Data is the building block in all research, as results are only as good as the data upon which the conclusions were formed. However, researchers may receive minimal training on how to use the de-identified data systems and methods for achieving, assessing, or controlling the quality of research data (Nahm, 2012; Zozus et al., 2019).

De-identified data systems are defined as systems/tools that allow users to drag and drop search terms from a hierarchical ontology into a Venn diagram-like interface. Investigators can perform an initial analysis on the de-identified cohort. Furthermore, de-identified data systems have no features to indicate the data quality or assist in identifying the data quality; these systems only provide counts.

Informatics is the science of how to use data, information, and knowledge to improve human health and the delivery of healthcare services (American Medical Informatics Association, 2022).

Clinical Informatics is the application of informatics and information technology to deliver healthcare services. For example, patient portals, electronic medical records (EMRs), telehealth, healthcare apps, and a variety of data reporting tools (American Medical Informatics Association, 2022).

The case presented in this paper focuses on the quality of data obtained from two de-identified systems (Epic Slicerdicer and i2b2).The purpose of this paper is to discuss the quality of the data (counts) generated from the two systems, understand the potential causes of the data quality issues, and propose steps to improve the quality and increase the trust of the generated counts by comparing the accuracy, consistency, validity, and understandability of the outcomes from the two systems.

The proposed steps for maintaining the data quality among different systems aim to help data managers, information systems teams, and informaticists at a healthcare organization monitor and sustain data quality as part of their business intelligence, data governance, and data democratization processes. The quality improvement steps proposed are generic and contributes in adding generic and essential steps to automate data curation and data governance to tackle various data quality problem.

The remainder of this paper is organized as follows. In the following section, we introduce the importance of data quality to clinical research informatics, the study case and study method and materials presented in the Importance of Data Quality to Clinical Research Informatics, Case Study Goals, and Methodology section. The findings and the discussion part, and the proposed steps to ensure data quality are discussed in Discussion section. Conclusions are drawn and work contribution is discussed in Conclusion section.

Data quality refers to the degree data meets the expectations of data consumers and their intended use of the data (Pipino et al., 2002; Halimeh, 2011; AbuHalimeh and Tudoreanu, 2014). In clinical informatics, this depends on the study conducted (Nahm, 2012; Zozus et al., 2019).

The meaning of data quality lies in how the data is perceived and used by its consumer. Identifying data quality involves two stages: first, highlighting which characteristics (Dimensions) are important (Figure 1) and second, determining how these dimensions affect the population in question (Halimeh, 2011; AbuHalimeh and Tudoreanu, 2014).

This paper focuses on a subset of data quality dimensions, which we term de-identified data quality dimensions (DDQD). We think these dimensions should be mainly considered to maintain the data quality in de-identified systems because the absence of any of these dimensions will affect the overall quality of the data in the de-identified data systems. These dimensions are described in Table 1 below.

The impact of quality data and management is in performance and efficiency gains and the ability to extract new understandings. Poor clinical informatics data quality can cause glitches throughout an organization. This impact includes the quality of research outcomes, healthcare services, and decision-making.

Quality is not a simple scalar measure but can be defined on multiple dimensions, with each dimension yielding different meanings to different information consumers and processes (Halimeh, 2011; AbuHalimeh and Tudoreanu, 2014). Each dimension can be measured and assessed differently. Data quality assessment implies providing a value for each dimension about how much of the dimension or quality feature is achieved to enable adequate understanding and management. Data quality and the discipline of informatics are undistinguishable interconnected. Data quality depends on how data are collected, processed, and presented; this is what makes data quality very important and sometimes complicated because data collection and processing varies from one study to another. Clinical informatics data can include different data formats and types and could come from different resources.

The primary goal is to compare, identify and understand discrepancies in a patient count in i2b2 compared to Epic Slicerdicer (Galaxy, 2021). The secondary goal was to create a data dictionary that clinical researchers would easily understand. For example, if they wanted a count of patients with asthma, they would know (1) what diagnoses were used to identify patients, (2) where these diagnoses were captured, and (3) that this count matched existing clinical knowledge.

The case described below is from one of the healthcare organizations wanted to have the ability to ingest other sources of research-specific data, such as genomic information, and the existing products did not have a way to do that. After deliberation i2b2 (The i2b2 tranSMART Foundation, 2021) was chosen as the data model for their clinical data warehouse. Prior to going live with users, however, it is very important and essential to validate that the data in their Clinical Data Warehouse (CDW) was accurate.

The discrepancies led to major information quality issues such as data inconsistency and data accuracy both affects the believability and the validity of the data which also are major data quality measures. The discrepancies noted above are likely due to several factors. First, Slicerdicer counts patients for every race selected instead of i2b2, which only takes the first race field, this because two data models were used to pattern race and ethnicity variables in i2b2 to the 1997 OMB race categories and the 2003 OMB variables, which contains a more granular set of race and ethnicity categories. The mapping then was done to ‘bundle” the other races to a more general set of categories. This could be the reason why there is a reduction of concepts because maybe the map is incomplete.

Secondly, the purpose of the Extract-Load-Transform (ETL) process is to load the warehouse with integrated and cleansed data. Data quality focuses on the contents of the individual records to ensure the data loaded into the target destination is accurate, reliable, and consistent, so the ETL code should be evaluated to ensure the data extracted generally match what researchers want. In our case, understanding what diagnosis most researchers are interested in—they may want encounter diagnosis instead of including problem list and medical history. Thirdly, the causes for data quality issues are format differences or conversion errors (Azeroual et al., 2019; Souibgui et al., 2019).

Lastly, data loss could be present in the ETL process, which is one of the challenges in ETL processes because of the nature of the source systems. Data losses arise from the disparities among the source operational systems. Source systems are very diverse and disparate because of the increased amount of data, modification of data formats, and modification of and deriving new data elements.

In general, data integration with heterogeneous systems is not an easy task. This is mainly due to the fact that many data exchange channels must be developed in order to allow an exchange of data between the systems (Berkhoff et al., 2012) and to solve problems related to the provision of interoperability between systems on the level of data (Macura, 2014).

The success of any de-identified data tool depends largely on the quality of the data used, and the mapping process which is intertwined with the extraction and transformation components. The ETL process is a crucial component in determining the quality of the data generated by an information system.

This study proved that the discrepancies in the data used in the data pull process led to major information quality issues such as data inconsistency and data accuracy which both affects the believability and the validity of the data which also are major data quality measures.

Our contribution in this paper is to propose a set of steps that together form guidelines for a method or automated procedures and tools to manage data quality and data governance in a multifaceted, diverse information environment such as healthcare organizations and to enhance the data quality among the de-identified data tools.

Future plan is to study more clinical informatics tools such TriNetX and other sets of medical data to assess the quality of the counts obtained from these tools.

The author confirms being the sole contributor of this work and has approved it for publication.

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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