Abstract
The creation, management, and use of Electronic Medical Records (EMR) is a central issue for the medical community and is a high priority for many governments around the world. Collecting, storing, and managing EMR is expensive and difficult due to a set of demanding requirements for quality attributes (reliability, availability, security), multiple types of data (real-time data, historical data, medical rules, medical vocabularies), and data operations (raising alarms, pattern detection, or predictions). The traditional approach uses a combination of multiple data management systems such as databases, rule engines, data mining engines, event processing engines and more. Having multiple data management systems leads to “islands of data”, missed correlations, and frequent false alarms. However, recent advances in database technology have added functionality to database systems such as temporal support, continuous queries, notifications, rules managers, event processing and data mining. This chapter describes a prototype, SICU, that using those advanced functionalities, implements a complete, single-system EMR engine to monitor patients in emergency care units. SICU was designed as a proof-of-concept EMR system that manages real-time data (vitals and laboratory data), historic data (past clinical information), medical knowledge (in the form of rules) and issues appropriate alarms with the correct level of criticality and personalized by doctor or patient. In addition, using data mining models built from real patient profiles, SICU is able to predict if patients will have a cardiac arrest in the following 24 hours. The prototype has shown a way to significantly enhance evidence based medicine and is therefore of great interest to the medical community. The lessons can be applied to other domains such as smart utility grids.
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Keywords
- Electronic Medical Record
- Electronic Medical Record System
- Continuous Query
- Rule Manager
- Registered Query
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Guerra, D., Bizarro, P., Gawlick, D. (2011). Smart Patient Care. In: Helmer, S., Poulovassilis, A., Xhafa, F. (eds) Reasoning in Event-Based Distributed Systems. Studies in Computational Intelligence, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19724-6_10
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DOI: https://doi.org/10.1007/978-3-642-19724-6_10
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