The Value of an Emergency Medicine Virtual Observation Unit.

STUDY OBJECTIVE: We implemented a virtual observation unit in which emergency department (ED) patients receive observation-level care at home. Our primary aim was to compare this new care model to in-person observation care in terms of brick-and-mortar ED length of stay (inclusive of ED observation unit time) as well as secondarily on inpatient admission and 72-hour return visits (overall and with admission). METHODS: In a retrospective analysis of electronic health record data on ED observation patients from January 1, 2022 to December 29, 2022 from an academic urban ED, we used propensity matching to compare virtual to in-person observation patients on outcomes of interest. Patients were matched 1:1 based on age, sex, Charlson Comorbidity Index, and reason for observation. We also conducted real-time review of all virtual observation cases for potential safety concerns. RESULTS: Of 8,218 observation stays, 361 virtual observation patients were matched with 361 in-person observation patients. Virtual observation patients experienced lower median brick-and-mortar ED + EDOU LOS [14.6 (IQR 10.2, 18.9) versus 33.3 (IQR 28.1, 38.1) hours] and lower inpatient admission rates (10.2% [SD 5.0] versus 24.7% [SD 11.3]). The 72-hour return rate was higher for virtual observation patients (3.6% [SD 3.0] versus 2.5% [SD 3.0]). Among those with return visits, the rate of inpatient admission was higher among virtual observation patients (53.8% [SD 3.2] versus 11.1% [13.0]). There were no significant patient safety events recorded. CONCLUSION: Virtual observation unit patients used fewer hours in ED and ED observation relative to on-site observation patients. This new care delivery model warrants further study because it has the potential to positively impact ED capacity.

Development of a predictive model for identifying women vulnerable to HIV in Chicago.

INTRODUCTION: Researchers in the United States have created several models to predict persons most at risk for HIV. Many of these predictive models use data from all persons newly diagnosed with HIV, the majority of whom are men, and specifically men who have sex with men (MSM). Consequently, risk factors identified by these models are biased toward features that apply only to men or capture sexual behaviours of MSM. We sought to create a predictive model for women using cohort data from two major hospitals in Chicago with large opt-out HIV screening programs. METHODS: We matched 48 newly diagnosed women to 192 HIV-negative women based on number of previous encounters at University of Chicago or Rush University hospitals. We examined data for each woman for the two years prior to either their HIV diagnosis or their last encounter. We assessed risk factors including demographic characteristics and clinical diagnoses taken from patient electronic medical records (EMR) using odds ratios and 95% confidence intervals. We created a multivariable logistic regression model and measured predictive power with the area under the curve (AUC). In the multivariable model, age group, race, and ethnicity were included a priori due to increased risk for HIV among specific demographic groups. RESULTS: The following clinical diagnoses were significant at the bivariate level and were included in the model: pregnancy (OR 1.96 (1.00, 3.84)), hepatitis C (OR 5.73 (1.24, 26.51)), substance use (OR 3.12 (1.12, 8.65)) and sexually transmitted infections (STIs) chlamydia, gonorrhoea, or syphilis. We also a priori included demographic factors that are associated with HIV. Our final model had an AUC of 0.74 and included healthcare site, age group, race, ethnicity, pregnancy, hepatitis C, substance use, and STI diagnosis. CONCLUSIONS: Our predictive model showed acceptable discrimination between those who were and were not newly diagnosed with HIV. We identified risk factors such as recent pregnancy, recent hepatitis C diagnosis, and substance use in addition to the traditionally used recent STI diagnosis that can be incorporated by health systems to detect women who are vulnerable to HIV and would benefit from preexposure prophylaxis (PrEP).

Evolution of an Electronic Health Record Based-Human Immunodeficiency Virus (HIV) Screening Program in an Urban Emergency Department for Diagnosing Acute and Chronic HIV Infection.

BACKGROUND: Since 2006, Centers for Disease Control and Prevention guidelines recommend routine opt-out human immunodeficiency virus (HIV) testing among sexually active 13- to 64-year-olds. Earlier diagnosis and treatment of HIV infection reduces morbidity and mortality and can limit transmission to others. OBJECTIVE: Our aim was to increase HIV testing, diagnosis, and linkage to care in the emergency department (ED). METHODS: Beginning May 4, 2015, we utilized our electronic health record (EHR) to enhance HIV testing in patients seen in the Rush University Medical Center emergency department in Chicago, IL, who were 13-64 years of age, did not have HIV listed on their problem list, and did not have an HIV antigen/antibody (Ag/Ab) test in the EHR within the past rolling 12-month period. Strategies included use of a "Best Practice Advisory" and later auto-order screening linked to a complete blood count order. RESULTS: Our baseline HIV test rate was 2.5% of the target population by age (average of 93 tests per month). From May 4, 2015 to January 31, 2019, 137,749 patients of 240,091 ED visits met our test criteria and 23,588 (17.1% of the target population) HIV Ag/Ab tests were performed, resulting in 164 positive tests. We identified 18 acute seroconverters, 51 new chronically infected persons, and 95 known infected, many of who had not disclosed their status. Our positive test rate was 0.70%, which dropped to 0.29% if only newly diagnosed individuals were counted. CONCLUSIONS: EHR enhancements in a large urban ED identifies both newly diagnosed acute and chronically HIV-infected persons. Identification of previously diagnosed patients offers an opportunity to relink them to care.

