---
_id: '11577'
abstract:
- lang: eng
text: The establishment of a digital healthcare system is a national and community
task. The Federal Ministry of Education and Research in Germany is providing funding
for consortia consisting of university hospitals among others participating in
the "Medical Informatics Initiative". Exchange of medical data between research
institutions necessitates a place where meta information for this data is made
accessible. Within these consortia different metadata registry solutions were
chosen. To promote interoperability between these solutions, we have examined
whether the portal of Medical Data Models is eligible for managing and communicating
metadata and relevant information across different data integration centres of
the Medical Informatics Initiative and beyond. Apart from the MDM-portal, some
ISO 11179-based systems such as Samply.MDR as well as openEHR-based solutions
are going to be applyed. In this paper, we have focused on the creation of a mapping
model between the CDISC ODM standard and the Samply.MDR import format. In summary,
it can be stated that the mapping model is feasible and promote the exchangeability
between different metadata registry approaches.
author:
- first_name: AK
full_name: Kock-Schoppenhauer, AK
last_name: Kock-Schoppenhauer
- first_name: H
full_name: Ulrich, H
last_name: Ulrich
- first_name: S
full_name: Wagen-Zink, S
last_name: Wagen-Zink
- first_name: P
full_name: Duhm-Harbeck, P
last_name: Duhm-Harbeck
- first_name: J
full_name: Ingenerf, J
last_name: Ingenerf
- first_name: P
full_name: Neuhaus, P
last_name: Neuhaus
- first_name: M
full_name: Dugas, M
last_name: Dugas
- first_name: Philipp
full_name: Bruland, Philipp
id: '75847'
last_name: Bruland
orcid: 0000-0001-6939-7630
citation:
ama: 'Kock-Schoppenhauer A, Ulrich H, Wagen-Zink S, et al. Compatibility Between
Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR. Studies
in health technology and informatics. 2018;247:221-225.'
apa: 'Kock-Schoppenhauer, A., Ulrich, H., Wagen-Zink, S., Duhm-Harbeck, P., Ingenerf,
J., Neuhaus, P., Dugas, M., & Bruland, P. (2018). Compatibility Between Metadata
Standards: Import Pipeline of CDISC ODM to the Samply.MDR. Studies in Health
Technology and Informatics, 247, 221–225.'
bjps: 'Kock-Schoppenhauer A et al. (2018) Compatibility Between Metadata
Standards: Import Pipeline of CDISC ODM to the Samply.MDR. Studies in health
technology and informatics 247, 221–225.'
chicago: 'Kock-Schoppenhauer, AK, H Ulrich, S Wagen-Zink, P Duhm-Harbeck, J Ingenerf,
P Neuhaus, M Dugas, and Philipp Bruland. “Compatibility Between Metadata Standards:
Import Pipeline of CDISC ODM to the Samply.MDR.” Studies in Health Technology
and Informatics 247 (2018): 221–25.'
chicago-de: 'Kock-Schoppenhauer, AK, H Ulrich, S Wagen-Zink, P Duhm-Harbeck, J Ingenerf,
P Neuhaus, M Dugas und Philipp Bruland. 2018. Compatibility Between Metadata Standards:
Import Pipeline of CDISC ODM to the Samply.MDR. Studies in health technology
and informatics 247: 221–225.'
din1505-2-1: 'Kock-Schoppenhauer, AK
; Ulrich, H ; Wagen-Zink,
S ; Duhm-Harbeck, P ; Ingenerf, J ; Neuhaus,
P ; Dugas, M ; Bruland,
Philipp: Compatibility Between Metadata Standards: Import Pipeline of CDISC
ODM to the Samply.MDR. In: Studies in health technology and informatics
Bd. 247, IOS Press (2018), S. 221–225'
havard: 'A. Kock-Schoppenhauer, H. Ulrich, S. Wagen-Zink, P. Duhm-Harbeck, J. Ingenerf,
P. Neuhaus, M. Dugas, P. Bruland, Compatibility Between Metadata Standards: Import
Pipeline of CDISC ODM to the Samply.MDR., Studies in Health Technology and Informatics.
