Emergency Department Wait Times Research Articles

Predictors of Hospital Admission for Pediatric Cyclic Vomiting Syndrome

To identify predictors of hospitalization in pediatric patients presenting to an emergency department (ED) for a cyclic vomiting syndrome (CVS) attack. We retrospectively reviewed patients with CVS seen at our institution between 2015 and 2018 and included those who met the Rome IV criteria for CVS. We identified all CVS-related ED visits and subsequently performed a case-control analysis, utilizing multivariate logistic regression, to identify clinical and demographic factors that may predict hospitalization. In total, 219 patients with CVS (usingInternational Statistical Classification of Diseases and Related Health Problems, 10th Revision) were identified, of which 65% met the inclusion criteria (median age 11years). We identified 152 CVS-related ED visits, of which 62% resulted in hospitalization. Factors found to predict hospitalization using multivariate analyses included male sex (P=.04), younger age (P=.027), delayed presentation (>24hours) to the ED (P<.001), and longer wait time prior treatment with antiemetics (P=.029). One-quarter of all patients with CVS had presented to the ED and nearly two-thirds of these ED visits resulted in hospitalization. A delayed presentation to the ED following the onset of symptoms was the strongest independent predictor of hospital admission, alongside male sex, younger age, and longer ED wait times before treatment with antiemetics. These findings suggest that early intervention may be key to successfully mitigating the risk of hospitalization for a CVS attack.

The critical care literature 2019.

An emergency physician (EP) is often the first health care provider to evaluate, resuscitate, and manage a critically ill patient. In recent years, the annual hours of critical care delivered in emergency departments across the United States has steadily increased. From 2006 to 2014, emergency department (ED) visits for critically ill patients increased approximately 80%. In addition to seeing more critically ill patients, EPs are often tasked with providing critical care long beyond the initial resuscitation period. In fact, more than 50% of ED patients that require admission to the ICU remain in the ED for more than 6h. Prolonged ED wait times for critically ill patients to be transferred to the ICU is associated with increased hospital, 30-day, and 90-day mortality. It is during these early hours of critical illness, while the patient is in the ED, where lives can be saved or lost. Therefore, it is important for the EP to be knowledgeable about recent developments in resuscitation and critical care medicine. This review summarizes important articles published in 2019 pertaining to the resuscitation and care of select critically ill patients. We chose these articles based on our opinion of the importance of the study findings and their application to emergency medicine. The following topics are covered: sepsis, rapid sequence intubation, mechanical ventilation, neurocritical care, post-cardiac arrest care, and ED-based ICUs.

Implementation evaluation of pre-hospital blood collection in regional Australia: a mixed methods study

BackgroundIn response to increasing emergency department presentations and wait times in Australia, multiple strategies and models of care have been implemented with varying results. One effective strategy has been the implementation of pre-hospital blood collection by paramedics when they insert an intravenous cannula. This research aims to determine the efficiency of and barriers to wider implementation of a pre-hospital blood collection trial in a regional context. In particular, to evaluate the impact of the pre-hospital blood collection on time to pathology results and error rates, and paramedic opinion. MethodsThis retrospective controlled cohort study was conducted over 12 months from August 2018. Emergency and pathology data were used to determine the haemolysis and error rates, as well as the time to result availability of pre-hospital blood collection samples compared to in hospital samples arrived by ambulance. To determine the facilitators and barriers to wider implementation a survey of 48 paramedics was conducted following completion of the 12-month trial. The survey was informed by the Theoretical Domains Framework, a behavior change theory associated with improved uptake when applied. FindingsOverall 237 samples were collected. There was a 65% (51min) reduction in time taken for samples to be received at pathology and a 38% (50min) improvement in time taken for results to return from pathology for patients arrived by ambulance. There were no labelling errors in the pre-hospital blood collection group or change in haemolysis rates. The majority (79%) of paramedics who completed the survey were optimistic about the protocol improving patient outcomes and 89% regarded the change in practice as acceptable. Three main themes emerged: 1. Training, environmental challenges and adequate equipment; 2. increased efficiency and improved patient care and 3. Prerequisites to implementing a new practice. Integration of Quantitative and Qualitative data resulted in 10 key influencers of behavior that need to be addressed in any future implementation. Conclusion and implications for practiceThe introduction of pre-hospital phlebotomy reduced the time to blood results availability by 38% and resulted in fewer labelling errors. Wider implementation is supported by paramedics, but more training is required.

