Cited by 20Medical Coding and Health InformationElectronic Health Records SystemsRace, Genetics, and Society

Improving hospital reporting of patient race and ethnicity—approaches to data auditing

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Zingmond, David S., Parikh, Punam, Louie, Rachel, Lichtensztajn, Daphne Y., Ponce, Ninez, Hasnain‐Wynia, Romana, Gomez, Scarlett Lin·2015·Health Services Research

Abstract

Objective: To investigate new metrics to improve the reporting of patient race and ethnicity (R/E) by hospitals. Data Sources: California Patient Discharge Database (PDD) and birth registry, 2008–2009, Healthcare and Cost Utilization Project's State Inpatient Database, 2008–2011, cancer registry 2000–2008, and 2010 US Census Summary File 2. Study Design: We examined agreement between hospital reported R/E versus self‐report among mothers delivering babies and a cancer cohort in California. Metrics were created to measure root mean squared differences (RMSD) by hospital between reported R/E distribution and R/E estimates using R/E distribution within each patient's zip code of residence.RMSD comparisons were made to corresponding “gold standard” facility‐level measures within the maternal cohort for California and six comparison states. Data Collection: Maternal birth hospitalization (linked to the state birth registry) and cancer cohort records linked to preceding and subsequent hospitalizations. Hospital discharges were linked to the corresponding Census zip code tabulation area using patient zip code. Principal Findings: Overall agreement between the PDD and the gold standard for the maternal cohort was 86 percent for the combined R/E measure and 71 percent for race alone. The RMSD measure is modestly correlated with the summary level gold standard measure for R/E (r = 0.44). The RMSD metric revealed general improvement in data agreement and completeness across states. “Other” and “unknown” categories were inconsistently applied within inpatient databases. Conclusions: Comparison between reported R/E and R/E estimates using zip code level data may be a reasonable first approach to evaluate and track hospital R/E reporting. Further work should focus on using more granular geocoded data for estimates and tracking data to improve hospital collection of R/E data.

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  • Misclassification bias was noted as a problem or potential problem in 18 of the 26 (69%) articles about the plausibility of race/ethnicity data.

    “Misclassification bias, that is, incorrect assignment, was noted as a problem or a potential problem in 18 of the 26 (69%) articles about the plausibility of race/ethnicity data.11,13,23–38”

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Authors
Zingmond, D. S., Parikh, P., Louie, R., Lichtensztajn, D. Y., Ponce, N., Hasnain‐Wynia, R., & Gomez, S. L.
Affiliations
  • University of California, Los Angeles (US)
  • Cancer Prevention Institute of California (US)
  • Patient-Centered Outcomes Research Institute (US)
Corresponding author
Zingmond, David S.
Venue
Health Services Research
Publisher
Wiley
Published
2015-06-15
Volume
50
Issue
S1
Pages
1372-1389
ISSN
  • 0017-9124 (ISSN-L)
  • 1475-6773
OpenAlex
W2101546221
Cited by
20
References
4
Citations by year
2025: 1 · 2024: 3 · 2023: 1 · 2022: 4 · 2021: 1 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 3 · 2015: 1
FWCI
3.85
Type
journal article
Language
en
Open access
Locations
  • Health Services Research (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Office of Statewide Health Planning and Development, State of California
  • Agency for Healthcare Research and Quality (1R01HS019963, R01HS019963)
Topics
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Race, Genetics, and Society
Keywords
hospital discharge data, data auditing, cancer hospitalization, census data linkage, data quality
MeSH terms
Adult, Birth Rate, California, Data Collection, Ethnicity, Female, Health Services Research, Hospital Information Systems, Humans, Infant, Newborn, Medical Record Linkage, Middle Aged, Neoplasms, Patient Discharge, Pregnancy, Registries, Databases, Factual, Censuses, Racial Groups, Quality Improvement

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