Full-Text
Keywords: big data; data; ETL; framework; integration; knowledge; medical records; extract-transform and load big data; data; ETL; framework; integration; knowledge; medical records; extract-transform and load
Show Figures

Figure 1

MDPI and ACS Style

Helgheim, B.I.; Maia, R.; Ferreira, J.C.; Martins, A.L. Merging Data Diversity of Clinical Medical Records to Improve Effectiveness. Int. J. Environ. Res. Public Health 2019, 16, 769. https://doi.org/10.3390/ijerph16050769

AMA Style

Helgheim BI, Maia R, Ferreira JC, Martins AL. Merging Data Diversity of Clinical Medical Records to Improve Effectiveness. International Journal of Environmental Research and Public Health. 2019; 16(5):769. https://doi.org/10.3390/ijerph16050769

Chicago/Turabian Style

Helgheim, Berit I., Rui Maia, Joao C. Ferreira, and Ana L. Martins. 2019. "Merging Data Diversity of Clinical Medical Records to Improve Effectiveness" International Journal of Environmental Research and Public Health 16, no. 5: 769. https://doi.org/10.3390/ijerph16050769

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop