Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions

Abstract

Introduction:Electronic health record (EHR) data are known to have significant data quality issues, yet the practice and frequency of assessing EHR data is unknown. We sought to understand current practices and attitudes towards reporting data quality assessment (DQA) results by data professionals.Methods:The project was conducted in four Phases: (1) examined current DQA practices among informatics/CER stakeholders via engagement meeting (07/2014); (2) characterized organizations conducting DQA by interviewing key personnel and data management professionals (07-08/2014); (3) developed and administered an anonymous survey to data professionals (03-06/2015); and (4) validated survey results during a follow-up informatics/CER stakeholder engagement meeting (06/2016).Results:The first engagement meeting identified the theme of unintended consequences as a primary barrier to DQA. Interviewees were predominantly medical groups serving distributed networks with formalized DQAs. Consistent with the interviews, most survey (N=111) respondents utilized DQA processes/programs. A lack of resources and clear definitions of how to judge the quality of a dataset were the most commonly cited individual barriers. Vague quality action plans/expectations and data owners not trained in problem identification and problem-solving skills were the most commonly cited organizational barriers. Solutions included allocating resources for DQA, establishing standards and guidelines, and changing organizational culture.Discussion:Several barriers affecting DQA and reporting were identified. Community alignment towards systematic DQA and reporting is needed to overcome these barriers.Conclusion:Understanding barriers and solutions to DQA reporting is vital for establishing trust in the secondary use of EHR data for quality improvement and the pursuit of personalized medicine.

Validation process of a high‐resolution database in a paediatric intensive care unit—Describing the perpetual patient's validation

“…As there were no guidelines specifically designed to guarantee high‐resolution data quality, 9,14 we elaborated the first complete validation procedure. Our validation procedure was inspired by previously published experiences 9,10,30,32‐34 and guidelines 13‐15,28,35 regarding data quality assessment in the field of medical DB collected at a lower rate or in a restricted area. To evaluate the quality of the data, we chose to perform an external validation procedure.…”

“…However, a validation step of the collected data is necessary before considering this DB suitable for research purposes 9‐11 . Indeed, the value of research findings depends on data quality 12,13 . Several guidelines or frameworks were elaborated to evaluate and report the quality of DBs and national registries and to guide designers of DBs at each step of the data collection 12,14,15 .…”

Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets

“…Assessment of data quality is key for ensuring that the available data and information are credible and such assessments are essential when establishing trust for reuse of the data (Callahan 2017). Trusted data are perceived as worthy of use in decision making environments where the metadata is sufficient to adequately describe the data, e.g., information about the dataset author and data timeliness.…”

“…Investigating barriers to assessing and reporting data quality information in medical bioinformatics, Callahan et al (2017) identified issues, at both organizational and individual levels, that contribute to deficiencies in quality assurance. Such organizational issues include inadequate support, unclear expectations and insufficient training such as the absence of best practices.…”

Healthcare data heterogeneity and its contribution to machine learning performance

“…In any case, the common key is the idea of a DQ assessment is imperative to assure the data fitness-to-use. In this sense, DQ assessment methods emerge as an artifact helping in the community acceptance of the results of research works [7], and standardizing these methods could lead the transparency and consistency of DQ concept [8].…”