Using ontologies to integrate and share resuscitation data from diverse medical devices.

OBJECTIVE: To propose a method for standardised data representation and demonstrate a technology that makes it possible to translate data from device dependent formats to this standard representation format. METHODS AND RESULTS: Outcome statistics vary between emergency medical systems organising resuscitation services. Such differences indicate a potential for improvement by identifying factors affecting outcome, but data subject to analysis have to be comparable. Modern technology for communicating information makes it possible to structure, store and transfer data flexibly. Ontologies describe entities in the world and how they relate. Letting different computer systems refer to the same ontology results in a common understanding on data content. Information on therapy such as shock delivery, chest compressions and ventilation should be defined and described in a standardised ontology to enable comparison and combining data from diverse sources. By adding rules and logic data can be merged and combined in new ways to produce new information. An example ontology is designed to demonstrate the feasibility and value of such a standardised structure. CONCLUSIONS: The proposed technology makes possible capturing and storing of data from different devices in a structured and standardised format. Data can easily be transformed to this standardised format, compared and combined independent of the original structure.

Representing resuscitation data-Considerations on efficient analysis of quality of cardiopulmonary resuscitation.

OBJECTIVE: To discuss challenges in representing resuscitation data from Utstein style reports and devices like defibrillators with focus on unified and efficient handling of variety of resuscitation research objectives. METHODS AND RESULTS: Information on therapy such as shock delivery, chest compressions and ventilation can be extracted from defibrillators. A method for merging this information with cardiac rhythm annotated from ECGs, yields a representation of the resuscitation episode with cardiac rhythm also giving information on response to therapy. These data should be synchronised to an electronic Utstein report. With modern technology for communicating information it is possible to structure, store and transport data flexibly so that data captured with devices from different manufacturers can be combined. CONCLUSIONS: A scheme for representing resuscitation data should combine essential information stored in different locations after a resuscitation attempt. The resulting representation should enable data analysis to enable studies of the relationship between therapy and patient response. As the complexity and amount of data generated during resuscitation efforts are ever increasing, the time is mature for using modern information technology tools to provide infrastructure for efficient data management and analysis to identify and meet future challenges in resuscitation data analysis.