Assessing an electronic self-report method for improving quality of ethnicity and race data in the Veterans Health Administration
| Autor: | 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 |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | JAMIA open, vol 6, iss 2 JAMIA Open |
| ISSN: | 2574-2531 |
| Popis: | 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) or data completeness (89.9% vs 91%, P > .05). Both had better data completeness than Standard-EHR (P Conculsions eScreening is a promising method for improving ethnicity and race data accuracy and completeness in VA. |
| Databáze: | OpenAIRE |
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