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Methods Inf Med 2017; 56(03): 238-247
DOI: 10.3414/ME16-01-0057
Paper
Schattauer GmbH

Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data[*]

Georgy Kopanitsa
1   Institute Cybernetic Center, Tomsk Polytechnic University, Tomsk, Russia
2   Tomsk State University of Architecture and Building, Tomsk, Russia
› Author Affiliations Funding The research was funded by the Grant of a Russian President # AAAA-A16-116120810057-8.
Further Information

Publication History

received: 02 May 2016

accepted in revised form: 10 January 2017

Publication Date:
24 January 2018 (online)

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Summary

Background: The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse.

Objectives: In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration.

Materials and Methods: Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS.

Results: Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records’ normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users.

Conclusions: The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid data set with known structure and constraints. The standard based nature of the archetype approach allows an easy integration of CDSSs with existing EHR systems.

Keywords

Archetypes - semantic interoperability - electronic health record - openEHR - clinical decision support systems

* Supplementary material published on our website https://doi.org/10.3414/ME16-01-0057


 

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