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.

Role of ED crowding relative to trauma quality care in a Level 1 Trauma Center.

OBJECTIVE: Trauma Quality Improvement Program participation among all trauma centers has shown to improve patient outcomes. We aim to identify trauma quality events occurring during the Emergency Department (ED) phase of care. METHODS: This is a single-center observational study using consecutively registered data in local trauma registry (Jan 1, 2016-Jun 30, 2017). Four ED crowding scores as determined by four different crowding estimation tools were assigned to each enrolled patient upon arrival to the ED. Patient related (age, gender, race, severity of illness, ED disposition), system related (crowding, night shift, ED LOS), and provider related risk factors were analyzed in a multivariate logistic regression model to determine associations relative to ED quality events. RESULTS: Total 5160 cases were enrolled among which, 605 cases were deemed ED quality improvement (QI) cases and 457 cases were ED provider related. Similar percentages of ED QI cases (10-12%) occurred across the ED crowding status range. No significant difference was appreciated in terms of predictability of ED QI cases relative to different crowding status after adjustment for potential confounders. However, an adjusted odds ratio of 1.64 (95% CI, 1.17-2.30, p < 0.01) regarding ED LOS ≥2 h predictive of ED related quality issues was noted when analyzed using multivariate logistic regression. CONCLUSION: Provider related issues are a common contributor to undesirable outcomes in trauma care. ED crowding lacks significant association with poor trauma quality care. Prolonged ED LOS (≥2 h) appears to be linked with unfavorable outcomes in ED trauma care.

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.

Risks predicting prolonged hospital discharge boarding in a regional acute care hospital.

BACKGROUND: Prolonged hospital discharge boarding can impact patient flow resulting in upstream Emergency Department crowding. We aim to determine the risks predicting prolonged hospital discharge boarding and their direct and indirect effects on patient flow. METHODS: Retrospective review of a single hospital discharge database was conducted. Variables including type of disposition, disposition boarding time, case management consultation, discharge medications prescriptions, severity of illness, and patient homeless status were analyzed in a multivariate logistic regression model. Hospital charges, potential savings of hospital bed hours, and whether detailed discharge instructions provided adequate explanations to patients were also analyzed. RESULTS: A total of 11,527 admissions was entered into final analysis. The median discharge boarding time was approximately 2 h. Adjusted Odds Ratio (AOR) of patients transferring to other hospitals was 7.45 (95% CI 5.35-10.37), to court or law enforcement custody was 2.51 (95% CI 1.84-3.42), and to a skilled nursing facility was 2.48 (95% CI 2.10-2.93). AOR was 0.57 (95% CI 0.47-0.71) if the disposition order was placed during normal office hours (0800-1700). AOR of early case management consultation was 1.52 (95% CI 1.37-1.68) versus 1.73 (95% CI 1.03-2.89) for late consultation. Eighty-eight percent of patients experiencing discharge boarding times within 2 h of disposition expressed positive responses when questioned about the quality of explanations of discharge instructions and follow-up plans based on satisfaction surveys. Similar results (86% positive response) were noted among patients whose discharge boarding times were prolonged (> 2 h, p = 0.44). An average charge of $6/bed/h was noted in all hospital discharges. Maximizing early discharge boarding (≤ 2 h) would have resulted in 16,376 hospital bed hours saved thereby averting $98,256.00 in unnecessary dwell time charges in this study population alone. CONCLUSION: Type of disposition, case management timely consultation, and disposition to discharge dwell time affect boarding and patient flow in a tertiary acute care hospital. Efficiency of the discharge process did not affect patient satisfaction relative to the perceived quality of discharge instruction and follow-up plan explanations. Prolonged disposition to discharge intervals result in unnecessary hospital bed occupancy thereby negatively impacting hospital finances while delivering no direct benefit to patients.

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.

Using the LACE index to predict hospital readmissions in congestive heart failure patients.

