Authors:
Keisuke Ogawa
1
;
Kazunori Matsumoto
1
;
Masayuki Hashimoto
1
;
Tatsuaki Hamai
1
and
Yoshiaki Kondo
2
Affiliations:
1
KDDI R&D Labs and Inc., Japan
;
2
Tohoku University Graduate School of Medicine, Wise Solutions and Inc., Japan
Keyword(s):
Electronic medical records, Timeline, visualization of medical data, XEUS, Word completion, Adaptive data mergence, Social patient list, Mobile, XML.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Cardiovascular Technologies
;
Clinical Problems and Applications
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Management and Quality
;
Data Manipulation
;
Data Visualization
;
Decision Support Systems
;
Devices
;
Distributed and Mobile Software Systems
;
Electronic Health Records and Standards
;
Enterprise Information Systems
;
Evaluation and Use of Healthcare IT
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Medical and Nursing Informatics
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Ontologies and the Semantic Web
;
Pervasive Health Systems and Services
;
Physiological Computing Systems
;
Sensor Networks
;
Society, e-Business and e-Government
;
Software Engineering
;
Software Systems in Medicine
;
Wearable Sensors and Systems
;
Web Information Systems and Technologies
Abstract:
In this paper, we propose a novel electronic medical record system (EMR) based on a brand new concept to support doctors’ cognition and medical analysis wherever they are. Conventional EMR systems have the advantage of helping doctors easily retrieve and manage mass medical records. On the other hand, medical records have been expected to support doctors’ planning. Most conventional EMR systems don’t have an appropriate function for such purpose, however. Because of its poor user interface, which is similar to legacy medical records written on paper, they can’t help doctors analyze medical data that occurs chronologically. To attain that purpose, the system has to have the ability to visualize medical data that occurs over various time spans. This is because the relationship among the different medical data should be observable when we look at it over various time spans. In addition, doctors aren’t always at their desks, so they can’t always use EMR systems with a desktop PC. Therefo
re, in view of these problems, we propose a system that has timeline interface which visualizes medical data that occurs over various time spans and its client application works on a mobile device. In this manner, the system can support doctors’ cognition and medical analysis wherever they are. In addition, we are verifying this system in the medical field.
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