Using clinician text notes in electronic medical record data to validate transgender-related diagnosis codes
Transgender individuals are vulnerable to negative health risks and outcomes, but research remains limited because data sources, such as electronic medical records (EMRs), lack standardized collection of gender identity information. Most EMR do not include the gold standard of self-identified gender...
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| Published in | Journal of the American Medical Informatics Association : JAMIA Vol. 25; no. 7; pp. 905 - 908 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.07.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1067-5027 1527-974X 1527-974X |
| DOI | 10.1093/jamia/ocy022 |
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| Abstract | Transgender individuals are vulnerable to negative health risks and outcomes, but research remains limited because data sources, such as electronic medical records (EMRs), lack standardized collection of gender identity information. Most EMR do not include the gold standard of self-identified gender identity, but International Classification of Diseases (ICDs) includes diagnostic codes indicating transgender-related clinical services. However, it is unclear if these codes can indicate transgender status. The objective of this study was to determine the extent to which patients' clinician notes in EMR contained transgender-related terms that could corroborate ICD-coded transgender identity.
Data are from the US Department of Veterans Affairs Corporate Data Warehouse. Transgender patients were defined by the presence of ICD9 and ICD10 codes associated with transgender-related clinical services, and a 3:1 comparison group of nontransgender patients was drawn. Patients' clinician text notes were extracted and searched for transgender-related words and phrases.
Among 7560 patients defined as transgender based on ICD codes, the search algorithm identified 6753 (89.3%) with transgender-related terms. Among 22 072 patients defined as nontransgender without ICD codes, 246 (1.1%) had transgender-related terms; after review, 11 patients were identified as transgender, suggesting a 0.05% false negative rate.
Using ICD-defined transgender status can facilitate health services research when self-identified gender identity data are not available in EMR. |
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| AbstractList | Transgender individuals are vulnerable to negative health risks and outcomes, but research remains limited because data sources, such as electronic medical records (EMRs), lack standardized collection of gender identity information. Most EMR do not include the gold standard of self-identified gender identity, but International Classification of Diseases (ICDs) includes diagnostic codes indicating transgender-related clinical services. However, it is unclear if these codes can indicate transgender status. The objective of this study was to determine the extent to which patients' clinician notes in EMR contained transgender-related terms that could corroborate ICD-coded transgender identity.
Data are from the US Department of Veterans Affairs Corporate Data Warehouse. Transgender patients were defined by the presence of ICD9 and ICD10 codes associated with transgender-related clinical services, and a 3:1 comparison group of nontransgender patients was drawn. Patients' clinician text notes were extracted and searched for transgender-related words and phrases.
Among 7560 patients defined as transgender based on ICD codes, the search algorithm identified 6753 (89.3%) with transgender-related terms. Among 22 072 patients defined as nontransgender without ICD codes, 246 (1.1%) had transgender-related terms; after review, 11 patients were identified as transgender, suggesting a 0.05% false negative rate.
Using ICD-defined transgender status can facilitate health services research when self-identified gender identity data are not available in EMR. Transgender individuals are vulnerable to negative health risks and outcomes, but research remains limited because data sources, such as electronic medical records (EMRs), lack standardized collection of gender identity information. Most EMR do not include the gold standard of self-identified gender identity, but International Classification of Diseases (ICDs) includes diagnostic codes indicating transgender-related clinical services. However, it is unclear if these codes can indicate transgender status. The objective of this study was to determine the extent to which patients' clinician notes in EMR contained transgender-related terms that could corroborate ICD-coded transgender identity.ObjectiveTransgender individuals are vulnerable to negative health risks and outcomes, but research remains limited because data sources, such as electronic medical records (EMRs), lack standardized collection of gender identity information. Most EMR do not include the gold standard of self-identified gender identity, but International Classification of Diseases (ICDs) includes diagnostic codes indicating transgender-related clinical services. However, it is unclear if these codes can indicate transgender status. The objective of this study was to determine the extent to which patients' clinician notes in EMR contained transgender-related terms that could corroborate ICD-coded transgender identity.Data are from the US Department of Veterans Affairs Corporate Data Warehouse. Transgender patients were defined by the presence of ICD9 and ICD10 codes associated with transgender-related clinical