BACKGROUND: The LACE index has been used to predict the risk of unplanned readmission within 30 days after hospital discharge in both medical and surgical patients. The aim of this study is to validate the accuracy of using the LACE index in CHF patients. METHODS: This was a retrospective study. The LACE index score was calculated on each patient who was admitted to hospital due to an acute CHF exacerbation. Operational and clinical variables were collected from patients including basic clinical characteristics, length of hospitalization, comorbidities, number of previous ED visits in the past 6 months before the index admission, and the number of post discharge ED revisits at 30, 60, and 90 days. All variables were analyzed by multivariate logistic regression to determine the association between clinical variables and the hospital unplanned readmissions. C-statistic was used to discriminate those patients with high risk of readmissions. RESULTS: Of the 253 patients included in the study, 24.50% (62/253) experienced unplanned readmission to hospital within 30 days after discharge. The LACE index was slightly higher in patients readmitted versus patients not readmitted (12.17 ± 2.22 versus 11.80 ± 1.92, p = 0.199). Adjusted odds ratios based on logistic regression of all clinical variables showed only the number of previous ED visits (OR 1.79, 95% CI 1.30-2.47, p < 0.001), history of myocardial infarction (OR 2.51, 95% CI 1.02-6.21, p = 0.045), and history of peripheral vascular disease (OR 10.75, 95% CI 1.52-75.73, p = 0.017) increased the risk of unplanned readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of ED revisits (15.04% vs 0%) within 30 days from the index discharge than those with low LACE scores (p = 0.030). CONCLUSION: The LACE index may not accurately predict unplanned readmissions within 30 days from hospital discharge in CHF patients. The LACE high risk index may have utility as a screening tool to predict high risk ED revisits after hospital discharge.

The inaccuracy of determining overcrowding status by using the national ED overcrowding study tool.

BACKGROUND: Emergency department (ED) crowding has become more common, and perceptions of crowding vary among different health care providers. The National Emergency Department Overcrowding Study (NEDOCS) tool is the most commonly used tool to estimate ED crowding but still uncertain of its reliability in different ED settings. OBJECTIVE: The objectives of this study are to determine the accuracy of using the NEDOCS tool to evaluate overcrowding in an extremely high-volume ED and assess the reliability and consistency of different providers' perceptions of ED crowding. MATERIAL AND METHODS: This was a 2-phase study. In phase 1, ED crowding was determined by the NEDOCS tool. The ED length of stay and number of patients who left without being seen were analyzed. In phase 2, a survey of simulated ED census scenarios was completed by different providers. The interrater and intrarater agreements of ED crowding were tested. RESULTS: In phase 1, the subject ED was determined to be overcrowded more than 75% of the time in which nearly 50% was rated as severely overcrowded by the NEDOCS tool. No statistically significant difference was found in terms of the average length of stay and the number of left without being seen patients under different crowding categories. In phase 2, 88 surveys were completed. A moderate level of agreement between health care providers was reached (κ = 0.5402, P < .0001). Test-retest reliability among providers was high (r = 0.8833, P = .0007). The strength of agreement between study groups and the NEDOCS was weak (κ = 0.3695, P < .001). CONCLUSION: Using the NEDOCS tool to determine ED crowding might be inaccurate in an extremely high-volume ED setting.

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.

Association between burnout and wellness culture among emergency medicine providers.

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.

Dedicated homeless clinics reduce inappropriate emergency department utilization.

BACKGROUND: The homeless patient population is known to have a high occurrence of inappropriate emergency department (ED) utilization. The study hospital initiated a dedicated homeless clinic targeting patients experiencing homelessness with a combination of special features. We aim to determine whether this mode of care can reduce inappropriate ED utilization among homeless patients. METHODS: We conducted a retrospective observational study from July 1, 2017 to Dec 31, 2017. The study enrolled all homeless patients who visited any hospital regular clinic, dedicated homeless clinic, and ED at least once during the study period. ED homeless patients were divided into four groups (A: no clinic visits; B: those who only visited hospital regular clinic; C: those who only visited dedicated homeless clinic; and D: those who visited both hospital regular clinic and dedicated homeless clinic). The New York University algorithm was used to determine appropriate ED utilization. We compared inappropriate ED utilization among patients from these groups. Multivariate logistic regression was used to determine the risks of different clinical visits in association with inappropriate ED utilization. RESULTS: A total of 16,323 clinic and 8511 ED visits occurred among 5022 unique homeless patients, in which 2450 unique patients were seen in hospital regular clinic, 784 patients in dedicated homeless clinic, 688 patients in both hospital regular clinic and dedicated homeless clinic, and 1110 patients with no clinic visits. Twenty-nine percent (230/784) of patients from dedicated homeless clinic utilized the ED, among which 21% (175/844) of their ED visits were considered inappropriate. In contrast, 40% of patients from hospital regular clinic utilized the ED, among which 29% were inappropriate (P < 0.001). The adjusted odds ratio (OR) was 0.61 (95% confidence interval [CI] = 0.50-0.74, P < 0.001) on dedicated homeless clinic predicting inappropriate ED visits in multivariate logistic regression. CONCLUSION: Implementing a dedicated homeless clinic with these features can reduce ED inappropriate utilization among patients experiencing homelessness.

