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Objective: Non-Hispanic Black (NHB) and Hispanic/Latino (Hispanic) patients wait longer in the emergency department (ED) to see practitioners when compared with non-Hispanic White (NHW) patients. We investigate factors contributing to longer wait times for NHB and Hispanic patients using a linear decomposition approach. Methods: This retrospective observational study included patients presenting to one tertiary hospital ED from 2019 to 2021. Median wait times among NHW, NHB, and Hispanic were calculated with multivariable linear regressions. The extent to which demographic, clinical, and hospital factors explained the differences in average wait time among the three groups were analyzed with Blinder‒Oaxaca post-linear decomposition model. Results: There were 310,253 total patients including 34.7% of NHW, 34.7% of NHB, and 30.6% of Hispanic patients. The median wait time in NHW was 9 min (interquartile range [IQR] 4‒47 min), in NHB was 13 min (IQR 4‒59 min), and in Hispanic was 19 min (IQR 5‒78 min, p < 0.001). The top two contributors of average wait time difference were mode of arrival and triage acuity level. Post-linear decomposition analysis showed that 72.96% of the NHB‒NHW and 87.77% of the Hispanic‒NHW average wait time difference were explained by variables analyzed. Conclusion: Compared to NHW patients, NHB and Hispanic patients typically experience longer ED wait times, primarily influenced by their mode of arrival and triaged acuity levels. Despite these recognized factors, there remains 12%‒27% unexplained factors at work, such as social determinants of health (including implicit bias and systemic racism) and many other unmeasured confounders, yet to be discovered.

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OBJECTIVE: Patient portal (PP) use has rapidly increased in recent years. However, the PP use status among houseless patients is largely unknown. We aim to determine 1) the PP use status among Emergency Department (ED) patients experiencing houselessness, and 2) whether PP use is linked to the increase in patient clinic visits. METHODS: This is a single-center retrospective observational study. From March 1, 2019, to February 28, 2021, houseless patients who presented at ED were included. Their PP use status, including passive PP use (log-on only PP) and effective PP use (use PP of functions) was compared between houseless and non-houseless patients. The number of clinic visits was also compared between these two groups. Lastly, a multivariate logistic regression was analyzed to determine the association between houseless status and PP use. RESULTS: We included a total of 236,684 patients, 13% of whom (30,956) were houseless at time of their encounter. Fewer houseless patients had effective PP use in comparison to non-houseless patients (7.3% versus 11.6%, p < 0.001). In addition, a higher number of clinic visits were found among houseless patients who had effective PP use than those without (18 versus 3, p < 0.001). The adjusted odds ratio of houseless status associated with PP use was 0.48 (95% CI 0.46-0.49, p < 0.001). CONCLUSIONS: Houselessness is a potential risk factor preventing patient portal use. In addition, using patient portals could potentially increase clinic visits among the houseless patient population.

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Background: Patient portals tethered to electronic health records (EHR) have become vital to patient engagement and better disease management, specifically among adults with multimorbidity. We determined individual and neighborhood factors associated with patient portal use (MyChart) among adult patients with multimorbidity seen in an Emergency Department (ED). Methods: This study adopted a cross-sectional study design and used a linked database of EHR from a single ED site to patients' neighborhood characteristics (i.e., zip code level) from the American Community Survey. The study population included all adults (age > 18 years), with at least one visit to an ED and multimorbidity between 1 January 2019 to 31 December 2020 (N = 40,544). Patient and neighborhood characteristics were compared among patients with and without MyChart use. Random-intercept multi-level logistic regressions were used to analyze the associations of patient and neighborhood factors with MyChart use. Results: Only 19% (N = 7757) of adults with multimorbidity used the patient portal. In the fully adjusted multi-level model, at the patient level, having a primary care physician (AOR = 5.55, 95% CI 5.07−6.07, p < 0.001) and health insurance coverage (AOR = 2.41, 95% CI 2.23−2.61, p < 0.001) were associated with MyChart use. At the neighborhood level, 4.73% of the variation in MyChart use was due to differences in neighborhood factors. However, significant heterogeneity existed in patient portal use when neighborhood characteristics were included in the model. Conclusions: Among ED patients with multimorbidity, one in five adults used patient portals. Patient-level factors, such as having primary care physicians and insurance, may promote patient portal use.

