RACE/ETHNICITY IN MEDICAL CHARTS AND ADMINISTRATIVE DATABASES OF PATIENTS SERVED BY COMMUNITY HEALTH CENTERS
Open accessAbstract
OBJECTIVE: The objective of this study was to measure the agreement in classification of patients' race/ethnicity in the medical charts and the automated practice management systems (PMSs) of seven community health centers. SETTING: Community health centers are on the frontlines of providing primary care to the under-served and racial/ethnic minorities. Public and private investments in information technology and the increasing use of automated disease registries hold promise to improve care and reduce ethnic and racial disparities. However, data quality may limit the accuracy of race/ethnicity classification and the ability to measure the effect of population-based clinical quality improvements. DESIGN/PARTICIPANTS: In a cross-sectional study, a probability sample of 947 patients with encounters in 2002 was selected from 79,119 patients. Each PMS used a single data field with a pick list that combined ethnicity and race. Race/ethnicity on registration forms completed by patients was abstracted from medical charts. Race/ethnicity classifications were aggregated into seven major categories: Asian/Pacific Islander, Black/African-American, Native American, White, Hispanic/Latino, Other, Missing/Unknown. OUTCOME MEASURES: The sensitivity, positive predictive value, and proportion of agreement were outcome measures of agreement between information in the medical chart and PMS. RESULTS: The overall proportion of agreement (PA) between the medical chart (reference) and PMS was 87%. The PA varied significantly by health center (95%-74%). Hispanic/Latino had the highest sensitivity (91%) and positive predictive value (95%) and White the lowest (84% and 80%, respectively). CONCLUSIONS: In broad categories, correspondence of race/ethnicity classifications in medical charts and PMS was good, although health centers varied. A careful appraisal of data quality of race/ethnicity is warranted before administrative databases are used in clinical quality improvement programs or research to assess health disparities.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several studies found that the plausibility of patient race/ethnicity information in their dataset was questionable because the data were incomplete; plausibility was low because a patient's race was reported correctly in one area but as 'other' in another.
“For example, several studies found that the plausibility of the patient race/ethnicity information in their dataset was questionable because the data were incomplete; in other words, plausibility was low because a patient’s race was reported correctly in one area, but as “other” in another.21,86,87”
From The quality of social determinants data in the electronic health record: a systematic review (2021) · cites this paper as [87]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Agreement between administrative data and patients’ self-reports of race/ethnicity
Kressin, N. R., Chang, B.-H., Hendricks, A., & Kazis, L. E. (2003) · American Journal of Public Health · doi:10.2105/ajph.93.10.1734 · Cited by 234 · see citing works
Four-year review of the use of race and ethnicity in epidemiologic and public health research
Comstock, R. D. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh084 · Cited by 142 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Politizer RM, Yoon J, Shi L, Hughes RG, Regan J, Gaston MH. Inequality in America: the contribution of health centers in reducing and eliminating disparities in access to care. Med Care Res Rev. 2001;58:234-248.
- Maizlish NA, Shaw B, Hendry K. Glycemic control in diabetic patients served by commu- nity health centers. Am J Med Qual. 2004; 19:172-179.
- Centers for Disease Control and Prevention. Use of race and ethnicity in public health surveillance. Summary of the CDC/ASTDR workshop. Morb Mortal Wkly Rep. 1993; 42(RR-10):1-17.
- Moscou S, Anderson MR, Kaplan JB, Valencia L. Validity of racial/ethnic classifica- tions in medical records data: an exploratory study. Am J Public Health. 2003;93: 1084-1086.
- Sondik EJ, Lucas JW, Madans JH, Smith SS. Race/ethnicity and the 2000 Census: implica- tions for public health. Am J Public Health. 2000;90:1709-1713.
Cited by
Sorted by citations
The quality of social determinants data in the electronic health record: a systematic review
Cook, L. A., Sachs, J., & Weiskopf, N. G. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab199 · Cited by 153 · see citing works
- Authors
- Maizlish, N., & Herrera, L.
- ORCID
- Maizlish, Neil
- Venue
- Digital Repository at the University of Maryland
- Publisher
- Digital Repository at the University of Maryland
- Published
- 2006
- Volume
- 16
- Issue
- 2
- Pages
- 483-7
- OpenAlex
- W1488159655
- Cited by
- 9
- References
- 2
- Citations by year
- 2025: 1 · 2023: 1 · 2021: 1 · 2015: 1 · 2014: 2 · 2012: 2
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- PubMed (publishedVersion)
- University Libraries (University of Maryland) (submittedVersion)
- Digital Repository at the University of Maryland (University of Maryland College Park)
- Funders
- Agency for Healthcare Research and Quality
- 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
- medical records, practice management systems, community health centers, data quality, data agreement, health disparities, racial and ethnic minorities, Hispanic patients, administrative databases, clinical quality improvement
- MeSH terms
- California, Community Health Centers, Cross-Sectional Studies, Ethnicity, Humans, Medical Records, Patients, Practice Management, Racial Groups
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
