Terri Elizabeth Workman

Introduction: Problematic gambling is an increasing public health concern that also affects U.S. Veterans. Problematic gambling in Veterans correlates with several other factors, including socio-economic issues like homelessness and legal problems, and mental health issues like post-traumatic stress disorder, and suicide attempt. Our objective was to analyze differences between U.S. Veterans experiencing problematic gambling compared to similar U.S. Veterans who did not, according to diagnostic codes. Materials and Methods: Electronic health records from the U.S. Department of Veterans Affairs (VA) for health care encounters between 2016-01-01 and 2024-12-31 served as data for this study. We performed a one-to-one match on age, race, ethnicity, gender, and VA station for 775 Veterans diagnosed with problematic gambling by International Classification of Diseases 10 ^th edition codes, to Veterans not receiving one of the diagnostic codes relevant to gambling. We then compared comorbidity diagnoses, marital status, and VA care encounters, applying chi-squared and t-test analyses. Results: There were many statistically significant differences in care use, marital status, and comorbidities. Veterans with problematic gambling were more likely to experience other substance abuse, including opioid use disorder (22.5% vs 8.4%) and alcohol use disorder (56.3% vs 25.9%). Veterans with problematic gambling were also more likely to experience mental illness, including major depressive disorder (64.9% vs 46.7%), post-traumatic stress disorder (50.8% vs 43.1%), and suicide attempt (5.3% vs 3.0%), and socio-economic issues, including economic hardship (28.7% vs 16.3%), homelessness (33.9% vs 14.8%), legal problems (13.4% vs 6.8%) and social/family problems (53.6% vs 46.5%). Veterans experiencing problematic gambling also were less likely to be married and received more VA outpatient care. Conclusions: The findings suggest that problematic gambling in Veterans correlates with worse outcomes when comparing similar Veterans receiving care through the same facilities. It is feasible to screen for problematic gambling in primary care encounters, however the extent of screening in VA care is unknown. Screening for problematic gambling in VA primary care may enable providers to assist patients experiencing problematic gambling to find directed care.

Objectives: This study aims to analyze differences between lesbian, gay, bisexual, transgender, and queer (LGBTQ+) and non-LGBTQ+ Veterans with post-traumatic stress disorder (PTSD) in terms of demographics, comorbidities, and medical care usage, including differences by sex of record, including separate analyses for transgender and non-transgender Veterans.Methods: Chi-square, t-test, ANOVA Welch one-way testing, and absolute standardized difference analyses were conducted on a cohort of 277,539 Veterans diagnosed with PTSD.Results: The study found significant differences, particularly concerning positive LGBTQ+ status and sex of record. There were significant differences found in age, marital status, and medical care usage, as well as pain, mental health, and substance use disorder diagnoses. Differences in having experienced military sexual trauma, crime, or maltreatment were especially significant, with increased percentages among LGBTQ+ individuals, and sex of record females. In separate analyses, there were similar differences among transgender and non-transgender Veterans, with similar increased risks for sex of record females.Conclusion: Our findings suggest an intersectionality of LGBTQ+ status and sex of record in the context of PTSD. These findings may help guide future research, policy, and interventions.

Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes, which are included in electronic health records. We sought to identify problematic opioid use from a full range of clinical notes and compare the demographic and clinical characteristics of patients identified as having problematic opioid use exclusively in clinical notes to patients documented through ICD opioid use disorder diagnostic codes. We developed and applied a natural language processing (NLP) tool that combines rule-based pattern analysis and a trained support vector machine to the clinical notes of a patient cohort (n = 222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. The NLP tool achieved 96.6% specificity, 90.4% precision/PPV, 88.4% sensitivity/recall, and 94.4% accuracy on unseen test data. NLP exclusively identified 57,331 patients; 6997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, and more care encounters, and were less likely to be married. Patients in both these groups had substantially elevated comorbidity levels compared with patients not documented through either method as experiencing problematic opioid use. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.

Background: Electronic health records (EHRs) are a data source for opioid research. Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes. Objectives: Our goals were 1) to identify problematic opioid use from a full range of clinical notes; and 2) to compare the characteristics of patients identified as having problematic opioid use, exclusively documented in clinical notes, to those having documented ICD opioid use disorder diagnostic codes. Materials and Methods: We developed and applied a natural language processing (NLP) tool to the clinical notes of a patient cohort (n=222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. We compared the demographic and clinical characteristics of patients identified only through NLP, to those of patients identified through ICD codes. Results: NLP exclusively identified 57,331 patients; 6,997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, more care encounters, and less likely to be married. Patients in the NLP and ICD groups had substantially elevated comorbidity levels compared to patients not documented as experiencing problematic opioid use. Conclusions: NLP is a feasible approach for identifying problematic opioid use not otherwise recorded by ICD codes. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.

Background: Pain assessment is performed in many healthcare systems, such as the Veterans Health Administration, but prior studies have not assessed whether pain screening varies in sexual and gender minority populations that include individuals who identify as lesbian, gay, bisexual, and/or transgender (LGBT). Objective: The purpose of this study was to evaluate pain screening and reported pain of LGBT Veterans compared to non-LGBT Veterans. Methods: Using a retrospective cross-sectional cohort, data from the Corporate Data Warehouse, a national repository with clinical/administrative data, were analyzed. Veterans were classified as LGBT using natural language processing. We used a robust Poisson model to examine the association between LGBT status and binary outcomes of pain screening, any pain, and persistent pain within one year of entry in the cohort. All models were adjusted for demographics, mental health, substance use, musculoskeletal disorder(s), and number of clinic visits. Results: There were 1,149,486 Veterans (218,154 (19%) classified as LGBT) in our study. Among LGBT Veterans, 94% were screened for pain compared to 89% among those not classified as LGBT (non-LGBT) Veterans. In adjusted models, LGBT Veterans’ probability of being screened for pain compared to non-LGBT Veterans was 2.5% higher (95% CI 2.3%, 2.6%); risk of any pain was 2.1% lower (95% CI 1.6%, 2.6%); and there was no significant difference between LGBT and non-LGBT Veterans in persistent pain (RR = 1.00, 95% CI (0.99, 1.01), p = 0.88). Conclusions: In a nationwide sample, LGBT Veterans were more likely to be screened for pain but had lower self-reported pain scores, though adjusted differences were small. It was notable that transgender and Black Veterans reported the greatest pain. Reasons for these findings require further investigation.

