Ubiquitous information for ubiquitous computing: expressing clinical data sets with openEHR archetypes.

Ubiquitous computing requires ubiquitous access to information and knowledge. With the release of openEHR Version 1.0 there is a common model available to solve some of the problems related to accessing information and knowledge by improving semantic interoperability between clinical systems. Considerable work has been undertaken by various bodies to standardise Clinical Data Sets. Notwithstanding their value, several problems remain unsolved with Clinical Data Sets without the use of a common model underpinning them. This paper outlines these problems like incompatible basic data types and overlapping and incompatible definitions of clinical content. A solution to this based on openEHR archetypes is motivated and an approach to transform existing Clinical Data Sets into archetypes is presented. To avoid significant overlaps and unnecessary effort during archetype development, archetype development needs to be coordinated nationwide and beyond and also across the various health professions in a formalized process.

Towards a comprehensive electronic patient record to support an innovative individual care concept for premature infants using the openEHR approach.

PURPOSE: This paper introduces the modelling of a prototype neonatology electronic patient record (EPR) using openEHR archetypes. The EPR is necessary to support the complex communication tasks of the innovative concept of 'Developmental, Family-Centred, Individual Care of Premature Infants and Newborns' established for the Department of Neonatology at Heidelberg University Hospital. METHODS: The data to be documented was analysed and modelled using the five step openEHR data modelling approach (odma). RESULTS: The analysis revealed a total of 1818 items, which could be arranged into 70 clinical concepts. The items and concepts were then mapped to 132 openEHR archetypes. Fifty-eight of these archetypes could be reused either directly or via specialisation from the existing openEHR archetypes. A further 67 archetypes were newly developed. To combine and constrain archetypes for local settings 16 templates were developed. CONCLUSION: By using the five step openEHR data modelling approach, semantic interoperability, and a reduced need for repeated documentation of the same data can be realised. This is of major importance within the hospital as well as for trans-institutional data exchange.

Expressing clinical data sets with openEHR archetypes: a solid basis for ubiquitous computing.

PURPOSE: The purpose of this paper is to analyse the feasibility and usefulness of expressing clinical data sets (CDSs) as openEHR archetypes. For this, we present an approach to transform CDS into archetypes, and outline typical problems with CDS and analyse whether some of these problems can be overcome by the use of archetypes. METHODS: Literature review and analysis of a selection of existing Australian, German, other European and international CDSs; transfer of a CDS for Paediatric Oncology into openEHR archetypes; implementation of CDSs in application systems. RESULTS: To explore the feasibility of expressing CDS as archetypes an approach to transform existing CDSs into archetypes is presented in this paper. In case of the Paediatric Oncology CDS (which consists of 260 data items) this lead to the definition of 48 openEHR archetypes. To analyse the usefulness of expressing CDS as archetypes, we identified nine problems with CDS that currently remain unsolved without a common model underpinning the CDS. Typical problems include incompatible basic data types and overlapping and incompatible definitions of clinical content. A solution to most of these problems based on openEHR archetypes is motivated. With regard to integrity constraints, further research is required. CONCLUSIONS: While openEHR cannot overcome all barriers to Ubiquitous Computing, it can provide the common basis for ubiquitous presence of meaningful and computer-processable knowledge and information, which we believe is a basic requirement for Ubiquitous Computing. Expressing CDSs as openEHR archetypes is feasible and advantageous as it fosters semantic interoperability, supports ubiquitous computing, and helps to develop archetypes that are arguably of better quality than the original CDS.