EP.WE.844Two-week bowel cancer referrals during the COVID pandemic

Abstract Aims The aims were to assess the impact of the COVID-19 pandemic on the waiting times for patients referred via the two-week pathway for suspected colorectal cancer. We also examined the use of Faecal Immunochemical Test (FIT) alongside the presenting complaints in triaging/prioritising patients for further imaging and/or endoscopic investigations appropriately. Methods A list of all patients referred via the two-week pathway to the West Suffolk Hospital for suspected colorectal cancers from 30/01/2020 to 19/07/2020 was compiled. The main four red flag symptoms were change in bowel habit (CIBH), anorectal bleeding, anaemia and weight loss. A subset of 235 patients were closely examined regarding their presenting complaints, FIT, imaging and endoscopy results with analysis of outcomes. Results 127 male versus 108 female patients were included. 59.61% of patients who were eligible for the FIT test received one. Mean waiting time for FIT positive patients was 42.39 (95% CI) versus 61.10 (95% CI) for FIT negative patients. Patients with one or two red flags symptoms had a mean waiting time of 44.81 days (95% CI 35.79-53.82) and 47.91 days (95% CI 38.07-57.75) respectively. Patients with three red flag symptoms had a mean waiting time of 28.2 days (95% CI 17.94-38.39). There was a statistically significant difference in mean waiting time between patients having 1-2 symptoms and patients with three symptoms (p < 0.005). Conclusions Despite delays during the COVID pandemic particularly for endoscopy, high risk and FIT positive patients were prioritised. Waiting times were still higher than advised national guidelines.

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P042 Five-year review of a tertiary paediatric sleep service for complex patients

SLEEP Advances ◽

10.1093/sleepadvances/zpab014.090 ◽

2021 ◽

Vol 2 (Supplement_1) ◽

pp. A35-A35

Author(s):

A Griffiths ◽

S Preston ◽

A Adams ◽

M Vandeleur

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Diagnostic Study ◽

Sleep Study ◽

Clinical Model ◽

Home Study ◽

Sleep Studies ◽

Mean Waiting Time ◽

Bed Capacity ◽

The Impact

Abstract Introduction Our paediatric sleep unit commenced service for children with complex medical problems in July 2015. Service capacity includes 12 inpatient level 1 studies (two neonates) and one home study per week. FTE includes senior scientists 2.6, sleep technologists 1.7, administration 1.0, nursing 0.7 and medical 1.2. The primary aim of this study was to evaluate activity during the first 5-years. The secondary aim was to document the impact of the COVID-19 pandemic. Methods Sleep unit operational & diagnostic data were collected from sleep booking sheets, sleep study reports, electronic medical records. Descriptive statistics are presented. Results A total of 2186 sleep studies were performed (July 2015 to June 2020) with a range of 368–472 studies per annum. Overall, 61.7% were diagnostic studies, 20.8% titration studies (CPAP, oxygen, bi-level or invasive ventilation), 10% neonatal and 7.5% home studies. Between 2016–2020, the average waiting time (days) for a neonatal study was 16, a titration study was 106, a diagnostic study was 110 and a home study was 76. Further delays were caused by the COVID19 pandemic. Mean waiting time rose 229% from 108 days (Feb 2020) to 355 days (Feb 2021). Referrals for sleep studies have exceeded bed capacity since the beginning of the pandemic. Discussion This audit describes activity in a tertiary complex paediatric sleep service during the first 5 years. The service has struggled on current FTE and bed capacity to manage waiting times, exacerbated further by the COVID-19 pandemic. A new business and clinical model are warranted.

