The status of patient portal use among Emergency Department patients experiencing houselessness: A large-scale single-center observational study.

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.

Using HEART2 score to risk stratify chest pain patients in the Emergency Department: an observational study.

BACKGROUND: A significant number of chest pain patients had previous cardiac imaging tests (CIT) performed before being presented to the Emergency Department (ED). The HEART (history, electrocardiogram, age, risk factors, and troponin) score has been used to risk-stratify chest pain patients in the ED, but not particularly for patients with CIT performed. We aim to modify the current HEART score with the addition of most recent CIT findings (referred to as HEART2 score), to predict a 30-day major adverse cardiac event (MACE) among ED chest pain patients, compare the performance accuracy of using HEART versus HEART2 score for 30-day MACE outcome predictions, and further determine the value of HEART2 in a subset group of ED chest pain patients (i.e., ones with previous CIT). METHODS: This is a single-center observational study. We included chest pain patients with HEART scores calculated during their index ED visits. A modified HEART2 score was developed with the addition of CIT findings as one of the HEART2 components. Patients were divided into three groups, including low (≤ 3), moderate (4-6), and high-risk HEART/HEART2 scores (≥ 7). MACE occurrence of a patient with different risks of HEART and HEART2 scores and overall performance accuracy of HEART versus HEART2 score predicting MACE outcomes were compared. RESULTS: We included a total of 9419 chest pain patients at ED, among which one out of five patients (1874/9419) had previous CIT performed. Fewer (38.2%) of such patients had low-risk HEART scores in comparison to 55.5% of low-risk HEART2 scores (p < 0.001). The MACE outcomes were similar in low-risk HEART patients compared with low-risk HEART2 patients (2.2% versus 3.1%, p = 0.3021). The overall performance accuracy of using the HEART2 score to stratify chest pain patients with previous CIT was better than using the HEART score's (AUC 0.74 versus 0.71, p = 0.0082). CONCLUSIONS: Using the HEART2 score might be suitable to stratify low-to-moderate risk chest pain patients at ED with a similar 30-days MACE occurrence compared to the HEART score. More importantly, with the use of similar low-risk criteria (HEART2 ≤ 3), over 45% more chest pain patients with previous CIT performed could be discharged directly from ED.

Validation of a simplified comorbidity evaluation predicting clinical outcomes among patients with coronavirus disease 2019 - A multicenter retrospective observation study.

OBJECTIVES: We compared and validated the performance accuracy of simplified comorbidity evaluation compared to the Charlson Comorbidity Index (CCI) predicting COVID-19 severity. In addition, we also determined whether risk prediction of COVID-19 severity changed during different COVID-19 pandemic outbreaks. METHODS: We enrolled all patients whose SARS-CoV-2 PCR tests were performed at six different hospital Emergency Departments in 2020. Patients were divided into three groups based on the various COVID-19 outbreaks in the US (first wave: March-May 2020, second wave: June-September 2020, and third wave: October-December 2020). A simplified comorbidity evaluation was used as an independent risk factor to predict clinical outcomes using multivariate logistic regressions. RESULTS: A total of 22,248 patients were included, for which 7023 (32%) patients tested COVID-19 positive. Higher percentages of COVID-19 patients with more than three chronic conditions had worse clinical outcomes (i.e., hospital and intensive care unit admissions, receiving invasive mechanical ventilations, and in-hospital mortality) during all three COVID-19 outbreak waves. CONCLUSIONS: This simplified comorbidity evaluation was validated to be associated with COVID clinical outcomes. Such evaluation did not perform worse when compared with CCI to predict in-hospital mortality.

Two-step predictive model for early detection of emergency department patients with prolonged stay and its management implications.

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.

HEART Score Risk Stratification of Low-Risk Chest Pain Patients in the Emergency Department: A Systematic Review and Meta-Analysis.