services, and a 3:1 comparison group of nontransgender patients was drawn. Patients' clinician text notes were extracted and searched for transgender-related words and phrases.MethodsData are from the US Department of Veterans Affairs Corporate Data Warehouse. Transgender patients were defined by the presence of ICD9 and ICD10 codes associated with transgender-related clinical services, and a 3:1 comparison group of nontransgender patients was drawn. Patients' clinician text notes were extracted and searched for transgender-related words and phrases.Among 7560 patients defined as transgender based on ICD codes, the search algorithm identified 6753 (89.3%) with transgender-related terms. Among 22 072 patients defined as nontransgender without ICD codes, 246 (1.1%) had transgender-related terms; after review, 11 patients were identified as transgender, suggesting a 0.05% false negative rate.ResultsAmong 7560 patients defined as transgender based on ICD codes, the search algorithm identified 6753 (89.3%) with transgender-related terms. Among 22 072 patients defined as nontransgender without ICD codes, 246 (1.1%) had transgender-related terms; after review, 11 patients were identified as transgender, suggesting a 0.05% false negative rate.Using ICD-defined transgender status can facilitate health services research when self-identified gender identity data are not available in EMR.ConclusionsUsing ICD-defined transgender status can facilitate health services research when self-identified gender identity data are not available in EMR. |
| Author | Cashy, John Blosnich, John R Kauth, Michael R Shipherd, Jillian C Fine, Michael J Gordon, Adam J Brown, George R |
| AuthorAffiliation | 11 Mountain Home VA Medical Center, Mountain Home, TN, 37684, USA 4 LGBT Health Program, Office of Patient Care Services, Department of Veterans Affairs, Washington, DC, 20420, USA 8 Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA 6 Department of Psychiatry, Boston University, Boston, MA, 02118. USA 1 Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15240, USA 3 MIRECC, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15213, USA 9 Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Houston, TX, 77021, USA 2 Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA 5 VA Boston Healthcare System, National Center for PTSD, Women’s Health Sciences Division, Boston, MA, 02130, USA 7 South Central MIRECC, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA 10 Department of Psychiatry and Behavioral Sciences, Eas |
| AuthorAffiliation_xml | – name: 9 Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Houston, TX, 77021, USA – name: 1 Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15240, USA – name: 2 Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA – name: 7 South Central MIRECC, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA – name: 3 MIRECC, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15213, USA – name: 4 LGBT Health Program, Office of Patient Care Services, Department of Veterans Affairs, Washington, DC, 20420, USA – name: 5 VA Boston Healthcare System, National Center for PTSD, Women’s Health Sciences Division, Boston, MA, 02130, USA – name: 10 Department of Psychiatry and Behavioral Sciences, East Tennessee State University, Johnson, TN, 37604, USA – name: 6 Department of Psychiatry, Boston University, Boston, MA, 02118. USA – name: 8 Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA – name: 11 Mountain Home VA Medical Center, Mountain Home, TN, 37684, USA |
| Author_xml | – sequence: 1 givenname: John R surname: Blosnich fullname: Blosnich, John R organization: Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15240, USA – sequence: 2 givenname: John surname: Cashy fullname: Cashy, John organization: Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15240, USA – sequence: 3 givenname: Adam J surname: Gordon fullname: Gordon, Adam J organization: Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15240, USA, Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA, MIRECC, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15213, USA – sequence: 4 givenname: Jillian C surname: Shipherd fullname: Shipherd, Jillian C organization: LGBT Health Program, Office of Patient Care Services, Department of Veterans Affairs, Washington, DC, 20420, USA, VA Boston Healthcare System, National Center for PTSD, Women’s Health Sciences Division, Boston, MA, 02130, USA, Department of Psychiatry, Boston University, Boston, MA, 02118. USA – sequence: 5 givenname: Michael R surname: Kauth fullname: Kauth, Michael R organization: LGBT Health Program, Office of Patient Care Services, Department of Veterans Affairs, Washington, DC, 20420, USA, South Central MIRECC, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA, Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety, Houston, TX, 77021, USA – sequence: 6 givenname: George R surname: Brown fullname: Brown, George R organization: Department of Psychiatry and Behavioral Sciences, East Tennessee State University, Johnson, TN, 37604, USA, Mountain Home VA Medical Center, Mountain Home, TN, 37684, USA – sequence: 7 givenname: Michael J surname: Fine fullname: Fine, Michael J organization: Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, 15240, USA, Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29635362$$D View this record in MEDLINE/PubMed |
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| Title | Using clinician text notes in electronic medical record data to validate transgender-related diagnosis codes |
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