The challenges and opportunities of continuous data quality improvement for healthcare administration data

Abstract Background Various data quality issues have prevented healthcare administration data from being fully utilized when dealing with problems ranging from COVID-19 contact tracing to controlling healthcare costs. Objectives (i) Describe the currently adopted approaches and practices for understanding and improving the quality of healthcare administration data. (ii) Explore the challenges and opportunities to achieve continuous quality improvement for such data. Materials and Methods We used a qualitative approach to obtain rich contextual data through semi-structured interviews conducted at a state health agency regarding Medicaid claims and reimbursement data. We interviewed all data stewards knowledgeable about the data quality issues experienced at the agency. The qualitative data were analyzed using the Framework method. Results Sixteen themes emerged from our analysis, collected under 4 categories: (i) Defect characteristics: Data defects showed variability, frequently remained obscure, and led to negative outcomes. Detecting and resolving them was often difficult, and the work required often exceeded the organizational boundaries. (ii) Current process and people issues: The agency adopted primarily ad-hoc, manual approaches to resolving data quality problems leading to work frustration. (iii) Challenges: Communication and lack of knowledge about legacy software systems and the data maintained in them constituted challenges, followed by different standards used by various organizations and vendors, and data verification difficulties. (iv) Opportunities: Training, tool support, and standardization of data definitions emerged as immediate opportunities to improve data quality. Conclusions Our results can be useful to similar agencies on their journey toward becoming learning health organizations leveraging data assets effectively and efficiently.