Assessing the quality of electronic health record data and patient self-report data

Meredith N. Zozus, Anita Walden, Marcia Byers, Thomas Powell, Pei Wang, Maryam Garza, Guilherme Del Fiol, Jessica Tenenbaum, Matthew Nix, Carl Pieper

Research output: Contribution to conferencePaperpeer-review

Abstract

Knowing the accuracy of self-reported medical data is critical to using the data in clinical decision-making and research. The same is true for data in Electronic Health Records (EHRs). For these data, accuracy reported in the literature varies widely leaving little to guide researchers in selection of the most accurate data source. This study addresses this gap by comparing patient self-report and EHR data and is the most extensive study to date in the accuracy of clinical data. The study design, data collection and preliminary results for race data are reported here. The initial comparison of race data in a small group of participating clinics showed a 33% discrepancy rate. Further, bias was evident in that all of the discrepant records were from patients reporting Hispanic ethnicity. Initial characterization of the results identified process differences among the clinics and lack of identification with the race categories among patients. The extent of variability in discrepancy rates across facilities and other data elements remains to be characterized but the necessity for accuracy assessment has been demonstrated.

Original languageEnglish (US)
StatePublished - 2017
Event22nd MIT International Conference on Information Quality, ICIQ 2017 - Little Rock, United States
Duration: Oct 6 2017Oct 7 2017

Conference

Conference22nd MIT International Conference on Information Quality, ICIQ 2017
CountryUnited States
CityLittle Rock
Period10/6/1710/7/17

Keywords

  • Data accuracy
  • Data quality
  • Data quality assessment
  • Electronic health record

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Information Systems

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