Emergency Department Wait Times Research Articles

Quality of care and emergency department throughput during the COVID-19 pandemic in a community health system Pandemic in a Community Health System

Objective: This retrospective study explores the strategic plan formulated by AHMC Health System in California, USA, to sustain and improve quality of care and emergency department (ED) efficiency during the COVID-19 pandemic. It also analyzes the plan’s outcomes.Background: The COVID-19 pandemic has posed challenges for both individuals and healthcare industries alike, impacting decision-making and access to care. AHMC faced staff and resource shortages, patient reluctance, and difficulties adapting to rapidly evolving public health guidelines. These challenges highlighted the critical need for effective plans to maintain or improve healthcare quality and ED performance.Methods: AHMC adopted a comprehensive three-layer strategic plan in 2020. The first layer, “Pandemic Response,” focused on leadership, staff training and education, infection control, new treatments, and employee vaccination rates. The second layer, “ED Throughput,” set objectives for metrics such as door-to-doctor (door-to-doc) time, ancillary turnaround time (TAT), ED length of stay (LOS), and the left-without-being-seen (LWBS) rates. Progress was monitored through monthly improvement meetings. The third layer, “Quality Excellence,” tracked improvements in COVID-adapted objectives on quality initiatives, based on CMS Quality Star Ratings, Leapfrog Hospital Safety Grades, and Yelp review scores.Results: By 2023, the three-layer strategic plan had led to many improvements in the quality of care and ED efficiency. AHMC identified 22,287 positive COVID-19 cases, expanded its ventilator inventory by 50%, and enhanced patient outcomes by applying updated treatments. Additionally, AHMC saw a 3% reduction in ED wait times and sustained its overall patient satisfaction rates, CMS Quality Star Rating, and Leapfrog Hospital Safety Grade scores.Conclusions: AHMC’s three-layer strategic plan showed effectiveness in maintaining quality of care and ED efficiency during the COVID-19 pandemic. By focusing on “Pandemic Response,” “ED Throughput,” and “Quality Excellence,” AHMC was able to adapt to the rapidly evolving public health guidelines, expand its capacity to treat COVID-19 patients and sustain its overall patient safety, satisfaction, and quality ratings. The implementation of this plan highlights the importance of proactive and comprehensive strategies in managing healthcare crises.

Impact of Nurse Practitioner Role in Emergency Departments

Background Overcrowding and long wait times in the emergency department (ED) have resulted in decreased patient satisfaction and quality of care. One of the solutions proposed to address wait times is the introduction of the nurse practitioner (NP) role in the ED. We present a systematic mixed studies review protocol that aims to gather and analyze available knowledge on the impact of the NP role in the ED on patients, other healthcare providers, and organizations. Methods The review will employ a mixed studies analysis approach. Data will be gathered from peer-reviewed and grey literature in English with no time limit. All international publications on the impact of NP role implementation that meets the inclusion criteria in the ED setting will be included. Each study will be appraised for quality using the mixed methods appraisal tool and data extracted by two independent authors. In the presence of conflict, a third author will provide a resolution. Study characteristics and findings will be synthesized using descriptive analysis, meta-analysis, and a three-stage thematic analysis approach. The review results will be presented using the PRISMA checklist for systematic reviews. Conclusions The systematic review will present current evidence on the impact of NP role implementation in the ED setting. The results are anticipated to support decisions and policymakers in their quest to decrease ED wait times and improve the quality of patient care in healthcare settings. Keywords: Nursing, Nurse Practitioner, Emergency Department, Patient Care, Systematic Review

A Granular Approach to Optimal and Fair Patient Placement in Hospital Emergency Departments

