Assessing Data Quality for Healthcare Systems Data Used in Clinical Research 

Assessing Data Quality is an NIH Collaboratory guidance document for pragmatic clinical trials that reuse routine healthcare data (especially EHR data) to identify participants, define cohorts, and measure outcomes. It explains why data quality must be demonstrated to support credible research conclusions and offers a practical, multidimensional framework. The guide emphasizes three research-critical dimensions: completeness (are the right variables present and sufficiently populated), accuracy/correctness (do values reflect the clinical truth), and consistency/concordance (are values stable and aligned across sources, time, sites, and related fields). It outlines concrete assessment approaches, including metadata review, missingness profiling, logic and range checks, cross-field validation, source-to-source comparisons, targeted chart review, and ongoing monitoring during trial conduct.

Assessing Data Quality for Healthcare Systems Data Used in Clinical Research 

Background—Electronic health information routinely collected during healthcare delivery and reimbursement can help address the need for evidence about the real-world effectiveness, safety, and quality of medical care. Often, distributed networks that combine information from multiple sources are needed to generate this real-world evidence.

Objective—We provide a set of field-tested best practices and a set of recommendations for data quality checking for comparative effectiveness research (CER) in distributed data networks.Methods—Explore the requirements for data quality checking and describe data quality approaches undertaken by several existing multi-site networks.

Results—There are no established standards regarding how to evaluate the quality of electronic health data for CER within distributed networks. Data checks of increasing complexity are often employed, ranging from consistency with syntactic rules to evaluation of semantics and consistency within and across sites. Temporal trends within and across sites are widely used, as are checks of each data refresh or update. Rates of specific events and exposures by age group, sex, and month are also common.

Discussion—Secondary use of electronic health data for CER holds promise but is complex, especially in distributed data networks that incorporate periodic data refreshes. The viability of a learning health system is dependent on a robust understanding of the quality, validity, and optimal secondary uses of routinely collected electronic health data within distributed health data networks. Robust data quality checking can strengthen confidence in findings based on distributed data network.

Monitoring Intervention Fidelity and Adaptations (Webpage)

This webpage from the NIH Collaboratory describes strategies for anticipating how to work with health systems that could adapt the ePCT intervention and includes real-world case studies from the NIH Collaboratory Trials.