scholarly journals Erratum for Cromwell DA, Griffiths DA 2002

2003 ◽  
Vol 26 (1) ◽  
pp. 219
Author(s):  
DA Cromwell ◽  
DA Griffiths
Keyword(s):  
Waiting Time ◽  
Information Services ◽  
Time Information

Erratum for Cromwell DA, Griffiths DA 2002, 'Waiting time information services: how well do different statistics forecast a patient's wait?'

scholarly journals Waiting time information services: how well do different statistics forecast a patient's wait?

2002 ◽  
Vol 25 (6) ◽  
pp. 75 ◽  
Author(s):  
David A. Cromwell ◽  
David A. Griffths
Keyword(s):  
Waiting Time ◽  
Public Hospital ◽  
Elective Surgery ◽  
Waiting Times ◽  
Information Services ◽  
Waiting List ◽  
Web Based ◽  
Time Information ◽  
Sydney Australia ◽  
Patient Waiting

This study investigates how accurately the waiting times of patients about to join a waiting list are predicted by the types of statistics disseminated via web-based waiting time information services. Data were collected at a public hospital in Sydney, Australia, on elective surgery activity and waiting list behaviour from July 1995 to June 1998.The data covered 46 surgeons in 10 surgical specialties. The accuracy of the tested statistics varied greatly, being affected more by the characteristics and behaviour of a surgeon's waiting list than by how the statistics were derived. For those surgeons whose waiting times were often over six months, commonly used statistics can be very poor at forecasting patient waiting times.

Waiting time information services: what are the implications of waiting list behaviour for their design?

2002 ◽  
Vol 25 (4) ◽  
pp. 40 ◽  
Author(s):  
David Cromwell ◽  
David Griffiths
Keyword(s):  
Waiting Time ◽  
Elective Surgery ◽  
Waiting Times ◽  
Waiting Lists ◽  
Information Services ◽  
Waiting List ◽  
Surgical Units ◽  
Time Information ◽  
Over Time ◽  
Sydney Australia

In some countries, patients requiring elective surgery can access comparative waiting time information for various surgical units. What someone can deduce from this information will depend upon how the statistics are derived, and how waiting lists behave. However, empirical analyses of waiting list behaviour are scarce. This study analysed three years of waiting list data collected at one hospital in Sydney, Australia. The results highlight various issues that raise questions about using particular waiting time statistics to make inferences about patient waiting times. In particular, the results highlight the considerable variation in behaviour that can exist between surgeons in the same specialty, and that can occur over time.

scholarly journals Waiting time information in the Italian NHS: A citizen perspective

Health Policy ◽  
2020 ◽  
Vol 124 (8) ◽  
pp. 796-804
Author(s):  
Sabina De Rosis ◽  
Elisa Guidotti ◽  
Sara Zuccarino ◽  
Giulia Venturi ◽  
Francesca Ferré
Keyword(s):  
Waiting Time ◽  
Time Information

Dynamic control strategy based on passenger choice behavior with real-time information

SIMULATION ◽  
2017 ◽  
Vol 95 (9) ◽  
pp. 809-822
Author(s):  
Wensi Wang ◽  
Zhihui Tian ◽  
Yonglei Jiang ◽  
Lan Wu ◽  
Jianqiao Gao
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Control Strategy ◽  
Choice Behavior ◽  
Dynamic Control ◽  
Holding Time ◽  
Level Model ◽  
Real Time Information ◽  
Time Information ◽  
Passenger Choice

Real-time control strategies are important methods for high-frequency transit to counteract the effects of bus bunching in passenger waiting time. This paper extends previous literature with the development of an optimization model for multiple lines in a corridor capable of executing a dynamic control strategy based on passenger choice behavior with real-time information. The bi-level model integrates “passenger perceptions,”“service selection,” and “control strategy” effectively. The upper level model is a control model with the objective of minimizing the total waiting time of passengers in the system composed of common lines to decide whether a bus arriving at the hub should be held and its holding time. The lower level model is an allocation model with the utilization of a Nested Logit model to study passenger choice behavior. In addition, a heuristic algorithm is introduced to solve the problem. The effectiveness of the model is evaluated with the data of two lines in Dalian city of China. The results show that the control strategy proposed in this paper outperforms the simple control strategy without passenger choice behavior, where the waiting time of passengers, the number of buses that need to hold, and bus holding time are all reduced.

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