scholarly journals Emergency department waiting times: do the raw data tell the whole story?

2014 ◽  
Vol 38 (1) ◽  
pp. 65 ◽  
Author(s):  
Janette Green ◽  
James Dawber ◽  
Malcolm Masso ◽  
Kathy Eagar
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Peer Group ◽  
Waiting Times ◽  
Geographic Location ◽  
Cross Sectional ◽  
Raw Data ◽  
Performance Targets ◽  
Triage Category ◽  
Sectional Analysis

Objective To determine whether there are real differences in emergency department (ED) performance between Australian states and territories. Methods Cross-sectional analysis of 2009−10 attendances at an ED contributing to the Australian non-admitted patient ED care database. The main outcome measure was difference in waiting time across triage categories. Results There were more than 5.8 million ED attendances. Raw ED waiting times varied by a range of factors including jurisdiction, triage category, geographic location and hospital peer group. All variables were significant in a model designed to test the effect of jurisdiction on ED waiting times, including triage category, hospital peer group, patient socioeconomic status and patient remoteness. When the interaction between triage category and jurisdiction entered the model, it was found to have a significant effect on ED waiting times (P < 0.001) and triage was also significant (P < 0.001). Jurisdiction was no longer statistically significant (P = 0.248 using all triage categories and 0.063 using only Australian Triage Scale 2 and 3). Conclusions Although the Council of Australian Governments has adopted raw measures for its key ED performance indicators, raw waiting time statistics are misleading. There are no consistent differences in ED waiting times between states and territories after other factors are accounted for. What is known about the topic? The length of time patients wait to be treated after presenting at an ED is routinely used to measure ED performance. In national health agreements with the federal government, each state and territory in Australia is expected to meet waiting time performance targets for the five ED triage categories. The raw data indicate differences in performance between states and territories. What does this paper add? Measuring ED performance using raw data gives misleading results. There are no consistent differences in ED waiting times between the states and territories after other factors are taken into account. What are the implications for practitioners? Judgements regarding differences in performance across states and territories for triage waiting times need to take into account the mix of patients and the mix of hospitals.

scholarly journals Measuring and Analyzing Waiting Time Indicators of Patients’ Admitted in Emergency Department: A Case Study

2015 ◽  
Vol 8 (1) ◽  
pp. 143 ◽  
Author(s):  
Saeed Amina ◽  
Ahmad Barrati ◽  
Jamil Sadeghifar ◽  
Marzeyh Sharifi ◽  
Zahra Toulideh ◽  
...  
Keyword(s):  
Emergency Department ◽  
Waiting Time ◽  
Waiting Times ◽  
Hospital Data ◽  
Governance System ◽  
Cross Sectional ◽  
Average Waiting Time ◽  
Study Population

<p><strong>BACKGROUND</strong><strong> </strong><strong>&amp;</strong><strong> </strong><strong>AIMS:</strong> Measuring and analyzing of provided services times in Emergency Department is the way to improves quality of hospital services. The present study was conducted with aim measuring and analyzing patients waiting time indicators in Emergency Department in a general hospital in Iran.</p> <p><strong>MATERIAL</strong><strong> </strong><strong>&amp;</strong><strong> </strong><strong>METHODS:</strong> This cross-sectional, observational study was conducted during April to September 2012. The study population consisted of 72 patients admitted to the Emergency Department at Baharlo hospital. Data collection was carried out by workflow forms. Data were analyzed by t.<strong> </strong>test and ANOVA.</p> <p><strong>RESULTS:</strong> The average waiting time for patients from admission to enter the triage 5 minutes, the average time from triage to physician visit 6 minute and the average time between examinations to leave ED was estimated 180 minutes. The total waiting time in the emergency department was estimated at about 210 minutes. The significant<strong> </strong>correlation between marital status of patients (P=0.03), way of arrive to ED (P=0.02) and type of shift work (P=0.01) with studied time indicators were observed.</p> <p><strong>CONCLUSION:</strong> According to results and comparing with similar studies, the average waiting time of patients admitted to the studied hospital is appropriate. Factors such as: Utilizing clinical governance system and attendance of resident Emergency Medicine Specialist have performed an important role in reducing of waiting times in ED.</p>

scholarly journals Disease phenotype analyses of relapsing-remitting multiple sclerosis in Canada and Saudi Arabia