Prolonged emergency department (ED) length of stay (LOS) is associated with detrimental effects on patient care quality and outcomes. There is evidence that certain groups of patients experience longer LOS based on their gender or race, especially with regard to the part of LOS that is attributable to waiting to be seen by a clinician. This work tackles the patient prioritization and placement aspects of ED operations with the goal of improving throughput and wait time in a fair, equitable way. We present a novel Mixed Integer Linear Programming (MILP) predictive-prescriptive formulation that incorporates a breakdown of predicted patient ED LOS into actionable pieces. We incorporate considerations for fairness and reformulate the MILP formulation into a compact and computationally tractable formulation that can be solved efficiently in real time. To deal with uncertainty, we propose a sampling-based solution, and provide provable guarantees regarding its convergence, stability and sample complexity. The proposed solution increases the throughput of the ED by [Formula: see text] and decreases the average wait time by [Formula: see text] compared to current hospital practice. In addition, the method is near-optimal in terms of throughput, and produces high-quality solutions in terms of average wait time compared to a clairvoyant oracle. Our proposed approach demonstrates desirable properties when it comes to fairness in patient prioritization, illustrating a path for addressing hidden biases in patient ED wait times and hospital operations as a whole. This work was conducted in collaboration with a large US academic medical center. Data from more than 40,000 patient visits were used to shape and evaluate the predictive-prescriptive models. An important practical contribution is translating a complex algorithm’s output into recommendations that can be operationalized in the context of existing processes in the ED. Specifically, we develop an interpretable metamodel that is trained to mimic the predictive-prescriptive algorithm’s decisions and provides a transparent set of rules for patient placement. The method will be used by the hospital to improve patient flow and quality of care as well as to support more fair and consistent bed allocation decisions.

Applications of Artificial Intelligence in Emergency Departments to Improve Wait Times: Protocol for an Integrative Living Review.

Long wait times in the emergency department (ED) are a major issue for health care systems all over the world. The application of artificial intelligence (AI) is a novel strategy to reduce ED wait times when compared to the interventions included in previous research endeavors. To date, comprehensive systematic reviews that include studies involving AI applications in the context of EDs have covered a wide range of AI implementation issues. However, the lack of an iterative update strategy limits the use of these reviews. Since the subject of AI development is cutting edge and is continuously changing, reviews in this area must be frequently updated to remain relevant. This study aims to provide a summary of the evidence that is currently available regarding how AI can affect ED wait times; discuss the applications of AI in improving wait times; and periodically assess the depth, breadth, and quality of the evidence supporting the application of AI in reducing ED wait times. We plan to conduct a living systematic review (LSR). Our strategy involves conducting continuous monitoring of evidence, with biannual search updates and annual review updates. Upon completing the initial round of the review, we will refine the search strategy and establish clear schedules for updating the LSR. An interpretive synthesis using Whittemore and Knafl's framework will be performed to compile and summarize the findings. The review will be carried out using an integrated knowledge translation strategy, and knowledge users will be involved at all stages of the review to guarantee applicability, usability, and clarity of purpose. The literature search was completed by September 22, 2023, and identified 17,569 articles. The title and abstract screening were completed by December 9, 2023. In total, 70 papers were eligible. The full-text screening is in progress. The review will summarize AI applications that improve ED wait time. The LSR enables researchers to maintain high methodological rigor while enhancing the timeliness, applicability, and value of the review. DERR1-10.2196/52612.

An Investigation into Demographic Disparities in Emergency Department Disposition Decisions

We investigate the presence of health disparities in emergency department (ED) disposition decisions and if crowding levels might have an exacerbating role. Using data from a large, academic ED, we find statistically significant associations between ED disposition decisions and patient sex, race, as well as ethnicity, with male, Caucasian, and non-Hispanic patients being more likely to be admitted to the hospital compared with, respectively, female, African-American, and Hispanic patients. In line with earlier findings in other studies, we find that longer waiting times, suggesting higher levels of ED crowding, is associated with higher rates of admission. Moreover, longer ED wait times modified sex differences, suggesting that the disposition disparity in female patients might be exacerbated when the ED is more crowded.

Novel Uses of Methadone Under the “72-Hour Rule” to Facilitate Transitions of Care and Low-Dose Buprenorphine Induction in an Outpatient Bridge Clinic

