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BMC Med Res Methodol. 2017 Feb 28;17(1):36. doi: 10.1186/s12874-017-0299-3.

Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned.

Author information

1
Biomedical Informatics and Public Health department, Hôpital Européen Georges Pompidou, AP-HP, 10 Rue Leblanc, 75015, Paris, France. yannick.girardeau@aphp.fr.
2
Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1138, Centre de Recherche des Cordeliers, F-75006, Paris, France. yannick.girardeau@aphp.fr.
3
Institute of Medical Informatics, University of Münster, Münster, Germany.
4
Biomedical Informatics and Public Health department, Hôpital Européen Georges Pompidou, AP-HP, 10 Rue Leblanc, 75015, Paris, France.
5
Université Paris Descartes, Paris, France, Paris Sorbonne Cité, Paris, France.
6
Assistance Publique - Hôpitaux de Paris, Unité d'épidémiologie et de recherche clinique, Hôpital européen Georges Pompidou, Paris, France.
7
INSERM, U1142, LIMICS, AP-HP, F-75006, Paris, France.
8
Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1138, Centre de Recherche des Cordeliers, F-75006, Paris, France.

Abstract

BACKGROUND:

The development of Electronic Health Records (EHRs) in hospitals offers the ability to reuse data from patient care activities for clinical research. EHR4CR is a European public-private partnership aiming to develop a computerized platform that enables the re-use of data collected from EHRs over its network. However, the reproducibility of queries may depend on attributes of the local data. Our objective was 1/ to describe the different steps that were achieved in order to use the EHR4CR platform and 2/ to identify the specific issues that could impact the final performance of the platform.

METHODS:

We selected three institutional studies covering various medical domains. The studies included a total of 67 inclusion and exclusion criteria and ran in two University Hospitals. We described the steps required to use the EHR4CR platform for a feasibility study. We also defined metrics to assess each of the steps (including criteria complexity, normalization quality, and data completeness of EHRs).

RESULTS:

We identified 114 distinct medical concepts from a total of 67 eligibility criteria Among the 114 concepts: 23 (20.2%) corresponded to non-structured data (i.e. for which transformation is needed before analysis), 92 (81%) could be mapped to terminologies used in EHR4CR, and 86 (75%) could be mapped to local terminologies. We identified 51 computable criteria following the normalization process. The normalization was considered by experts to be satisfactory or higher for 64.2% (43/67) of the computable criteria. All of the computable criteria could be expressed using the EHR4CR platform.

CONCLUSIONS:

We identified a set of issues that could affect the future results of the platform: (a) the normalization of free-text criteria, (b) the translation into computer-friendly criteria and (c) issues related to the execution of the query to clinical data warehouses. We developed and evaluated metrics to better describe the platforms and their result. These metrics could be used for future reports of Clinical Trial Recruitment Support Systems assessment studies, and provide experts and readers with tools to insure the quality of constructed dataset.

KEYWORDS:

Clinical trial; Clinical trial recruitment system; Electronic health records; Patient recruitment

PMID:
28241798
PMCID:
PMC5329914
DOI:
10.1186/s12874-017-0299-3
[Indexed for MEDLINE]
Free PMC Article

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