An Emergency Department-based system intervention to improve osteoporosis screening for older adults at high-risk of fracture.

Falls and osteoporosis are risk factors for fragility fractures. Bone mineral density (BMD) assessment is associated with better preventative osteoporosis care, but it is underutilized by those at high fracture risk. We created a novel electronic medical record (EMR) alert-driven protocol to screen patients in the Emergency Department (ED) for fracture risk and tested its feasibility and effectiveness in generating and completing referrals for outpatient BMD testing after discharge. The EMR alert was configured in 2 tertiary-care EDs and triggered by the term "fall" in the chief complaint, age (≥65 years for women, ≥70 years for men), and high fall risk (Morse score ≥ 45). The alert electronically notified ED study staff of potentially eligible patients. Participants received osteoporosis screening education and had BMD testing ordered. From November 15, 2020 to December 4, 2021, there were 2,608 EMR alerts among 2,509 patients. We identified 558 patients at high-risk of fracture who were screened for BMD testing referral. Participants were excluded for: serious illness (N = 141), no documented health insurance to cover BMD testing (N = 97), prior BMD testing/recent osteoporosis care (N = 58), research assistant unavailable to enroll (N = 53), concomitant fracture (N = 43), bedridden status (N = 38), chief complaint of fall documented in error (N = 38), long-term care residence (N = 34), participation refusal (N = 32), or hospitalization (N = 3). Of the 16 participants who had BMD testing ordered, 7 scheduled and 5 completed BMD testing. EMR alerts can help identify subpopulations who may benefit from osteoporosis screening, but there are significant barriers to identifying eligible and willing patients for screening in the ED. In our study targeting an innovative venue for osteoporosis care delivery, only about 1% of patients at high-risk of fracture scheduled BMD testing after an ED visit. Adequate resources during and after an ED visit are needed to ensure that older adults participate in preventative osteoporosis care.

Pregnancy, Opioid Use Disorder and COVID-19: An Evaluation of Acute Care Presentations During a Pandemic.

INTRODUCTION: The opioid epidemic has been exacerbated by the COVID-19 pandemic, resulting in increased acute care opioid-related and overdose visits. We sought to assess how the pandemic may have impacted an obstetric cohort impacted by opioid misuse in the acute care context. METHODS: A retrospective review of acute care presentations of patients with concomitant pregnancy (Z33.1) and opioid-related diagnostic codes (T10 codes and/or F11) was conducted over a 24-month period (pre-COVID = March 2019 through February 2020, post-COVID = March 2020 through February 2021). Descriptive statistics and χ2 analysis of pre- versus post-COVID presentations were performed. RESULTS: A total of 193 individuals, 104 (53.9%) pre- and 89 (46.1%) post-COVID, accounting for 292 total encounters, 160 (54.8%) pre- and 132 (45.2%) post-COVID, were seen for acute care visits ( P = 0.84). Age ( P = 0.15), race ( P = 0.59), and insurance status ( P = 0.17) were similar pre- versus post-COVID. The majority of presentations, pre- (40.4%) and post-COVID (44.9%), were for opioid withdrawal ( P = 0.74). Although post-COVID individuals were more likely to lack prenatal care (48.3% versus 39.4% pre-COVID), this trend was not significant ( P = 0.19). Similar proportions of individuals were affected by pregnancy complications (51.9% pre-, 44.9% post-COVID; P = 0.30). Similar proportions of individuals were affected by adverse pregnancy outcomes (44.2% pre-, 48.3% post-COVID; P = 0.64). CONCLUSION: The COVID-19 pandemic did not have a statistically significant effect on opioid-related acute care presentations or outcomes for obstetric patients. In this acute care cohort, however, opioid misuse had significant general impact on pregnancy complications and outcomes, suggesting unmet needs in this population.

Race and Other Disparate Demographic Variables Identified Among Emergency Department Boarders.