Background Federal regulations restrict methadone for opioid use disorder (OUD) treatment to licensed opioid treatment programs (OTPs). However, providers in other settings can administer methadone for opioid withdrawal under the “72-hour rule” while linking to further care. Prior work has demonstrated that methadone initiation in a low-barrier bridge clinic is associated with high OTP linkage and 1-month retention rates. We describe 2 other novel applications of the 72-hour rule in which methadone withdrawal management facilitated linkage to inpatient hospitalization and outpatient buprenorphine induction. Case Presentations Patient 1 was a 46-year-old woman with OUD complicated by serious injection-related infections. Severe opioid withdrawal limited her ability to tolerate emergency department wait times and receive inpatient care. We administered methadone for opioid withdrawal in an outpatient bridge clinic immediately before emergency department referral; this enabled hospital admission for intravenous antibiotics and anticoagulation. Patient 2 was a 36-year-old man with OUD desiring buprenorphine treatment. He had been unable to complete traditional buprenorphine induction without experiencing precipitated withdrawal. Thus, we recommended a low-dose buprenorphine induction overlapping with a full opioid agonist. Given the patient's preference to stop using fentanyl immediately, he received 72 hours of methadone for withdrawal treatment during the induction phase and successfully transitioned to buprenorphine without significant concomitant fentanyl use. Conclusion In addition to facilitating OTP linkage, on-demand 72-hour methadone administration for opioid withdrawal can reduce barriers to acute medical care and buprenorphine treatment.

Lifting the curtain on the emergency department crisis: a multi-method reception study of Larry Saves the Canadian Healthcare System

BackgroundDespite growing evidence of the potential of arts-based modalities to translate knowledge and spark discussion on complex issues, applications to health policy are rare. This study explored the potential of a research-based theatrical video to increase public capacity and motivation to engage with the complex issues that make Emergency Department wait times such an intractable problem.MethodsLarry Saves the Canadian Healthcare System is a digital musical micro-series developed from extensive research examining system-level causes of Emergency crowding and the ineffectiveness of prevailing approaches. We released individual episodes and a revised full-length version on YouTube, using organic promotion strategies and paid advertising. We used YouTube Analytics to track views, engagement and viewer demographics, and content-analyzed viewer comments. We also conducted five university-based screenings; 92 students completed questionnaires, rating Larry on 16 descriptors using a 7-point Likert scale.ResultsFrom June 2022 through May 2023, Larry garnered over 100,000 views (76,752 of the full-length version, 35,535 of episodes), 1329 likes, 2780 shares, and 139 comments. Views and watch time were higher among women and positively associated with age. Among YouTube comments, the predominating themes were praise for the video and criticism of the healthcare system. Many commenters applauded the show’s accuracy, humor, and/or resonance with their experience; several shared healthcare horror stories. Students overwhelmingly agreed with all positive and disagreed with all negative descriptors, and nearly unanimously deemed the video informative, thought-provoking, and entertaining. Most also affirmed that it had increased their knowledge, interest, and confidence to participate in discussions about healthcare issues. Neither gender, primary language, nor employment in healthcare predicted ratings, but graduate students and those 25+ years old evaluated the video most positively.DiscussionThese findings highlight the promise of research-informed musical satire to inform and invigorate discourse on an urgent health policy problem. Larry has reached tens of thousands of viewers, garnered excellent feedback, and received high student ratings. Further research should directly assess educational and behavioural outcomes and explore what facilitative strategies could maximize this knowledge translation product’s potential to foster informed, impactful policy dialogue.

Patient Sensitivity to Emergency Department Waiting Time Announcements

Problem definition: Emergency department (ED) delay announcement systems are implemented in many countries. We answer three important questions pertaining to the operations and effectiveness of such systems by studying the public hospital network and ED waiting time (WT) announcement system in Hong Kong’s “universal” public healthcare system: (1) How many patients are aware of (and sensitive to) the ED WT announcements? (2) How sensitive are these patients to the announced WT? (3) How can the Hong Kong government improve the WT announcement system? Methodology/results: We study over 1.3 million patient visits to the 17 tier 1 public EDs. We structurally estimate the fraction of patients sensitive to the announced WT and their sensitivity to the announcements as well as patient characteristics that lead to higher sensitivity. In the patient’s ED choice decision, we estimate the trade-off between the travel distance to an ED and the expected WT at the ED. We find that 3.1% of the patients are sensitive to the announced WT, and they are willing to travel an additional 4.8 km to save one hour of waiting. Urgent patients are less likely to be sensitive to the delay announcement than less urgent patients, but those that are sensitive are more WT averse than their less urgent counterparts. Counterfactual analysis shows that the average actual WT and number of patients who leave without being seen can be reduced by 4.6% and 8.5%, respectively, by increasing the fraction of sensitive patients to 15.0% and, simultaneously, reducing the announced WT assessment window to one hour from the current level of three hours. Further improvement can be achieved by providing predicted WT information based on the current level of ED crowding or less extreme past performance—median WT rather than the currently used 95th percentile. Managerial implications: The Hong Kong government should utilize the two levers of the announcement system: the sensitive fraction of patients and information recency. Increasing the sensitive fraction can benefit the system when it is below a certain threshold level. However, administrators should exercise caution when the sensitive fraction becomes large and consider implementing additional measures to mitigate the negative effects of information delay. The sensitive group of patients can unfairly be punished for their proactiveness. Shortening the announced WT assessment window and providing predicted WT are possible alternatives that not only improve overall performance but also exhibit strong robustness to increases in the sensitive population. History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition. Funding: This work was supported by the Research Grants Council of the University Grants Committee through the Collaborative Research Fund [Grant C7162-20G] and General Research Fund [Grant 17502320]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0457 .