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Managing Queues with Heterogeneous Servers

Journal of Applied Probability ◽

10.1239/jap/1308662637 ◽

2011 ◽

Vol 48 (2) ◽

pp. 435-452 ◽

Cited By ~ 12

Author(s):

Jung Hyun Kim ◽

Hyun-Soo Ahn ◽

Rhonda Righter

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Structural Result ◽

Batch Arrivals ◽

Optimal Thresholds ◽

Mean Waiting Time ◽

Server Problem ◽

The Mean ◽

The Impact ◽

Intuitive Proof

We consider several versions of the job assignment problem for an M/M/m queue with servers of different speeds. When there are two classes of customers, primary and secondary, the number of secondary customers is infinite, and idling is not permitted, we develop an intuitive proof that the optimal policy that minimizes the mean waiting time has a threshold structure. That is, for each server, there is a server-dependent threshold such that a primary customer will be assigned to that server if and only if the queue length of primary customers meets or exceeds the threshold. Our key argument can be generalized to extend the structural result to models with impatient customers, discounted waiting time, batch arrivals and services, geometrically distributed service times, and a random environment. We show how to compute the optimal thresholds, and study the impact of heterogeneity in server speeds on mean waiting times. We also apply the same machinery to the classical slow-server problem without secondary customers, and obtain more general results for the two-server case and strengthen existing results for more than two servers.

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Patient waiting time Analysis at Service Points in the Ultrasound Unit of a Nigerian Teaching Hospital

Journal of Radiography and Radiation Sciences ◽

10.48153/jrrs/2020/gyli3928 ◽

2020 ◽

Vol 34 (1) ◽

pp. 29-36

Author(s):

F U Idigo ◽

N I Chijioke ◽

A C Anakwue ◽

U B Nwogu

Keyword(s):

Teaching Hospital ◽

Waiting Time ◽

Waiting Times ◽

Healthcare Facility ◽

Tertiary Hospital ◽

Radiology Department ◽

System Efficiency ◽

Cross Sectional ◽

Mean Waiting Time ◽

Significant Difference

Background: Quality of service, as perceived by patients in any healthcare facility is to a great extent, dependent on the waiting time. Reducing patients' waiting time increases patients' satisfaction and improves system efficiency. Purpose: To measure and analyze the waiting time of patients at the service points in the ultrasound unit of a Nigerian tertiary hospital and to determine the mean examination time for the different ultrasound investigations carried out. Methods: This prospective cross-sectional study was carried out in the ultrasound unit of the Radiology department at the University of Nigeria Teaching Hospital (UNTH) Ituku/Ozalla, Enugu. The waiting and examination times of patients were measured directly through observation of system operations. The waiting time at the various service points identified as costing, update, payment and examination were recorded. Mean, range and standard deviation of waiting and service times formed the descriptive statistics for the. For inferential statistics, ANOVA test was carried out to test for significance in the different service point waiting times, and the different examination times for the different investigations. Results: Mean waiting time was 3 hours 31 seconds and average exam time was 26 minutes 31 seconds. Analysis of variance on the service point where patients wait the most showed that the point after making the payment was the most significant. There was no significant difference found in the amount of time spent on different examinations (P < 0.05). Conclusion: Timely delivery of services is of optimum importance, considering the need for patient-centred service. With the information provided on the waiting time at the different service points in a typical teaching hospital ultrasound unit, departmental managers will be guided in the planning of the departmental operations, to enhance patient satisfaction and system efficiency.

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The impact of the COVID-19 pandemic on waiting times for elective surgery patients: A multicenter study

PLoS ONE ◽

10.1371/journal.pone.0253875 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0253875

Author(s):

Mikko Uimonen ◽

Ilari Kuitunen ◽

Juha Paloneva ◽

Antti P. Launonen ◽

Ville Ponkilainen ◽

...

