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

Author:

Schepens Maike H. J.12,Trompert Annemarie C.3,van Hooff Miranda L.45,van der Velde Erik67,Kallewaard Marjon6,Verberk-Jonkers Iris J. A. M.68,Cense Huib A.910,Somford Diederik M.11,Repping Sjoerd12,Tromp Selma C.613,Wouters Michel W. J. M.21314

Affiliation:

1. Cirka BV, Healthcare Strategy and Innovation, Zeist, The Netherlands

2. Department of Biomedical Data Sciences, LUMC, Leiden, The Netherlands

3. Dutch Institute for Clinical Auditing, Leiden, The Netherlands

4. Department of Orthopedics, Radboud UMC, Nijmegen, The Netherlands

5. Department of Orthopedics, Sint Maartenskliniek, Nijmegen, The Netherlands

6. Dutch Association of Medical Specialists, Utrecht, The Netherlands

7. Zorgverbeteraars, Healthcare IT Consulting, Roden, The Netherlands

8. Department of Nephrology, Maasstad Hospital, Rotterdam, The Netherlands

9. Department of Surgery, Rode Kruis Hospital, Beverwijk, The Netherlands

10. Department of Health System Innovation. Faculty of Economics and Business, Groningen University. Groningen, The Netherlands

11. Department of Urology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands

12. Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands

13. Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands

14. Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands

Abstract

Abstract 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.

Publisher

Georg Thieme Verlag KG

Subject

Health Information Management,Computer Science Applications,Health Informatics

Reference31 articles.

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