P38 Forecasting Emergency Department Waiting Times Using Deep Neural Networks

P38 Forecasting Emergency Department Waiting Times Using Deep Neural Networks

ParallelED—A novel screening and referral intervention using emergency department wait times to identify and address unmet social needs

AbstractBackgroundPeople arriving at the emergency department (ED) often have unmet health‐related social needs (HRSN). We implemented an intervention that used undergraduate student volunteers to screen patients in the ED waiting room (WR) for unmet social drivers of health and subsequently referred patients to community resources.MethodsThis cross‐sectional quality improvement study included patients who were approached to complete a HRSN screening questionnaire, subsequently referred to community resources, and followed up by phone from October 2021 to October 2022 in an ED WR of an academic medical center. Primary measures were the proportions of patients who had unmet HRSN and the proportions enrolled in a statewide database of social care resources—NCCARE360. Patient demographics and geospatial distribution were also assessed to better understand the population served.ResultsOur intervention reached 3297 unique patients, with 398 patients (12%) agreeing to complete screening. Of those screened, 93% were positive for at least one social need and 95% of the aforementioned were interested in receiving assistance. A total of 60% of those who screened positive were enrolled into NCCARE360. Persons identifying as female or non‐Hispanic Black were disproportionately represented at a higher rate among those who screened positive for at least one social need, with food and housing insecurity emerging as the most common referral categories.ConclusionOur results demonstrate patients’ willingness to be screened in the ED WR and a high identification of HRSN. Our findings show that idle time in the ED WR can be used to identify patients with unmet HRSN and refer them to resources.

Future leaders in a learning health system: Exploring the Health System Impact Fellowship.

The Canadian health system is reeling following the COVID-19 pandemic. Strains have become growing cracks, with long emergency department wait times, shortage of human health resources, and growing dissatisfaction from both clinicians and patients. To address long-needed health system reform in Canada, a modernization of training is required for the next generation health leaders. The Canadian Institutes of Health Research Health System Impact Fellowship (HSIF) is an example of a well-funded and connected training program which prioritizes embedded research and embedding technically trained scholars with health system partners. The program has been successful in the scope and impact of its training outcomes as well as providing health system partners with a pool of connected and capable scholars. Looking forward, integrating aspects of evidence synthesis from both domestic and international sources and adapting a general contractor approach to implementation within the HSIF could help catalyze learning health system reform in Canada.

Homelessness and Emergency Department Use: Wait Time Disparities Across Triage Acuity Levels.