Keyword(s):

Health Care ◽

Waiting Time ◽

Multicenter Study ◽

Elective Surgery ◽

Waiting Times ◽

Public Hospitals ◽

Mean Waiting Time ◽

The Mean ◽

The Impact ◽

Surgery Incidence

Background A concern has been that health care reorganizations during the first COVID-19 wave have led to delays in elective surgeries, resulting in increased complications and even mortality. This multicenter study examined the changes in waiting times of elective surgeries during the COVID-19 pandemic in Finland. Methods Data on elective surgery were gathered from three Finnish public hospitals for years 2017–2020. Surgery incidence and waiting times were examined and the year 2020 was compared to the reference years 2017–2019. The mean annual, monthly, and weekly waiting times were calculated with 95% confidence intervals (CI). The most common diagnosis groups were examined separately. Findings A total of 88 693 surgeries were included during the study period. The mean waiting time in 2020 was 92.6 (CI 91.5–93.8) days, whereas the mean waiting time in the reference years was 85.8 (CI 85.1–86.5) days, resulting in an average 8% increase in waiting times in 2020. Elective procedure incidence decreased rapidly in the onset of the first COVID-19 wave in March 2020 but recovered in May and June, after which the surgery incidence was 22% higher than in the reference years and remained at this level until the end of the year. In May 2020 and thereafter until November, waiting times were longer with monthly increases varying between 7% and 34%. In gastrointestinal and genitourinary diseases and neoplasms, waiting times were longer in 2020. In cardiovascular and musculoskeletal diseases, waiting times were shorter in 2020. Conclusion The health care reorganizations due to the pandemic have increased elective surgery waiting times by as much as one-third, even though the elective surgery rate increased by one-fifth after the lockdown.

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Utilization of the Feto-Maternal triage Program in Maternity Hospitals Of Baghdad City

Iraq Medical Journal ◽

10.22317/imj.v4i4.967 ◽

2020 ◽

Vol 4 (4) ◽

Author(s):

Wisam M. Muttaleb ◽

Ezedeen F. Baha’aldeen

Keyword(s):

Waiting Time ◽

Positive Influence ◽

Control Group ◽

Study Group ◽

Flow Sheet ◽

Age Group ◽

Mean Waiting Time ◽

Maternity Hospitals ◽

Significant Difference ◽

The Impact

Objectives: To evaluate the impact of fate-maternal triage program upon laboring women's outcomes. Methods: Quazi- experimental design was conducted upon laboring women seeking for care in Baghdad hospitals The study was conducted At Baghdad hospitals, which in both sides Al-Kargh (13 hospital) and Al-Resafa(23 hospital). The sample of the study consist of 280 laboring women, 140 women for each control and study group who are attending to the labor room in selected hospitals. Questionnaire format conducted as a flow sheet. It is designed and developed by the researcher depending on the feto-maternal triage index of AWHONN'S and Manchester triage system with some modification done by a researcher. Results:the mean age for both groups is 29.85 ± 8.64. More than a quarter of participants in the study group are within the age group of (21-25) years-old (n = 40; 28.6%), followed by those who are in the age group of (31-35) years-old (n = 36; 25.7%). For the control group, more than a third are in the age group of (21-25) years-old (n = 47; 33.6%), followed by those who are in the (31-35) years-old (n = 33; 23.6%).Concerning participants’ BMI, more than two-fifths in the study group are overweight (n = 61; 43.6%),for the control group, more than two-fifths in the study group are overweight (n = 61; 43.6%).More than a third of participants in the study group reported that the waiting time is (10-20) minutes and (21-30) minutes (n = 53; 37.9%) for each of them,For the control group, more than two-fifths reported that the waiting time is (31-60) minutes (n = 58; 41.4%).The mean waiting time for the control group is greater than that of the study group (1.68, 1.32) respectively. also more than a third of participants in the study group reported that the severity of cases are very urgent and emergency cases (n = 44; 31.4%), (n=23; 16.4%) respectively for each of them. For the control group, more than half of participants reported that the severity of case is very urgent and emergency cases (n = 44; 33.6 %),(n=20;14.3) respectively.There is a statistically significant difference in the mothers’ complications between the study and the control groups (χ2 = 13.755, df = 1, p-value = .000). This indicates a positive influence of the program in reducing the mother's complications. Conclusion: The study shows that the positive influence of the program in triage the cases according to its severity and according to the levels used in the triage.