COVID-19 Pandemic Leads to Decrease in Emergency Department Wait Times.

Public health and medical officials have been trying to reduce wait times in emergency departments (EDs) for years. Surprisingly, the coronavirus disease 2019 (COVID-19) pandemic seems to have done just that. But now people are concerned that wait times are getting shorter for the wrong reasons.

Innovative Use of Emergency Medicine Providers in an Urban Setting to Reduce Overutilization of 9-1-1.

Emergency Departments (ED) and Emergency Medical Services (EMS) are relied on to address nonemergent needs causing long ED wait times. Baltimore City EMS provided over 100,000 transports, many for low-acuity medical needs. Minor Definitive Care Now (MDCN) is designed to address low-acuity complaints and decrease ED visits. MDCN provides low-acuity 9-1-1 callers the option of on-scene evaluation and treatment. For patients requiring additional resources, but not needing an ED, an alternate destination is considered. Patients were screened low acuity by EMS personnel and voluntarily enrolled in MDCN. A questionnaire was given to patients after their visit to assess satisfaction. CRISP, a database for hospital visits in Maryland, was reviewed to assess if patients went to the ED after an MDCN visit. In 1year of service, 168 calls were screened, with 144 patients consenting to treatment by the MDCN team. Of enrolled patients: 94 (65%) were treated on the scene, 37 (26%) were transported to an urgent care facility, 1 (0.6%) was transported to their primary care provider for a same-day appointment, and 12 (8.4%) were transported to the ED after further evaluation. Of the 94 patients treated on scene, 3 (3.2%) presented to a local ED in the surrounding area within 72h. On review, there were no safety issues identified or deficits in the clinical care provided on scene. This innovative model of on-scene evaluation and treatment can potentially reduce transports, decrease ED wait times, and reduce costs, in an effective and efficient way.

Improving Timeliness of Internal Medicine Consults in the Emergency Department: A Quality Improvement Initiative.

Emergency department (ED) wait time is an important health system quality indicator. Prolonged consult to decision time (CTDT), the time it takes to reach a disposition decision after receiving a specialty consultation request, can contribute to increased overall length of stay in the ED. To identify delays in the consultation process for general internal medicine (GIM) and trial interventions to reduce CTDT. The study was conducted at a large tertiary teaching hospital with GIM inpatient wards at two campuses. Four interventions were trialed over sequential Plan-Do-Study-Act cycles: (1) process mapping, (2) resident education sessions, (3) audit and feedback of CTDT, and (4) adding a swing shift during peak consult volume. The primary outcome measures were mean CTDT for patients admitted to GIM and the proportion of admitted patients with CTDT of less than 3 hours. Mean CTDT decreased from 4.61 hours before intervention to 4.18 hours after intervention (p < .0001). The proportion of GIM patients with CTDT less than 3 hours increased from 25% to 33% (p < .0001). The interventions trialed led to a sustained reduction in CTDT over a 12-month period and demonstrated the effectiveness of education in influencing physician performance.

