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Using Existing Clinical Information Models for Dutch Quality Registries to Reuse Data and Follow COUMT Paradigm

Background  Reuse of health care data for various purposes, such as the care process, for quality measurement, research, and finance, will become increasingly important in the future; therefore, “Collect Once Use Many Times” (COUMT). Clinical information models (CIMs) can be used for content standar...

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Detalles Bibliográficos
Autores principales: Schepens, Maike H. J., Trompert, Annemarie C., van Hooff, Miranda L., van der Velde, Erik, Kallewaard, Marjon, Verberk-Jonkers, Iris J. A. M., Cense, Huib A., Somford, Diederik M., Repping, Sjoerd, Tromp, Selma C., Wouters, Michel W. J. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10156444/
https://www.ncbi.nlm.nih.gov/pubmed/37137338
http://dx.doi.org/10.1055/s-0043-1767681
Descripción
Sumario:Background  Reuse of health care data for various purposes, such as the care process, for quality measurement, research, and finance, will become increasingly important in the future; therefore, “Collect Once Use Many Times” (COUMT). Clinical information models (CIMs) can be used for content standardization. Data collection for national quality registries (NQRs) often requires manual data entry or batch processing. Preferably, NQRs collect required data by extracting data recorded during the health care process and stored in the electronic health record. Objectives  The first objective of this study was to analyze the level of coverage of data elements in NQRs with developed Dutch CIMs (DCIMs). The second objective was to analyze the most predominant DCIMs, both in terms of the coverage of data elements as well as in their prevalence across existing NQRs. Methods  For the first objective, a mapping method was used which consisted of six steps, ranging from a description of the clinical pathway to a detailed mapping of data elements. For the second objective, the total number of data elements that matched with a specific DCIM was counted and divided by the total number of evaluated data elements. Results  An average of 83.0% (standard deviation: 11.8%) of data elements in studied NQRs could be mapped to existing DCIMs . In total, 5 out of 100 DCIMs were needed to map 48.6% of the data elements. Conclusion  This study substantiates the potential of using existing DCIMs for data collection in Dutch NQRs and gives direction to further implementation of DCIMs. The developed method is applicable to other domains. For NQRs, implementation should start with the five DCIMs that are most prevalently used in the NQRs. Furthermore, a national agreement on the leading principle of COUMT for the use and implementation for DCIMs and (inter)national code lists is needed.