INTRODUCTION: Emergency department (ED) boarding, the process of holding patients in the ED due to a lack of inpatient beds after the decision is made to admit, has profound consequences. Increased ED boarding times are associated with adverse patient outcomes, including increased mortality. While previous studies have demonstrated racial disparities with regard to ED boarding, current literature lacks insight into discrepancies that may exist among other demographic groups as it pertains to ED boarding. We sought to review ED boarding times differentiated by demographic characteristics. METHODS: We conducted a retrospective review of all ED admissions from an academic ED in the Southeast from April-September 2019. The primary outcome assessed was boarding time, defined as time from decision to admit to ED departure. Patient demographic data including race, gender, and age were collected and analyzed. We performed descriptive statistics and chi-square analyses. RESULTS: The study population included 17,606 patients with a mean age of 56.3. Nearly half (49.8%) of the patients were female. Additionally, 43.8% of patients were Black and 48.6% White. For all admissions, there was no difference in mean boarding time among Black and White patients (5.2 ± 8.8 vs 5.2 ± 8.2 hours, P = 0.11). Among Emergency Severity Index (ESI) level I admissions, Black patients boarded longer than White patients (4.1 ± 0.3 vs 2.7 ± 0.3 hours, P = 0.009). Black patients also boarded significantly longer than White patients for psychiatric admissions (22.7 ± 23.7 vs 18.5 ± 19.4 hours, P <0.05). For all admissions, males boarded longer than females (5.5 ± 8.5 vs 4.9 ± 8.2 hours, P <.0001). Patients older than 75 boarded for less time (3.8 ± 6.2 hours) compared to younger groups (15-24: 6.4 ± 10.8 hours; 25-44: 6.6 ± 10.8; 45-64: 5.0 ± 7.6; and 64-75: 4.7 ± 6.7; all P <.05). CONCLUSION: This analysis demonstrated significant differences in ED boarding times between races among psychiatric and ESI I admissions, gender, and age. This data provides insight into differences in ED boarding times among demographic groups and provides a focal point for examining possible factors contributing to the observed differences.

Utilizing Laboratory Results to Identify Emergency Department Patients with Indications for HIV Pre-Exposure Prophylaxis.

People newly diagnosed with HIV often have previous contact with health care professionals, often on multiple occasions, including within emergency departments (EDs). Although EDs have become vital partners in routine screening and linkage to care for persons with HIV, ED engagement in HIV prevention efforts, to include HIV risk assessment and pre-exposure prophylaxis (PrEP) referral, are rare. In this study, we electronically queried the hospital electronic health record (EHR) for ED encounters in 2019 and 2020 for patients who screened negative for HIV (N = 26,914) to identify objective evidence of HIV acquisition risk due to recent sexual behavior (sexually transmitted infection screen positive for chlamydia, syphilis, gonorrhea, or trichomoniasis) or recent injection drug practices (urine drug screen positive for heroin, amphetamines, cocaine, or other opiates). In the reviewed period, we identified 1324 patients (4.92%) at sufficient risk to warrant PrEP referral (i.e., PrEP indications), including 304 (22.96%) due to sexual behavior and 1032 (77.95%) due to potential injection drug use. Notably, among adults with PrEP indications regardless of transmission risk group, persons who inject drugs (PWID) represented a significantly larger proportion within our ED cohort as compared with Centers for Disease Control and Prevention (CDC) estimates for the US population (77.95% vs. 6.34%, p < 0.0001). Among adults with PrEP indications due to sexual behavior specifically, women represented a significantly larger proportion within our ED cohort as compared with estimates for the US population (62.17% vs. 16.48%, p < 0.0001). Our results demonstrate that utilizing laboratory results within the EHR may be a simple low-resource option for identifying and engaging PrEP candidates, especially women and PWID.

Identification of Gout Flares in Chief Complaint Text Using Natural Language Processing.

Many patients with gout flares treated in the Emergency Department (ED) often do not receive optimal continuity of care after an ED visit. Thus, developing methods to identify patients with gout flares in the ED and referring them to appropriate outpatient gout care is required. While Natural Language Processing (NLP) has been used to detect gout flares retrospectively, it is much more challenging to identify patients prospectively during an ED visit where documentation is usually minimal. We annotate a corpus of ED triage nurse chief complaint notes for the presence of gout flares and implement a simple algorithm for gout flare ED alerts. We show that the chief complaint alone has strong predictive power for gout flares. We make available a de-identified version of this corpus annotated for gout mentions, which is to our knowledge the first free text chief complaint clinical corpus available.

Automated electronic medical record sepsis detection in the emergency department.

Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system. Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a "sepsis alert" for those with ≥2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ≥1 major organ dysfunction (SBP ≤ 90 mm Hg, lactic acid ≥2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool. Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2-48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%. Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.