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OBJECTIVES: Personal health records (PHR) use has improved individuals' health outcomes. The adoption of PHR remains low with documented racial disparities. We aim to determine factors associated with reducing racial and ethnic disparities among Hispanic adults in PHR use. METHODS: Participants included non-Hispanic White (NHW) and Hispanic adults (age ≥ 18 years) enrolled in Health Information National Trends Survey in 2018 and 2019. We identified PHR use as online medical record access in the last 12 months. We considered three factors (1. accessing mHealth Apps on the phone, 2. having a usual source of care, and 3. electronically communicating (e-communication) with healthcare providers) as facilitating PHR use. Multivariable logistic regressions with replicate weights were analyzed to determine factors associated with racial/ethnic disparities in PHR use after controlling for general characteristics (i.e., sex, age, education, insurance status, and income). RESULTS: A lower percentage of Hispanics than NHWs used PHR (42.0% vs. 53.5%, P < .001). When adjusted for individual general characteristics, the adjusted odds ratio (AOR) of e-communication with healthcare providers associated with PHR use was 1.49 (1.19-1.86, P < .001), AOR was 2.06 (1.62-2.6, P < .001) on accessing to mHealth App, and 2.60 (1.86-3.63, P < .001) on having a usual source of care. However, the racial difference was not statistically significant after adjusting three factors promoting PHR use (AOR = 0.90, 95% CI = 0.66, 1.22, P = .48). CONCLUSIONS: Ethnic disparities were reduced when PHR use was facilitated by having a usual source of care, active e-communication, and having access to mHealth apps. Interventions focusing on these three factors may potentially reduce racial/ethnic disparities.

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Background: Patient portal (PP) use varies among different patient populations, specifically among those with diabetes mellitus (DM). In addition, it is still uncertain whether PP use could be linked to improved clinical outcomes. Therefore, the aim of this paper was to determine PP use status for patients, recognize factors promoting PP use, and further identify the association between PP use and clinical outcome among diabetic patients of different races and ethnicities. Methods: This was a single-center cross-section study. Patients were divided into non-Hispanic white (NHW), non-Hispanic black (NHB), and Hispanic/Latino groups. PP use was compared among these three groups. Multivariate logistic regressions were used to determine factors associated with PP use, serum glycemic control, and emergency department (ED) hospitalizations. Results: A total of 77,977 patients were analyzed. The rate of PP use among patients of NHW (24%) was higher than those of NHB (19%) and Hispanic/Latinos (18%, P < 0.0001). The adjusted odds ratio (AOR) of insurance coverage associated with PP use was 2.12 (2.02 - 2.23, P < 0.0001), and having a primary care physician (PCP) associated with PP use was 3.89 (3.71 - 4.07, P < 0.0001). In terms of clinical outcomes, the AOR of PP use associated with serum glycemic control was 0.98 (0.90 - 1.05, P = 0.547) and ED hospitalization was 0.79 (0.73 - 0.86, P < 0.0001). Conclusion: PP use disparity occurred among NHB and Hispanic/Latino patients in the ED. Having insurance coverage and PCPs seem to correlate with PP use. PP use did not seem to associate with serum glycemic control among DM patients present in the ED but could possibly reduce patient hospitalizations.

