Children Who Leave the Emergency Department Without Being Seen: Why Did They Leave and What Would Make Them Stay?

Introduction: Children who visit emergency departments and leave without being seen represent a multifactorial problem. We aimed to compare the sociodemographic characteristics of children who left and of those who did not leave, as well as to evaluate parental reasoning, subsequent use of medical care and patient outcome.Material and Methods: This was a prospective case-control study of a random sample of children who left without being seen and their matched controls from an emergency department during a three-month period. We performed a phone questionnaire to obtain information concerning reasons for leaving, patient outcomes and general feedback.Results: During the study period, 18 200 patients presented to the emergency department, of whom 92 (0.5%) left without being seen. Fifty-five (59.8%) completed the questionnaire and there were 82 controls. The most common reasons for leaving were ‘excessive waiting time’ (92.7%) and ‘problem could wait’ (21.8%). A significantly higher number of patients who left sought further medical care (78.2% vs 11%) but they did not experience higher levels of unfavourable outcomes.Discussion: The waiting time seems to be the major factor that drives the decision to leave. The fact that parents felt safe in leaving and the low level of adverse outcomes highlights the low-acuity nature of the majority of patients who leave.Conclusion: Reducing the waiting times may be the logical strategic mean to decrease the rates of patients who leave without being seen. However, our data seems to indicate that the concerns surrounding clinical outcome after leaving may be partly unwarranted.

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HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462302000442 ◽

2002 ◽

Vol 18 (3) ◽

pp. 611-618

Author(s):

Markus Torkki ◽

Miika Linna ◽

Seppo Seitsalo ◽

Pekka Paavolainen

Keyword(s):

Hallux Valgus ◽

Waiting Time ◽

Varicose Vein ◽

Elective Surgery ◽

Waiting Times ◽

Waiting List ◽

Mean Waiting Time ◽

Number Of Patients ◽

Potential Problems ◽

Queue Discipline

Objectives: Potential problems concerning waiting list management are often monitored using mean waiting times based on empirical samples. However, the appropriateness of mean waiting time as an indicator of access can be questioned if a waiting list is not managed well, e.g., if the queue discipline is violated. This study was performed to find out about the queue discipline in waiting lists for elective surgery to reveal potential discrepancies in waiting list management. Methods: There were 1,774 waiting list patients for hallux valgus or varicose vein surgery or sterilization. The waiting time distributions of patients receiving surgery and of patients still waiting for an operation are presented in column charts. The charts are compared with two model charts. One model chart presents a high queue discipline (first in—first out) and another a poor queue discipline (random) queue. Results: There were significant differences in waiting list management across hospitals and patient categories. Examples of a poor queue discipline were found in queues for hallux valgus and varicose vein operations. Conclusions: A routine waiting list reporting should be used to guarantee the quality of waiting list management and to pinpoint potential problems in access. It is important to monitor not only the number of patients in the waiting list but also the queue discipline and the balance between demand and supply of surgical services. The purpose for this type of reporting is to ensure that the priority setting made at health policy level also works in practise.

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Assessing DNA rates within first time psychiatric referrals and the extent to which DNA rates are reduced by an SMS reminder service

BJPsych Open ◽

10.1192/bjo.2021.831 ◽

2021 ◽

Vol 7 (S1) ◽

pp. S315-S315

Author(s):

Henry Coates

Keyword(s):

