Discrepancies in Aggregate Patient Data between Two Sources with Data Originating from the Same Electronic Health Record: A Case Study

Abstract

Background: Data exploration in modern electronic health records (EHRs) is often aided by user-friendly graphical interfaces providing "self-service" tools for end users to extract data for quality improvement, patient safety, and research without prerequisite training in database querying. Other resources within the same institution, such as Honest Brokers, may extract data sourced from the same EHR but obtain different results leading to questions of data completeness and correctness.

Objectives: Our objectives were to (1) examine the differences in aggregate output generated by a "self-service" graphical interface data extraction tool and our institution's clinical data warehouse (CDW), sourced from the same database, and (2) examine the causative factors that may have contributed to these differences.

Methods: Aggregate demographic data of patients who received influenza vaccines at three static clinics and three drive-through clinics in similar locations between August 2020 and December 2020 was extracted separately from our institution's EHR data exploration tool and our CDW by our organization's Honest Brokers System. We reviewed the aggregate outputs, sliced by demographics and vaccination sites, to determine potential differences between the two outputs. We examined the underlying data model, identifying the source of each database.

Results: We observed discrepancies in patient volumes between the two sources, with variations in demographic information, such as age, race, ethnicity, and primary language. These variations could potentially influence research outcomes and interpretations.

Conclusion: This case study underscores the need for a thorough examination of data quality and the implementation of comprehensive user education to ensure accurate data extraction and interpretation. Enhancing data standardization and validation processes is crucial for supporting reliable research and informed decision-making, particularly if demographic data may be used to support targeted efforts for a specific population in research or quality improvement initiatives.

Fig. 1

Data model of OMOP and SlicerDicer showing a shared source for patient data with differing ETL rules. EHR, electronic health record; ETL, extract transform load; OMOP, Observational Medical Outcomes Partnership.

Fig. 2

In SlicerDicer, “Department” sites are grouped into a higher level “Location.” The drive-through vaccination sites were listed under “Department” and the static immunization sites were listed under “Location.”