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OBJECTIVES: During the coronavirus 2019 (COVID-19) pandemic, increased anxiety and depression were reported, with mixed findings among individuals of different races/ethnicities. This study examines whether anxiety and depression increased during the COVID-19 pandemic compared to the pre-COVD-19 period among different racial/ethnic groups in the US. METHODS: The Health Information National Trend Surveys 5 (HINTS 5) Cycle 4 data was analyzed. We used the time when the survey was administered as the pre-COVID-19 period (before March 11, 2020, weighted N = 77,501,549) and during the COVID-19 period (on and after March 11, 2020, weighted N = 37,222,019). The Patient Health Questionnaire (PHQ) was used to measure anxiety/depression and further compared before and during COVID-19. Separate multivariable logistic regression analyses were used to determine the association of the COVID-19 pandemic with anxiety/depression after adjusting for age, sex, insurance, income, and education. RESULT: A higher percentage of Non-Hispanic whites (NHW) with chronic conditions reported anxiety (24.3% vs. 11.5%, p = 0.0021) and depression (20.7% vs. 9.3%, p = 0.0034) during COVID-19 than pre-COVID-19. The adjusted odds ratio (AOR) of anxiety and depression for NHWs with chronic conditions during the COVID-19 pandemic was 2.02 (95% confidence interval of 1.10-3.73, p = 0.025) and 2.33 (1.17-4.65, p = 0.018) compared to NHWs who participated in the survey before the COVID-19. LIMITATIONS: Limited to the NHW US population. PHQ can only be used as the initial screening tool. CONCLUSION: The COVID-19 pandemic was associated with an increased prevalence of anxiety and depression among NHW adults with chronic conditions, but not among people of color.

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OBJECTIVE: Patient perceptions of physician trust and respect are important factors for patient satisfaction evaluations. However, perceptions are subjective by nature and can be affected by patient and physician demographic characteristics. We aim to determine the causal effect on patient-physician demographic concordance and patient perceptions of physician trust and respect in an emergency care setting. METHODS: We performed a causal effect analysis in an observational study setting. A near-real-time patient satisfaction survey was sent via telephone to patients within 72 h of discharge from an emergency department (ED). Patient-trust-physician (PTP) and physician-show-respect (PSR) scores were measured. Patient and physician demographics (age, gender, race, and ethnicity) were matched. Causal effect was analyzed to determine the direct effect of patient-physician demographic concordance on PTP/PSR scores. RESULTS: We enrolled 1815 patients. The treatment effect of patient-physician age concordance on PTP scores was -0.119 (p = 0.036). Other treatment effect of patient-physician demographic concordance on patient perception of physician trust and respect ranged from -0.02 to -0.2 (p > 0.05). CONCLUSION: Patient-physician age concordance may cause a negative effect on patient perception of physician trust. Otherwise, patient-physician demographic concordance has no effect on patient perceptions of physician trust and respect.

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BACKGROUND: Mobile applications related to health and wellness (mHealth apps) are widely used to self-manage chronic conditions. However, research on whether mHealth apps facilitate self-management behaviors of individuals with chronic conditions is sparse. We aimed to evaluate the association of mHealth apps with different types of self-management behaviors among patients with chronic diseases in the United States. METHODS: This is a cross-sectional observational study. We used data from adult participants (unweighted n = 2340) of the Health Information National Trends Survey in 2018 and 2019. We identified three self-management behaviors: (1) resource utilization using electronic personal health records; (2) treatment discussions with healthcare providers; and (3) making healthcare decisions. We analyzed the association of mHealth apps to self-management behaviors with multivariable logistic and ordinal regressions. RESULTS: Overall, 59.8% of adults (unweighted number = 1327) used mHealth apps. Adults using mHealth apps were more likely to use personal health records (AOR = 3.11, 95% CI 2.26-4.28), contact healthcare providers using technology (AOR = 2.70, 95% CI 1.93-3.78), and make decisions on chronic disease management (AOR = 2.59, 95% CI 1.93-3.49). The mHealth apps were associated with higher levels of self-management involvement (AOR = 3.53, 95% CI 2.63-4.72). CONCLUSION: Among individuals with chronic conditions, having mHealth apps was associated with positive self-management behaviors.