Data Collection ◽

Waiting Time ◽

Text Messaging ◽

Positive Impact ◽

Waiting Times ◽

National Guidelines ◽

Medical Specialties ◽

Number Of Patients ◽

First Time ◽

Communication Preference

Aims1) To assess the average wait time for patients to be offered an appointment and to establish any correlations between longer waiting times and 'Did not attend (DNA)' rates 2) To assess the number of patients who have opted into the text message appointment reminder service and whether this had an effect on DNA rates.BackgroundResearch has indicated that the Did Not Attend (DNA) rate in Psychiatry is estimated at 20%, twice that of other medical specialties (1). With NHS Digital estimating that DNAs cost the NHS £1 Billion per annum, there has been much interest in reducing the rate of DNAs within Psychiatry (2). Findings have shown that short waiting times are associated with higher rates of attendance (3). In addition, poor appointment attendance within Psychiatry is also associated with increased disease severity and higher rates of hospital admission (4).MethodWe conducted retrospective data collection on 99 patients referred to Professor Oyebode between January 2018 and August 2019. Our data collection involved assessing time the referral was received, time to first appointment and the patient's communication preference (e.g. whether they opted in to the SMS alert service). All data collection was conducted through use of RIO and coded/ammonized into a Excel spreadsheet. No sampling methods were employed and our population only consisted of first-time referrals to Professor Oyebodes clinic.Result1) We found no correlation between a longer waiting time to first appointment and an increased DNA rate.2) All patient waiting times between 1st January - 31st August were within the maximum limit set by national guidelines3) Opting into the text messaging service remains severely low. Of the patients audited, 95% had not completed a communication preference form. Overall, it is still unclear whether the text messaging service has a positive impact on DNA rates.ConclusionOur data have shown no significant correlation between a longer waiting time and an increased DNA rate for first time Psychiatry appointments. Secondly, we have concluded that between the audited period, waiting times were still within the maximum 18 week wait set by the Mental Health Standards. Finally, we can conclude that uptake of the text messaging service remains very low at 4%. Due to a limited sample size of only 4 patients, it is still unclear from this audit whether opting into the text messaging services will have a positive decrease on the number of DNA's.

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OPTIMASI SISTEM ANTRIAN PADA LAYANAN KESEHATAN MASYARAKAT MENGGUNAKAN PENDEKATAN SIMULASI

JURNAL MANAJEMEN INDUSTRI DAN LOGISTIK ◽

10.30988/jmil.v3i1.41 ◽

2019 ◽

Vol 3 (1) ◽

pp. 14-22

Author(s):

Widya Setia Findari ◽

Yohanes Anton Nugroho

Keyword(s):

Waiting Time ◽

Six Sigma ◽

Waiting Times ◽

Community Health Center ◽

Observation Data ◽

Simulation Approach ◽

Simulation Test ◽

Queue System ◽

Number Of Patients ◽

Patient Service

Abstract : The purpose of this study is to optimize service time in a community health center. The average number of patients visiting is 100 to 300 per day. In certain units there is a heavy queue of patients which increases service waiting times, including registration units, inspection units, and pharmaceutical units. The initial observation data on the existing system shows the waiting time for patient services is 2,7 hours. This fact shows that the time of patient service on the existing system needs to be optimized so that the waiting time can be accelerated. This study offers a solution to optimize the service queue system using a simulation approach. The DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma method is used as a basis for analyzing the waiting time for services from an existing system. The results of the analysis are used in the simulation test to obtain improvement factors using several scenarios. The best simulation results are obtained with the scenario of adding operators in all units. Optimizing the waiting time of patient services using the best scenario simulation approach is shown by the decrease in waiting time of the queue system by 1,05 hours or 38,9% faster than the existing system.Keywords: System Optimizing; Public Health; Queue; Simulation; DMAIC Six SigmaAbstrak : Tujuan penelitian ini adalah untuk mengoptimalkan waktu tunggu pelayanan di sebuah pusat kesehatan masyarakat (Puskesmas). Rata-rata jumlah pasien yang berkunjung adalah 100 hingga 300 per hari. Pada beberapa unit tertentu terjadi antrian pasien yang padat sehingga meningkatkan waktu tunggu pelayanan, antara lain unit pendaftaran, unit pemeriksaan, dan unit farmasi. Data pengamatan awal pada sistem yang ada menunjukkan waktu tunggu pelayanan pasien adalah 2,7 jam. Fakta ini menunjukkan bahwa waktu pelayanan pasien pada sistem yang ada perlu dioptimalkan agar waktu tunggu dapat dipercepat. Penelitian ini menawarkan solusi optimalisasi sistem antrian pelayanan dengan menggunakan pendekatan simulasi. Metode DMAIC (Define, Measure, Analyze, Improve, Control) Six Sigma digunakan sebagai dasar analisis waktu tunggu pelayanan dari sistem yang sudah ada. Hasil analisis digunakan pada uji simulasi untuk mendapatkan faktor perbaikan dengan menggunakan beberapa skenario. Hasil simulasi terbaik diperoleh dengan skenario penambahan operator di semua unit. Optimasi waktu tunggu pelayanan pasien dengan menggunakan pendekatan simulasi skenario terbaik ditunjukkan oleh penurunan waktu tunggu sistem antrian sebesar 1,05 jam atau 38,9% lebih cepat dari sistem yang sudah ada.Kata kunci: Optimasi Sistem, Layanan Kesehatan, Antrian, Simulasi, DMAIC Six Sigma