Assessing Patient Acceptance of an Automated Algorithm to Identify Ciswomen for HIV Pre-Exposure Prophylaxis.

The use of HIV pre-exposure prophylaxis (PrEP) in cisgender women (ciswomen) lags far behind their need. Data elements from the electronic medical record (EMR), including diagnosis of a sexually transmitted infection (STI), can be incorporated into automated algorithms for identifying clients who are most vulnerable to HIV and would benefit from PrEP. However, it is unknown how women feel about the use of such technology. In this study, we assessed women's attitudes and opinions about an automated EMR-based HIV risk algorithm and determined if their perspectives varied by level of HIV risk. Respondents were identified using best practice alerts or referral to a clinic for STI symptoms from January to December 2021 in Chicago, IL. Participants were asked about HIV risk factors, their self-perceived HIV risk, and their thoughts regarding an algorithm to identify ciswomen who could benefit from PrEP. Most of the 112 women who completed the survey (85%) thought they were at low risk for HIV, despite high rates of STI diagnoses. The majority were comfortable with the use of this algorithm, but their comfort level dropped when asked about the algorithm identifying them specifically. Ciswomen had mixed feelings about the use of an automated HIV risk algorithm, citing it as a potentially helpful and empowering tool for women, yet raising concerns about invasion of privacy and potential racial bias. Clinics must balance the benefits of using an EMR-based algorithm for ciswomen with their concerns about privacy and bias to improve PrEP uptake among particularly vulnerable women.

Concurrent Testing for COVID-19 and HIV Infection at 6 High-Volume Emergency Departments in a Priority Jurisdiction for Ending the HIV Epidemic in the United States.

BACKGROUND: The COVID-19 pandemic caused disruptions in access to routine HIV screening. SETTING: We assess HIV and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing across 6 emergency departments (EDs) in Cook County, Illinois. METHODS: We retrospectively analyzed the number of SARS-CoV-2 tests, HIV screens, and the proportion of concurrent tests (encounters with both SARS-CoV-2 and HIV testing), correlating with diagnoses of new and acute HIV infection. RESULTS: Five sites reported data from March 1, 2020, to February 28, 2021, and 1 site from September 1, 2020, to February 28, 2021. A total of 1,13,645 SARS-CoV-2 and 36,094 HIV tests were performed; 17,469 of these were concurrent tests. There were 102 new HIV diagnoses, including 25 acute infections. Concurrent testing proportions ranged from 6.7% to 37% across sites (P < 0.001). HIV testing volume correlated with the number of new diagnoses (r = 0.66, P < 0.01). HIV testing with symptomatic SARS-CoV-2 testing was strongly correlated with diagnosis of acute infections (r = 0.87, P < 0.001); this was not statistically significant when controlling for HIV testing volumes (r = 0.59, P = 0.056). Acute patients were more likely to undergo concurrent testing (21/25) versus other new diagnoses (29/77; odds ratio = 8.69, 95% CI: 2.7 to 27.8, P < 0.001). CONCLUSIONS: Incorporating HIV screening into SARS-CoV-2 testing in the ED can help maintain HIV screening volumes. Although all patients presenting to the ED should be offered opt-out HIV screening, testing individuals with symptoms of COVID-19 or other viral illness affords the opportunity to diagnose symptomatic acute and early HIV infection, rapidly link to care, and initiate treatment.

COVID-19 Outcomes in Hospitalized Patients With Neurodegenerative Disease: A Retrospective Cohort Study.

Background and Objectives: COVID-19 outcomes in patients with neurodegenerative disorders (NDs) are not well understood, and we hypothesize that there may be increased morbidity and mortality in this group. Methods: This was a retrospective cohort study performed at 3 hospitals in the Chicagoland area. All patients hospitalized with COVID-19 infection with ND during a 3-month period (March 15, 2020-June 15, 2020) were included and compared with age-matched controls (CL) at 1:1 ratio. Primary outcomes were death, intensive care unit (ICU) admission, and invasive ventilation. Secondary outcomes included presenting COVID-19 symptoms, development of encephalopathy, supplementary oxygen use, discharge disposition, and risk factors for mortality. Results: The study included 132 patients with neurodegenerative disorders and 132 age-matched CL. Ninety-day mortality (ND 19.7% vs CL 23.5%, p = 0.45) and ICU admission (ND 31.5% vs CL 35.9%, p = 0.43) rates were not significantly different between the 2 groups. Patients with ND had a lower rate of invasive ventilation (ND 11.4% vs CL 23.2%, p = 0.0075) and supplementary oxygen use (ND 83.2% vs CL 95.1%, p = 0.0012). Patients with ND were also more likely to have altered mental status or confusion as their presenting COVID-19 symptom and less likely to present with respiratory symptoms. Patients with ND were discharged to nursing home or hospice at higher rates compared with CL. Discussion: We found that there was no difference in short-term mortality of patients with ND hospitalized for COVID-19 compared with CL, but they may have higher rates of neurologic complications and disability. Future studies should address long-term outcomes.

