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.

HOW TO REPORT AND MONITOR THE PERFORMANCE OF WAITING LIST MANAGEMENT

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.

scholarly journals Increasing efficiency for reducing elective surgery waiting times: a multi-interventions approach

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
C Quercioli ◽  
G A Carta ◽  
G Cevenini ◽  
G Messina ◽  
N Nante ◽  
...  
Keyword(s):  
Waiting Time ◽  
General Surgery ◽  
Time Allocation ◽  
Optimal Allocation ◽  
Elective Surgery ◽  
Waiting Times ◽  
Operating Time ◽  
District General Hospital ◽  
Waiting List ◽  
Mean Waiting Time

Abstract Background Elective surgery long waiting times are a common problem in publicly funded health systems. They have been tackled allocating additional resources or using existing resources more efficiently but results are patchy. We studied the effectiveness of a multi-interventions project based on the reorganization of existing capacity. Methods In a district general hospital (Siena's Province, Italy) with 150 beds, 4 elective surgery operating rooms (ORs) opened 6 hours/day 5 days/week (surgery specialties: general surgery, orthopedics, gynecology and urology) in October 2018 a project for reducing surgery waiting times was implemented based on 3 key points: i) separation of the Day Surgery (DS) flow from that of the ordinary activity; ii) increase of available operating time through reorganization of personnel: 30 additional hours/week were made available; iii) allocation of operating sessions flexibly in proportion to the waiting list: the made-available hours were redistributed through an algorithm able to estimate the optimal allocation of surgical time blocks to minimize the length of waiting lists, taking account of the interventions priority class. The waiting time of the out from 1/10/2019 to 31/12/2019 (N = 635) was compared with that of the interventions carried out from 1/10/2018 to 31/12/2018 (N = 634) using t-test. Results Waiting times for non-urgent cases (that can be operated beyond 30 days) were significantly reduced for all specialties (p < 0.01) except urology. For general surgery, orthopedics and gynecology, DS interventions' mean waiting time decreases from 198 to 100 days (-50%) that one of ordinary interventions from 213 to 134 days (-37%). Waiting time for urgent cases (to be operated within 30 days) was also reduced. Conclusions A multi-interventions project based on using existing capacity (personnel and structures) more efficiently and improving planning methodologies resulted to be strongly effective in reducing waiting times for elective surgery. Key messages To effectively reduce surgical waiting times, a strategy is needed that involve the entire process: from surgical planning, to staff and structures organization. The flexible OR time allocation on the base of the waiting list is a key point to improve surgery planning and reduce waiting list.

Announcing Waiting Time in Emergency Departments

2019 ◽  
pp. 1-6
Author(s):  
Martin Lariviere ◽  
Sarang Deo
Keyword(s):  
Waiting Time ◽  
Emergency Departments ◽  
Waiting Times ◽  
Large Network ◽  
Regional Networks ◽  
Marketing Campaign ◽  
National Healthcare ◽  
Time Estimates ◽  
Patient Waiting Time ◽  
Patient Waiting

First National Healthcare (FNH) runs a large network of hospitals and has worked to systematically reduce waiting times in its emergency departments. One of FNH's regional networks has run a successful marketing campaign promoting its low ED waiting times that other regions want to emulate. The corporate quality manager must now determine whether to allow these campaigns to be rolled out and, if so, which waiting time estimates to use. Are the numbers currently being reported accurate? Is there a more accurate way of estimating patient waiting time that can be easily understood by consumers?

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

2017 ◽  
Vol 3 (4) ◽  
pp. 00020-2017 ◽  
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.

scholarly journals Has the increase in private health insurance uptake affected the Victorian public hospital surgical waiting list?

2002 ◽  
Vol 25 (6) ◽  
pp. 64 ◽  
Author(s):  
Brian Hanning
Keyword(s):  
Health Insurance ◽  
Public Sector ◽  
Private Sector ◽  
Uptake Rate ◽  
Public Hospital ◽  
Private Health Insurance ◽  
Elective Surgery ◽  
Waiting List ◽  
Limited Data ◽  
The Public

It was anticipated that increased uptake of Private Health Insurance (PHI) would reduce demand on public sector surgical waiting lists. The best measure of changed demand is the comparison of the actual cases added to that projected given previous trends in PHI uptake. Detailed Victorian data is available up to 2000-1.The total waiting list has varied little, reflecting significant decreases in both in patients added to and removed. There was a marked increase in private sector elective surgery cases coinciding with the fall in additions to the public sector waiting list and in public sector elective surgical cases. The June 2001 Victorian surgical waiting list would have been 69,599 not 41,838 if the PHI uptake rate had continued to fall in line with pre-1999 trends, and that of June 2002 about 100,000 compared to 40,458 in March 2002.Limited data from other states suggests the Victorian trends are representative of all Australia.

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