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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2017

Open Access 01-12-2017 | Research article

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

Authors: Yannick Girardeau, Justin Doods, Eric Zapletal, Gilles Chatellier, Christel Daniel, Anita Burgun, Martin Dugas, Bastien Rance

Published in: BMC Medical Research Methodology | Issue 1/2017

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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.
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Literature
1.
Morgan S, Grootendorst P, Lexchin J, Cunningham C, Greyson D. The cost of drug development: a systematic review. Health Policy Amst Neth. 2011;100:4–17.CrossRef
2.
El Fadly A, Rance B, Lucas N, Mead C, Chatellier G, Lastic P-Y, et al. Integrating clinical research with the Healthcare Enterprise: From the RE-USE project to the EHR4CR platform. J Biomed Inform. 2011;44:S94–S102.CrossRefPubMed
3.
Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012;13:395–405.CrossRefPubMed
4.
Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol. 2011;7:e1002141.CrossRefPubMedPubMedCentral
5.
Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc. 2013;20:117–21.CrossRefPubMed
6.
Sanderson IC, Obeid JS, Madathil KC, Gerken K, Fryar K, Rugg D, et al. Managing clinical research permissions electronically: A novel approach to enhancing recruitment and managing consents. Clin Trials Lond Engl. 2013;10:604–11.
7.
Cuggia M, Besana P, Glasspool D. Comparing semi-automatic systems for recruitment of patients to clinical trials. Int J Med Inf. 2011;80:371–88.CrossRef
8.
Séroussi B, Bouaud J. Using OncoDoc as a computer-based eligibility screening system to improve accrual onto breast cancer clinical trials. Artif Intell Med. 2003;29:153–67.CrossRefPubMed
9.
Grundmeier RW, Swietlik M, Bell LM. Research subject enrollment by primary care pediatricians using an electronic health record. AMIA Annu Symp Proc AMIA Symp. 2007;289–93.
10.
McGregor J, Brooks C, Chalasani P, Chukwuma J, Hutchings H, Lyons RA, et al. The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial. Trials. 2010;11:39.CrossRefPubMedPubMedCentral
11.
Köpcke F, Kraus S, Scholler A, Nau C, Schüttler J, Prokosch H-U, et al. Secondary use of routinely collected patient data in a clinical trial: an evaluation of the effects on patient recruitment and data acquisition. Int J Med Inf. 2013;82:185–92.CrossRef
12.
Beauharnais CC, Larkin ME, Zai AH, Boykin EC, Luttrell J, Wexler DJ. Efficacy and cost-effectiveness of an automated screening algorithm in an inpatient clinical trial. Clin Trials Lond Engl. 2012;9:198–203.CrossRef
13.
Köpcke F, Prokosch H-U. Employing computers for the recruitment into clinical trials: a comprehensive systematic review. J Med Internet Res. 2014;16:e161.
14.
Soto-Rey I, Trinczek B, Girardeau Y, Zapletal E, Ammour N, Doods J, et al. Efficiency and effectiveness evaluation of an automated multi-country patient count cohort system. BMC Med Res Methodol. 2015;15:44.CrossRefPubMedPubMedCentral
15.
Zapletal E, Rodon N, Grabar N, Degoulet P. Methodology of integration of a clinical data warehouse with a clinical information system: the HEGP case. Stud Health Technol Inform. 2010;160:193–7.PubMed
16.
Boussadi A, Caruba T, Zapletal E, Sabatier B, Durieux P, Degoulet P. A clinical data warehouse-based process for refining medication orders alerts. J Am Med Inform Assoc JAMIA. 2012;19:782–5.
17.
Ouagne D, Hussain S, Sadou E, Jaulent M-C, Daniel C. The Electronic Healthcare Record for Clinical Research (EHR4CR) information model and terminology. Stud Health Technol Inform. 2012;180:534–8.PubMed
18.
Bache R, Miles S, Taweel A. An adaptable architecture for patient cohort identification from diverse data sources. J Am Med Inform Assoc. 2013;20:e327–33.CrossRefPubMedPubMedCentral
19.
Bache R, Taweel A, Miles S, Delaney BC. An eligibility criteria query language for heterogeneous data warehouses. Methods Inf Med. 2015;54:41–4.CrossRefPubMed
20.
Ross J, Tu S, Carini S, Sim I. Analysis of eligibility criteria complexity in clinical trials. AMIA Summits Transl Sci Proc. 2010;2010:46–50.PubMedPubMedCentral
21.
Weng C, Tu SW, Sim I, Richesson R. Formal representation of eligibility criteria: a literature review. J Biomed Inform. 2010;43:451–67.CrossRefPubMed
22.
23.
Tu SW, Campbell JR, Glasgow J, Nyman MA, McClure R, McClay J, et al. The SAGE guideline model: achievements and overview. J Am Med Inform Assoc. 2007;14:589–98.CrossRefPubMedPubMedCentral
24.
Tu SW, Peleg M, Carini S, Bobak M, Ross J, Rubin D, et al. A practical method for transforming free-text eligibility criteria into computable criteria. J Biomed Inform. 2011;44:239–50.CrossRefPubMed
25.
Makam AN, Nguyen OK, Moore B, Ma Y, Amarasingham R. Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm. BMC Med Inform Decis Mak. 2013;13:81.CrossRefPubMedPubMedCentral
26.
Singh JA, Kundukulam JA, Bhandari M. A systematic review of validated methods for identifying orthopedic implant removal and revision using administrative data. Pharmacoepidemiol. Drug Saf. 2012;21 Suppl 1:265–73.
27.
Köpcke F, Trinczek B, Majeed RW, Schreiweis B, Wenk J, Leusch T, et al. Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence. BMC Med Inform Decis Mak. 2013;13:37.CrossRefPubMedPubMedCentral
Metadata
Title
Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned
Authors
Yannick Girardeau
Justin Doods
Eric Zapletal
Gilles Chatellier
Christel Daniel
Anita Burgun
Martin Dugas
Bastien Rance
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2017
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-017-0299-3

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