STUDY OBJECTIVE: The objectives of this systematic review and meta-analysis are to appraise the evidence in regard to the diagnostic accuracy of a low-risk History, ECG, Age, Risk Factors, and Troponin (HEART) score for prediction of major adverse cardiac events in emergency department (ED) patients. These included 4 subgroup analyses: by geographic region, the use of a modified low-risk HEART score (traditional HEART score [0 to 3] in addition to negative troponin results), using conventional versus high-sensitivity troponin assays in the HEART score, and a comparison of different post-ED-discharge patient follow-up intervals. METHODS: We searched MEDLINE, EBSCO, Web of Science, and Cochrane Database for studies on the diagnostic performance of low-risk HEART scores to predict major adverse cardiac events among ED chest pain patients. Two reviewers independently screened articles for inclusion, assessed the quality of studies with both an adapted Quality Assessment of Diagnostic Accuracy Studies version 2 tool and an internally developed tool that combined components of the Quality in Prognostic Studies; Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies; and Grading of Recommendations Assessment, Development and Evaluation. Pooled sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios were calculated. RESULTS: There were 25 studies published from 2010 to 2017, with a total of 25,266 patients included in the final meta-analysis, of whom 9,919 (39.3%) were deemed to have low-risk HEART scores (0 to 3). Among patients with low-risk HEART scores, short-term major adverse cardiac events (30 days to 6 weeks) occurred in 2.1% of the population (182/8,832) compared with 21.9% of patients (3,290/15,038) with non-low-risk HEART scores (4 to 10). For patients with HEART scores of 0 to 3, the pooled sensitivity of short-term major adverse cardiac event predictions was 0.96 (95% confidence interval [CI] 0.93 to 0.98), specificity was 0.42 (95% CI 0.36 to 0.49), positive predictive value was 0.19 (95% CI 0.14 to 0.24), negative predictive value was 0.99 (95% CI 0.98 to 0.99), positive likelihood ratio was 1.66 (95% CI 1.50 to 1.85), and negative likelihood ratio was 0.09 (95% CI 0.06 to 0.15). Subgroup analysis showed that lower short-term major adverse cardiac events occurred among North American patients (0.7%), occurred when modified low-risk HEART score was used (0.8%), or occurred when high-sensitivity troponin was used for low-risk HEART score calculations (0.8%). CONCLUSION: In this meta-analysis, despite its use in different patient populations, the troponin type used, and timeline of follow-up, a low-risk HEART score had high sensitivity, negative predictive value, and negative likelihood ratio for predicting short-term major adverse cardiac events, although risk of bias and statistical heterogeneity were high.

Common step-wise interventions improved primary care clinic visits and reduced emergency department discharge failures: a large-scale retrospective observational study.

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.

Optimal Measurement Interval for Emergency Department Crowding Estimation Tools.

STUDY OBJECTIVE: Emergency department (ED) crowding is a barrier to timely care. Several crowding estimation tools have been developed to facilitate early identification of and intervention for crowding. Nevertheless, the ideal frequency is unclear for measuring ED crowding by using these tools. Short intervals may be resource intensive, whereas long ones may not be suitable for early identification. Therefore, we aim to assess whether outcomes vary by measurement interval for 4 crowding estimation tools. METHODS: Our eligible population included all patients between July 1, 2015, and June 30, 2016, who were admitted to the JPS Health Network ED, which serves an urban population. We generated 1-, 2-, 3-, and 4-hour ED crowding scores for each patient, using 4 crowding estimation tools (National Emergency Department Overcrowding Scale [NEDOCS], Severely Overcrowded, Overcrowded, and Not Overcrowded Estimation Tool [SONET], Emergency Department Work Index [EDWIN], and ED Occupancy Rate). Our outcomes of interest included ED length of stay (minutes) and left without being seen or eloped within 4 hours. We used accelerated failure time models to estimate interval-specific time ratios and corresponding 95% confidence limits for length of stay, in which the 1-hour interval was the reference. In addition, we used binomial regression with a log link to estimate risk ratios (RRs) and corresponding confidence limit for left without being seen. RESULTS: Our study population comprised 117,442 patients. The time ratios for length of stay were similar across intervals for each crowding estimation tool (time ratio=1.37 to 1.30 for NEDOCS, 1.44 to 1.37 for SONET, 1.32 to 1.27 for EDWIN, and 1.28 to 1.23 for ED Occupancy Rate). The RRs of left without being seen differences were also similar across intervals for each tool (RR=2.92 to 2.56 for NEDOCS, 3.61 to 3.36 for SONET, 2.65 to 2.40 for EDWIN, and 2.44 to 2.14 for ED Occupancy Rate). CONCLUSION: Our findings suggest limited variation in length of stay or left without being seen between intervals (1 to 4 hours) regardless of which of the 4 crowding estimation tools were used. Consequently, 4 hours may be a reasonable interval for assessing crowding with these tools, which could substantially reduce the burden on ED personnel by requiring less frequent assessment of crowding.