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P.032 The impact of waiting time on the assessment of the first seizure onset in pediatrics

Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques ◽

10.1017/cjn.2017.117 ◽

2017 ◽

Vol 44 (S2) ◽

pp. S21-S21

Author(s):

AA Khan ◽

J Lim ◽

B Janzen ◽

A Amiraslany ◽

S Almubarak

Keyword(s):

Medical Care ◽

Waiting Time ◽

Waiting Times ◽

Seizure Outcome ◽

Seizure Onset ◽

Pediatric Neurology ◽

Mean Waiting Time ◽

The Mean ◽

The Impact

Background: Childhood epilepsy has increased in global incidence. Children with epilepsy require immediate healthcare evaluation and monitoring. Waiting times between first seizure onset and pediatric neurology assessment may impact seizure outcome at follow-up. Quality of medical care for children with first seizure onset will be assessed and the impact of pediatric neurology clinic waiting times on seizure outcomes will be determined Methods: This retrospective study, based on chart review, includes patients with first seizure evaluation at the Royal University Hospital in Saskatoon between January 2012 and December 2015. The interim period before first assessment and other factors were studied in relation to seizure outcome on follow-up. Results: 1158 patients were assessed. 378 (32.6%) patients had first seizure clinic assessment. 197 (52%) had epileptic events. 181 (48%) had non-epileptic events. The mean age of patients was 8.8 years. The mean waiting time for assessment by a pediatric neurologist was 4.33 months. The mean duration of follow-up was 20.9 months. At the last seizure assessment, 132 patients were free of seizures and 65 patients had a recurrence of seizures. Conclusions: First seizure assessment is crucial for management of children with epilepsy. Waiting time and other factors may influence seizure outcome, representing opportunities to improve standard medical care.

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Managing Queues with Heterogeneous Servers

Journal of Applied Probability ◽

10.1017/s002190020000797x ◽

2011 ◽

Vol 48 (02) ◽

pp. 435-452 ◽

Cited By ~ 2

Author(s):

Jung Hyun Kim ◽

Hyun-Soo Ahn ◽

Rhonda Righter

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Structural Result ◽

Batch Arrivals ◽

Optimal Thresholds ◽

Mean Waiting Time ◽

Server Problem ◽

The Mean ◽

The Impact ◽

Intuitive Proof

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493 Evaluation of Patient Waiting Times for Plain Radiographs Whilst Attending Outpatient Clinics in A Tertiary Orthopaedic Centre

British Journal of Surgery ◽

10.1093/bjs/znab134.435 ◽

2021 ◽

Vol 108 (Supplement_2) ◽

Author(s):

Z Hayat ◽

E Kinene ◽

S Molloy

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Time Frame ◽

Outpatient Clinics ◽

Contributing Factors ◽

Time Data ◽

Work Related ◽

Mean Waiting Time ◽

Appointment Time ◽

Related Stress

Abstract Introduction Reduction of waiting times is key to delivering high quality, efficient health care. Delays experienced by patients requiring radiographs in orthopaedic outpatient clinics are well recognised. Method To establish current patient and staff satisfaction, questionnaires were circulated over a two-week period. Waiting time data was retrospectively collected including appointment time, arrival time and the time at which radiographs were taken. Results 84% (n = 16) of radiographers believed patients would be dissatisfied. However, of the 296 patients questioned, 56% (n = 165) were satisfied. Most patients (89%) felt the waiting time should be under 30 minutes. Only 36% were seen in this time frame. There was moderate negative correlation (R=-0.5); higher waiting times led to increased dissatisfaction. Mean waiting time was 00:37 and the maximum 02:48. Key contributing factors included volume of patients, staff shortages (73.7%), equipment shortages (57.9%) and incorrectly filled request forms. Eight (42.1%) had felt unwell from work related stress. Conclusions A concerted effort is needed to improve staff and patient opinion. There is scope for change post COVID. Additional training and exploring ways to avoid overburdening the department would benefit. Numerous patients were open to different days or alternative sites. Funding requirements make updating equipment, expanding the department and recruiting more staff challenging.

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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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