2015 ◽  
Vol 42 (S1) ◽  
pp. S32-S32
Author(s):  
M Alluqmani ◽  
M Alqermli ◽  
G Blevins ◽  
B Alotibi ◽  
F Giuliani ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Saudi Arabia ◽  
Geographic Location ◽  
Clinical Findings ◽  
Cross Sectional ◽  
Relapsing Remitting ◽  
Similar Disease ◽  
The Mean ◽  
Sectional Analysis ◽  
Relapsing Remitting Multiple Sclerosis

Background: Multiple sclerosis (MS) exhibits a spectrum of clinical findings, especially in relapsing-remitting MS (RR-MS). To assess the effects of geographic location and ethnicity on RR-MS phenotype, we investigated RR-MS patients in Canada and Saudi Arabia. Methods: A retrospective cross-sectional analysis of patients receiving active care in MS Clinics was performed in Medina, Saudi Arabia and Edmonton, Alberta. Demographic and clinical data was collected for each patient. Results: 98 patients with treated RR-MS were recruited (n=51, Medina; n=47, Edmonton); 40 patients were Caucasian (Edmonton) while 46 patients were Bedouin (Medina). Although the disease duration was longer in the Edmonton (5.7+2.3 yr) compared to the Medina group (4.4+1.4 yr) (p<0.05), the mean age of RR-MS onset, relapse rate and EDSS change were similar. The female:male ratio was comparable in Edmonton (35:12) and Medina (32:19), as was the risk of optic neuritis. The likelihood of an infratentorial lesion-associated presentation differed (Edmonton, n=23; Medina; n=13) among groups (p<0.05). Spinal cord lesions on MRI were more frequent in Edmonton (n=18) compared to Medina (n=1) patients (p<0.05). Conclusions: Despite differences in location, ethnicity, and a predominance of infratentorial lesion burden the Edmonton group, the RR-MS phenotype displayed similar disease severity and trajectory in these cohorts.

Polypharmacy and Potentially Inappropriate Medication Use in Older Blacks with Diabetes Mellitus Presenting to the Emergency Department

2021 ◽  
pp. 089826432110455
Author(s):  
Ginah Nightingale ◽  
Emily M. Scopelliti ◽  
Robin Casten ◽  
Monica Woloshin ◽  
Shu Xiao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Emergency Department ◽  
Controlled Trial ◽  
Inappropriate Medication ◽  
Medication Use ◽  
Potentially Inappropriate Medication ◽  
Cross Sectional ◽  
Randomized Controlled ◽  
Inappropriate Medication Use ◽  
Sectional Analysis

Background: Medication-related problems in older Blacks with diabetes mellitus (DM) are not well established. Objectives: To describe the frequency of medication-related problems in older Blacks with DM presenting to the emergency department (ED). Methods: The study was a cross-sectional analysis of baseline data from a randomized controlled trial evaluating Blacks aged ≥60 years of age presenting to the ED. Polypharmacy, potentially inappropriate medication (PIM) use, and anticholinergic score were evaluated. Results: Of 168 patients (median age = 68, range 60–92), most ( n = 164, 98%) were taking ≥5 medications, and 67 (39.9%) were taking a PIM. A majority ( n = 124, 74%) were taking a medication with an anticholinergic score ≥1. Number of medications was correlated with number of PIMs ( r = .22, p = .004) and anticholinergic score ( r = .50, p < .001). Conclusion: Polypharmacy and PIM use was common in older Blacks with DM.

scholarly journals Reasons patients leave their nearest healthcare service to attend Karen Park Clinic, Pretoria North

2013 ◽  
Vol 5 (1) ◽  
Author(s):  
Agnes T. Masango- Makgobela ◽  
Indiran Govender ◽  
John V. Ndimande
Keyword(s):  
Healthcare Provider ◽  
Waiting Time ◽  
Healthcare Service ◽  
Waiting Times ◽  
Healthcare Providers ◽  
Descriptive Study ◽  
Staff Attitudes ◽  
Quality Service ◽  
Cross Sectional