247 (2018) 221–225.'
ieee: 'A. Kock-Schoppenhauer et al., “Compatibility Between Metadata Standards:
Import Pipeline of CDISC ODM to the Samply.MDR.,” Studies in health technology
and informatics, vol. 247, pp. 221–225, 2018.'
mla: 'Kock-Schoppenhauer, AK, et al. “Compatibility Between Metadata Standards:
Import Pipeline of CDISC ODM to the Samply.MDR.” Studies in Health Technology
and Informatics, vol. 247, 2018, pp. 221–25.'
short: A. Kock-Schoppenhauer, H. Ulrich, S. Wagen-Zink, P. Duhm-Harbeck, J. Ingenerf,
P. Neuhaus, M. Dugas, P. Bruland, Studies in Health Technology and Informatics
247 (2018) 221–225.
ufg: 'Kock-Schoppenhauer, AK u. a.: Compatibility Between Metadata Standards:
Import Pipeline of CDISC ODM to the Samply.MDR., in: Studies in health technology
and informatics 247 (2018), S. 221–225.'
van: 'Kock-Schoppenhauer A, Ulrich H, Wagen-Zink S, Duhm-Harbeck P, Ingenerf J,
Neuhaus P, et al. Compatibility Between Metadata Standards: Import Pipeline of
CDISC ODM to the Samply.MDR. Studies in health technology and informatics. 2018;247:221–5.'
date_created: 2024-06-24T07:36:34Z
date_updated: 2024-07-18T13:40:32Z
department:
- _id: DEP5024
external_id:
pmid:
- '29677955'
intvolume: ' 247'
keyword:
- CDISC ODM
- MDR
- data elements
- mapping
- metadata registry
language:
- iso: eng
page: 221-225
pmid: '1'
publication: Studies in health technology and informatics
publication_identifier:
eissn:
- 1879-8365
issn:
- 0926-9630
publication_status: published
publisher: ' IOS Press'
status: public
title: 'Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to
the Samply.MDR.'
type: journal_article
user_id: '83781'
volume: 247
year: '2018'
...
---
_id: '11576'
abstract:
- lang: eng
text: Data dictionaries provide structural meta-information about data definitions
in health information technology (HIT) systems. In this regard, reusing healthcare
data for secondary purposes offers several advantages (e.g. reduce documentation
times or increased data quality). Prerequisites for data reuse are its quality,
availability and identical meaning of data. In diverse projects, research data
warehouses serve as core components between heterogeneous clinical databases and
various research applications. Given the complexity (high number of data elements)
and dynamics (regular updates) of electronic health record (EHR) data structures,
we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard.
Metadata of two large hospitals were automatically inserted into two CMDWs containing
16,230 forms and 310,519 data elements. Automatic updates of metadata are possible
as well as semantic annotations. A CMDW allows metadata discovery, data quality
assessment and similarity analyses. Common data models for distributed research
networks can be established based on similarity analyses.
author:
- first_name: Philipp
full_name: Bruland, Philipp
id: '75847'
last_name: Bruland
orcid: 0000-0001-6939-7630
- first_name: J
full_name: Doods, J
last_name: Doods
- first_name: M
full_name: Storck, M
last_name: Storck
- first_name: M
full_name: Dugas, M
last_name: Dugas
citation:
ama: Bruland P, Doods J, Storck M, Dugas M. What Information Does Your EHR Contain?
Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification
and Data Access Within Distributed Clinical Research Networks. Studies in health
technology and informatics. 2017;245:313-317.
apa: Bruland, P., Doods, J., Storck, M., & Dugas, M. (2017). What Information
Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW)
to Support Identification and Data Access Within Distributed Clinical Research
Networks. Studies in Health Technology and Informatics, 245, 313–317.
bjps: Bruland P et al. (2017) What Information Does Your EHR Contain?
Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification
and Data Access Within Distributed Clinical Research Networks. Studies in health
technology and informatics 245, 313–317.
chicago: 'Bruland, Philipp, J Doods, M Storck, and M Dugas. “What Information Does
Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW)
to Support Identification and Data Access Within Distributed Clinical Research
Networks.” Studies in Health Technology and Informatics 245 (2017): 313–17.'
chicago-de: 'Bruland, Philipp, J Doods, M Storck und M Dugas. 2017. What Information
Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW)
to Support Identification and Data Access Within Distributed Clinical Research
Networks. Studies in health technology and informatics 245: 313–317.'
din1505-2-1: 'Bruland, Philipp ; Doods, J ; Storck,
M ; Dugas, M: What Information
Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW)
to Support Identification and Data Access Within Distributed Clinical Research
Networks. In: Studies in health technology and informatics Bd. 245 (2017),
S. 313–317'
havard: P. Bruland, J. Doods, M. Storck, M. Dugas, What Information Does Your EHR
Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support
Identification and Data Access Within Distributed Clinical Research Networks.,
Studies in Health Technology and Informatics. 245 (2017) 313–317.
