Initializing a hospital-wide data quality program. The AP-HP experience.

BACKGROUND AND OBJECTIVES

Data Quality (DQ) programs are recognized as a critical aspect of new-generation research platforms using electronic health record (EHR) data for building Learning Healthcare Systems. The AP-HP Clinical Data Repository aggregates EHR data from 37 hospitals to enable large-scale research and secondary data analysis. This paper describes the DQ program currently in place at AP-HP and the lessons learned from two DQ campaigns initiated in 2017.

MATERIALS AND METHODS

As part of the AP-HP DQ program, two domains - patient identification (PI) and healthcare services (HS) - were selected for conducting DQ campaigns consisting of 5 phases: defining the scope, measuring, analyzing, improving and controlling DQ. Semi-automated DQ profiling was conducted in two data sets - the PI data set containing 8.8 M patients and the HS data set containing 13,099 consultation agendas and 2122 care units. Seventeen DQ measures were defined and DQ issues were classified using a unified DQ reporting framework. For each domain, actions plans were defined for improving and monitoring prioritized DQ issues.

RESULTS

Eleven identified DQ issues (8 for the PI data set and 3 for the HS data set) were categorized into completeness (n = 6), conformance (n = 3) and plausibility (n = 2) DQ issues. DQ issues were caused by errors from data originators, ETL issues or limitations of the EHR data entry tool. The action plans included sixteen actions (9 for the PI domain and 7 for the HS domain). Though only partial implementation, the DQ campaigns already resulted in significant improvement of DQ measures.

CONCLUSION

DQ assessments of hospital information systems are largely unpublished. The preliminary results of two DQ campaigns conducted at AP-HP illustrate the benefit of the engagement into a DQ program. The adoption of a unified DQ reporting framework enables the communication of DQ findings in a well-defined manner with a shared vocabulary. Dedicated tooling is needed to automate and extend the scope of the generic DQ program. Specific DQ checks will be additionally defined on a per-study basis to evaluate whether EHR data fits for specific uses.