Background and Aims: In deep learning, a major difficulty in identifying suicidality and its risk factors in clinical notes is the lack of training samples given the small number of true positive instances among the number of patients screened. This paper describes a novel methodology that identifies suicidality in clinical notes by addressing this data sparsity issue through zero‐shot learning. Our general aim was to develop a tool that leveraged zero‐shot learning to effectively identify suicidality documentation in all types of clinical notes.Methods: US Veterans Affairs clinical notes served as data. The training data set label was determined using diagnostic codes of suicide attempt and self‐harm. We used a base string associated with the target label of suicidality to provide auxiliary information by narrowing the positive training cases to those containing the base string. We trained a deep neural network by mapping the training documents’ contents to a semantic space. For comparison, we trained another deep neural network using the identical training data set labels, and bag‐of‐words features.Results: The zero‐shot learning model outperformed the baseline model in terms of area under the curve, sensitivity, specificity, and positive predictive value at multiple probability thresholds. In applying a 0.90 probability threshold, the methodology identified notes documenting suicidality but not associated with a relevant ICD‐10‐CM code, with 94% accuracy.Conclusion: This method can effectively identify suicidality without manual annotation.

There is widespread use of dietary supplements, some prescribed but many taken without a physician’s guidance. There are many potential interactions between supplements and both over-the-counter and prescription medications in ways that are unknown to patients. Structured medical records do not adequately document supplement use; however, unstructured clinical notes often contain extra information on supplements. We studied a group of 377 patients from three healthcare facilities and developed a natural language processing (NLP) tool to detect supplement use. Using surveys of these patients, we investigated the correlation between self-reported supplement use and NLP extractions from the clinical notes. Our model achieved an F1 score of 0.914 for detecting all supplements. Individual supplement detection had a variable correlation with survey responses, ranging from an F1 of 0.83 for calcium to an F1 of 0.39 for folic acid. Our study demonstrated good NLP performance while also finding that self-reported supplement use is not always consistent with the documented use in clinical records.

Background: Individuals released from carceral facilities have high rates of hospitalization and death, especially in the weeks immediately after their return to community settings. During this transitional process, individuals leaving incarceration are expected to engage with multiple providers working in separate, complex systems, including health care clinics, social service agencies, community-based organizations, and probation and parole services. This navigation is often complicated by individuals’ physical and mental health, literacy and fluency, and socioeconomic status. Personal health information technology, which can help people access and organize their health information, could improve the transition from carceral systems to the community and mitigate health risks upon release. Yet, personal health information technologies have not been designed to meet the needs and preferences of this population nor tested for acceptability or use. Objective: The objective of our study is to develop a mobile app to create personal health libraries for individuals returning from incarceration to help bridge the transition from carceral settings to community living. Methods: Participants were recruited through Transitions Clinic Network clinic encounters and professional networking with justice-involved organizations. We used qualitative research methods to assess the facilitators and barriers to developing and using personal health information technology for individuals returning from incarceration. We conducted individual interviews with people just released from carceral facilities (n=~20) and providers (n=~10) from the local community and carceral facilities involved with the transition for returning community members. We used rigorous rapid qualitative analysis to generate thematic output characterizing the unique circumstances impacting the development and use of personal health information technology for individuals returning from incarceration and to identify content and features for the mobile app based on the preferences and needs of our participants. Results: As of February 2023, we have completed 27 qualitative interviews with individuals recently released from carceral systems (n=20) and stakeholders (n=7) who support justice-involved individuals from various organizations in the community. Conclusions: We anticipate that the study will characterize the experiences of people transitioning from prison and jails to community settings; describe the information, technology resources, and needs upon reentry to the community; and create potential pathways for fostering engagement with personal health information technology. International Registered Report Identifier (IRRID): DERR1-10.2196/44748

Objective: Disclosure of sexual orientation and gender identity correlates with better outcomes, yet data may not be available in structured fields in electronic health record data. To gain greater insight into the care of sexual and gender-diverse patients in the Veterans Health Administration (VHA), we examined the documentation patterns of sexual orientation and gender identity through extraction and analyses of data contained in unstructured electronic health record clinical notes. Methods: Salient terms were identified through authoritative vocabularies, the research team’s expertise, and frequencies, and the use of consistency in VHA clinical notes. Term frequencies were extracted from VHA clinical notes recorded from 2000 to 2018. Temporal analyses assessed usage changes in normalized frequencies as compared with nonclinical use, relative growth rates, and geographic variations. Results: Over time most terms increased in use, similar to Google ngram data, especially after the repeal of the “Don’t Ask Don’t Tell” military policy in 2010. For most terms, the usage adoption consistency also increased by the study’s end. Aggregated use of all terms increased throughout the United States. Conclusion: Term usage trends may provide a view of evolving care in a temporal continuum of changing policy. These findings may be useful for policies and interventions geared toward sexual and gender-diverse individuals. Despite the lack of structured data, the documentation of sexual orientation and gender identity terms is increasing in clinical notes.