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BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) have shown a range of clinical outcomes. Previous studies have reported that patient comorbidities are predictive of worse clinical outcomes, especially when patients have multiple chronic diseases. We aim to: 1) derive a simplified comorbidity evaluation and determine its accuracy of predicting clinical outcomes (i.e., hospital admission, intensive care unit (ICU) admission, ventilation, and in-hospital mortality); and 2) determine its performance accuracy in comparison to well-established comorbidity indexes. METHODS: This was a single-center retrospective observational study. We enrolled all emergency department (ED) patients with COVID-19 from March 1, 2020, to December 31, 2020. A simplified comorbidity evaluation (COVID-related high-risk chronic condition (CCC)) was derived to predict different clinical outcomes using multivariate logistic regressions. In addition, chronic diseases included in the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) were scored, and its accuracy of predicting COVID-19 clinical outcomes was also compared with the CCC. RESULTS: Data were retrieved from 90,549 ED patient visits during the study period, among which 3,864 patients were COVID-19 positive. Forty-seven point nine percent (1,851/3,864) were admitted to the hospital, 9.4% (364) patients were admitted to the ICU, 6.2% (238) received invasive mechanical ventilation, and 4.6% (177) patients died in the hospital. The CCC evaluation correlated well with the four studied clinical outcomes. The adjusted odds ratios of predicting in-hospital death from CCC was 2.84 (95% confidence interval (CI): 1.81 - 4.45, P < 0.001). C-statistics of CCC predicting in-hospital all-cause mortality was 0.73 (0.69 - 0.76), similar to those of the CCI's (0.72) and ECI's (0.71, P = 0.0513). CONCLUSIONS: CCC can accurately predict clinical outcomes among patients with COVID-19. Its performance accuracies for such predictions are not inferior to those of the CCI or ECI's.

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OBJECTIVE: Burnout is a common occurrence among healthcare providers and has been associated with provider wellness culture. However, this association has not been extensively studied among emergency medicine (EM) providers. We aim to determine the association between EM provider burnout and their culture of wellness, and to elicit the independent wellness culture domains most predictive of burnout prevention. METHODS: This was a multi-center observational study. We enrolled EM physicians and advanced practice providers from sixteen different emergency departments (EDs). Provider wellness culture and burnout surveys were performed. The wellness culture domains included in this study are personal/organizational value alignment, provider appreciation, leadership quality, self-controlled scheduling, peer support, and family support. Correlations between each wellness culture domain and burnout were analyzed by Pearson correlation co-efficiency, and their associations were measured by multivariate logistic regression with adjustments of other confounders. RESULTS: A total of 242 ED provider surveys were entered for final analysis. The overall burnout rate was 54% (130/242). Moderate correlations were found between burnout and two wellness culture domains (value alignment: r=-0.43, P<0.001 and provider appreciation: r=-0.49, P<0.001). The adjusted odds ratio of provider appreciation associated with burnout was 0.44 (95% confidence interval, 0.25-0.77; P=0.004), adjusted odds ratio of family support was 0.67 (95% confidence interval, 0.48-0.95; P=0.025). CONCLUSION: ED providers have a relatively high burnout rate. Provider burnout might have certain associations with wellness culture domains. Provider appreciation and family support seem to play important roles in burnout protection.

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OBJECTIVE: To develop a novel model for predicting Emergency Department (ED) prolonged length of stay (LOS) patients upon triage completion, and further investigate the benefit of a targeted intervention for patients with prolonged ED LOS. MATERIALS AND METHODS: A two-step model to predict patients with prolonged ED LOS (>16 h) was constructed. This model was initially used to predict ED resource usage and was subsequently adapted to predict patient ED LOS based on the number of ED resources using binary logistic regressions and was validated internally with accuracy. Finally, a discrete event simulation was used to move patients with predicted prolonged ED LOS directly to a virtual Clinical Decision Unit (CDU). The changes of ED crowding status (Overcrowding, Crowding, and Not-Crowding) and savings of ED bed-hour equivalents were estimated as the measures of the efficacy of this intervention. RESULTS: We screened a total of 123,975 patient visits with final enrollment of 110,471 patient visits. The overall accuracy of the final model predicting prolonged patient LOS was 67.8%. The C-index of this model ranges from 0.72 to 0.82. By implementing the proposed intervention, the simulation showed a 12% (1044/8760) reduction of ED overcrowded status - an equivalent savings of 129.3 ED bed-hours per day. CONCLUSIONS: Early prediction of prolonged ED LOS patients and subsequent (simulated) early CDU transfer could lead to more efficiently utilization of ED resources and improved efficacy of ED operations. This study provides evidence to support the implementation of this novel intervention into real healthcare practice.

