Towards a content agnostic computable knowledge repository for data quality assessment

Rajan, Naresh Sundar; Gouripeddi, Ramkiran; Mo, Peter; Madsen, Randy; Facelli, Julio C.

doi:10.1016/j.cmpb.2019.05.017

Cited by 23 publications

(32 citation statements)

References 11 publications

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“…Therefore, a standardized reporting scheme describing data quality would be essential to assess whether research questions could be appropriately answered with a data collection at hand. 29 The interpretation of the new world registries would be challenging as well.…”

Section: Discussionmentioning

confidence: 99%

Recent Trends in Patient Registries for Health Services Research

Stausberg

Harkener

Semler³

2021

Methods Inf Med

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Background Patient registries are an established methodology in health services research. Since more than 150 years, registries collect information concerning groups of similar patients to answer research questions. Elaborated recommendations about an appropriate development and an efficient operation of registries are available. However, the scene changes rapidly. Objectives The aim of the study is to describe current trends in registry research for health services research. Methods Registries developed within a German funding scheme for model registries in health services research were analyzed. The observations were compared with recent recommendations of the Agency for Healthcare Research and Quality (AHRQ) on registries in the 21st century. Results Analyzing six registries from the funding scheme revealed the following trends: recruiting healthy individuals, representing familial or other interpersonal relationships, recording of patient-reported experiences or outcomes, accepting participants as study sites, active informing of participants, integrating the registry with other data collections, and transferring data from the registry to electronic patient records. This list partly complies with the issues discussed by the AHRQ. The AHRQ structured its ideas in five chapters, increasing focus on the patient, engaging patients as partners, digital health and patient registries, direct-to-patient registry, and registry networks. Conclusion For the near future, it can be said that the concept and the design of a registry should place the patient in the center. Registries will be increasingly linked together and interconnected with other data collections. New challenges arise regarding the management of data quality and the interpretation of results from less controlled settings. Here, further research related to the methodology of registries is needed.

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Section: Discussionmentioning

confidence: 99%

Recent Trends in Patient Registries for Health Services Research

Stausberg

Harkener

Semler³

2021

Methods Inf Med

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“…It should be noted that, although more recent DQCs exist (e.g. Gürdür et al , 2019; Huang, 2018; Liu et al , 2020; Rajan et al , 2019; Rasool and Warraich, 2018; Teh et al , 2020), the ones shown are still frequently cited in current DQ research. In the tables, the final two columns show the number of Scopus (Sc) citations and the number of Google Scholar (GS) citations (citations counts were conducted on August 14, 2020).…”

Section: Descriptive Findingsmentioning

confidence: 99%

Understanding the differences across data quality classifications: a literature review and guidelines for future research

Haug

2021

IMDS

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PurposeNumerous data quality (DQ) definitions in the form of sets of DQ dimensions are found in the literature. The great differences across such DQ classifications (DQCs) imply a lack of clarity about what DQ is. For an improved foundation for future research, this paper aims to clarify the ways in which DQCs differ and provide guidelines for dealing with this variance.Design/methodology/approachA literature review identifies DQCs in conference and journal articles, which are analyzed to reveal the types of differences across these. On this basis, guidelines for future research are developed.FindingsThe literature review found 110 unique DQCs in journals and conference articles. The analysis of these articles identified seven distinct types of differences across DQCs. This gave rise to the development of seven guidelines for future DQ research.Research limitations/implicationsBy identifying differences across DQCs and providing a set of guidelines, this paper may promote that future research, to a greater extent, will converge around common understandings of DQ.Practical implicationsAwareness of the identified types of differences across DQCs may support managers when planning and conducting DQ improvement projects.Originality/valueThe literature review did not identify articles, which, based on systematic searches, identify and analyze existing DQCs. Thus, this paper provides new knowledge on the variance across DQCs, as well as guidelines for addressing this.

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“…The research gap in article [39] is the focus on data quality assessment based on metrics which are not sufficient for the contextual data quality; however, our proposed model supports deep learning and semantic ontology based data quality assessment methodology. To analyze data quality of the healthcare data, authors [40] developed the data quality knowledge repositories; that contain the characteristics of data quality rules. These rules have been used to measure the quality of healthcare data.…”

Section: Background and Related Workmentioning

confidence: 99%

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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Towards a content agnostic computable knowledge repository for data quality assessment

Cited by 23 publications

References 11 publications

Recent Trends in Patient Registries for Health Services Research

Recent Trends in Patient Registries for Health Services Research

Understanding the differences across data quality classifications: a literature review and guidelines for future research

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

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