Comparing different approaches to two-level modelling of electronic health records.
Electronic Health Record (EHR) systems are being developed to improve the communication of patient data. The health care domain includes many different types of data and concepts, of which some are constantly changing, and some are more lasting. This makes the development of an EHR a complex task. In order to improve the handling of this complexity, a new two-level modelling approach in EHR system development has emerged, using a concept of archetypes as the pivot in the representation of the health care knowledge. The key issue in this approach involves dividing the problem field into two separate models: A generic information model and a domain knowledge model. By analysis of how this layering has been carried out in two different two-level EHR systems - the OpenEHR (formerly the Australian GEHR, Good Electronic Health Record) and the EHR project of Aarhus County, Denmark. We have identified critical meta model parameters influencing the ability of the modelling paradigm to meet the expectation for easy handling of the development process (flexibility) and the capability to manage changing models (dynamics). The OpenEHR has defined the division line in such a way that it makes the generic model small and the domain model large. The opposite is the case of the Aarhus EHR system, where the information model is large, and the knowledge model is small. A small information model and a large knowledge model make more of the system changeable, but it also makes it less flexible to develop. The opposite is the case for a large information model and a small knowledge model.
Michelsen
Line
L
Department of Health Science and Technology Aalborg University, Denmark.
Pedersen
Signe S
SS
Tilma
Helene B
HB
Andersen
Stig K
SK
D001315:Australia; D003199:Computer Systems; D003718:Denmark; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval; D016347:Medical Records Systems, Computerized; D008962:Models, Theoretical