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Evaluation of the United States Centers for Disease Control and Prevention (CDC)-defined HCV-related risk factors are not consistently performed as part of routine care, rendering risk-based testing susceptible to clinician bias and missed diagnoses. This work uses natural language processing (NLP) and machine learning to identify patients who are at high risk for HCV infection. Models were developed and validated to predict patients with newly identified HCV infection (detectable RNA or reported HCV diagnosis). We evaluated models with three types of variables: structured (structured-based model), semi-structured and free-text notes (text-based model), and all variables (full-set model). We applied each model to three stratifications of data: patients with no history of HCV prior to 2020, patients with a history of HCV prior to 2020, and all patients. We used XGBoost and ten-fold C-statistic cross-validation to evaluate the generalizability of the models. There were 3564 unique patients, 487 with HCV infection. The average C-statistics on the structured-based, text-based, and full-set models for all the patients were 0.777 (95% CI: 0.744–0.810), 0.677 (95% CI: 0.631–0.723), and 0.774 (95% CI: 0.735–0.813), respectively. The full-set model performed slightly better than the structured-based model and similar to text-based models for patients with no history of HCV prior to 2020; average C-statistics of 0.780, 0.774, and 0.759, respectively. NLP was able to identify six more risk factors inconsistently coded in structured elements: incarceration, needlestick, substance use or abuse, sexually transmitted infections, piercings, and tattoos. The availability of model options (structured-based or text-based models) with a similar performance can provide deployment flexibility in situations where data is limited.

Background & objectives Screening for hepatitis C virus is the first critical decision point for preventing morbidity and mortality from HCV cirrhosis and hepatocellular carcinoma and will ultimately contribute to global elimination of a curable disease. This study aims to portray the changes over time in HCV screening rates and the screened population characteristics following the 2020 implementation of an electronic health record (EHR) alert for universal screening in the outpatient setting in a large healthcare system in the US mid-Atlantic region. Methods Data was abstracted from the EHR on all outpatients from 1/1/2017 through 10/31/2021, including individual demographics and their HCV antibody (Ab) screening dates. For a limited period centered on the implementation of the HCV alert, mixed effects multivariable regression analyses were performed to compare the timeline and characteristics of those screened and un-screened. The final models included socio-demographic covariates of interest, time period (pre/post) and an interaction term between time period and sex. We also examined a model with time as a monthly variable to look at the potential impact of COVID-19 on screening for HCV. Results Absolute number of screens and screening rate increased by 103% and 62%, respectively, after adopting the universal EHR alert. Patients with Medicaid were more likely to be screened than private insurance (ORadj 1.10, 95% CI: 1.05, 1.15), while those with Medicare were less likely (ORadj 0.62, 95% CI: 0.62, 0.65); and Black (ORadj 1.59, 95% CI: 1.53, 1.64) race more than White. Conclusions Implementation of universal EHR alerts could prove to be a critical next step in HCV elimination. Those with Medicare and Medicaid insurance were not screened proportionately to the national prevalence of HCV in these populations. Our findings support increased screening and re-testing efforts for those at high risk of HCV.

Background & objectivesScreening for hepatitis C virus is the first critical decision point for preventing morbidity and mortality from HCV cirrhosis and hepatocellular carcinoma and will ultimately contribute to global elimination of a curable disease. This study aims to portray the changes over time in HCV screening rates and the screened population characteristics following the 2020 implementation of an electronic health record (EHR) alert for universal screening in the outpatient setting in a large healthcare system in the US mid-Atlantic region.MethodsData was abstracted from the EHR on all outpatients from 1/1/2017 through 10/31/2021, including individual demographics and their HCV antibody (Ab) screening dates. For a limited period centered on the implementation of the HCV alert, mixed effects multivariable regression analyses were performed to compare the timeline and characteristics of those screened and un-screened. The final models included socio-demographic covariates of interest, time period (pre/post) and an interaction term between time period and sex. We also examined a model with time as a monthly variable to look at the potential impact of COVID-19 on screening for HCV.ResultsAbsolute number of screens and screening rate increased by 103% and 62%, respectively, after adopting the universal EHR alert. Patients with Medicaid were more likely to be screened than private insurance (ORadj 1.10, 95% CI: 1.05, 1.15), while those with Medicare were less likely (ORadj 0.62, 95% CI: 0.62, 0.65); and Black (ORadj 1.59, 95% CI: 1.53, 1.64) race more than White.ConclusionsImplementation of universal EHR alerts could prove to be a critical next step in HCV elimination. Those with Medicare and Medicaid insurance were not screened proportionately to the national prevalence of HCV in these populations. Our findings support increased screening and re-testing efforts for those at high risk of HCV.

Background The opioid epidemic across the U.S. poses an array of public health concerns, especially HCV transmission. HCV is now widely curable, yet incident rates are increasing due to the opioid epidemic. Despite the established trajectory from oral prescription opioids (OPOs) to opioid use disorder (OUD), OUD to injection drug use (IDU), and IDU to hepatitis C virus (HCV), OPOs are not a defined risk factor (RF) for HCV infection. The objective of this study was to observe rates of HCV testing and Ab reactivity (HCVAb+) in patients receiving OPOs to substantiate them as a RF, ultimately contributing to HCV elimination. Methods Data from MedStar Health patients receiving OPOs from 1/2017 to 12/2018 were collected and analyzed using chi-squared or student t -tests and logistic regression for uni- or multi-variable analyses, respectively. Statistical significance was defined as P

Background Coronavirus infection (COVID) presents with flu-like symptoms and can cause serious complications. Here, we discuss the presentation and outcomes of COVID in an ambulatory setting along with distribution of positive cases amongst healthcare workers (HCWs). Method Patients who visited the COVID clinic between 03/11/2020 and 06/14/2020 were tested based on the CDC guidelines at the time using PCR-detection methods. Medical records were reviewed and captured on a RedCap database. Statistical analysis was performed using both univariate and bivariate analysis using Fischer’s exact test with 2-sided P values. Results Of the 2471 evaluated patients, 846 (34.2%) tested positive for COVID. Mean age of positivity was 43.4 years (SD ± 15.4), 60.1% were female and 49% were Black. 58.7% of people tested had a known exposure, and amongst those with exposure, 57.3% tested positive. Ninety-four patients were hospitalized (11.1%), of which 22 patients (23.4%) required ICU admission and 10 patients died. The overall death rate of patients presenting to clinic was 0.4%, or 1.2% amongst positive patients. Median length of hospital stay was 6 days (range 1-51). Symptoms significantly associated with COVID included: anosmia, fever, change in taste, anorexia, myalgias, cough, chills, and fatigue. Increased risk of COVID occurred with diabetes, whereas individuals with lung disease or malignancy were not associated with increased risk of COVID. Amongst COVID positive HCWs, the majority were registered nurses (23.4%), most working in general medicine (39.8%) followed by critical care units (14.3%). Discussion/Conclusion Blacks and females had the highest infection rates. There was a broad range in presentation from those who are very ill and require hospitalization and those who remain ambulatory. The above data could assist health care professionals perform a targeted review of systems and co-morbidities, allowing for appropriate patient triage.