The need for harmonized structured documentation and chances of secondary use – Results of a systematic analysis with automated form comparison for prostate and breast cancer

Krumm, Rainer; Semjonow, Axel; Tio, Joke; Duhme, Heike; Bürkle, Thomas; Haier, Jörg; Dugas, Martin; Breil, Bernhard

doi:10.1016/j.jbi.2014.04.008

Cited by 23 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data interchange, a key process in the data collection phase, is an original ODM use case, was the focus of ODM v1.0, and has been covered broadly in the literature [2, 13, 26, 35, 43, 45, 46, 52, 53, 57, 58, 60, 62–64, 73–85]. ODM’s basic hierarchical structure is particularly well suited for data capture [84].…”

Section: 0 Resultsmentioning

confidence: 99%

“…In this case, using semantically annotated ODM helped drive automated transformations while preserving the original semantics. The open-source compareODM tool in Dugas et al [81] compared semantically enriched ODM forms and was able to automatically derive the differences between two versions of a form including identical, matching, or similar data items [43]. ODM has been used to integrate clinical research data into the i2b2 (Informatics for Integrating Biology and the Bedside) data model including both ontology and fact data [26, 46].…”

Section: 0 Resultsmentioning

confidence: 99%

“…Research exists on the automated analysis of CRFs [43, 81], but none have analyzed the CRF from a regulatory perspective. The benefits proposed in the 2007 FDA pilot [87] could be explored.…”

Section: 0 Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Hume

Aerts

Sarnikar

et al. 2016

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Introduction In order to further advance research and development on the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) standard, the existing research must be well understood. This paper presents a methodological review of the ODM literature. Specifically, it develops a classification schema to categorize the ODM literature according to how the standard has been applied within the clinical research data lifecycle. This paper suggests areas for future research and development that address ODM’s limitations and capitalize on its strengths to support new trends in clinical research informatics. Methods A systematic scan of the following databases was performed: (1) ABI/Inform, (2) ACM Digital, (3) AIS eLibrary, (4) Europe Central PubMed, (5) Google Scholar, (5) IEEE Xplore, (7) PubMed, and (8) ScienceDirect. A Web of Science citation analysis was also performed. The search term used on all databases was “CDISC ODM.” The two primary inclusion criteria were: (1) the research must examine the use of ODM as an information system solution component, or (2) the research must critically evaluate ODM against a stated solution usage scenario. Out of 2,686 articles identified, 266 were included in a title level review, resulting in 183 articles. An abstract review followed, resulting in 121 remaining articles; and after a full text scan 69 articles met the inclusion criteria. Results As the demand for interoperability has increased, ODM has shown remarkable flexibility and has been extended to cover a broad range of data and metadata requirements that reach well beyond ODM’s original use cases. This flexibility has yielded research literature that covers a diverse array of topic areas. A classification schema reflecting the use of ODM within the clinical research data lifecycle was created to provide a categorized and consolidated view of the ODM literature. The elements of the framework include: (1) EDC (Electronic Data Capture) and EHR (Electronic Health Record) infrastructure; (2) planning; (3) data collection; (4) data tabulations and analysis; and (5) study archival. The analysis reviews the strengths and limitations of ODM as a solution component within each section of the classification schema. This paper also identifies opportunities for future ODM research and development, including improved mechanisms for semantic alignment with external terminologies, better representation of the CDISC standards used end-to-end across the clinical research data lifecycle, improved support for real-time data exchange, the use of EHRs for research, and the inclusion of a complete study design. Conclusions ODM is being used in ways not originally anticipated, and covers a diverse array of use cases across the clinical research data lifecycle. ODM has been used as much as a study metadata standard as it has for data exchange. A significant portion of the literature addresses integrating EHR and clinical research data. The simplicity and readability of ODM has likely contributed to its succe...

show abstract

Section: 0 Resultsmentioning

confidence: 99%

Section: 0 Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Hume

Aerts

Sarnikar

et al. 2016

Journal of Biomedical Informatics

View full text Add to dashboard Cite

show abstract

“…From my perspective, missing semantic annotation in databases is the root cause for data integration problems [32]. Semantically annotated data items and forms facilitate to compare documentation approaches [33,34], help to avoid redundant data entry by integration of information systems [35] and foster data analysis [36]. Content standards with semantics are evolving, both from regulatory bodies (e.g.…”

Section: Resultsmentioning

confidence: 99%

Clinical Research Informatics: Recent Advances and Future Directions

Dugas¹

2015

Yearb Med Inform

View full text Add to dashboard Cite

“…However, single source approaches may be the most difficult to put into practice, requiring an EHR platform that can be adapted to local and dynamically changing documentation needs as well as support from local routine IT departments and/or vendors to implement the respective data entry forms and work-flows. Harmonizing this integration of clinical care and research documentation across multiple sites creates additional challenges both concerning differences in local work-flows and semantic issues [12].…”

Section: General Options For Setting Up a Disease Registrymentioning

confidence: 99%

Registries in Clinical Epidemiology: the European Surveillance System on Contact Allergies (ESSCA)

Uter¹,

Schnuch²,

Wilkinson³

et al. 2016

Methods Inf Med

View full text Add to dashboard Cite

SummaryBackground: Disease registries rely on consistent electronic data capturing (EDC) pertinent to their objectives; either by using existing electronic data as far as available, or by implementing specific software solutions.Objectives: To describe the current practice of an international disease registry (European Surveillance System on Contact Allergies, ESSCA, www.essca-dc.org) against different state of the art approaches for EDC.Methods: Since 2002, ESSCA is collecting data, currently from 53 departments in 12 countries. Departmental EDC software ranges from spreadsheets to comprehensive “patch test software” based on a relational database. In the Erlangen data centre, such diverse data is imported, converted to a common format, quality checked and pooled for scientific analyses.Results: Feed-back to participating departments for quality control is provided by standardised reports. Varying author teams publish scientific analyses addressing the objective of contact allergy surveillance.Conclusions: Although ESSCA represents a historically grown, heterogeneous network and not one unified approach to EDC, some of its features have contributed to its viability in the last 12 years and may be useful to consider for similar investigator-initiated networks.

show abstract

The need for harmonized structured documentation and chances of secondary use – Results of a systematic analysis with automated form comparison for prostate and breast cancer

Abstract: Identifying common data elements in medical forms from different settings with systematic and automated form comparison is feasible.

Cited by 23 publications

References 16 publications

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Clinical Research Informatics: Recent Advances and Future Directions

Registries in Clinical Epidemiology: the European Surveillance System on Contact Allergies (ESSCA)

Contact Info

Product

Resources

About