A Survey of Data Quality Measurement and Monitoring Tools

JsonCurer: Data Quality Management for JSON Based on an Aggregated Schema

High-quality data is critical to deriving useful and reliable information. However, real-world data often contains quality issues undermining the value of the derived information. Most existing research on data quality management focuses on tabular data, leaving semi-structured data under-exploited. Due to the schema-less and hierarchical features of semi-structured data, discovering and fixing quality issues is challenging and time-consuming. To address the challenge, this paper presents JsonCurer, an interactive visualization system to assist with data quality management in the context of JSON data. To have an overview of quality issues, we first construct a taxonomy based on interviews with data practitioners and a review of 119 real-world JSON files. Then we highlight a schema visualization that presents structural information, statistical features, and quality issues of JSON data. Based on a similarity-based aggregation technique, the visualization depicts the entire JSON data with a concise tree, where summary visualizations are given above each node, and quality issues are illustrated using Bubble Sets across nodes. We evaluate the effectiveness and usability of JsonCurer with two case studies. One is in the domain of data analysis while the other concerns quality assurance in MongoDB documents.

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews

BACKGROUND

Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.

OBJECTIVE

This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.

METHODS

A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.

RESULTS

A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.

CONCLUSIONS

The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

The impact of data quality monitoring of a multicenter prospective registry of cardiac implantable electronic devices

Data quality monitoring plays a crucial role in multicenter prospective registries. By maintaining high data accuracy, completeness, and consistency, researchers can improve the overall quality and reliability of the registry data, enabling meaningful conclusions and supporting evidence-based decisions. The purpose of the present study was to evaluate data quality metrics (completeness, accuracy, and temporal plausibility) of a Multicenter Registry of Cardiac Implantable Electronic Devices (CIEDs) and to perform a direct data audit of a random sample of records to assess the agreement levels with the source documents. The CIED Registry was a prospective, multicenter, real-world observational study carried out from January 2020 to December 2022 in five designated centers across Sao Paulo, Brazil. We assessed the data quality of the CIED Registry by using two distinct approaches:•Dynamic data monitoring using features of the REDCap (Research Electronic Data Capture) software, including data reports and data quality rules•Direct data audit in which information from a random sample of 10 % of cases from the coordinating center was compared with original source documents Our findings suggest that the methodological approach applied to the CIED Registry resulted in high data completeness, accuracy, temporal plausibility, and excellent agreement levels with the source documents.

Data Quality in Health Care: Main Concepts and Assessment Methodologies

INTRODUCTION

 In the health care environment, a huge volume of data is produced on a daily basis. However, the processes of collecting, storing, sharing, analyzing, and reporting health data usually face with numerous challenges that lead to producing incomplete, inaccurate, and untimely data. As a result, data quality issues have received more attention than before.

OBJECTIVE

 The purpose of this article is to provide an insight into the data quality definitions, dimensions, and assessment methodologies.

METHODS

 In this article, a scoping literature review approach was used to describe and summarize the main concepts related to data quality and data quality assessment methodologies. Search terms were selected to find the relevant articles published between January 1, 2012 and September 31, 2022. The retrieved articles were then reviewed and the results were reported narratively.

RESULTS

 In total, 23 papers were included in the study. According to the results, data quality dimensions were various and different methodologies were used to assess them. Most studies used quantitative methods to measure data quality dimensions either in paper-based or computer-based medical records. Only two studies investigated respondents' opinions about data quality.

CONCLUSION

 In health care, high-quality data not only are important for patient care, but also are vital for improving quality of health care services and better decision making. Therefore, using technical and nontechnical solutions as well as constant assessment and supervision is suggested to improve data quality.