805 A Dangerous Wait: The Impact of Prolonged Waiting Times in Endocrine Surgery

Abstract Aim In Northern Ireland on 31/12/19 90,514 patients were awaiting admission/day case procedure. The 2019/2020 Ministerial waiting time target states that by March 2020, 55% of patients should not wait longer than 13 weeks for inpatient/day case treatment, and no patient should wait longer than 52 weeks. This audit investigates the impact of long waiting times in endocrine surgery and how they impact patient safety. Method Data was collected from the endocrine surgery waiting list in the Royal Victoria Hospital, Belfast, up to 6/2/20. Number of days spent on the waiting list, disease complications and the number of days before they occurred were collated. Results 118 patients were awaiting endocrine surgery. The average waiting time was 533 days. 21 patients experience 27 complications related to their endocrine disease whilst waiting for surgery. The average duration before complications was 490 days; 4 required admission, 11 required medical intervention and 3 required a surgical intervention. Conclusions The average waiting time for endocrine surgery is greater than 52 weeks. In Northern Ireland no one should be waiting more than 52 weeks. The length of the waiting list has resulted in 1 in 5 experiencing complications and prolonged suffering from under-treated disease. This is a significant patient safety concern. Urgent action to address waiting lists is required and the disruption caused by COVID-19 should be used as a catalyst for reform.

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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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Weights for Waits: Lessons from Salisbury

Journal of Health Services Research & Policy ◽

10.1177/135581960000500205 ◽

2000 ◽

Vol 5 (2) ◽

pp. 83-88 ◽

Cited By ~ 27

Author(s):

Alastair Lack ◽

Rhiannon Tudor Edwards ◽

Angela Boland

Keyword(s):

Health Care ◽

Waiting Time ◽

Resource Use ◽

Unmet Need ◽

Resource Planning ◽

Waiting Lists ◽

Waiting List ◽

Case Mix ◽

Day Case ◽

The Impact

Objectives: This paper describes a waiting list patients' points scheme under development in Salisbury, UK, for the fair management of elective inpatient and day case waiting lists. The paper illustrates how points can be assigned to patients on a waiting list to indicate their relative unmet need, and illustrates the impact on case mix and resource use of the implementations of the points system versus ‘first come, first served’. The paper explores a range of philosophical and technical questions raised by the points system. Methods: The Salisbury Priority Scoring System enables surgeons to assign relative priority to patients at the time they are placed on a waiting list for elective health care. Points are assigned to patients to reflect the rate of progress of their disease, pain or distress, disability or dependence on others, loss of usual occupation and time already waited. In recognition of the need for resource planning alongside the prioritisation of elective inpatients and day case waiting lists, a range of iso-resource groups has been developed for all procedures on these lists. These categorise procedures in terms of their resource use (i.e. bed days and theatre time required). Results: In a modelling exercise, application of the Salisbury Points Scheme to a ‘first come, first served' orthopaedic waiting list produced considerable changes in the order of patients to be treated. Only seven patients appeared in the first 20 patients to be treated under both regimes. The Salisbury Scheme required fewer resources to treat its first 20 patients than ‘first come, first served' and met more Salisbury-defined ‘need’;, but eliminated fewer days of waiting from the list. Conclusions: Development of a points scheme and iso-resource groupings opens up opportunities for more sophisticated purchasing, based on treating patients in order of unmet need rather than according to arbitrary maximum waiting time guarantees, as has been the dominant policy on waiting lists pursued in the UK, Australia, and Sweden, to date. However, such schemes raise three issues: first, the necessity of defining need as a composite of clinical and social factors; second the necessity to determine the acceptability of explicit prioritisation to both health care professionals and patients; third, the thorny issue of whether such prioritisation schemes will lead to ‘gaming’ by well-meaning general practitioners and specialists, aiming to secure the priority of their own patients and clinical specialty. Rigorous piloting of schemes, such as that developed at Salisbury, will be required to identify their dynamic effect over time on case mix, waiting time and resource use.

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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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EP.WE.727Upper GI Surgery Clinic Referrals; Can We Improve the Patient Experience?