85 Parent and youth experiences of emergency department care for suspected long bone fractures in children

Abstract Background Long bone fractures are a frequent reason for visits to the pediatric emergency department (ED) where patients obtain the definitive care they require. Youth are rarely involved in quality assessment and improvement initiatives. Objectives To describe parent and youth experiences of their ED care for suspected long bone fractures. Design/Methods This prospective survey study of parents/youth consulting a tertiary care pediatric ED used surveys co-constructed by ED clinicians and parent partners. English/French speaking patients aged 10 to 18 years old with suspected long-bone fracture at triage – and their parents – were eligible to complete the survey during their ED stay. Electronic surveys were developed for parents and youth and pre-tested with 12 youth and 14 parents. Descriptive data was generated using SPSS v.25 (IBM Inc.). Results Three hundred surveys were distributed to eligible participants from March 2018 to June 2019 and 249 surveys were completed (148 parents, 101 youth). At least one member from 189 families completed a survey. Parents and youth participants had median ages of 44 and 12 years old, respectively. Most patients (98%) had a radiograph as part of their work-up, usually in the ED (76%); Twenty-seven percent were referred with radiographs. Most families (61%) received a diagnosis of long-bone fracture and were referred to orthopaedics for outpatient follow-up. Families’ median length of stay was 4h08min (range 43 min-14h30min). Parents (88mm) and youth (81mm) reported high median satisfaction with their overall ED care on 100 mm visual analog scales. Most parents (81%) and youth (75%) reported receiving just enough information from ED staff, though only half (50%) of the parents received information on how to care for their child at home. More than 90% of parents believed that the ED team treated their child’s pain. Eighty-one percent of youth reported that pain was treated sufficiently although 63% believed it had been treated quickly. Most parents (56%) and youth (72%) reported their ED stay was longer than anticipated; parents’ perceptions did not correlate with ED length of stay on logistic regression (p=0.014), while youth’s did (p=0.009). Few parents (33%) or youth (32%) reported receiving regular information on ED wait times. Conclusion Parent and youth perspectives of their ED care are complementary, and both should be considered in patient-oriented quality improvement initiatives.

Emergency Department Wait Times in NYS Hospitals on Prevalence of Hospital-Acquired C. difficile Infection

<h3>Background</h3> C. difficile is the most common hospital-acquired infection, as it is resistant to many sterilization and disinfection measures and can persist in the environment for extended periods of time. Given the fast-paced, high-volume care environment of emergency departments (ED), higher levels of C. difficile contamination are often found in the ED, compared to other areas of the hospital. This study examined the relationship between average patient wait times in the ED before admission, and overall hospital-acquired C. difficile infection (HA-CDI) rates in New York State acute care hospitals. <h3>Methods</h3> A time series (2013-2015), fixed-effects regression analysis was conducted comparing each facility's annual average ED wait time for admitted patients, to that facility's average (HA-CDI) rates, limited to patients entering through the ED. This model controlled for known clinical and non-clinical predictors of HA-CDI: average length of stay; case mix index; total discharges, a measure of hospital size; and percent Medicare discharges, a proxy for advanced age. <h3>Results</h3> ED wait times had a significant and positive relationship with HA-CDI rates. Facilities experience an additional .003 cases of HA-CDI/1,000 patient discharges with every additional minute patients spend in that facility's emergency department (P < .001), on average. Standardized coefficients were also calculated to indicate variable effect size in the model. ED wait times had the largest effect size (.310), indicating they explain more of the variance in HA-CDI rates for patients entering through the ED than some of the best-known predictors of HA-CDI. <h3>Conclusions</h3> The relationship between ED wait times and eventual HA-CDI warrants further exploration. These findings suggest efforts to reduce ED wait times for admitted patients, or more rigorous environmental cleanliness strategies in the ED, as possible avenues for HA-CDI prevention.

