Analysis of Open Health Data Quality Using Data Object-Driven Approach to Data Quality Evaluation: Insights From a Latvian Context

Abstract

This research focuses on the analysis of the quality of open health data that are freely available and can be used by everyone for their own purposes. The quality of open data is crucial as it can lead to unreliable decision-making and financial losses, however, the quality of open health data has even more critical role. Despite its importance, this topic is rarely discussed. Therefore, the previously proposed data object-driven approach to data quality evaluation is applied to open health data in Latvia in order to (a) evaluate their quality, highlighting common quality issues that should be considered by both, users and data publishers, (b) demonstrate that the used approach is suitable for given purpose as it is simple enough, and ensures the involvement of users even without IT and data quality knowledge (domain experts) in the data quality analysis examining data for their own purposes. The proposed solution seems to be useful in establishing communication between data users and publishers, improving the overall quality of data.

Rethinking clinical study data: why we should respect analysis results as data

“…In particular, since the ARDM will make analysis results more easily accessible and reusable also to an audience who may only have a limited understanding of how to assess the quality of the underlying raw data. In this wider context, it may beneficial to use data quality evaluation approaches that were developed for a non-technical audience or for an audience without subject-matter (domain) expertise 35 . This will allow the audience to interpret the results taking the quality of the underlying raw data into account.…”

Towards Data Quality Runtime Verification

“…However, data object-driven model can be used by wide audience without need to make in-depth analysis of the basics of the approach as it usually happens with other approaches where at least an exploration of the list of dimensions, their meanings and criteria under each of them, needs a lot of time for every particular solution as criteria differentiate from case to case [3], [6], [7]. At the same time, the proposed approach already demonstrated its effectiveness by applying it to real datasets [4], [6] - [8].…”

Using Secondary Health Data in Research

“…There are, however, in many cases fees for access to data (Windle, 2010;Burton, Banner, Elliot, Knoppers & Banks, 2017;Wise, 2019), so researchers need to balance the costs of access to the data against its overall utility and include consideration of fees in budget proposals (National Academy of Sciences, 2009). The advent of comprehensive national and supra-national big data curation projects and standards developing organisations has begun to increase accessibility to verified secondary data and further lowered the cost of research using secondary data (Hammond, Jaffe, & Kush, 2009;Wise, 2019;Nikiforova, 2019). Clinical research networks that use a common data model such as the EMIF (EU) and SHRINE (US) are currently in the process of extending their scope and resourcing for the purpose of easing the access, storage and transmissibility of secondary data for use in medical research (Burton, Banner, Elliot, Knoppers & Banks, 2017;Martin-Sanchez, Aguiar-Pulido, Lopez-Campos, Peek & Saachi, 2017;Pezoulas et al, 2019).…”