British Journal of Surgery ◽

10.1093/bjs/znab308.088 ◽

2021 ◽

Vol 108 (Supplement_7) ◽

Author(s):

Mohamed Abouelazayem ◽

Raluca Belchita

Keyword(s):

Patient Experience ◽

Waiting Time ◽

Waiting Times ◽

Patient Records ◽

Electronic Patient Records ◽

Post Discharge ◽

Average Waiting Time ◽

Upper Gi

Abstract Aim To review the new referrals to the Upper GI surgery clinic for appropriateness, investigations requested, and waiting times and to identify potential pathways to reduce waiting times and improve the patient experience. Method Patients who attended the UGI clinic over 2 months period were identified. Data were collected from GP referrals and Electronic Patient Records. Follow up, post-discharge appointments, and Did Not Attends were excluded. Data collected included time from referral to first clinic, symptoms, investigations requested, suitability for a pathway, and appropriateness of referral. A first clinic outcome was concluded from reading the GP referral, there were 5 outcomes to choose from; direct to another specialty, discharge back to GP, clinic, surgery, pre-investigate and clinic. Results 147 referrals were analysed. The average waiting time from referral to the first clinic was 51 days (range 7-119 days). 73% of the referrals were GP referrals and 27% from other specialties. The most common referral was for gallstones and the most common 2 outcomes were Pre-investigate and surgery. Conclusion Most of the investigations and outcomes suggested from the project were the same as those from clinic letters. The following pathways can be developed to cut waiting times and costs for the trust:

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Measuring and Analyzing Waiting Time Indicators of Patients’ Admitted in Emergency Department: A Case Study

Global Journal of Health Science ◽

10.5539/gjhs.v8n1p143 ◽

2015 ◽

Vol 8 (1) ◽

pp. 143 ◽

Cited By ~ 6

Author(s):

Saeed Amina ◽

Ahmad Barrati ◽

Jamil Sadeghifar ◽

Marzeyh Sharifi ◽

Zahra Toulideh ◽

...

Keyword(s):

Emergency Department ◽

Waiting Time ◽

Waiting Times ◽

Hospital Data ◽

Governance System ◽

Cross Sectional ◽

Average Waiting Time ◽

Study Population

BACKGROUND & AIMS: Measuring and analyzing of provided services times in Emergency Department is the way to improves quality of hospital services. The present study was conducted with aim measuring and analyzing patients waiting time indicators in Emergency Department in a general hospital in Iran. MATERIAL & METHODS: This cross-sectional, observational study was conducted during April to September 2012. The study population consisted of 72 patients admitted to the Emergency Department at Baharlo hospital. Data collection was carried out by workflow forms. Data were analyzed by t. test and ANOVA. RESULTS: The average waiting time for patients from admission to enter the triage 5 minutes, the average time from triage to physician visit 6 minute and the average time between examinations to leave ED was estimated 180 minutes. The total waiting time in the emergency department was estimated at about 210 minutes. The significant correlation between marital status of patients (P=0.03), way of arrive to ED (P=0.02) and type of shift work (P=0.01) with studied time indicators were observed. CONCLUSION: According to results and comparing with similar studies, the average waiting time of patients admitted to the studied hospital is appropriate. Factors such as: Utilizing clinical governance system and attendance of resident Emergency Medicine Specialist have performed an important role in reducing of waiting times in ED.

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Impact of streaming "fast track" emergency department patients

Australian Health Review ◽

10.1071/ah060525 ◽

2006 ◽

Vol 30 (4) ◽

pp. 525 ◽

Cited By ~ 50

Author(s):

Debra O'Brien ◽

Aled Williams ◽

Kerrianne Blondell ◽

George A Jelinek

Keyword(s):