Introduction Certain patient groups perceive specific barriers to accessing primary care, resulting in increased emergency department (ED) use for non-emergency conditions. There is evidence coming from other countries that homeless people are treated differently in accessing emergency services.Examination of ED wait time by demographic characteristics provides pertinent information to identify subgroups that are more subject to the consequences or causes of access block and delayed treatment. Methods We analyzed five years of Emergency Department Information System (EDIS) visit records of the largest tertiary care center in Atlantic Canada. The wait time from triage to seeing a physician was the outcome, housing status was the main exposure, and age and gender were the predictors. Quantile regressions were carried out to identify the influence of homeless visits in meeting the Canadian Association of Emergency Physicians (CAEP) wait time benchmarks for each Canadian Triage and Acuity Scale (CTAS) level. The classification and regression tree method was used to quantify and classify the demographic subgroups contributing to wait time disparities across CTAS levels. Results Homeless visit medianwait times that exceeded the three-hour CAEP benchmark were significantly longer for urgent (by 40 minutes, CI: 25-55), semi-urgent (by 31 minutes, CI: 17-45), and non-urgent (by 57 minutes, CI: 25-89) than acuity level-matched domiciled visitwait times. At the 50th percentile, one-hourbenchmark homeless triaged as semi-urgent waited (median=20 minutes, CI: 12-28) longer, and no other triage-level differences were found at this benchmark. Homeless emergent-level visits that exceeded the three-hour benchmark were 28 minutes, on average, shorter than domiciled patients of the same acuity level. Homeless females above 40 stayed the longest for non-urgent care (mean=173 minutes), 82 minutes longer (p=0.0001) than age-gender-acuity level-matched domiciled patients. Homelessness was the most prominent ED wait time classifier for non-urgent, ED visits.Overall, homeless patients triaged as CTAS-5 waited 30 minutes longer (p=0.0001) than domiciled patients triaged as CTAS-5.Homeless male 16-20-year-olds waited the shortest time of 72 minutes. Conclusion Homelessness-related wait time disparities exist in the low acuity non-urgent-level ED visits more than in the other levels, supporting the theory that lack of primary care access is a driver of ED use in this group. Our acuity level analysis supports that homeless people of a certain age (older) and gender groups (female) wait longer than their age-gender-matched domiciled patients to be seen by a physician in low acuity level presentations. Given the pattern of the homeless being seen earlier or statistically similar in emergent-level visits compared to matched domiciled patients and that 16-20-year-old homeless males were seen on average within 72 minutes (the shortest mean wait time reported for the triage level CTAS-5), we decline the notion of discrimination at the study site ED. If homeless patients' non-urgent needs were met elsewhere, pressure on the ED to meet benchmarks might be reduced.

Patient sex, racial and ethnic disparities in emergency department triage: A multi-site retrospective study

ObjectivesThere is limited evidence on sex, racial, and ethnic disparities in Emergency Department (ED) triage across diverse settings. We evaluated differences in the assignment of Emergency Severity Index (ESI) by patient sex and race/ethnicity, accounting for age, clinical factors, and ED operating conditions. MethodsWe conducted a multi-site retrospective study of adult patients presenting to high-volume EDs from January 2019–February 2020. Patient-level data were obtained and analyzed from three EDs (academic, metropolitan community, and rural community) affiliated with a large health system in the Southeastern United States. For the study outcome, ESI levels were grouped into three categories: 1–2 (highest acuity), 3, and 4–5 (lowest acuity). Multinomial logistic regression was used to compare ESI categories by patient race/ethnicity and sex jointly (referent = White males), adjusted for patient age, insurance status, ED arrival mode, chief complaint category, comorbidity score, time of day, day of week, and average ED wait time. ResultsWe identified 186,840 eligible ED visits with 56,417 from the academic ED, 69,698 from the metropolitan community ED, and 60,725 from the rural community ED. Patient cohorts between EDs varied by patient age, race/ethnicity, and insurance status. The majority of patients were assigned ESI 3 in the academic and metropolitan community EDs (61% and 62%, respectively) whereas 47% were assigned ESI 3 in the rural community ED. In adjusted analyses, White females were less likely to be assigned ESI 1–2 compared to White males although both groups were roughly comparable in the assignment of ESI 4–5. Non-White and Hispanic females were generally least likely to be assigned ESI 1–2 in all EDs. Interactions between ED wait time and race/ethnicity-sex were not statistically significant. ConclusionsThis retrospective study of adult ED patients revealed sex and race/ethnicity-based differences in ESI assignment, after accounting for age, clinical factors, and ED operating conditions. These disparities persisted across three different large EDs, highlighting the need for ongoing research to address inequities in ED triage decision-making and associated patient-centered outcomes.