Background: Many patients move from one healthcare provider or facility to another, disturbing the continuity that enhances holistic patient care.Objectives: To investigate the reasons given by patients for attending Karen Park Clinic rather than the clinic nearest to their homes.Methods: A cross-sectional descriptive study was conducted during 2010. Three hundred and fifty patients attending Karen Park Clinic were given questionnaires to complete, with the following variables: place of residence; previous attendance at the clinic nearest their home; services available at their nearest clinic; and their willingness to attend their nearest clinic in future.Results: Respondents were from Soshanguve (153; 43.7%), Mabopane (92; 26.3%), Garankuwa (29; 8.3%) and Hebron (20; 5.7%) and most were women (271; 77.4%) aged 26–45 (177; 50.6%). Eighty per cent (281) of the patients had visited their nearest clinic previously and 54 of these (19.2%) said they would not return. The reasons for this were: long waiting time (88; 25.1%); long queues (84; 24%); rude staff (60; 17%); and no medication (39; 11.1%).Conclusion: The majority of patients who had attended their nearest clinic were adamant that they would not return. It is necessary to reduce waiting times, thus reducing long queues. This can be achieved by having adequate, satisfied healthcare providers to render a quality service and by organising training for management. Patients can thus be redirected to their nearest clinic and the health centre’s capacity can be increased by procuring adequate drugs. There is a need to follow up on patients’ complaints about staff attitudes.

scholarly journals Assessment of patients waiting and service times in the ophthalmology clinic of a public tertiary hospital in Nigeria

2020 ◽  
Vol 54 (4) ◽  
pp. 231-237
Author(s):  
Lateefat B. Olokoba ◽  
Kabir A. Durowade ◽  
Feyi G. Adepoju ◽  
Abdulfatai B. Olokoba
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Sampling Technique ◽  
Tertiary Hospital ◽  
Cross Sectional Study ◽  
Ethical Review ◽  
Cross Sectional ◽  
Arrival Times ◽  
The Mean ◽  
Service Times

Introduction: Long waiting time in the out-patient clinic is a major cause of dissatisfaction in Eye care services. This study aimed to assess patients’ waiting and service times in the out-patient Ophthalmology clinic of UITH. Methods: This was a descriptive cross-sectional study conducted in March and April 2019. A multi-staged sampling technique was used. A timing chart was used to record the time in and out of each service station. An experience based exit survey form was used to assess patients’ experience at the clinic. The frequency and mean of variables were generated. Student t-test and Pearson’s correlation were used to establish the association and relationship between the total clinic, service, waiting, and clinic arrival times. Ethical approval was granted by the Ethical Review Board of the UITH. Result: Two hundred and twenty-six patients were sampled. The mean total waiting time was 180.3± 84.3 minutes, while the mean total service time was 63.3±52.0 minutes. Patient’s average total clinic time was 243.7±93.6 minutes. Patients’ total clinic time was determined by the patients’ clinic status and clinic arrival time. Majority of the patients (46.5%) described the time spent in the clinic as long but more than half (53.0%) expressed satisfaction at the total time spent at the clinic. Conclusion: Patients’ clinic and waiting times were long, however, patients expressed satisfaction with the clinic times.

scholarly journals Impact of streaming "fast track" emergency department patients

2006 ◽  
Vol 30 (4) ◽  
pp. 525 ◽  
Author(s):  
Debra O'Brien ◽  
Aled Williams ◽  
Kerrianne Blondell ◽  
George A Jelinek
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Waiting Time ◽  
Fast Track ◽  
Average Length ◽  
Waiting Times ◽  
Relative Reduction ◽  
Average Waiting Time ◽  
Discharged Patients ◽  
Track Area