ieee: P. Bruland, J. Doods, M. Storck, and M. Dugas, “What Information Does Your
EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support
Identification and Data Access Within Distributed Clinical Research Networks.,”
Studies in health technology and informatics, vol. 245, pp. 313–317, 2017.
mla: Bruland, Philipp, et al. “What Information Does Your EHR Contain? Automatic
Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and
Data Access Within Distributed Clinical Research Networks.” Studies in Health
Technology and Informatics, vol. 245, 2017, pp. 313–17.
short: P. Bruland, J. Doods, M. Storck, M. Dugas, Studies in Health Technology and
Informatics 245 (2017) 313–317.
ufg: 'Bruland, Philipp u. a.: What Information Does Your EHR Contain? Automatic
Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and
Data Access Within Distributed Clinical Research Networks., in: Studies in
health technology and informatics 245 (2017), S. 313–317.'
van: Bruland P, Doods J, Storck M, Dugas M. What Information Does Your EHR Contain?
Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification
and Data Access Within Distributed Clinical Research Networks. Studies in health
technology and informatics. 2017;245:313–7.
date_created: 2024-06-24T07:36:24Z
date_updated: 2024-07-18T13:42:49Z
department:
- _id: DEP5024
extern: '1'
external_id:
pmid:
- '29295106'
intvolume: ' 245'
keyword:
- Information Systems
- Metadata
- Semantics
language:
- iso: eng
page: 313-317
pmid: '1'
publication: Studies in health technology and informatics
publication_identifier:
eissn:
- 1879-8365
issn:
- 0926-9630
publication_status: published
status: public
title: What Information Does Your EHR Contain? Automatic Generation of a Clinical
Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed
Clinical Research Networks.
type: journal_article
user_id: '83781'
volume: 245
year: '2017'
...
---
_id: '11745'
abstract:
- lang: eng
text: "Background: Data capture is one of the most expensive phases during the conduct
of a clinical trial and the increasing use of electronic health records (EHR)
offers significant savings to clinical research. To facilitate these secondary
uses of routinely collected patient data, it is beneficial to know what data elements
are captured in clinical trials. Therefore our aim here is to determine the most
commonly used data elements in clinical trials and their availability in hospital
EHR systems.\r\n\r\nMethods: Case report forms for 23 clinical trials in differing
disease areas were analyzed. Through an iterative and consensus-based process
of medical informatics professionals from academia and trial experts from the
European pharmaceutical industry, data elements were compiled for all disease
areas and with special focus on the reporting of adverse events. Afterwards, data
elements were identified and statistics acquired from hospital sites providing
data to the EHR4CR project.\r\n\r\nResults: The analysis identified 133 unique
data elements. Fifty elements were congruent with a published data inventory for
patient recruitment and 83 new elements were identified for clinical trial execution,
including adverse event reporting. Demographic and laboratory elements lead the
list of available elements in hospitals EHR systems. For the reporting of serious
adverse events only very few elements could be identified in the patient records.\r\n\r\nConclusions:
Common data elements in clinical trials have been identified and their availability
in hospital systems elucidated. Several elements, often those related to reimbursement,
are frequently available whereas more specialized elements are ranked at the bottom
of the data inventory list. Hospitals that want to obtain the benefits of reusing
data for research from their EHR are now able to prioritize their efforts based
on this common data element list."
article_number: '159'
author:
- first_name: Philipp
full_name: Bruland, Philipp
id: '75847'
last_name: Bruland
orcid: 0000-0001-6939-7630
- first_name: Mark
full_name: McGilchrist, Mark
last_name: McGilchrist
- first_name: Eric
full_name: Zapletal, Eric
last_name: Zapletal
- first_name: Dionisio
full_name: Acosta, Dionisio
last_name: Acosta
- first_name: Johann
full_name: Proeve, Johann
last_name: Proeve
- first_name: Scott
full_name: Askin, Scott
last_name: Askin
- first_name: Thomas
full_name: Ganslandt, Thomas
last_name: Ganslandt
- first_name: Justin
full_name: Doods, Justin
last_name: Doods
- first_name: Martin
full_name: Dugas, Martin
last_name: Dugas
citation:
ama: Bruland P, McGilchrist M, Zapletal E, et al. Common data elements for secondary
use of electronic health record data for clinical trial execution and serious
adverse event reporting. BMC Medical Research Methodology. 2016;16(1).
