Random-order-of-service for heterogeneous customers: waiting time analysis

Abstract Objective The quantity of A-trains, a high-frequency pattern of free-running facial nerve electromyography, is correlated with the risk for postoperative high-grade facial nerve paresis. This correlation has been confirmed by automated analysis with dedicated algorithms and by visual offline analysis but not by audiovisual real-time analysis. Methods An investigator was presented with 29 complete data sets measured during actual surgeries in real time and without breaks in a random order. Data were presented either strictly via loudspeaker (audio) or simultaneously by loudspeaker and computer screen (audiovisual). Visible and/or audible A-train activity was then quantified by the investigator with the computerized equivalent of a stopwatch. The same data were also analyzed with quantification of A-trains by automated algorithms. Results Automated (auto) traintime (TT), known to be a small, yet highly representative fraction of overall A-train activity, ranged from 0.01 to 10.86 s (median: 0.58 s). In contrast, audio-TT ranged from 0 to 1,357.44 s (median: 29.69 s), and audiovisual-TT ranged from 0 to 786.57 s (median: 46.19 s). All three modalities were correlated to each other in a highly significant way. Likewise, all three modalities correlated significantly with the extent of postoperative facial paresis. As a rule of thumb, patients with visible/audible A-train activity < 1 minute presented with a more favorable clinical outcome than patients with > 1 minute of A-train activity. Conclusion Detection and even quantification of A-trains is technically possible not only with intraoperative automated real-time calculation or postoperative visual offline analysis, but also with very basic monitoring equipment and real-time good quality audiovisual analysis. However, the investigator found audiovisual real-time-analysis to be very demanding; thus tools for automated quantification can be very helpful in this respect.

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Waiting Time Analysis at University Hospitals Based on Visitor Psychology

Human Interface and the Management of Information. Designing Information - Lecture Notes in Computer Science ◽

10.1007/978-3-030-50020-7_14 ◽

2020 ◽

pp. 212-221

Author(s):

Shigeyoshi Iizuka ◽

Shozo Nishii ◽

Eriko Tanimoto ◽

Hiro Nakazawa ◽

Asuka Kodaka ◽

...

Keyword(s):

Waiting Time ◽

Time Analysis ◽

University Hospitals

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Patient arrival pattern and waiting time analysis for a tertiary hospital radiology department

British Journal of Healthcare Management ◽

10.12968/bjhc.2019.25.3.136 ◽

2019 ◽

Vol 25 (3) ◽

pp. 136-143

Author(s):

Felicitas Ugochinyere Idigo ◽

Kenneth Kalu Agwu ◽

Obinna Emmanuel Onwujekwe ◽

Mark Chukwudi Okeji ◽

Angel-Mary Chukwunyelu Anakwue

Keyword(s):

Waiting Time ◽

Tertiary Hospital ◽

Radiology Department ◽

Time Analysis ◽

Patient Arrival ◽

Arrival Pattern

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Modified cumulative distribution function in application to waiting time analysis in the continuous time random walk scenario

Journal of Physics A Mathematical and Theoretical ◽

10.1088/1751-8121/50/3/034002 ◽

2016 ◽

Vol 50 (3) ◽

pp. 034002 ◽

Cited By ~ 2

Author(s):

Rafał Połoczański ◽

Agnieszka Wyłomańska ◽

Monika Maciejewska ◽

Andrzej Szczurek ◽

Janusz Gajda

Keyword(s):

Distribution Function ◽

Random Walk ◽

Waiting Time ◽

Continuous Time ◽

Cumulative Distribution Function ◽

Continuous Time Random Walk ◽

Cumulative Distribution ◽

Time Analysis

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The Effect of First-Step Techniques from the Staggered Stance in American Football

Sports Medicine International Open ◽

10.1055/s-0043-103010 ◽

2017 ◽

Vol 01 (02) ◽

pp. E69-E73

Author(s):

Nikolas Knudsen ◽

Thomas Andersen

Keyword(s):

Reaction Time ◽

Random Order ◽

American Football ◽

Football Players ◽

Ground Reaction Forces ◽

Time Analysis ◽

Sprint Time ◽

Reaction Forces ◽

Force Profiles ◽

Time Linear

AbstractThe purpose of this study was to evaluate 3 different starting techniques from the staggered stance with regards to sprint time, reaction time, linear impulse and power. 11 male amateur American football players volunteered to participate in a testing session consisting of twelve 5 m sprints, 4 in each technique (normal (NORM), backwards false step (BFS) and forwards false step (FFS)) in random order. Sprint starts were performed on force plates to investigate ground reaction forces, reaction time and total sprint time. Analysis showed significant differences in sprint times, with NORM (1.77±0.10 s) being faster than FFS (1.81±0.12 s) and BFS (2.01±0.13 s), and FFS being faster than BFS, although no differences were found in reaction time. In terms of mean force and power, NORM (331.1±39.2N, 542.2±72.3W) and FFS (320.8±43.2N, 550.9±81.4W) were significantly larger than BFS (256.9±36.2N, 443.5±61.1W). This indicates that when starting from a staggered stance, the BFS is inferior to the others and should be avoided. However, since the force profiles of the NORM and the FFS were similar, the differences in sprint time could arise from a technique bias towards the NORM start.

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