Fulfillment, burnout and resilience in emergency medicine-Correlations and effects on patient and provider outcomes.

BACKGROUND: Healthcare provider wellness have been reported to correlate with patient care outcomes. It is not understood whether synergistic effects may exist between them. OBJECTIVE: We aim to investigate three provider wellness markers and determine their associations with provider self-reported medical errors and intent-to-leave outcomes among Emergency Department (ED) providers. DESIGN: This is a multi-center retrospective study. METHOD: Three wellness domains include professional fulfillment (PF), burnout (BO), and personal resilience (PR). Two outcomes measured as provider self-reported medical errors and provider intent-to-leave. Correlations between wellness markers and outcomes were analyzed. When adjusted for other confounders (provider demographics, provider experience, and operational environment), a multivariate logistic regression analysis was performed to further determine the interactions among these three domains on provider wellness affecting patient and provider related outcomes. RESULTS: Total 242 surveys were collected from providers at 16 different EDs. The median score of PF were 2.83 among physicians and 2.67 among APPs, BO were 1.00 (physicians) and 0.95 (APPs), and PR were 0.88 (physicians) and 0.81 (APPs). The median scores of self-reported medical errors were 1.50 (physicians) and 0.95 (APPs), and intent-to-leave were 1.00 (physicians and APPs). High correlations occurred among PF, BO, and PR. When analyzed together, high PF, low BO, and high PR functioned as a protective effect on provider intent-to-leave (adjusted odds ratios = 0.09, 95% CI 0.03-0.30). CONCLUSION: High correlations occurred among three provider wellness markers with no significant difference between physicians and APPs. Providers with high PR, low BO, and high PR tended to be more stable in their jobs.

Status of Emergency Department Seventy-Two Hour Return Visits Among Homeless Patients.

BACKGROUND: We aim to externally validate the status of emergency department (ED) appropriate utilization and 72-h ED returns among homeless patients. METHODS: This is a retrospective single-center observational study. Patients were divided into two groups (homeless versus non-homeless). Patients' general characteristics, clinical variables, ED appropriate utilization, and ED return disposition deviations were compared and analyzed separately. RESULTS: Study enrolled a total of 63,990 ED visits. Homeless patients comprised 9.3% (5,926) of visits. Higher ED 72-h returns occurred among homeless patients in comparison to the non-homeless patients (17% versus 5%, P < 0.001). Rate of significant ED disposition deviations (e.g., admission, triage to operation room, or death) on return visits were lower in homeless patients when compared to non-homeless patient populations (15% versus 23%, P < 0.001). CONCLUSIONS: Though ED return rate was higher among homeless patients, return visit case management seems appropriate, indicating that 72-h ED returns might not be an optimal healthcare quality measurement for homeless patients.

Identifying diverse concepts of discharge failure patients at emergency department in the USA: a large-scale retrospective observational study.

