Initializing a hospital-wide data quality program. The AP-HP experience. (November 2019)

• Record Type: Journal Article
• Authors: Daniel, Christel; Serre, Patricia; Orlova, Nina; Bréant, Stéphane; Paris, Nicolas; Griffon, Nicolas
• Is Part Of: Computer methods and programs in biomedicine. Volume 181(2020)
• Publication Date: 2019-11
• Subjects: Data accuracy -- Data quality -- Electronic health records -- Data warehousing -- Observational Studies as Topic; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.10.016
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: What is already known on the topic? EHR data is increasingly used for clinical research, effectiveness studies, innovation in digital health, and clinical decision support. Studies have shown that EHR data contain errors that can have negative impacts on findings generated from these data and affect research results. The promised benefits of Learning Health System can only be achieved if the quality of the data is sufficient to support various secondary uses. In the healthcare domain, there are no generally accepted methods to best characterize and quantifying data quality. Data quality assessments of hospital information systems are largely unpublished. What has been added by the paper? AP-HP has set up a clinical data repository aggregating data from 8, 8M patients collected in 37 hospitals of the greater Paris area. The importance of systematic data quality assessment has been emphasized in the context of this AP-HP clinical data repository project. First benefits of the engagement into data quality campaigns in the patient identification and healthcare services domains have been demonstrated. Manual and automated data quality checks have increased awareness about data quality issues within the AP-HP direction and across hospitals. The data quality campaigns contributed to position data quality at the forefront of data governance. Abstract: 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.