Patient Waiting Time in Emergency Department of a Tertiary Level Hospital

Background: Patient waiting time is an important factor of utilizing hospital service, patient satisfaction, quality of management etc. Hence, emergency department (ED) performance can be best assessed by measuring patients’ waiting time and affecting factors at this department.&#x0D; Objective: To determine patient waiting time at ED and associated factors in a tertiary level hospital.&#x0D; Materials and methods: This cross sectional study was conducted in ED of Chattogram Medical College &amp; Hospital (CMCH), Bangladesh. A total of 175 patients or their attendants at ED were enrolled by convenient sampling technique. Data were collected by face- to- face interview with pretested semi-structured questionnaire and observational checklist. Descriptive statistics was used for analysis by MS Excel and SPSS 21st version windows software.&#x0D; Result: Around half (47.40%) was male followed by female (32%) and children (20.6%). Mean (± SD) age of the patients was 33.69 ± 21.33 years. More than half (52.6%) were from rural areas followed by urban areas (47.7%). Average monthly family income was Tk. 23891.43(±13033.76). Mean (±SD) waiting time for collecting ticket, consultation with doctor, physical examination, and nursing services were 8.06(±7.98) minutes, 5.46(±6.95) minutes, 2.30(±1.11) minutes and 8(±6.22) minutes respectively. More than two fifth (41.1%) patients had waiting time within 5 minutes. The ratio of doctor/patient, nurse/patient, ticket provider/patient and trolleys &amp; wheel chairs bearer/patient were 1:115, 1:58, 1:115 and 1:40 respectively. Majority of the patients (57.7%) were transferred to Inpatient Department (IPD). Statistically significant relationship was found between satisfaction on overall management and overall waiting time of ED (χ2 = 22.47, df =6, p=0.001).&#x0D; Conclusion: Majority (48.1%) found satisfied with waiting time at the emergency department.&#x0D; JOPSOM 2019; 38(2): 15-21

Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time.

Emergency Department (ED) overcrowding is a major global healthcare issue. Many research studies have been conducted to predict ED wait time using various machine learning prediction models to enhance patient experience and improve care efficiency and resource allocation. In this paper, we used Long Short-Term Memory (LSTM) recurrent neural networks to build a model to predict ED wait time in the next 2 hours using a randomly generated patient timestamp dataset of a typical patient hospital journey. Compared with Linear Regression model, the average mean absolute error for the LSTM model is decreased by 9.7% (3 minutes) (p < 0.01). The LSTM model statistically outperforms the LR model, however, both models could be practically useful in ED wait time prediction.

Using Long Short-Term Memory (LSTM) Neural Networks to Predict Emergency Department Wait Time.

Emergency Department (ED) overcrowding is a major global healthcare issue. In this paper, we used Long Short-Term Memory (LSTM) recurrent neural networks to build a model to predict ED wait time in the next 2 hours using a randomly generated patient timestamp dataset of a typical patient hospital journey. Compared with Linear Regression model, the average mean absolute error for the LSTM model is decreased by 15% (3 minutes) (p<0.001). The LSTM model statistically outperforms the LR model, however, both models could be practically useful in ED wait time prediction.

P105: Observational study of distribution of time and activities over the course of an emergency physician's shift

Introduction: The growing scrutiny to improve Emergency Department (ED) wait times and patient flow have resulted in many efforts to increase efficiency and maximize patient throughput via systems improvements. This study investigates areas of efficiency improvement from the Emergency Physician (EP) perspective by examining EP workflow in a two phased observational time-motion study. In the initial phase, the distribution of time and activities of EPs were dissected to identify potential sources for streamlining to maximize physician productivity. The first phase was of the study was completed during the period immediately preceding the implementation of an Electronic Health Records (EHR). The second phase of the study will repeat the analysis one year post EHR implementation. This data will be dissected to again identify sources for streamlining in an EHR environment and to identify shifts in work flow from a paper-based system. Methods: An observational time motion study was conducted at St. Mary's Hospital ED, in Kitchener Ontario. An observer was paired with an EP for the duration of an 8 hour shift, to a total of 14 shifts in the first phase of the study. Nine task categories were measured concurrently with a stopwatch application on a tablet, along with the number of interruptions experienced by the EP. Means of each category were calculated and converted to percentages, representing the amount of time per 8 hour shift dedicated to each activity. The second phase will be repeated in Fall 2020, 1 year after EHR implementation. Results: A total of 14 shifts were observed, accounting for 112 hours of observation. EP's time was allocated amongst the following categories: direct patient interaction (40.8%), documentation (27.1%), reviewing patient results (18.4%), communicating with ED staff (7.63%), personal activities (5.7%), writing orders (5.1%), communicating with consultants (3.3%), teaching (1.7%) and medical information searches (1.3%). On average, EPs experienced 15.8 interruptions over the course of an 8 hour shift. Conclusion: In a paper charting system, the direct patient interaction accounts for the largest timeshare over the course of a given shift. However, the next two largest categories, documentation and reviewing patient data, both represent areas of potential streamlining via clerical improvements. Additionally, detailed measurements of EPs’ activities have proven feasible and provides the potential for future insight into the impact of EHR's on EP workflow.

