Development of a framework to assess the quality of data sources in healthcare settings.

OBJECTIVE: The purpose of this study was to develop a framework to assess the quality of healthcare data sources. MATERIALS AND METHODS: First, a systematic review was performed and a thematic analysis of included literature conducted to identify items relating to the quality of healthcare data sources. Second, expert advisory group meetings were held to explore experts' perception of the results of the review and identify gaps in the findings. Third, a framework was developed based on the findings. RESULTS: Synthesis of the review results and expert advisory group meetings resulted in 8 parent themes and 22 subthemes. The parent themes were: Governance, leadership, and management; Data; Trust; Context; Monitoring; Use of information; Standardization; Learning and training. The 22 subthemes were: governance, finance, organization, characteristics, time, data management, data quality, ethics, access, security, quality improvement, monitoring and feedback, dissemination, analysis, research, standards, linkage, infrastructure, documentation, definitions and classification, learning, and training. DISCUSSION: The herein presented framework was developed using a robust methodology which included reviewing literature and extracting data source quality items, filtering, and matching items, developing a list of themes, and revising them based on expert opinion. To the best of our knowledge, this study is the first to apply a systematic approach to identify aspects related to the quality of healthcare data sources. CONCLUSIONS: The framework, can assist those using healthcare data sources to identify and assess the quality of a data source and inform whether the data sources used are fit for their intended use.

Modelling temporal data in knowledge graphs: a systematic review protocol.

Background: The benefits of having high-quality healthcare data are well established. However, high-dimensionality and irregularity of healthcare data pose challenges in their management. Knowledge graphs have gained increasing popularity in many domains, as a method for representing data to overcome such challenges. However, little is known about their suitability for use with healthcare data. One important factor in representing data is "time". Data with time related attributes are considered, temporal data. Temporal data are frequently observed in healthcare and the management of rapidly changing patient data is an ongoing challenge. Traditionally, data models have focused on presenting static data and do not account for temporal data. Temporal data models ensure time consistency in data models and assist analysing the history of data and predicting the future trends in data. Knowledge graphs can include temporal data models and are therefore of interest to the field of healthcare data management. As such, the herein aim is to outline a protocol for an inter-disciplinary systematic review of approaches, applications and challenges in modelling temporal data in knowledge graphs so that we can inform the application of knowledge graphs to healthcare data. Method: The research questions is, what are the existing approaches in modelling temporal data in RDF based knowledge graphs. Two sub-questions on applications, and challenges will also be evaluated. ACM digital library, IEEE Xplore and Scopus will be searched for this review. The search will be limited to peer-reviewed literature referring to knowledge graphs based on Resource Description Framework (RDF). A narrative synthesis of the papers will be conducted. Conclusion: The findings of this systematic review will be useful for data engineers to better represent data and perform analytics through temporal data modelling. They can be applied in the context of healthcare data and the current challenges faced in managing rapidly changing patient data.