Objective: Fast track systems to stream emergency department (ED) patients with low acuity conditions have been introduced widely, resulting in reduced waiting times and lengths of stay for these patients. We aimed to prospectively assess the impact on patient flows of a fast track system implemented in the emergency department of an Australian tertiary adult teaching hospital which deals with relatively few low acuity patients. Methods: During the 12-week trial period, patients in Australasian Triage Scale (ATS) categories 3, 4 and 5 who were likely to be discharged were identified at triage and assessed and treated in a separate fast track area by ED medical and nursing staff rostered to work exclusively in the area. Results: The fast track area managed 21.6% of all patients presenting during its hours of operation. There was a 20.3% (?18 min; 95%CI, ?26 min to ?10 min) relative reduction in the average waiting time and an 18.0% (?41 min; 95%CI, ?52 min to ?30 min) relative reduction in the average length of stay for all discharged patients compared with the same period the previous year. Compared with the 12-week period before the fast track trial, there was a 3.4% (?2.1 min; 95%CI, ?8 min to 4 min) relative reduction in the average waiting time and a 9.7% (?20 min; 95%CI, ?31 min to ?9 min) relative reduction in the average length of stay for all discharged patients. There was no increase in the average waiting time for admitted patients. This was despite major increases in throughput and access block in the study period. Conclusion: Streaming fast track patients in the emergency department of an Australian tertiary adult teaching hospital can reduce waiting times and length of stay for discharged patients without increasing waiting times for admitted patients, even in an ED with few low acuity patients.

scholarly journals An Evaluation of a Modified CTAS at an Accident and Emergency Department in a Developing Country

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Shalini Pooransingh ◽  
L. K. Teja Boppana ◽  
Isaac Dialsingh
Keyword(s):  
Emergency Department ◽  
Admission Rate ◽  
Cross Sectional Study ◽  
Structural Factors ◽  
Cross Sectional ◽  
Admission Rates ◽  
Accident And Emergency ◽  
Triage Category ◽  
Accident And Emergency Department ◽  
Design And Methods

Objectives. To review the modified Canadian Triage and Acuity Scale used in an accident and emergency department in Trinidad and Tobago. Design and Methods. A cross-sectional study was carried out. Times from assignment of triage category to being seen by a physician were collected from the patient notes on the days of presentation and compared to the reference standards. Times from decision to admit to obtaining a bed were also recorded. Results. 200 patients were included in the study. The median waiting time for patients in the immediate/blue category was 3 minutes (range = 3); for the red category, it was 31.2 minutes (range = 121.8); in the yellow category, it was 61.8 minutes (range = 805.2). The overall admission rate was 30.5%, with an admission rate of 25% for the blue category; 20% of patients in the red category waited more than 4 hours for a hospital bed. Conclusion. The patients assigned to the blue category were being seen almost immediately. Less critical persons wait longer than the reference times and this may be due to structural factors such as staffing. The admission rates per category highlighted a low admission rate for the blue category (25%), which is unusual. This study highlights the need for a further study to review clinical presentation, assignment to triage category, and outcomes.

scholarly journals Factors Associated With Patient Waiting Time at a Medical Outpatient Clinic: A Case Study of University of Nairobi Health Services

2021 ◽  
Vol 6 (12) ◽  
pp. 915-918
Author(s):  
Rebecca Bisanju Wafula (BSCN, MSCHSM) ◽  
Dr. Richard Ayah (MBCHB, MSC, PHD)
Keyword(s):  
Health Services ◽  
Health Care Providers ◽  
Waiting Time ◽  
Outpatient Clinic ◽  
Health Workers ◽  
Waiting Times ◽  
Care Providers ◽  
Cross Sectional ◽  
Patient Waiting Time ◽  
Patient Waiting

Background: Long waiting time in outpatient clinics is a constant challenge for patients and the health care providers. Prolonged waiting times are associated with poor adherence to treatment, missed appointment and failure or delay in initiation of treatment and is a major factor towards the perception of the patient towards the care received. Objective: To determine the waiting time and associated factors among out patients attending staff clinic at University of Nairobi health services. Method: A cross-sectional study design was used and data collected from 384 ambulatory patients over a period of four weeks using an interviewer administered pretested structured exit questionnaire with a time-tracking section. Simple random sampling was used to select respondents in a walk- in outpatient clinic set up. Data was cleaned and analysed using Statistical Package for Social Sciences (SPSS) 20. Analysis of variance (ANOVA), and cross tabulation was used to establish associations between the independent variable and dependent variables. Results: In total 384 patients were tracked and interviewed. The average patient waiting time was 55.3mins.Most respondents (52%) suggested that improving availability of staff at their stations would help to reduce patient waiting time. In this study, gender (P=0.005) and availability of doctors (p=0.000) were found to affect patient waiting time with women waiting longer than the male patients. Conclusion: Majority of the patients spent about an hour at the facility to be served. Inadequate number of health workers was the main cause of long waiting time.

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