The role of patient perception of crowding in the determination of real-time patient satisfaction at Emergency Department.

OBJECTIVE: To evaluate the associations between real-time overall patient satisfaction and Emergency Department (ED) crowding as determined by patient percepton and crowding estimation tool score in a high-volume ED. DESIGN: A prospective observational study. SETTING: A tertiary acute hospital ED and a Level 1 trauma center. PARTICIPANTS: ED patients. INTERVENTION(S): Crowding status was measured by two crowding tools [National Emergency Department Overcrowding Scale (NEDOCS) and Severely overcrowded-Overcrowded-Not overcrowded Estimation Tool (SONET)] and patient perception of crowding surveys administered at discharge. MAIN OUTCOME MEASURE(S): ED crowding and patient real-time satisfaction. RESULTS: From 29 November 2015 through 11 January 2016, we enrolled 1345 participants. We observed considerable agreement between the NEDOCS and SONET assessment of ED crowding (bias = 0.22; 95% limits of agreement (LOAs): -1.67, 2.12). However, agreement was more variable between patient perceptions of ED crowding with NEDOCS (bias = 0.62; 95% LOA: -5.85, 7.09) and SONET (bias = 0.40; 95% LOA: -5.81, 6.61). Compared to not overcrowded, there were overall inverse associations between ED overcrowding and patient satisfaction (Patient perception OR = 0.49, 95% confidence limit (CL): 0.38, 0.63; NEDOCS OR = 0.78, 95% CL: 0.65, 0.95; SONET OR = 0.82, 95% CL: 0.69, 0.98). CONCLUSIONS: While heterogeneity exists in the degree of agreement between objective and patient perceived assessments of ED crowding, in our study we observed that higher degrees of ED crowding at admission might be associated with lower real-time patient satisfaction.

Roles of disease severity and post-discharge outpatient visits as predictors of hospital readmissions.

BACKGROUND: Risks prediction models of 30-day all-cause hospital readmissions are multi-factorial. Severity of illness (SOI) and risk of mortality (ROM) categorized by All Patient Refined Diagnosis Related Groups (APR-DRG) seem to predict hospital readmission but lack large sample validation. Effects of risk reduction interventions including providing post-discharge outpatient visits remain uncertain. We aim to determine the accuracy of using SOI and ROM to predict readmission and further investigate the role of outpatient visits in association with hospital readmission. METHODS: Hospital readmission data were reviewed retrospectively from September 2012 through June 2015. Patient demographics and clinical variables including insurance type, homeless status, substance abuse, psychiatric problems, length of stay, SOI, ROM, ICD-10 diagnoses and medications prescribed at discharge, and prescription ratio at discharge (number of medications prescribed divided by number of ICD-10 diagnoses) were analyzed using logistic regression. Relationships among SOI, type of hospital visits, time between hospital visits, and readmissions were also investigated. RESULTS: A total of 6011 readmissions occurred from 55,532 index admissions. The adjusted odds ratios of SOI and ROM predicting readmissions were 1.31 (SOI: 95 % CI 1.25-1.38) and 1.09 (ROM: 95 % CI 1.05-1.14) separately. Ninety percent (5381/6011) of patients were readmitted from the Emergency Department (ED) or Urgent Care Center (UCC). Average time interval from index discharge date to ED/UCC visit was 9 days in both the no readmission and readmission groups (p > 0.05). Similar hospital readmission rates were noted during the first 10 days from index discharge regardless of whether post-index discharge patient clinic visits occurred when time-to-event analysis was performed. CONCLUSIONS: SOI and ROM significantly predict hospital readmission risk in general. Most readmissions occurred among patients presenting for ED/UCC visits after index discharge. Simply providing early post-discharge follow-up clinic visits does not seem to prevent hospital readmissions.