P132: Optimizing a physician surge protocol to address emergency department wait times during times of increased patient demand

Background: Emergency Department overcrowding remains a significant problem. Interventions have often focused on areas external to the ED, with patient flow in the ED receiving less attention. Efforts to address ED flow are complicated by daily fluctuations in patient volume and acuity. Our local protocol brings in additional physicians when internal metrics indicate patient demand can't be met by current physician resources (a ‘surge’ period). However, anecdotal evidence suggests a lack of satisfaction and efficacy. We therefore undertook a project to improve our local management of these surge periods. Aim Statement: To improve the effectiveness of an ED Physician Surge Protocol to allow for a physician scheduling strategy that is reflective of the needs of the ED. Measures &amp; Design: This project consists of 3 phases. Phase 1 was an analysis of current surge metrics (including frequency, temporal patterns and physician response), with concurrent literature search to identify any best practices or easily addressable protocol changes, with first planned PDSA cycle. Phase 2 is a mixed methods survey of local staff to identify barriers and enablers of our current protocol, concurrent with a national survey of current practices. Phase 3 will be the implementation of a revised protocol, followed by a second mixed methods survey and analysis of metrics of interest. Evaluation/Results: Analysis of surge data (Oct 2018-Oct 2019) demonstrated a high volume of surges per month (78.7 +/- 10.9), highest at Foothills Medical Centre (94.3). Across all sites, afternoon periods had highest frequency of surges (absolute peak 1400 - 1500) with a secondary peak 2200–2300, both peaks occurring most frequently on weekends (Fri-Sun) However, physician response to surge calls was &lt; 10% (5.8-9.1%), with no discernable temporal pattern, even accounting for the significant number of automatic surge calls cancelled by clinicians. Analysis of data, in addition to literature review and engagement with senior administration suggested no immediate protocol changes, therefore project moved to 2nd phase. This phase is currently in progress, with planned analysis using Pareto Chart methodology. Discussion/Impact: Our initial data clearly demonstrates that current procedures are inadequate to address this ongoing issue, with no readily apparent solutions. Analysis of local barriers and enablers is currently underway, in addition to a national survey, with the results expected to inform an extensive redesign of current procedures.

The interplay of roles and routines: situating, performances and patterning in the emergency department

PurposeWhile previous research acknowledges the influence of roles on routine dynamics, roles are largely taken for granted. The purpose of this paper aims at examining how roles and routines interplay in accomplishing work in organizations.Design/methodology/approachA four-year ethnography of an emergency department (ED) at a university hospital was conducted through observations, interviews and documents.FindingsRoles and routines are formed by scripted and unscripted patterns, which are brought into performances following a situational assessment. Performances trigger patterning processes prompting the co-construction of role and routine patterns.Practical implicationsThis study highlights the importance of designing flexible structures. Managers can benefit from identifying unscripted patterns critical to work performance and making them part of scripted patterns. Managers should contemplate the influence that individuals, their relations and context have on how work is done.Social implicationsThis study suggests that the existence of different patterns impacts the length of wait times in EDs, a societal issue worldwide because of the effects that waiting can have on the patient's health condition and the unnecessary costs it carries. This study can help design solutions to decrease wait times.Originality/valueThis paper contributes to research on routine dynamics by providing a more nuanced explanation of the sources of endogenous change and how these enable organizational stability and flexibility.