doi:10.1186/s12874-016-0259-3
apa: Bruland, P., McGilchrist, M., Zapletal, E., Acosta, D., Proeve, J., Askin,
S., Ganslandt, T., Doods, J., & Dugas, M. (2016). Common data elements for
secondary use of electronic health record data for clinical trial execution and
serious adverse event reporting. BMC Medical Research Methodology, 16(1),
Article 159. https://doi.org/10.1186/s12874-016-0259-3
bjps: Bruland P et al. (2016) Common Data Elements for Secondary Use
of Electronic Health Record Data for Clinical Trial Execution and Serious Adverse
Event Reporting. BMC Medical Research Methodology 16.
chicago: Bruland, Philipp, Mark McGilchrist, Eric Zapletal, Dionisio Acosta, Johann
Proeve, Scott Askin, Thomas Ganslandt, Justin Doods, and Martin Dugas. “Common
Data Elements for Secondary Use of Electronic Health Record Data for Clinical
Trial Execution and Serious Adverse Event Reporting.” BMC Medical Research
Methodology 16, no. 1 (2016). https://doi.org/10.1186/s12874-016-0259-3.
chicago-de: Bruland, Philipp, Mark McGilchrist, Eric Zapletal, Dionisio Acosta,
Johann Proeve, Scott Askin, Thomas Ganslandt, Justin Doods und Martin Dugas. 2016.
Common data elements for secondary use of electronic health record data for clinical
trial execution and serious adverse event reporting. BMC Medical Research Methodology
16, Nr. 1. doi:10.1186/s12874-016-0259-3,
.
din1505-2-1: 'Bruland,
Philipp ; McGilchrist, Mark
; Zapletal, Eric ; Acosta,
Dionisio ; Proeve, Johann
; Askin, Scott ; Ganslandt,
Thomas ; Doods, Justin ;
u. a.: Common data elements for secondary use of electronic health record
data for clinical trial execution and serious adverse event reporting. In: BMC
Medical Research Methodology Bd. 16. London, Springer Science and Business
Media LLC (2016), Nr. 1'
havard: P. Bruland, M. McGilchrist, E. Zapletal, D. Acosta, J. Proeve, S. Askin,
T. Ganslandt, J. Doods, M. Dugas, Common data elements for secondary use of electronic
health record data for clinical trial execution and serious adverse event reporting,
BMC Medical Research Methodology. 16 (2016).
ieee: 'P. Bruland et al., “Common data elements for secondary use of electronic
health record data for clinical trial execution and serious adverse event reporting,”
BMC Medical Research Methodology, vol. 16, no. 1, Art. no. 159, 2016, doi:
10.1186/s12874-016-0259-3.'
mla: Bruland, Philipp, et al. “Common Data Elements for Secondary Use of Electronic
Health Record Data for Clinical Trial Execution and Serious Adverse Event Reporting.”
BMC Medical Research Methodology, vol. 16, no. 1, 159, 2016, https://doi.org/10.1186/s12874-016-0259-3.
short: P. Bruland, M. McGilchrist, E. Zapletal, D. Acosta, J. Proeve, S. Askin,
T. Ganslandt, J. Doods, M. Dugas, BMC Medical Research Methodology 16 (2016).
ufg: 'Bruland, Philipp u. a.: Common data elements for secondary use of electronic
health record data for clinical trial execution and serious adverse event reporting,
in: BMC Medical Research Methodology 16 (2016), H. 1.'
van: Bruland P, McGilchrist M, Zapletal E, Acosta D, Proeve J, Askin S, et al. Common
data elements for secondary use of electronic health record data for clinical
trial execution and serious adverse event reporting. BMC Medical Research Methodology.
2016;16(1).
date_created: 2024-07-18T13:31:36Z
date_updated: 2024-07-18T13:33:42Z
department:
- _id: DEP5024
doi: 10.1186/s12874-016-0259-3
extern: '1'
intvolume: ' 16'
issue: '1'
keyword:
- Clinical trials
- Common data elements
- Data quality
- Electronic health records
- Metadata
- Secondary use
language:
- iso: eng
place: London
publication: BMC Medical Research Methodology
publication_identifier:
issn:
- 1471-2288
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Common data elements for secondary use of electronic health record data for
clinical trial execution and serious adverse event reporting
type: scientific_journal_article
user_id: '83781'
volume: 16
year: '2016'
...