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

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

British Journal of Surgery ◽

10.1093/bjs/znab308.100 ◽

2021 ◽

Vol 108 (Supplement_7) ◽

Author(s):

Ramez Antakia ◽

Vladimir Popa-Nimigean ◽

Thomas Athisayaraj

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Bowel Habit ◽

Red Flags ◽

National Guidelines ◽

Mean Waiting Time ◽

Significant Difference ◽

Immunochemical Test ◽

Red Flag ◽

The Impact

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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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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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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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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Some reflections on the Renewal-theory paradox in queueing theory

Journal of Applied Mathematics and Stochastic Analysis ◽

10.1155/s104895339800029x ◽

1998 ◽

Vol 11 (3) ◽

pp. 355-368 ◽

Cited By ~ 3

Author(s):

Robert B. Cooper ◽

Shun-Chen Niu ◽

Mandyam M. Srinivasan

Keyword(s):

Waiting Time ◽

Queueing Theory ◽

Waiting Times ◽

Renewal Theory ◽

Vacation Models ◽

Polling Models ◽

Mean Waiting Time ◽

The Mean ◽

Expository Paper ◽

Set Up

The classical renewal-theory (waiting time, or inspection) paradox states that the length of the renewal interval that covers a randomly-selected time epoch tends to be longer than an ordinary renewal interval. This paradox manifests itself in numerous interesting ways in queueing theory, a prime example being the celebrated Pollaczek-Khintchine formula for the mean waiting time in the M/G/1 queue. In this expository paper, we give intuitive arguments that explain why the renewal-theory paradox is ubiquitous in queueing theory, and why it sometimes produces anomalous results. In particular, we use these intuitive arguments to explain decomposition in vacation models, and to derive formulas that describe some recently-discovered counterintuitive results for polling models, such as the reduction of waiting times as a consequence of forcing the server to set up even when no work is waiting.

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On the stochastic ordering of waiting times for patterns in sequences of random digits

Journal of Applied Probability ◽

10.2307/3213691 ◽

1984 ◽

Vol 21 (4) ◽

pp. 730-737 ◽

Cited By ~ 2

Author(s):

Gunnar Blom

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Stochastic Ordering ◽

Mean Waiting Time ◽

Coin Tossing ◽

The Mean

Random digits are collected one at a time until a pattern with given digits is obtained. Blom (1982) and others have determined the mean waiting time for such a pattern. It is proved that when a given pattern has larger mean waiting time than another pattern, then the waiting time for the former is stochastically larger than that for the latter. An application is given to a coin-tossing game.

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High emergency organ allocation rule in lung transplantation: a simulation study

ERJ Open Research ◽

10.1183/23120541.00020-2017 ◽

2017 ◽

Vol 3 (4) ◽

pp. 00020-2017 ◽

Cited By ~ 3

Author(s):

Julien Riou ◽

Pierre-Yves Boëlle ◽

Jason D. Christie ◽

Gabriel Thabut

Keyword(s):

Lung Transplantation ◽

Waiting Time ◽

Waiting Times ◽

Organ Allocation ◽

Waiting List ◽

Allocation Rule ◽

Long Term Effects ◽

Term Survival ◽

The Impact

The scarcity of suitable organ donors leads to protracted waiting times and mortality in patients awaiting lung transplantation. This study aims to assess the short- and long-term effects of a high emergency organ allocation policy on the outcome of lung transplantation.We developed a simulation model of lung transplantation waiting queues under two allocation strategies, based either on waiting time only or on additional criteria to prioritise the sickest patients. The model was informed by data from the United Network for Organ Sharing. We compared the impact of these strategies on waiting time, waiting list mortality and overall survival in various situations of organ scarcity.The impact of a high emergency allocation strategy depends largely on the organ supply. When organ supply is sufficient (>95 organs per 100 patients), it may prevent a small number of early deaths (1 year survival: 93.7% against 92.4% for waiting time only) without significant impact on waiting times or long-term survival. When the organ/recipient ratio is lower, the benefits in early mortality are larger but are counterbalanced by a dramatic increase of the size of the waiting list. Consequently, we observed a progressive increase of mortality on the waiting list (although still lower than with waiting time only), a deterioration of patients’ condition at transplant and a decrease of post-transplant survival times.High emergency organ allocation is an effective strategy to reduce mortality on the waiting list, but causes a disruption of the list equilibrium that may have detrimental long-term effects in situations of significant organ scarcity.

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