Games People Play: Lessons on Performance Measure Gaming from New Zealand Comment on "Gaming New Zealand's Emergency Department Target: How and Why Did It Vary Over Time and Between Organisations?"

For decades, observers have noted that gaming of performance measurement appears to be both endemic and endlessly creative. A recent study by Tenbensel and colleagues provides a detailed look at gaming of a health system performance measure—emergency department (ED) wait times—within four hospitals in New Zealand. Combined, these four hospitals handled more than 25% of the ED visits in the country each year. Tenbensel and colleagues examine whether the New Zealand ED wait time target was set appropriately and whether we can trust any performance measure statistics that are not independently verified or audited. Their thoughtprovoking examination is relevant to anyone working in quality improvement and provides a valuable set of tools for detecting gaming in performance measurement.

Scheduling of Physicians with Time-Varying Productivity Levels in Emergency Departments

Emergency department (ED) overcrowding and long patient wait times have been studied extensively in the literature due to its relevance. We propose a novel approach to assign physicians to shifts such that ED wait times are reduced without adding new physicians. In particular, we extend the physician rostering problem by including the heterogeneity between emergency physicians with regard to their productivity levels (measured as patient-per-hour rate) and by including the stochastic nature of patient arrivals and physician productivity. We formulate the physician rostering problem as a two-stage stochastic program and solve it with a sample average approximation and the L-shaped method. To formulate the problem, we perform a data analysis to investigate the major drivers of physician productivity levels using patient visit data from our partner ED, and find that it is sufficient to only consider individual physicians and the shift hour in which they are scheduled to provide emergency care. In particular, the workload and occupancy levels in the ED have a significant impact on physician productivity levels but the magnitude of this impact is minor. A simulation study calibrated using real data shows that the new scheduling from our formulation can reduce patient wait times by as much as 20%, compared to the current scheduling at our study ED. We also demonstrate how to incorporate physician preference in scheduling through physician clustering based on their productivity levels. Our simulation results show that EDs can receive almost full benefit even when the number of clusters is fairly small.

Using Emergency Department Data to Inform Specialty Strategy: Analyzing the Distribution of 13,777 Consecutive Immediate Orthopaedic Consults in an Urban Community Emergency Department

Introduction:Although an estimated 20% of emergency department (ED) visits are musculoskeletal in nature, it is unclear which of these require urgent orthopaedic consultation and which orthopaedic subspecialties are best suited for these consults. When an ED's internal staff does not have necessary orthopaedic coverage, transfer to other facilities is required. However, transfers pose major inconveniences and financial burdens to patients and can lead to long ED wait times, hospital financial loss from walk outs, decreased patient safety, and decreased staff morale. Therefore, it is beneficial for a hospital to have the appropriate staff readily available for consults. Data can be used to assess the orthopaedic needs of an ED. We evaluated the nature of urgent ED consults requesting the timely presence of an orthopaedic provider.Methods:Between the years 2008 and 2017, the Orthopaedics Department of this Health System saw 13,777 patients from the ED requesting immediate consult from an orthopaedic provider. We retrospectively analyzed the distribution of anatomic areas and nature of these injuries for these visits.Results:Hand, foot, and ankle consults comprised 75% of the volume. Knee, hip, and spine accounted for 15% of consults. Most injuries were fractures. Infections and sprains were also common.Discussion:By determining and understanding this distribution, orthopaedic departments can improve their organization to better respond to urgent ED consults, allowing for the proper delivery of orthopaedic point-of-care service to patients, increased revenue for the hospital, proper availability of core competencies, and increased value to the healthcare delivery system as a whole. We also believe that the trends observed in our data are largely generalizable to EDs serving urban communities similar to ours. Thus, these results can help inform a synergistic strategy for the system comprising EDs, urgent care clinics, and orthopaedic surgeons servicing them.