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

Global Journal of Health Science ◽

10.5539/gjhs.v8n1p143 ◽

2015 ◽

Vol 8 (1) ◽

pp. 143 ◽

Cited By ~ 6

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

BACKGROUND & AIMS: 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. MATERIAL & METHODS: 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. test and ANOVA. RESULTS: 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 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. CONCLUSION: 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.

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Impact of streaming "fast track" emergency department patients

Australian Health Review ◽

10.1071/ah060525 ◽

2006 ◽

Vol 30 (4) ◽

pp. 525 ◽

Cited By ~ 50

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.

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INTERNATIONAL EXPERIENCE IN REDUCING THE WAITING TIME FOR PLANNED MEDICAL CARE: THE POSSIBILITY OF APPLYING THIS EXPERIENCE IN RUSSIA

Public Administration Issues ◽

10.17323/1999-5431-2021-0-4-137-158 ◽

2021 ◽

Vol 0 (4) ◽

pp. 137-158

Author(s):

Anna Akhmetova ◽

◽

Elena Shevchenko ◽

Taras Sharamko ◽

Tatyana Aleshina ◽

...

Keyword(s):

Health Care ◽

Health Services ◽

Medical Care ◽

Waiting Time ◽

State Policy ◽

Waiting Times ◽

Social Needs ◽

Time Limits ◽

Foreign Experience ◽

Demand For Health

The imbalance between the demand for health services and their supply leads to a decrease in the availability of health care. The aim of the study is to analyze the key mechanisms of the policy on reducing the waiting time for planned medical care. The issues of ensuring the guarantee for maximum time limits are studied; the foreign experience of managing waiting times for medical care is reviewed, the possibility of applying it in Russian practice is analyzed; the possibilities of reducing waiting times at the level of medical organizations are considered. The review of foreign experience shows a purposeful state policy to reduce waiting times, and allows us to determine the most effective measures. In Russia, the guaranteed maximum patient waiting times are shorter than in most of the countries reviewed, however, state resources do not support these guarantees; there is no unified state approach for monitoring, and no well-thought-out mechanism for their regulation, based on both system capabilities and social needs. Taking into account the studied international and Russian experience, the recommendations for creating a system for managing the waiting time for planned medical care in Russia are proposed.

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Improving process quality for pediatric emergency department

International Journal of Health Care Quality Assurance ◽

10.1108/ijhcqa-11-2012-0117 ◽

2014 ◽

Vol 27 (4) ◽

pp. 336-346 ◽

Cited By ~ 9

Author(s):

Byungjoon B.J. Kim ◽

Theodore R. Delbridge ◽

Dawn B. Kendrick

Keyword(s):

Flow Control ◽

Waiting Time ◽

Pediatric Patients ◽

Waiting Times ◽

Discrete Event ◽

Process Quality ◽

Content Type ◽

Arrival Times ◽

Number Of Patients ◽

Control Management

Purpose – Overcrowding in emergency departments (EDs) leads to longer waiting times and results in higher number of patients leaving the ED without being seen by a physician. EDs need to improve quality for patients’ waiting time and length of stay (LoS) from the perspective of process and flow control management. The paper aims to discuss these issues. Design/methodology/approach – The retrospective case study was performed using the computerized ED patient time logs from arrival to discharge between July 1, 2009 and June 30, 2010. Patients were divided into two groups either adult or pediatric with a cutoff age of 18. Patients’ characteristics were measured by arrival time periods, waiting times before being seen by a physician, total LoS and acuity levels. A discrete event simulation was applied to the comparison of quality performance measures. Findings – Statistically significant differences were found between the two groups in terms of arrival times, acuity levels, waiting time stratified for various arrival times and acuity levels. The process quality for pediatric patients could be improved by redesign of patient flow management and medical resource. Research limitations/implications – The results are limited to a case of one community and ED. This study did not analyze the characteristic of leaving the ED without being seen by a physician. Practical implications – Separation of pediatric patients from adult patients in an ED can reduce the waiting time before being seen by a physician and the total staying time in the ED for pediatric patients. It can also lessen the chances for pediatric patients to leave the ED without being seen by a physician. Originality/value – A process and flow control management scheme based on patient group characteristics may improve service quality and lead to a better patient satisfaction in ED.

