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

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 ◽

10.1239/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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Are our patients waiting too long for cardiac surgery?

European Heart Journal ◽

10.1093/ehjci/ehaa946.2690 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

R Menezes Fernandes ◽

T.F Mota ◽

J.S Bispo ◽

H Costa ◽

D Bento ◽

...

Keyword(s):

Cardiac Surgery ◽

Clinical Outcomes ◽

Waiting Time ◽

Independent Predictor ◽

Valvular Disease ◽

Prognostic Impact ◽

Mean Waiting Time ◽

The Mean ◽

The Impact

Abstract Introduction Recommended pre-established waiting periods in patients referred for cardiac surgery aim to improve clinical outcomes. Purpose To determine the prognostic impact of the delay until cardiac surgery. Methods We conducted a retrospective study encompassing patients referred to cardiac surgery from a Cardiology Department, since January 2016 to December 2018. Clinical characteristics, diagnostic studies and follow-up were analysed. Primary endpoints were global mortality and re-hospitalization rates at follow-up. Independent predictors of clinical outcomes were identified through a binary logistic regression analysis, considering p=0,05. Results A total of 591 patients were included, with 71,1% male predominance and a mean age of 68,6±11,36 years old. 55,2% of patients had severe valvular disease (aortic – 38,6%, mitral – 9,6%, mixed valvular disease – 2,9%), and 37,1% had surgical coronary artery disease. The mean left ventricle ejection fraction was 56,1% ± 12,2% and the mean Euroscore II was 3,7%. 120 patients (20,3%) required more than one type of surgical intervention. 360 patients (60,9%) were referred to elective procedures, with a mean waiting time of 129,4 days and only 29,2% of them were operated in a 6-week period. The remaining 39,1% of patients needed urgent/emergent surgery, and the mean time until the intervention was 27,2 days (70,1% operated in 2-weeks). Mean waiting time was higher for valvular patients comparing with coronary patients (110,7 vs 48 days; p<0,001). 9,8% and 4,6% of patients were re-hospitalized or died while waiting for surgery, respectively. In a median follow-up of 520 days since the surgical referral, 25,5% of patients were re-hospitalized and 13,7% died. Waiting time was an independent predictor of global mortality (p=0,018), as well as arterial hypertension (p=0,002), severe valvular disease (p<0,001) and higher Euroscore II values (p=0,023). Waiting for surgery in an out-patient setting (p=0,011) and higher Euroscore II values (p=0,002) were independent predictors of re-hospitalization. Conclusion In our study, waiting time until surgery was an independent predictor of global mortality. Efforts should be made to enable referral surgical centres to timely respond to the needs of the population, considering the impact that delaying the appropriate treatment can have on the survival of these patients. Funding Acknowledgement Type of funding source: None

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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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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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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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THEORETICAL ANALYSIS OF MEAN WAITING TIME FOR MESSAGE DELIVERY IN LATTICE AD HOC NETWORKS

Journal of Circuits System and Computers ◽

10.1142/s0218126610007006 ◽

2010 ◽

Vol 19 (08) ◽

pp. 1711-1741

Author(s):

AKIRA OTSUKA ◽

KEISUKE NAKANO ◽

KAZUYUKI MIYAKITA

Keyword(s):

Ad Hoc Networks ◽

Waiting Time ◽

Ad Hoc ◽

Waiting Times ◽

Traffic Patterns ◽

Mobile Nodes ◽

Approximate Methods ◽

Mean Waiting Time ◽

The Mean ◽

Hoc Networks

In ad hoc networks, the analysis of connectivity performance is crucial. The waiting time to deliver message M from source S to destination D is a measure of connectivity that reflects the effects of mobility, and some approximate methods have been proposed to theoretically analyze the mean waiting time in one-dimensional ad hoc networks that consist of mobile nodes moving along a street. In this paper, we extend these approximate methods to analyze the mean waiting time in two-dimensional networks with a lattice structure with various flows of mobile nodes. We discuss how the mean waiting times behave in such complicated street networks and how to approximate two kinds of mean waiting times. We show that our approximate methods can successfully compute the mean waiting times for even traffic patterns and roughly estimate them for uneven traffic patterns in two-dimensional lattice networks. In these analyses, we consider two shadowing models to investigate how shadowing affects the waiting time. We also discuss the effect of different positions of S on the mean waiting time.

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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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Delay Analysis of anM/G/1/KPriority Queueing System with Push-out Scheme

Mathematical Problems in Engineering ◽

10.1155/2007/14504 ◽

2007 ◽

Vol 2007 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yutae Lee ◽

Bong Dae Choi ◽

Bara Kim ◽

Dan Keun Sung

Keyword(s):

Communication Networks ◽

Waiting Time ◽

Queueing System ◽

Waiting Times ◽

Loss Probability ◽

Mean Waiting Time ◽

The Mean ◽

Recursive Equations ◽

Push Out ◽

Stieltjes Transforms

This paper considers anM/G/1/Kqueueing system with push-out scheme which is one of the loss priority controls at a multiplexer in communication networks. The loss probability for the model with push-out scheme has been analyzed, but the waiting times are not available for the model. Using a set of recursive equations, this paper derives the Laplace-Stieltjes transforms (LSTs) of the waiting time and the push-out time of low-priority messages. These results are then utilized to derive the loss probability of each traffic type and the mean waiting time of high-priority messages. Finally, some numerical examples are provided.

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