Secondary use of routinely collected patient data in a clinical trial: An evaluation of the effects on patient recruitment and data acquisition

Köpcke, Felix; Kraus, Stefan; Scholler, Axel; Nau, Carla; Schüttler, J.; Prokosch, Hans‐Ulrich; Ganslandt, Thomas

doi:10.1016/j.ijmedinf.2012.11.008

Cited by 34 publications

(45 citation statements)

References 13 publications

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“…The effect seemed to correlate with an increase in screening efficiency that similarly doubled the number of screened patients. A comparatively minor increase in recruited patients of 14% from 306 to 348 in the same week was reported by Köpcke et al [69] who addressed pure oversight of otherwise well-organized manual recruiters. They also found 7% of the manually included patients did not fulfill the trial’s eligibility criteria.…”

Section: Resultsmentioning

confidence: 71%

“…The latter is generally the case for retrospective trials and feasibility studies. Typical examples include Payne et al [97], Thadani et al [115], and Köpcke et al [69] who required an administrator to develop a Structured Query Language (SQL)-based query. Based on 16 years of COSTAR research queries, Murphy [85] created the graphical interface Informatics for Integrating Biology and the Bedside (i2b2) to allow investigators to parameterize query templates themselves.…”

Section: Resultsmentioning

confidence: 99%

“…If the CTRSS has no clinical/research user (ie, the direct user is IT staff), it usually transforms the raw result of the reasoning algorithm into a patient list which is subsequently handed out to the researcher [60,80,103,105,115,119]. This is the preferred mode of communication if eligibility assessments are only required once [69,97]. However, when the target users are either treating physicians or clinical investigators, the mode of communication also needs to accommodate data security regulations and the trial’s temporal requirements.…”

Section: Resultsmentioning

confidence: 99%

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Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Köpcke

Prokosch²

2014

J Med Internet Res

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BackgroundMedical progress depends on the evaluation of new diagnostic and therapeutic interventions within clinical trials. Clinical trial recruitment support systems (CTRSS) aim to improve the recruitment process in terms of effectiveness and efficiency.ObjectiveThe goals were to (1) create an overview of all CTRSS reported until the end of 2013, (2) find and describe similarities in design, (3) theorize on the reasons for different approaches, and (4) examine whether projects were able to illustrate the impact of CTRSS.MethodsWe searched PubMed titles, abstracts, and keywords for terms related to CTRSS research. Query results were classified according to clinical context, workflow integration, knowledge and data sources, reasoning algorithm, and outcome.ResultsA total of 101 papers on 79 different systems were found. Most lacked details in one or more categories. There were 3 different CTRSS that dominated: (1) systems for the retrospective identification of trial participants based on existing clinical data, typically through Structured Query Language (SQL) queries on relational databases, (2) systems that monitored the appearance of a key event of an existing health information technology component in which the occurrence of the event caused a comprehensive eligibility test for a patient or was directly communicated to the researcher, and (3) independent systems that required a user to enter patient data into an interface to trigger an eligibility assessment. Although the treating physician was required to act for the patient in older systems, it is now becoming increasingly popular to offer this possibility directly to the patient.ConclusionsMany CTRSS are designed to fit the existing infrastructure of a clinical care provider or the particularities of a trial. We conclude that the success of a CTRSS depends more on its successful workflow integration than on sophisticated reasoning and data processing algorithms. Furthermore, some of the most recent literature suggest that an increase in recruited patients and improvements in recruitment efficiency can be expected, although the former will depend on the error rate of the recruitment process being replaced. Finally, to increase the quality of future CTRSS reports, we propose a checklist of items that should be included.

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Section: Resultsmentioning

confidence: 71%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Köpcke

Prokosch²

2014

J Med Internet Res

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“…As an increased amount of data is produced in EHRs, the of data gathered in process of providing care for reseach is re-used to facilitate patient identification for new clinical trials [3,7], to collect care data within clinical research setting [5] or to be combined with DNA biorepositories for identifying accurately phenotyped cases and controls for large-scale genomic studies [12] or to conduct pharmacogenetic studies [14,15]. Utilizing the availability of patient data from federated EHR systems in many different sites, as well as in international multilingual settings is still challenging [2].…”

Section: Discussionmentioning

confidence: 99%

Information Technology for Clinical, Translational and Comparative Effectiveness Research

Daniel

Choquet²

2014

Yearb Med Inform

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SummaryObjective: To select and summarize key contributions to current research in the field of Clinical Research Informatics (CRI). Method: A bibliographic search using a combination of MeSH and free terms search over PubMed was performed followed by a blinded review. Results: The review process resulted in the selection of four papers illustrating various aspects of current research efforts in the area of CRI. The first paper tackles the challenge of extracting accurate phenotypes from Electronic Healthcare Records (EHRs). Privacy protection within shared de-identified, patient-level research databases is the focus of the second selected paper. Two other papers exemplify the growing role of formal representation of clinical data -in metadata repositories -and knowledge -in ontologies -for supporting the process of reusing data for clinical research. Conclusions: The selected articles demonstrate how concrete platforms are currently achieving interoperability across clinical research and care domains and have reached the evaluation phase. When EHRs linked to genetic data have the potential to shift the research focus from research driven patient recruitment to phenotyping in large population, a key issue is to lower patient re-identification risks for biomedical research databases. Current research illustrates the potential of knowledge engineering to support, in the coming years, the scientific lifecycle of clinical research.

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“…While data collection in care settings should ideally permit data reuse for epidemiology, public health, or research, it is still difficult to reuse the data produced in care setting. 28 In this context, several standards are available to support the recording of health data in health information systems. They have been developed to standardize either DEs and information models (Health Level Seven (HL7), 29 OpenEHR (EN 13606) 30 ), value sets or terminologies (LOINC, 31 SNOMED CT 32 for clinical terms, Orphanet 1 for orphan drugs and rare disease diagnosis, Human Phenotype Ontology (HPO) 33 for signs, Online Mendelian Inheritance in Man (OMIM) 34 for genes, Human Genome Variation Society (HGVS) Mutnomen, 35 and GenATLAS 36 38 to support interoperability between CDISC (clinical research) and HL7 (electronic health records).…”

Section: Interoperability and Standardsmentioning

confidence: 99%

A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research

Choquet

Maaroufi

Carrara

et al. 2014

Journal of the American Medical Informatics Association

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Secondary use of routinely collected patient data in a clinical trial: An evaluation of the effects on patient recruitment and data acquisition

Cited by 34 publications

References 13 publications

Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Employing Computers for the Recruitment into Clinical Trials: A Comprehensive Systematic Review

Information Technology for Clinical, Translational and Comparative Effectiveness Research

A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research

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