Self-Check-In Kiosks Utilization and Their Association With Wait Times in Emergency Departments in the United States.

Delayed care in emergency departments (EDs) is a serious problem in the United States. Patient wait time is considered a critical measure of delayed care in EDs. Several strategies have been employed by EDs to reduce wait time, including implementation of self-check-in kiosks. However, the effect of kiosks on wait time in EDs is understudied. To assess the association between patient wait time and utilization of self-check-in kiosks in EDs. To investigate a series of other patient-, ED-, and hospital-level predictors of wait time in EDs. Using data from the 2015 and 2016 National Hospital Ambulatory Medical Care Survey, we analyzed 40,528 ED visits by constructing a multivariable linear regression model of the log-transformed wait time data as an outcome, then computing percent changes in wait times. During the study period, about 9% of EDs in the United States implemented kiosks. In our linear regression model, the wait time in EDs with kiosk self-check-in services was 56.8% shorter (95% confidence interval ̶ 130% to ̶ 6.4%, p<0.05) compared with EDs without kiosk services. In addition to kiosks, patients' day of visit, arrival time, triage assessment, arrival by ambulance, chronic medical conditions, ED boarding, hospitals' full-capacity protocol, and hospitals' location were significant predictors of wait time. Self-check-in kiosks are associated with shorter ED wait time in the United States. However, prolonged ED wait time continues to be a system-wide problem, and warrants multilayered interventions to address this challenge for those who are in acute need of immediate care.

An Analysis for the Use of Simulation Modeling in Reducing Patient Waiting Time in Emergency Departments (EDs) in Hospitals

The emergency department (ED) plays crucial role in providing 24-hour healthcare services to the ill with speed, accuracy, and sympathy. ED faces the problem of patient waiting time, which leads to patient dissatisfaction and patient crowding. This paper presents a systematic literature review of simulation of ED in healthcare systems from 1970 to 2013. The objectives of this review are to highlight the importance and role of simulation studies to solve the problem of patient waiting time faced by the ED. It also discusses how simulation can be better applied as a tool to solve these problems. The authors found that these simulation studies focus important insights into ED problems, but they also had some limitations that should be addressed.

Emergency Department Waiting Times and Determinants of Prolonged Length of Stay in a Botswana Referral Hospital

Introduction: Emergency medicine is a critical component of quality public health service. In fact length of stay and waiting times in the Emergency department are key indicators of quality. The aim of this study was to determine waiting times and determinants of prolonged length of stay (LOS) in the Princess Marina Hospital Emergency Department. Methods: This was a retrospective observational study. It was done at Princess Marina, a referral hospital in Gaborone, Botswana. Triage forms of patients who presented between 19/11/ 2018 and 18/12/2018 were reviewed. Data from patient files was used to determine time duration from triage to being reviewed by a doctor, time duration from review by emergency doctor to patients’ disposition and the time duration from patient’s triage to disposition (length of stay). Prolonged length of stay was defined as duration > 6 hours. Results: A total of 1052 files repre- senting patients seen over a 1-month period were reviewed. 72.5% of the patients had a prolonged length of stay. The median emergency doctor waiting time was 4.5 hours (IQR 1.6 - 8.3 hours) and the maximum was 27.1 hours. The median length of stay in the emergency department was 9.6 hours (IQR 5.8 - 14.6 hours) and the maximum was 45.9 hours. Patient’s age (AOR 1.01), mental status (AOR 0.61), admission to internal medicine service (AOR 5.12) and pediatrics admissions (AOR 0.11) were significant predictors of prolonged length of stay in the emergency department. Conclusion: Princess Marina Hospital emergency department waiting times and length of stay are long. Age, normal mental status and internal medicine admission were independent predictors of prolonged stay (>6 hours). Admission to the pediatrics service was associated with shorter length of stay. There is a need for interventions to address the long waiting times and length of stay. Interventions should particularly focus on the identified predictors.