The role of charity care and primary care physician assignment on ED use in homeless patients.

OBJECTIVE: Homeless patients are a vulnerable population with a higher incidence of using the emergency department (ED) for noncrisis care. Multiple charity programs target their outreach toward improving the health of homeless patients, but few data are available on the effectiveness of reducing ED recidivism. The aim of this study is to determine whether inappropriate ED use for nonemergency care may be reduced by providing charity insurance and assigning homeless patients to a primary care physician (PCP) in an outpatient clinic setting. METHODS: A retrospective medical records review of homeless patients presenting to the ED and receiving treatment between July 2013 and June 2014 was completed. Appropriate vs inappropriate use of the ED was determined using the New York University ED Algorithm. The association between patients with charity care coverage, PCP assignment status, and appropriate vs inappropriate ED use was analyzed and compared. RESULTS: Following New York University ED Algorithm standards, 76% of all ED visits were deemed inappropriate with approximately 77% of homeless patients receiving charity care and 74% of patients with no insurance seeking noncrisis health care in the ED (P=.112). About 50% of inappropriate ED visits and 43.84% of appropriate ED visits occurred in patients with a PCP assignment (P=.019). CONCLUSIONS: Both charity care homeless patients and those without insurance coverage tend to use the ED for noncrisis care resulting in high rates of inappropriate ED use. Simply providing charity care and/or PCP assignment does not seem to sufficiently reduce inappropriate ED use in homeless patients.

Racial disparity in emergency department triage.

BACKGROUND: Previous reports of lower triage acuity scores and longer Emergency Department (ED) wait times for African Americans compared to Caucasians had insufficient information to determine if this was due to bias or appropriately based on medical history and clinical presentation. OBJECTIVE: (1) Determine if African Americans are assigned lower triage acuity scores (TAS) after adjusting for a number of demographic and clinical variables likely to affect triage scores. (2) Determine if lower TAS translate into clinically significant longer wait times to assignment to a treatment area. METHODS: This was a retrospective matched cohort design analysis of de-identified data extracted from the ED electronic medical record system, which included demographic and clinical information, as well as TAS, and ED process times. Triage scores were assigned using a 5-point scale (ESI), with 1 being most urgent and 5 being least urgent. Mean TAS and wait times to a treatment area for specific chief complaints were compared by race; after adjusting for age, gender, insurance status, time of day, day of week, presence of co-morbidities, and abnormal vital signs using a 1:1 matched case analysis. RESULTS: The overall mean TAS for African Americans was 2.97 vs. 2.81 for Caucasians (difference of 0.18; p<0.001), translating to a lower acuity rating. African Americans had a significantly longer wait time to a treatment area compared to case-matched Caucasians (10.9min; p<0.001), with much larger differences in wait times noted within certain specific chief complaint categories. CONCLUSION: Our current study supports the hypothesis that racial bias may influence the triage process.

The Association of Name Recognition, Empathy Perception, and Satisfaction With Resident Physicians' Care Amongst Patients in an Academic Emergency Department.