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BACKGROUND: The use of ABCD3-I score for Transient ischemic attack (TIA) evaluation has not been widely investigated in the ED. We aim to determine the performance and cost-effectiveness of an ABCD3-I based pathway for expedited evaluation of TIA patients in the ED. METHODS: We conducted a single-center, pre- and post-intervention study among ED patients with possible TIA. Accrual occurred for seven months before (Oct. 2016-April 2017) and after (Oct. 2017-April 2018) implementing the ABCD3-I algorithm with a five-month wash-in period (May-Sept. 2017). Total ED length of stay (LOS), admissions to the hospital, healthcare cost, and 90-day ED returns with subsequent stroke were analyzed and compared. RESULTS: Pre-implementation and post-implementation cohorts included 143 and 118 patients respectively. A total of 132 (92%) patients were admitted to the hospital in the pre-implementation cohort in comparison to 28 (24%) patients admitted in the post-implementation cohort (p < 0.001) with similar 90-day post-discharge stroke occurrence (2 in pre-implementation versus 1 in post-implementation groups, p > 0.05). The mean ABCD2 scores were 4.5 (1.4) in pre- and 4.1 (1.3) in post-implementation cohorts (p = 0.01). The mean ABCD3-I scores were 4.5 (1.8) in post-implementation cohorts. Total ED LOS was 310 min (201, 420) in pre- and 275 min (222, 342) in post-implementation cohorts (p > 0.05). Utilization of the ABCD3-I algorithm saved an average of over 40% of total healthcare cost per patient in the post-implementation cohort. CONCLUSIONS: The initiation of an ABCD3-I based pathway for TIA evaluation in the ED significantly decreased hospital admissions and cost with similar 90-day neurological outcomes.

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OBJECTIVES: Provider efficiency has been reported in the literature but there is a lack of efficiency analysis among emergency medicine (EM) residents. We aim to compare efficiency of EM residents of different training levels and determine if EM resident efficiency is affected by emergency department (ED) crowding. METHODS: We conducted a single-center retrospective observation study from July 1, 2014, to June 30, 2017. The number of new patients per resident per hour and provider-to-disposition (PTD) time of each patient were used as resident efficiency markers. A crowding score was assigned to each patient upon the patient's arrival to the ED. We compared efficiency among EM residents of different training levels under different ED crowding statuses. Dynamic efficiency changes were compared monthly through the entire academic year (July to next June). RESULTS: The study enrolled a total of 150,920 patients. A mean of 1.9 patients/hour was seen by PGY-1 EM residents in comparison to 2.6 patients/hour by PGY-2 and -3 EM residents. Median PTD was 2.8 hours in PGY-1 EM residents versus 2.6 hours in PGY-2 and -3 EM residents. There were no significant differences in acuity across all patients seen by EM residents. When crowded conditions existed, residency efficiency increased, but such changes were minimized when the ED became overcrowded. A linear increase of resident efficiency was observed only in PGY-1 EM residents throughout the entire academic year. CONCLUSION: Resident efficiency improved significantly only during their first year of EM training. This efficiency can be affected by ED crowding.

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BACKGROUND: It is critical to understand whether providing health insurance coverage, assigning a dedicated Primary Care Physician (PCP), and arranging timely post-Emergency Department (ED) clinic follow-up can improve compliance with clinic visits and reduce ED discharge failures. We aim to determine the benefits of providing these common step-wise interventions and further investigate the necessity of urgent PCP referrals on behalf of ED discharged patients. METHODS: This is a single-center retrospective observational study. All patients discharged from the ED over the period Jan 1, 2015 through Dec 31, 2017 were included in the study population. Step-wise interventions included providing charity health insurance, assigning a dedicated PCP, and providing ED follow-up clinics. PCP clinic compliance and ED discharge failures were measured and compared among groups receiving different interventions. RESULT: A total of 227,627 patients were included. Fifty-eight percent of patients receiving charity insurance had PCP visits in comparison to 23% of patients without charity insurance (p < 0.001). Seventy-seven percent of patients with charity insurance and PCP assignments completed post-ED discharge PCP visits in comparison to only 4.5% of those with neither charity insurance nor PCP assignments (p < 0.001). CONCLUSIONS: Step-wise interventions increased patient clinic follow-up compliance while simultaneously reducing ED discharge failures. Such interventions might benefit communities with similar patient populations.

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