The rise of big clinical databases

Cook, Jonathan; Collins, Gary S.

doi:10.1002/bjs.9723

Cited by 127 publications

(85 citation statements)

References 70 publications

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“…Although data quality should ideally be checked continuously, such checks may be particularly relevant following: the first data extraction from a hospital or other data source, inconsistencies present in the data, the introduction of a new hospital information system, or reorganization or fusion of hospitals [1]. In order to keep track of changing registration systems in individual hospitals, a questionnaire might be submitted once a year on new developments in the hospital that could impact the registration or data extraction.…”

Section: Discussionmentioning

confidence: 99%

“…For health care researchers it seems very attractive to utilize these data maximally [1, 2]. Unfortunately, the use of routinely collected electronic health record (EHR) data potentially leads to quality issues, resulting from the fact that the data were not registered for research purposes but rather for clinical management or financial administration.…”

Section: Introductionmentioning

confidence: 99%

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Validation of multisource electronic health record data: an application to blood transfusion data

Hoeven

Bruijne

Kemper

et al. 2017

BMC Med Inform Decis Mak

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BackgroundAlthough data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice.MethodsThe approach consists of identifying existing validation frameworks for EHR data or linked data, selecting validity concepts from these frameworks and establishing quantifiable validity outcomes for each concept. The approach distinguishes external validation concepts (e.g. concordance with external reports, previous literature and expert feedback) and internal consistency concepts which use expected associations within the dataset itself (e.g. completeness, uniformity and plausibility). In an example case, the selected concepts were applied to a transfusion dataset and specified in more detail.ResultsApplication of the approach to a transfusion dataset resulted in a structured overview of data validity aspects. This allowed improvement of these aspects through further processing of the data and in some cases adjustment of the data extraction. For example, the proportion of transfused products that could not be linked to the corresponding issued products initially was 2.2% but could be improved by adjusting data extraction criteria to 0.17%.ConclusionsThis stepwise approach for validating linked multisource data provides a basis for evaluating data quality and enhancing interpretation. When the process of data validation is adopted more broadly, this contributes to increased transparency and greater reliability of research based on routinely collected electronic health records.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0504-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of multisource electronic health record data: an application to blood transfusion data

Hoeven

Bruijne

Kemper

et al. 2017

BMC Med Inform Decis Mak

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“…recalibration). Secondly, clinical data for developing a CPR are increasingly available from widespread use of electronic health records [76]. Thirdly, researchers may be attracted to creating a new CPR because they view this as more novel and therefore more likely to be published in higher impact journals.…”

Section: Discussionmentioning

confidence: 99%

Why do authors derive new cardiovascular clinical prediction rules in the presence of existing rules? A mixed methods study

et al. 2017

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BackgroundResearchers should examine existing evidence to determine the need for a new study. It is unknown whether developers evaluate existing evidence to justify new cardiovascular clinical prediction rules (CPRs).ObjectiveWe aimed to assess whether authors of cardiovascular CPRs cited existing CPRs, why some authors did not cite existing CPRs, and why they thought existing CPRs were insufficient.MethodDerivation studies of cardiovascular CPRs from the International Register of Clinical Prediction Rules for Primary Care were evaluated. We reviewed the introduction sections to determine whether existing CPRs were cited. Using thematic content analysis, the stated reasons for determining existing cardiovascular CPRs insufficient were explored. Study authors were surveyed via e-mail and post. We asked whether they were aware of any existing cardiovascular CPRs at the time of derivation, how they searched for existing CPRs, and whether they thought it was important to cite existing CPRs.ResultsOf 85 derivation studies included, 48 (56.5%) cited existing CPRs, 33 (38.8%) did not cite any CPR, and four (4.7%) declared there was none to cite. Content analysis identified five categories of existing CPRs insufficiency related to: (1) derivation (5 studies; 11.4% of 44), (2) construct (31 studies; 70.5%), (3) performance (10 studies; 22.7%), (4) transferability (13 studies; 29.5%), and (5) evidence (8 studies; 18.2%). Authors of 54 derivation studies (71.1% of 76 authors contacted) responded to the survey. Twenty-five authors (46.3%) reported they were aware of existing CPR at the time of derivation. Twenty-nine authors (53.7%) declared they conducted a systematic search to identify existing CPRs. Most authors (90.7%) indicated citing existing CPRs was important.ConclusionCardiovascular CPRs are often developed without citing existing CPRs although most authors agree it is important. Common justifications for new CPRs concerned construct, including choice of predictor variables or relevance of outcomes. Developers should clearly justify why new CPRs are needed with reference to existing CPRs to avoid unnecessary duplication.

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“…Traditionally, CQRs function like large observational studies that monitor health status or outcomes following interventions. By collecting data on large numbers of people over many years, sometimes including everyone receiving an intervention in a region or country, CQRs have enabled precise and generalizable conclusions about treatment outcomes at a population level . For these reasons, registries have become an attractive data source for clinicians and researchers who are wanting to answer important healthcare‐related questions.…”

mentioning

confidence: 85%