Factors Influencing Patient Waiting Time in Emergency Department of Khunyangu Sub-County Hospital, Busia Kenya

Background: Waiting time remains an important indicator of quality health services. The emergency department is the most critical area of any hospital. For patients who are in urgent need of hospitalization, delayed admission often leads to exacerbation of the patient's condition and may threaten the patient's life. In recent years, the flow of patients to the Emergency Departments of Western countries has steadily increased thus generating overcrowding and extended waiting times. Developing countries face daily challenges in the Emergency Department through huge exposure to several patients seen per day on average. The study therefore sought to establish factors influencing patient waiting time in the emergency department of Khuyangu Sub-County Hospital, Busia Kenya.
 Material and Methods: Descriptive cross-sectional research design guided the conduct of this study. Data collection was conducted on 191 patients and healthcare workers over four weeks using an interviewer-administered pretested structured questionnaire. Both descriptive statistics and inferential statistics were used for data analysis. To establish associations between the independent variable and dependent variables, correlations and cross-tabulations were used. Analysis of variance (ANOVA) was used to determine the influence of independent variables on the dependent variable.
 Findings: The findings demonstrated an R-squared value of 0.368. It was observed that there was a weak positive correlation that was statistically significant (r=.281, N=191, p=.000) between the availability of healthcare workers at their workstations and waiting time. There was a weak positive correlation that was also statistically significant (r=.228, N=191, p=.002) between communication on waiting time in areas where there was no health worker to attend to the patient and the waiting time.
 Conclusion and recommendations: The findings can be used to develop waiting time guidelines and improve waiting in the ED. This study recommends that hospital management should address the identified causes of delay to enable patients to get timely services. In addition, healthcare workers should be available at their workstations. Additional research is necessary to further evaluate the impact and utility of the emergency department.

Disparities in Emergency Department Waiting Times for Acute Gastrointestinal Bleeding: Results From the National Hospital Ambulatory Medical Care Survey, 2009-2018.

The primary aim of this study was to assess waiting time (WT) across different racial groups to determine whether racial disparities exist in patients presenting with gastrointestinal bleeding (GIB) to the United States emergency departments (EDs). Using the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2009 to 2018, we compared WT of patients with GIB across different racial/ethnic groups, including nonhispanic white (NHW), African American (AA), Hispanic White (HW), and Nonhispanic other. Multinomial logistic regression was applied to adjust the outcomes for possible confounders. We also assessed the trend of the WT over the study interval and compared the WT between the first (2009) and last year (2018) of the study interval. There were an estimated 7.8 million ED visits for GIB between 2009 and 2018. Mean WT ranged from 48 minutes in NHW to 68 minutes in AA. After adjusting for gender, age, geographic regions, payment type, type of GI bleeding, and triage status, multinomial logistic regression showed significantly higher waiting time for AA patients than NHW (OR 1.01, P=0.03). The overall trend showed a significant decrease in the mean WT (P value<0.001). In 2009, AA waited 69 minutes longer than NHW (P value<0.001), while in 2018, this gap was erased with no statistically significant difference (P value=0.26). Racial disparities among patients presenting with GIB are present in the United States EDs. African Americans waited longer for their first visits. Over time, ED wait time has decreased, leading to a decline in the observed racial disparity.

Participatory Modeling with Discrete-Event Simulation: A Hybrid Approach to Inform Policy Development to Reduce Emergency Department Wait Times

We detail a case study using a participatory modeling approach in the development and use of discrete-event simulations to identify intervention strategies aimed at reducing emergency department (ED) wait times in a Canadian health policy setting. A four-stage participatory modeling approach specifically adapted to the local policy environment was developed to engage stakeholders throughout the modeling processes. The participatory approach enabled a provincial team to engage a broad range of stakeholders to examine and identify the causes and solutions to lengthy ED wait times in the studied hospitals from a whole-system perspective. Each stage of the approach was demonstrated through its application in the case study. A novel and key feature of the participatory modeling approach was the development and use of a multi-criteria framework to identify and prioritize interventions to reduce ED wait times. We conclude with a discussion on lessons learned, which provide insights into future development and applications of participatory modeling methods to facilitate policy development and build multi-stakeholder consensus.

Evaluating a new emergency department avoidance service for older people: patient and relative experiences

BackgroundHigh emergency department (ED) usage by older individuals for non-emergencies is a global concern. ED avoidance initiatives have proven effective in addressing this issue. To specifically cater to individuals aged...