OBJECTIVES: Identifying patients who are at high risk for discharge failure allows for implementation of interventions to improve their care. However, discharge failure is currently defined in literature with great variability, making targeted interventions more difficult. We aim to derive a screening tool based on the existing diverse discharge failure models. DESIGN, SETTING AND PARTICIPANTS: This is a single-centre retrospective cohort study in the USA. Data from all patients discharged from the emergency department were collected from 1 January 2015 through 31 December 2017 and followed up within 30 days. METHODS: Scoring systems were derived using modified Framingham methods. Sensitivity, specificity and area under the receiver operational characteristic (AUC) were calculated and compared using both the broad and restricted discharge failure models. RESULTS: A total of 227 627 patients were included. The Screening for Healthcare fOllow-Up Tool (SHOUT) scoring system was derived based on the broad and restricted discharge failure models and applied back to the entire study cohort. A sensitivity of 80% and a specificity of 71% were found in SHOUT scores to identify patients with broad discharge failure with AUC of 0.83 (95% CI 0.83 to 0.84). When applied to a 3-day restricted discharge failure model, a sensitivity of 86% and a specificity of 60% were found to identify patients with AUC of 0.79 (95% CI 0.78 to 0.80). CONCLUSION: The SHOUT scoring system was derived and used to screen and identify patients that would ultimately become discharge failures, especially when using broad definitions of discharge failure. The SHOUT tool was internally validated and can be used to identify patients across a wide spectrum of discharge failure definitions.

Emergency Medicine Resident Efficiency and Emergency Department Crowding.

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.

Large observational study on risks predicting emergency department return visits and associated disposition deviations.

OBJECTIVE: A common emergency department (ED) patient care outcome metric is 72-hour ED return visits (EDRVs). Risks predictive of EDRV vary in different studies. However, risk differences associated with related versus unrelated EDRV and subsequent EDRV disposition deviations (EDRVDD) are rarely addressed. We aim to compare the potential risk patterns predictive of related and unrelated EDRV and further determine those potential risks predictive of EDRVDD. METHODS: We conducted a large retrospective observational study from September 1, 2015 through June 30, 2016. ED Patient demographic characteristics and clinical metrics were compared among patients of 1) related; 2) unrelated; and 3) no EDRVs. EDRVDD was defined as obvious disposition differences between initial ED visit and return visits. A multivariate multinomial logistic regression was performed to determine the independent risks predictive of EDRV and EDRVDD after adjusting for all confounders. RESULTS: A total of 63,990 patients were enrolled; 4.65% were considered related EDRV, and 1.80% were unrelated. The top risks predictive of EDRV were homeless, patient left without being seen, eloped, or left against medical advice. The top risks predictive of EDRVDD were geriatric and whether patients had primary care physicians regardless as to whether patient returns were related or unrelated to their initial ED visits. CONCLUSION: Over 6% of patients experienced ED return visits within 72 hours. Though risks predicting such revisits were multifactorial, similar risks were identified not only for ED return visits, but also for return ED visit disposition deviations.

Standardized Reporting System Use During Handoffs Reduces Patient Length of Stay in the Emergency Department.

BACKGROUND: Emergency department (ED) shift handoffs are potential sources of delay in care. We aimed to determine the impact that using standardized reporting tool and process may have on throughput metrics for patients undergoing a transition of care at shift change. METHODS: We performed a prospective, pre- and post-intervention quality improvement study from September 1 to November 30, 2015. A handoff procedure intervention, including a mandatory workshop and personnel training on a standard reporting system template, was implemented. The primary endpoint was patient length of stay (LOS). A comparative analysis of differences between patient LOS and various handoff communication methods were assessed pre- and post-intervention. Communication methods were entered a multivariable logistic regression model independently as risk factors for patient LOS. RESULTS: The final analysis included 1,006 patients, with 327 comprising the pre-intervention and 679 comprising the post-intervention populations. Bedside rounding occurred 45% of the time without a standard reporting during pre-intervention and increased to 85% of the time with the use of a standard reporting system in the post-intervention period (P < 0.001). Provider time (provider-initiated care to patient care completed) in the pre-intervention period averaged 297 min, but decreased to 265 min in the post-intervention period (P < 0.001). After adjusting for other communication methods, the use of a standard reporting system during handoff was associated with shortened ED LOS (OR = 0.60, 95% CI 0.40 - 0.90, P < 0.05). CONCLUSIONS: Standard reporting system use during emergency physician handoffs at shift change improves ED throughput efficiency and is associated with shorter ED LOS.