The Association of an Alpha-2 Adrenergic Receptor Agonist and Mortality in Patients With COVID-19.

There is a need for treatments to reduce coronavirus disease 2019 (COVID-19) mortality. Alpha-2 adrenergic receptor (α2 AR) agonists can dampen immune cell and inflammatory responses as well as improve oxygenation through physiologic respiratory parameters. Therefore, α2 AR agonists may be effective in reducing mortality related to hyperinflammation and acute respiratory failure in COVID-19. Dexmedetomidine (DEX) is an α2 AR agonist used for sedation. We performed a retrospective analysis of adults at Rush University System for Health hospitals between March 1, 2020 and July 30, 2020 with COVID-19 requiring invasive mechanical ventilation and sedation (n = 214). We evaluated the association of DEX use and 28-day mortality from time of intubation. Overall, 28-day mortality in the cohort receiving DEX was 27.0% as compared to 64.5% in the cohort that did not receive DEX (relative risk reduction 58.2%; 95% CI 42.4-69.6). Use of DEX was associated with reduced 28-day mortality on multivariable Cox regression analysis (aHR 0.19; 95% CI 0.10-0.33; p < 0.001). Adjusting for time-varying exposure to DEX also demonstrated that DEX was associated with reduced 28-day mortality (aHR 0.51; 95% CI 0.28-0.95; p = 0.03). Earlier DEX use, initiated <3.4 days from intubation, was associated with reduced 28-day mortality (aHR 0.25; 95% CI 0.13-0.50; p < 0.001) while later DEX use was not (aHR 0.64; 95% CI 0.27-1.50; p = 0.30). These results suggest an α2 AR agonist might reduce mortality in patients with COVID-19. Randomized controlled trials are needed to confirm this observation.

External validation of an opioid misuse machine learning classifier in hospitalized adult patients.

BACKGROUND: Opioid misuse screening in hospitals is resource-intensive and rarely done. Many hospitalized patients are never offered opioid treatment. An automated approach leveraging routinely captured electronic health record (EHR) data may be easier for hospitals to institute. We previously derived and internally validated an opioid classifier in a separate hospital setting. The aim is to externally validate our previously published and open-source machine-learning classifier at a different hospital for identifying cases of opioid misuse. METHODS: An observational cohort of 56,227 adult hospitalizations was examined between October 2017 and December 2019 during a hospital-wide substance use screening program with manual screening. Manually completed Drug Abuse Screening Test served as the reference standard to validate a convolutional neural network (CNN) classifier with coded word embedding features from the clinical notes of the EHR. The opioid classifier utilized all notes in the EHR and sensitivity analysis was also performed on the first 24 h of notes. Calibration was performed to account for the lower prevalence than in the original cohort. RESULTS: Manual screening for substance misuse was completed in 67.8% (n = 56,227) with 1.1% (n = 628) identified with opioid misuse. The data for external validation included 2,482,900 notes with 67,969 unique clinical concept features. The opioid classifier had an AUC of 0.99 (95% CI 0.99-0.99) across the encounter and 0.98 (95% CI 0.98-0.99) using only the first 24 h of notes. In the calibrated classifier, the sensitivity and positive predictive value were 0.81 (95% CI 0.77-0.84) and 0.72 (95% CI 0.68-0.75). For the first 24 h, they were 0.75 (95% CI 0.71-0.78) and 0.61 (95% CI 0.57-0.64). CONCLUSIONS: Our opioid misuse classifier had good discrimination during external validation. Our model may provide a comprehensive and automated approach to opioid misuse identification that augments current workflows and overcomes manual screening barriers.

Effect of an obesity best practice alert on physician documentation and referral practices.

The Centers for Medicare & Medicaid Services Electronic Health Record Meaningful Use Incentive Program requires physicians to document body mass index (BMI) and a follow-up treatment plan for adult patients with BMI ≥ 25. To examine the effect of a best practice alert on physician documentation of obesity-related care and referrals to weight management treatment, in a cluster-randomized design, 14 primary care clinics at an academic medical center were randomized to best practice alert intervention (n = 7) or comparator (n = 7). The alert was triggered when both height and weight were entered and BMI was ≥30. Both intervention and comparator clinics could document meaningful use by selecting a nutrition education handout within the alert. Intervention clinics could also select a referral option from the list of clinic and community-based weight management programs embedded in the alert. Main outcomes were proportion of eligible patients with (1) obesity-related documentation and (2) referral. There were 26,471 total primary care encounters with 12,981 unique adult patients with BMI ≥ 30 during the 6-month study period. Documentation doubled (17 to 33%) with implementation of the alert. However, intervention clinics were not significantly more likely to refer patients to weight management than comparator clinics (2.8 vs. 1.3%, p = 0.07). Although the alert was associated with increased physician meaningful use compliance, it was not an effective strategy for improving patient access to weight management services. Further research is needed to understand system-level characteristics that influence obesity management in primary care.