Anecdotes Drive Attitudes, Data Drives Decisions: Optimizing the Emergency Department Workup Prior to Surgical Consultations

Emergency department (ED) wait times and ED length of stay (LOS) have a significant impact on patient morbidity and mortality and patient satisfaction. Consultation-to-decision time can contribute to increased wait times and LOS in the ED. Up to 40% of patients presenting to the ED require consultation from subspecialty services. We hypothesize that in surgical patients, completion of workup prior to consultation will decrease consultation-to-decision time, ED wait times, and LOS in the ED. A retrospective review was conducted at a single site including all overnight general surgery consultations from the ED over 2-months. Data collected included wait times, LOS, and workup completed prior to consultation. Summary statistics were calculated and bivariate tests were performed using t-tests for continuous variables. Time to final surgical plan and LOS in ED were evaluated for 137 patients comparing "complete" and "incomplete" workups at time of consultation. It was considered a "complete" workup if labs and imaging were resulted prior to time of consult. If any baseline tests were not ordered prior to time of consult, it was considered an "incomplete" workup. Analysis demonstrated an average time of 4.9 and 2.5 hours for consultation-to-decision time for "incomplete" and "complete" workups respectively (p < 0.0001). For LOS in ED, there was an average of 11.4 and 7.9 hours for "incomplete" and "complete" workups respectively (p < 0.0001). There is a significant difference in consultation-to-decision time and LOS in the ED when consultation is performed following a complete versus incomplete workup for surgical patients independent of inherent wait times for testing to result and the need for additional testing requested from consulting services. Developing strategies to optimize workups from the ED prior to surgical consultation, including the development of care pathways, could significantly decrease patient wait times and LOS in the ED.

Covid-19 and its Impact on Hospitals

&#x0D; &#x0D; &#x0D; This qualitative study explored the extent to which the Covid-19 pandemic has impacted Canadian hospitals and emergency department (ED) wait times. A purposive sampling procedure was used for this study to conduct a content analysis on a sample of 50 of the most recent and relevant comments that included reactions, personal experiences, and possible solutions towards ED wait times from a CBC News article. A coding procedure examined any frequent themes and subcategories in the comments. Results showed six consistently present categorical themes: Wait Times, Shortage of Workers, Underfunded Healthcare System, Unrelated Covid-19 Symptoms, Avoidance, and Solutions. Furthermore, additional subcategories were determined from the themes. This study analyzes the intense backlogs of surgical cases and waiting rooms, resulting in adverse patient outcomes. Additionally, this study explores the underfunded and understaffed healthcare system, the stresses healthcare workers face daily, and possible solutions to mend this broken healthcare system.&#x0D; &#x0D; &#x0D;

Racial and Ethnic Disparities in Emergency Department Wait Times for Headache.

Headache is a common complaint of individuals seeking treatment in the emergency department (ED). Because pain is subjective, medical evaluation is susceptible to implicit bias that can lead to disparities in wait times. The aim of this study was to determine whether there are racial and ethnic disparities in ED wait times for headache. Our study used the 2015-2018 National Hospital Ambulatory Care Surveys (NHAMCS), a nationally representative sample of ambulatory care visits to EDs. Our sample consisted of visits made by adults for headaches, which were identified using ICD-10 diagnosis codes and NHAMCS reason for visit codes. There were 12,301,655 ED visits for headache represented by our sample. The mean wait time for headache visits was 38.1min (95%CI: 31.1, 45.0). The mean wait time for Non-Hispanic White patients, non-Hispanic Black patients, Hispanic patients, and the other race and ethnicity groups were 34.7min (95%CI: 27.5, 42.0), 46.4min (95%CI: 26.5, 66.4), 37.9min (95%CI: 19.4, 56.3), and 21.0min (95%CI: 6.3, 35.7) respectively. After controlling for patient- and hospital-level covariates, visits by non-Hispanic Black patients had 40% (95%CI: -0.01, 0.81, p = 0.056) longer wait times and visits by Hispanic patients had 39% (95%CI: -0.03, 0.80, p = 0.068) longer wait times than visits by non-Hispanic White patients. While our findings suggest that there may be longer wait times for visits by non-Hispanic Black and Hispanic patients compared to visits by non-Hispanic White patients, further research is needed to confirm these findings and determine causes of wait times disparities in the ED.