Methods for examining data quality in healthcare integrated data repositories

Huser, Vojtech; Kahn, Michael G.; Brown, Jeffrey S.; Gouripeddi, Ramkiran

doi:10.1142/9789813235533_0059

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…Because the handling of study data varies greatly across studies, interoperability issues may arise, and the provision of interfaces to facilitate data transfer will be an important future extension of our work. Therefore, an alignment of data quality related metadata with standards for information exchange such as HL7 FHIR [ 66 ] and common data models to enable data quality assessments without additional efforts in a harmonized fashion across data sets is a main objective [ 53 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

Schmidt

Struckmann

Enzenbach

et al. 2021

BMC Med Res Methodol

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Background No standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments. Methods Developments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP). Results The data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website. Conclusions The presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

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Section: Discussionmentioning

confidence: 99%

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

Schmidt

Struckmann

Enzenbach

et al. 2021

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…Again, leveraging existing data collection, or linking electronic health records across institutions into registries, as is done with the National Patient-Centered Clinical Research Network and similar initiatives, may limit the burden of such efforts. [90][91][92][93][94][95] A comprehensive database of clinical data, drug utilisation, and outcomes would help identify potential signals of efficacy and safety, aid in the design of clinical trials, and, for pragmatically designed trials, could serve as a data collection platform. Efforts to ensure rigorous and high-quality data collection, including adherence to regulatory guidance, where applicable, are essential to maximise the benefit of registries to drug development.…”

Section: Future Directionsmentioning

confidence: 99%

An evidence-based review of the use of vasoactive and inotropic medications in post-operative paediatric patients after cardiac surgery with cardiopulmonary bypass from 2000 to 2020

et al. 2020

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Background: Infants with moderate-to-severe CHD frequently undergo cardiopulmonary bypass surgery in childhood. Morbidity and mortality are highest in those who develop post-operative low cardiac output syndrome. Vasoactive and inotropic medications are mainstays of treatment for these children, despite limited evidence supporting their use. Methods: To help inform clinical practice, as well as the conduct of future trials, we performed a systematic review of existing literature on inotropes and vasoactives in children after cardiac surgery using the PubMed and EMBASE databases. We included studies from 2000 to 2020, and the patient population was defined as birth – 18 years of age. Two reviewers independently reviewed studies to determine final eligibility. Results: The final analysis included 37 papers. Collectively, selected studies reported on 12 different vasoactive and inotropic medications in 2856 children. Overall evidence supporting the use of these drugs in children after cardiopulmonary bypass was limited. The majority of studies were small with 30/37 (81%) enrolling less than 100 patients, 29/37 (78%) were not randomised, and safety and efficacy endpoints differed widely, limiting the ability to combine data for meta-analyses. Conclusion: Vasoactive and inotropic support remain critical parts of post-operative care for children after cardiopulmonary bypass surgery. There is a paucity of data for the selection and dosing of vasoactives and inotropes for these patients. Despite the knowledge gaps that remain, numerous recent innovations create opportunities to rethink the conduct of clinical trials in this high-risk population.

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“… 6 There are approaches described for evaluating quality of medical device data, 7 use of rule based approaches for data quality evaluation and management, 8 and outputs of workshops focusing on health data quality issues. 9 There has been a suggestion that quality informatics may become a specific area of health informatics. 10 Despite such recognition of the importance of data quality for widespread uses, evaluation of data utility for specific purposes has remained difficult.…”

Section: Introductionmentioning

confidence: 99%

Development of a data utility framework to support effective health data curation

Gordon

Barrett

Fennessy

et al. 2021

BMJ Health Care Inform

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ObjectivesThe value of healthcare data is being increasingly recognised, including the need to improve health dataset utility. There is no established mechanism for evaluating healthcare dataset utility making it difficult to evaluate the effectiveness of activities improving the data. To describe the method for generating and involving the user community in developing a proposed framework for evaluation and communication of healthcare dataset utility for given research areas.MethodsAninitial version of a matrix to review datasets across a range of dimensions wasdeveloped based on previous published findings regarding healthcare data. Thiswas used to initiate a design process through interviews and surveys with datausers representing a broad range of user types and use cases, to help develop afocused framework for characterising datasets.ResultsFollowing 21 interviews, 31 survey responses and testing on 43 datasets, five major categories and 13 subcategories were identified as useful for a dataset, including Data Model, Completeness and Linkage. Each sub-category was graded to facilitate rapid and reproducible evaluation of dataset utility for specific use-cases. Testing of applicability to >40 existing datasets demonstrated potential usefulness for subsequent evaluation in real-world practice.DiscussionTheresearch has developed an evidenced-based initial approach for a framework tounderstand the utility of a healthcare dataset. It likely to require further refinementfollowing wider application and additional categories may be required.ConclusionThe process has resulted in a user-centred designed framework for objectively evaluating the likely utility of specific healthcare datasets, and therefore, should be of value both for potential users of health data, and for data custodians to identify the areas to provide the optimal value for data curation investment.

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Methods for examining data quality in healthcare integrated data repositories

Cited by 16 publications

References 13 publications

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

An evidence-based review of the use of vasoactive and inotropic medications in post-operative paediatric patients after cardiac surgery with cardiopulmonary bypass from 2000 to 2020

Development of a data utility framework to support effective health data curation

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