Emergency Department ◽

Length Of Stay ◽

Waiting Time ◽

Fast Track ◽

Average Length ◽

Waiting Times ◽

Relative Reduction ◽

Average Waiting Time ◽

Discharged Patients ◽

Track Area

Objective: Fast track systems to stream emergency department (ED) patients with low acuity conditions have been introduced widely, resulting in reduced waiting times and lengths of stay for these patients. We aimed to prospectively assess the impact on patient flows of a fast track system implemented in the emergency department of an Australian tertiary adult teaching hospital which deals with relatively few low acuity patients. Methods: During the 12-week trial period, patients in Australasian Triage Scale (ATS) categories 3, 4 and 5 who were likely to be discharged were identified at triage and assessed and treated in a separate fast track area by ED medical and nursing staff rostered to work exclusively in the area. Results: The fast track area managed 21.6% of all patients presenting during its hours of operation. There was a 20.3% (?18 min; 95%CI, ?26 min to ?10 min) relative reduction in the average waiting time and an 18.0% (?41 min; 95%CI, ?52 min to ?30 min) relative reduction in the average length of stay for all discharged patients compared with the same period the previous year. Compared with the 12-week period before the fast track trial, there was a 3.4% (?2.1 min; 95%CI, ?8 min to 4 min) relative reduction in the average waiting time and a 9.7% (?20 min; 95%CI, ?31 min to ?9 min) relative reduction in the average length of stay for all discharged patients. There was no increase in the average waiting time for admitted patients. This was despite major increases in throughput and access block in the study period. Conclusion: Streaming fast track patients in the emergency department of an Australian tertiary adult teaching hospital can reduce waiting times and length of stay for discharged patients without increasing waiting times for admitted patients, even in an ED with few low acuity patients.

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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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How Should Lungs Be Allocated for Transplant?

Seminars in Respiratory and Critical Care Medicine ◽

10.1055/s-0037-1620265 ◽

2018 ◽

Vol 39 (02) ◽

pp. 126-137 ◽

Cited By ~ 10

Author(s):

Thomas Egan

Keyword(s):

Waiting Time ◽

Organ Procurement ◽

Waiting Times ◽

Organ Transplant ◽

Health Resources ◽

Organ Allocation ◽

Final Rule ◽

History Of ◽

End Stage ◽

The Impact

AbstractAs lung transplantation became established therapy for end-stage lung disease, there were not nearly enough suitable lungs from brain-dead organ donors to meet the need, leading to a focus on how lungs are allocated for transplant. Originally lungs were allocated by the United Network for Organ Sharing (UNOS) like hearts—by waiting time, first to listed recipients in the organ procurement organization of the donor, then to potential recipients in concentric 500 nautical mile circles. This resulted in long waiting times and increasing waitlist deaths. In 1999, the Health Resources and Services Administration published a Final Rule, requesting UNOS to review organ allocation algorithms to ensure that they complied with the desire to allocate organs based on urgency, avoiding futile transplants, and minimizing the role of waiting time in organ allocation. This led to development of the lung allocation score (LAS), which allocates lungs based on urgency and transplant benefit, introduced in 2005. The U.S. LAS system was adopted by Eurotransplant to allocate unused lungs between donor countries, and by both Germany and the Netherlands for lung allocation in their countries. This article will review the history of lung allocation, discuss the impact of LAS and its shortcomings, suggest recommendations to increase the number of lungs for transplant, and improve allocation of donated lungs. Ultimately, the goal of organ transplant research is to have so many organs to transplant that allocation systems are unnecessary.

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The Effect of Technology Investments on Hospital Budget and Patient Safety: A Simulation for Emergency Department

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9140.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4650-4656

Keyword(s):

Patient Safety ◽

Waiting Times ◽

Investment Decisions ◽

Resource Planning ◽

Rfid Technology ◽

Hospital Budget ◽

Hospital Managers ◽

Advanced Technologies ◽

Long Time ◽

The Impact

One of the most important problems for hospital management is long waiting times. Especially in Emergency Departments, which are very crowded in terms of the number of applicants, patients wait for a long time without any treatment. Long waiting times between stations affect the total length of stay of the patients in this department. In this case, it directly affects patient safety. Furthermore, patients can leave from hospitals because of not to take effective and rapid treatment or lack of resource. Patient satisfaction is negatively affected and the brand value of these hospitals may decrease. All these events trigger hospital budget also. Hospital managers who faced these problems before, should first review all their existing processes, identify roadmaps and plan improvements. They are able to make use of advanced technologies and make investment decisions for improvement processes. However, investment decisions can be quite costly and can effect of hospital budget economically. Therefore, the use of simulations to compare the results of investment decisions can provide much more accurate decisions. The purpose of this study is to support hospital managers in making decisions by monitoring the impact of investment decisions (technology or resource investment) on the hospital budget. Both investment decisions will be simulated comparatively, patient processes and resource planning will be improved by creating a sustainable management model with RFID Technology

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

Australian Health Review ◽

10.1071/ah020075 ◽

2002 ◽

Vol 25 (6) ◽

pp. 75 ◽

Cited By ~ 6

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.

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