Background: Recognition of the provider's name, provider empathy, and the patient's satisfaction with their care are patient-provider rapport measures. This study aimed to determine: 1) resident physicians' name recognition by patients in the emergency department; and 2) name recognition in association with patient perception of the resident's empathy and their satisfaction with the resident's care. Methods: This was a prospective observational study. A patient recognizing a resident physician was defined as the patient remembering a resident's name, understanding the level of training, and understanding a resident's role in patient care. A patient's perception of resident physician empathy was measured by the Jefferson Scale of Patient Perception of Physician Empathy (JSPPPE). Patient satisfaction of the resident was measured utilizing a real-time satisfaction survey. Multivariate logistic regressions were performed to determine the association amongst patient recognition of resident physicians, JSPPPE, and patient satisfaction after adjustments were made for demographics and resident training level. Results: We enrolled 30 emergency medicine resident physicians and 191 patients. Only 26% of studied patients recognized resident physicians. High JSPPPE scores were given by 39% of patients recognizing resident physicians compared to 5% of those who were not recognized (P = 0.013). High patient satisfaction scores were recorded in 31% of patients who recognized resident physicians compared to 7% who did not (P = 0.008). The adjusted odds ratios of patient recognition of resident physicians to high JSPPPE and high satisfaction scores were 5.29 (95% confidence interval (CI): 1.33 - 21.02, P = 0.018) and 6.12 (1.84 - 20.38, P = 0.003) respectively. Conclusions: Patient recognition of resident physicians is low in our study. However, patient recognition of resident physicians is associated with a higher patient perception of physician empathy and higher patient satisfaction. Our study suggests that resident education advocating for patient recognition of their healthcare provider's status needs to be emphasized as part of patient-centered health care.

Patient Portal Use Among Diabetic Patients With Different Races and Ethnicities.

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.

Role of HEART score in evaluating clinical outcomes among emergency department patients with different ethnicities.

OBJECTIVE: We aimed to examine the role of the HEART (history, EKG, age, risk factors, and troponin) score in the evaluation of six clinical outcomes among three groups of patients in the emergency department (ED). METHODS: We performed a retrospective observational study among three ED patient groups including White, Black, and Hispanic patients. ED providers used the HEART score to assess the need for patient hospital admission and for emergent cardiac imaging tests (CITs). HEART scores were measured using classification accuracy rates. Performance accuracies were measured in terms of HEART score in relation to four clinical outcomes (positive findings of CITs, ED returns, hospital readmissions, and 30-day major adverse cardiac events [MACE]). RESULTS: A high classification accuracy rate (87%) was found for use of the HEART score to determine hospital admission. HEART scores showed moderate accuracy (area under the receiver operating characteristic curve 0.66-0.78) in predicting results of emergent CITs, 30-day hospital readmissions, and 30-day MACE outcomes. CONCLUSIONS: Providers adhered to use of the HEART score to determine hospital admission. The HEART score may be associated with emergent CIT findings, 30-day hospital readmissions, and 30-day MACE outcomes, with no differences among White, Black, and Hispanic patient populations.

A Simplified Comorbidity Evaluation Predicting Clinical Outcomes Among Patients With Coronavirus Disease 2019.

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.

The influence of patient perception of physician empathy on patient satisfaction among attending physicians working with residents in an emergent care setting.

BACKGROUND: It is unclear whether the patient's perception of attending physician empathy and the patient's satisfaction can be affected when attending physicians work alongside residents. We aim to determine the influence residents may have on (1) patient perception of attending physician empathy and (2) patient satisfaction as it relates to their respective attending physicians. METHODS: This is a prospective single-center observational study. Patient perception of physician empathy was measured using Jefferson Scale of Patient Perception of Physician Empathy (JSPPPE) in both attendings and residents in the Emergency Department. Patient satisfaction with attending physicians and residents was measured by real-time patient satisfaction survey. Multivariate logistic regressions were performed to determine the association between patient satisfaction and JSPPPE after patient demographics, attending physician different experience, and residents with different years of training were adjusted. RESULTS: A total of 351 patients were enrolled. Mean JSPPPE scores were 30.1 among attending working alone, 30.1 in attending working with PGY-1 EM residents, 29.6 in attending working with PGY-2, and 27.8 in attending working with PGY-3 (p < 0.05). Strong correlation occurred between attending JSPPPE score and patient satisfaction to attending physicians (ρ > 0.5). The adjusted odds ratio was 1.32 (95% CI 1.23-1.41, p < 0.001) on attending's JSPPPE score predicting patient satisfaction to the attending physicians. However, there were no significant differences on patient satisfaction among four different groups. CONCLUSION: Empathy has strong correlation with patient satisfaction. Decreased patient perception of attending physician empathy was found when working with senior residents in comparison to working alone or with junior residents.

