Identification of Transgender People With Cancer in Electronic Health Records: Recommendations Based on CancerLinQ Observations

2021 ◽  
Vol 17 (3) ◽  
pp. e336-e342
Author(s):  
Ash B. Alpert ◽  
George A. Komatsoulis ◽  
Stephen C. Meersman ◽  
Elizabeth Garrett-Mayer ◽  
Suanna S. Bruinooge ◽  
...  
Keyword(s):  
Gender Identity ◽  
Electronic Health Records ◽  
Total Population ◽  
Female Gender ◽  
The United States ◽  
Current Data ◽  
Health Records ◽  
Transgender People ◽  
Transgender Identity ◽  
Electronic Health

PURPOSE: Cancer prevalence and outcomes data, necessary to understand disparities in transgender populations, are significantly hampered because gender identity data are not routinely collected. A database of clinical data on people with cancer, CancerLinQ, is operated by the ASCO and collected from practices across the United States and multiple electronic health records. METHODS: To attempt to identify transgender people with cancer within CancerLinQ, we used three criteria: (1) International Classification of Diseases 9/10 diagnosis (Dx) code suggestive of transgender identity; (2) male gender and Dx of cervical, endometrial, ovarian, fallopian tube, or other related cancer; and (3) female gender and Dx of prostate, testicular, penile, or other related cancer. Charts were abstracted to confirm transgender identity. RESULTS: Five hundred fifty-seven cases matched inclusion criteria and two hundred and forty-two were abstracted. Seventy-six percent of patients with Dx codes suggestive of transgender identity were transgender. Only 2% and 3% of the people identified by criteria 2 and 3 had evidence of transgender identity, respectively. Extrapolating to nonabstracted data, we would expect to identify an additional four individuals in category 2 and an additional three individuals in category 3, or a total of 44. The total population in CancerLinQ is approximately 1,300,000. Thus, our methods could identify 0.003% of the total population as transgender. CONCLUSION: Given the need for data regarding transgender people with cancer and the deficiencies of current data resources, a national concerted effort is needed to prospectively collect gender identity data. These efforts will require systemic efforts to create safe healthcare environments for transgender people.

scholarly journals Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?

2021 ◽  
Vol 18 (12) ◽  
pp. 6599
Author(s):  
Hale M. Thompson ◽  
Clair A. Kronk ◽  
Ketzel Feasley ◽  
Paul Pachwicewicz ◽  
Niranjan S. Karnik
Keyword(s):  
Data Collection ◽  
Gender Identity ◽  
Electronic Health Records ◽  
Data Quality ◽  
Hospital Admissions ◽  
The United States ◽  
Patient Comfort ◽  
United States Department ◽  
Health Records ◽  
Electronic Health

In 2015, the United States Department of Health and Human Services instantiated rules mandating the inclusion of sexual orientation and gender identity (SO/GI) data fields for systems certified under Stage 3 of the Meaningful Use of Electronic Health Records (EHR) program. To date, no published assessments have benchmarked implementation penetration and data quality. To establish a benchmark for a U.S. health system collection of gender identity and sex assigned at birth, we analyzed one urban academic health center’s EHR data; specifically, the records of patients with unplanned hospital admissions during 2020 (N = 49,314). Approximately one-quarter of patient records included gender identity data, and one percent of them indicated a transgender or nonbinary (TGNB) status. Data quality checks suggested limited provider literacy around gender identity as well as limited provider and patient comfort levels with gender identity disclosures. Improvements are needed in both provider and patient literacy and comfort around gender identity in clinical settings. To include TGNB populations in informatics-based research, additional novel approaches, such as natural language processing, may be needed for more comprehensive and representative TGNB cohort discovery. Community and stakeholder engagement around gender identity data collection and health research will likely improve these implementation efforts.

scholarly journals 1665. Using Electronic Health Records to Describe TB in Community Health Settings: a Cohort Analysis in a Large Safety-Net Population

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  
Keyword(s):  
United States ◽  
Risk Factors ◽  
Electronic Health Records ◽  
Safety Net ◽  
The United States ◽  
Research Network ◽  
Health Records ◽  
Treatment Regimens ◽  
Data Source ◽  
Electronic Health

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures

scholarly journals Adoption of Electronic Health Records and Perceptions of Financial and Clinical Outcomes Among Ophthalmologists in the United States

2018 ◽  
Vol 136 (2) ◽  
pp. 164 ◽  
Author(s):  
Michele C. Lim ◽  
Michael V. Boland ◽  
Colin A. McCannel ◽  
Arvind Saini ◽  
Michael F. Chiang ◽  
...  
Keyword(s):  
United States ◽  
Electronic Health Records ◽  
Clinical Outcomes ◽  
The United States ◽  
Health Records ◽  
Electronic Health

A Framework for Systematic Assessment of Clinical Trial Population Representativeness Using Electronic Health Records Data

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
The United States ◽  
Design Stage ◽  
Common Data Model ◽  
Free Text ◽  
Eligibility Criteria ◽  
Health Records ◽  
Electronic Health

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.

scholarly journals Impact of Obesity on Outcomes of Patients With Coronavirus Disease 2019 in the United States: A Multicenter Electronic Health Records Network Study

2020 ◽  
Vol 159 (6) ◽  
pp. 2221-2225.e6 ◽  
Author(s):  
Shailendra Singh ◽  
Mohammad Bilal ◽  
Haig Pakhchanian ◽  
Rahul Raiker ◽  
Gursimran S. Kochhar ◽  
...  
Keyword(s):  
United States ◽  
Electronic Health Records ◽  
The United States ◽  
Health Records ◽  
Electronic Health

scholarly journals Understanding optimisation processes of electronic health records (EHRs) in select leading hospitals: a qualitative study

2018 ◽  
Vol 25 (2) ◽  
pp. 109-125 ◽  
Author(s):  
Mark Chun Moon ◽  
Rebecca Hills ◽  
George Demiris
Keyword(s):  
United States ◽  
Qualitative Study ◽  
Electronic Health Records ◽  
Hospital Setting ◽  
The United States ◽  
Theory Approach ◽  
Health Records ◽  
High Performing ◽  
Advisory Groups ◽  
Electronic Health

BackgroundLittle is known about optimisation of electronic health records (EHRs) systems in the hospital setting while adoption of EHR systems continues in the United States.ObjectiveTo understand optimisation processes of EHR systems undertaken in leading healthcare organisations in the United States.MethodsInformed by a grounded theory approach, a qualitative study was undertaken that involved 11 in-depth interviews and a focus group with the EHR experts from the high performing healthcare organisations across the United States.ResultsThe study describes EHR optimisation processes characterised by prioritising exponentially increasing requests with predominant focus on improving efficiency of EHR, building optimisation teams or advisory groups and standardisation. The study discusses 16 types of optimisation that interdependently produced 16 results along with identifying 11 barriers and 20 facilitators to optimisation.ConclusionsThe study describes overall experiences of optimising EHRs in select high performing healthcare organisations in the US. The findings highlight the importance of optimising the EHR after, and even before, go-live and dedicating resources exclusively for optimisation.

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