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The 4-hour window: UK Government targets versus clinical priorities

European Psychiatry ◽

10.1016/j.eurpsy.2017.01.821 ◽

2017 ◽

Vol 41 (S1) ◽

pp. S563-S564

Author(s):

D. Cumming

Keyword(s):

Emergency Department ◽

United Kingdom ◽

Public Hospital ◽

Positive Response ◽

Psychiatric Patients ◽

Waiting Times ◽

Adverse Outcomes ◽

Department Of Health ◽

Competing Interest ◽

Image Position

IntroductionIn 2002, the Department of Health (United Kingdom) introduced a 4-hour target due to long waiting times. It is expected that 95% of patients who attend the A&E (Emergency) Department should be registered and admitted/discharged within 4 hours. Exceeding this is termed a “breach”.ObjectivesThe aim of this re-audit was to assess for a response following recommendations after an initial audit with concerning results. Forth Valley Royal is an acute public hospital in Central Scotland with 860 in-patient beds, covering a population of 300,000. It contains two general adult wards (42 beds), one IPCU (12 beds) and two Elderly wards (40 beds).MethodsReferral data was sourced across 4 consecutive months: April–July 2015 (initial audit) and October 2015–January 2016 (re-audit). These included all referrals from A&E to Psychiatry. Times were calculated for the 4 subprocesses listed in Table 1 below.Conclusion/discussionFollowing the initial audit, interventions such as training A&E staff to better manage psychiatric patients and encourage earlier referrals, led to a positive response in the re-audit (Subprocess 1). Breach rates reduced to 28% (from 35%) on re-audit. Less breaches (81% compared to 88%) were referred after 2-hours by A&E. Overall, the breach rates have reduced and they are less attributable to the A&E referring patients late. The outcome of patients leaving A&E without being seen by a psychiatrist was unknown – adverse outcomes would strengthen the debate to enforce the 4-hour window.Disclosure of interestThe author has not supplied his/her declaration of competing interest.Table 1Initial audit = 222 referrals (35% breach rate)Re-Audit = 348 referrals (28% breach rate)

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

Australian Health Review ◽

10.1071/ah13065 ◽

2014 ◽

Vol 38 (1) ◽

pp. 65 ◽

Cited By ~ 4

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.

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Predicting Patient Wait Times in the Phlebotomy Unit

Global Journal of Medical Research ◽

10.34257/gjmrkvol20is1pg1 ◽

2020 ◽

pp. 1-5

Author(s):

Dilek Orbatu ◽

Oktay Yıldırım ◽

Eminullah Yaşar ◽

Ali Rıza Şişman ◽

Süleyman Sevinç

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Wait Time ◽

High Accuracy ◽

Wait Times ◽

Blood Collection ◽

Time Prediction ◽

Number Of Patients ◽

Patient Will ◽

Patient Waiting Time

Patients frequently complain of long waiting times in phlebotomy units. Patients try to predict how long they will stay in the phlebotomy unit according to the number of patients in front of them. If it is not known how fast the queue is progressing, it is not possible to predict how long a patient will wait. The number of prior patients who will come to the phlebotomy unit is another important factor that changes the waiting time prediction. We developed an artificial intelligence (AI)-based system that predicts patient waiting time in the phlebotomy unit. The system can predict the waiting time with high accuracy by considering all the variables that may affect the waiting time. In this study, the blood collection performance of phlebotomists, the duration of the phlebotomy in front of the patient, and the number of prior patients who could come to the phlebotomy unit was determined as the main parameters affecting the waiting time. For two months, actual wait times and predicted wait times were compared. The wait time for 95 percent of the patients was predicted with a variance of ± 2 minutes. An AI-based system helps patients make predictions with high accuracy, and patient satisfaction can be increased.

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