Causal Effect Analysis of Demographic Concordance of Physician Trust and Respect in an Emergency Care Setting.

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.

The Role of Using HEART Score to Risk Stratify Chest Pain Among Emergency Department High Utilizers.

The HEART score is used to effectively risk stratify undifferentiated chest pain patients in the Emergency Department (ED). It is unclear whether such risk stratification can be applied among ED high utilizers. We aim to determine the efficacy and safety of using the HEART score to predict 30-day short-term major adverse cardiac events (MACE) in ED high utilizers. We conducted a retrospective, observational study in which ED high utilizers were defined as patients who had four or more ED visits within the past 12 months. ED high utilizers presenting at the study ED with chest pain were enrolled. Patients in which the HEART score was utilized were placed in the HEART group and patients with no HEART scores documented were placed to the usual care group. Hospital admissions and cardiac stress tests performed during the index hospitalizations, and 30-day MACE rates were analyzed and compared between the HEART and usual care groups. From January 1, 2017 to December 31, 2019, a total of 8,315 patient visits from ED high utilizers were enrolled. In the HEART group, 49% of ED visits were admitted with 20% receiving stress tests. A 30-day MACE outcome occurred among 1.4% of visits. In the usual care group, 44% of ED visits were admitted, with only 9% receiving index stress tests and a 1.5% of 30-day MACE occurrence (p=0.727). The study showed that similar short-term MACE outcomes occurred between patients using HEART scores and usual care to risk stratify chest pain among ED high utilizers.

Productivity, efficiency, and overall performance comparisons between attendings working solo versus attendings working with residents staffing models in an emergency department: A Large-Scale Retrospective Observational Study.

BACKGROUND AND OBJECTIVE: Attending physician productivity and efficiency can be affected when working simultaneously with Residents. To gain a better understanding of this effect, we aim to compare productivity, efficiency, and overall performance differences among Attendings working solo versus working with Residents in an Emergency Department (ED). METHODS: Data were extracted from the electronic medical records of all patients seen by ED Attendings and/or Residents during the period July 1, 2014 through June 30, 2017. Attending productivity was measured based on the number of new patients enrolled per hour per provider. Attending efficiency was measured based on the provider-to-disposition time (PDT). Attending overall performance was measured by Attending Performance Index (API). Furthermore, Attending productivity, efficiency, and overall performance metrics were compared between Attendings working solo and Attendings working with Residents. The comparisons were analyzed after adjusting for confounders via propensity score matching. RESULTS: A total of 15 Attendings and 266 Residents managing 111,145 patient encounters over the study period were analyzed. The mean (standard deviation) of Attending productivity and efficiency were 2.9 (1.6) new patients per hour and 2.7 (1.8) hours per patient for Attendings working solo, in comparison to 3.3 (1.9) and 3.0 (2.0) for Attendings working with Residents. When paired with Residents, the API decreased for those Attendings who had a higher API when working solo (average API dropped from 0.21 to 0.19), whereas API increased for those who had a lower API when working solo (average API increased from 0.13 to 0.16). CONCLUSION: In comparison to the Attending working solo staffing model, increased productivity with decreased efficiency occurred among Attendings when working with Residents. The overall performance of Attendings when working with Residents varied inversely against their performance when working solo.