Assessing an electronic self-report method for improving quality of ethnicity and race data in the Veterans Health Administration

Erin Almklov; Alicia J Cohen; Lauren E Russell; Maria K Mor; Michael J Fine; Leslie R M Hausmann; Ernest Moy; Donna L Washington; Kenneth T Jones; Judith A Long; James Pittman

doi:10.1093/jamiaopen/ooad020

Assessing an electronic self-report method for improving quality of ethnicity and race data in the Veterans Health Administration

JAMIA Open. 2023 Apr 12;6(2):ooad020. doi: 10.1093/jamiaopen/ooad020. eCollection 2023 Jul.

Authors

Erin Almklov^{1

2}, Alicia J Cohen^{3

4

5}, Lauren E Russell⁶, Maria K Mor^{7

8}, Michael J Fine^{7

9}, Leslie R M Hausmann^{7

9}, Ernest Moy⁶, Donna L Washington^{10

11}, Kenneth T Jones⁶, Judith A Long^{12

13}, James Pittman^{1

2

14}

Affiliations

¹ VA Center of Excellence for Stress and Mental Health, San Diego, California, USA.
² VA San Diego Healthcare System, San Diego, California, USA.
³ VA Center of Innovation in Long Term Services and Supports (LTSS-COIN), VA Providence Healthcare System, Providence, Rhode Island, USA.
⁴ Department of Family Medicine, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
⁵ Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA.
⁶ VHA Office of Health Equity, Washington, District of Columbia, USA.
⁷ VA Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA.
⁸ Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania, USA.
⁹ Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
¹⁰ VA HSR&D Center for the Study of Healthcare Innovation, Implementation and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA.
¹¹ Division of General Internal Medicine and Health Services Research, Department of Medicine, University of California, Los Angeles Geffen School of Medicine, Los Angeles, California, USA.
¹² Corporal Michael J. Crescenz VA Medical Center, VA Center for Health Equity Research and Promotion (CHERP), Philadelphia, Pennsylvania, USA.
¹³ Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
¹⁴ Department of Psychiatry, University of California San Diego, San Diego, California, USA.

Abstract

Objective: Evaluate self-reported electronic screening (eScreening) in a VA Transition Care Management Program (TCM) to improve the accuracy and completeness of administrative ethnicity and race data.

Materials and methods: We compared missing, declined, and complete (neither missing nor declined) rates between (1) TCM-eScreening (ethnicity and race entered into electronic tablet directly by patient using eScreening), (2) TCM-EHR (Veteran-completed paper form plus interview, data entered by staff), and (3) Standard-EHR (multiple processes, data entered by staff). The TCM-eScreening (n = 7113) and TCM-EHR groups (n = 7113) included post-9/11 Veterans. Standard-EHR Veterans included all non-TCM Gulf War and post-9/11 Veterans at VA San Diego (n = 92 921).

Results: Ethnicity: TCM-eScreening had lower rates of missingness than TCM-EHR and Standard-EHR (3.0% vs 5.3% and 8.6%, respectively, P < .05), but higher rates of "decline to answer" (7% vs 0.5% and 1.2%, P < .05). TCM-EHR had higher data completeness than TCM-eScreening and Standard-EHR (94.2% vs 90% and 90.2%, respectively, P < .05). Race: No differences between TCM-eScreening and TCM-EHR for missingness (3.5% vs 3.4%, P > .05) or data completeness (89.9% vs 91%, P > .05). Both had better data completeness than Standard-EHR (P < .05), which despite the lowest rate of "decline to answer" (3%) had the highest missingness (10.3%) and lowest overall completeness (86.6%). There was strong agreement between TCM-eScreening and TCM-EHR for ethnicity (Kappa = .92) and for Asian, Black, and White Veteran race (Kappas = .87 to .97), but lower agreement for American Indian/Alaska Native (Kappa = .59) and Native Hawaiian/Other Pacific Islander (Kappa = .50) Veterans.

Conculsions: eScreening is a promising method for improving ethnicity and race data accuracy and completeness in VA.

Keywords: Veterans; electronic screening; health equity; racial and ethnic disparities.

Published by Oxford University Press on behalf of the American Medical Informatics Association 2023.

Grants and funding

IK2 HX003013/HX/HSRD VA/United States