Do English NHS waiting time targets distort treatment priorities in orthopaedic surgery?

2005 ◽  
Vol 10 (3) ◽  
pp. 167-172 ◽  
Author(s):  
John Appleby ◽  
Seán Boyle ◽  
Nancy Devlin ◽  
Mike Harley ◽  
Anthony Harrison ◽  
...  
Keyword(s):  
Comparative Study ◽  
Waiting Time ◽  
Hip Replacement ◽  
Orthopaedic Surgery ◽  
Waiting Times ◽  
Admission Criteria ◽  
Before And After ◽  
Treatment Priorities ◽  
The Impact ◽  
Expected Increase

Objectives: To assess and quantify the impact of guarantees on maximum waiting times on clinical decisions to admit patients from waiting lists for orthopaedic surgery. Methods: Before and after comparative study, analysing changes in waiting times distributions between 1997/8 and 2001/2 for waiting list and booked inpatients and day cases admitted for elective treatments in trauma and orthopaedics in English hospitals. Results: The 2001/2 maximum waiting time target of 15 months did change the pattern of admissions for trauma and orthopaedic elective inpatients, with a net increase in admissions in that year, compared with 1997/8 (and over and above the 30,259 (7.6%) overall increase in all admissions) of patients who had waited around 15 months, of 9333. There was little indication that these additional admissions displaced shorter wait patients. In absolute and proportional terms, admissions increased for all waiting time categories except very short waiters – one to two weeks (an absolute fall of 2901 and a relative fall of 6591), and those waiting 40–41 weeks. The latter fall was only 111 patients in absolute terms (or 577 relative to the expected increase), however. The former much larger reduction may be an indication of clinical distortions, but it is unclear why very short wait (presumably more urgent) patients should disproportionately suffer compared with longer wait (presumably less urgent) cases. In addition, there was little indication that more minor cases usurped more major cases: 57% of the increase consisted of knee and hip replacement procedures, for example. Conclusions: While the 2001/2 waiting times target demonstrably changed admission patterns (and was a major contribution to the reduction in long waits), the extent to which this represented significant and clinically relevant distortions is questionable given the lack of widely accepted admission criteria. However, as targets become progressively tougher, there is a need to monitor consultants' concerns more closely.

scholarly journals Assessing the impact of a waiting time survey on reducing waiting times in urban primary care clinics in Cape Town, South Africa

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Johann Daniels ◽  
Virginia Zweigenthal ◽  
Gavin Reagon
Keyword(s):  
South Africa ◽  
Waiting Time ◽  
Waiting Times ◽  
Cape Town ◽  
Specific Factor ◽  
Primary Care Clinics ◽  
Before And After ◽  
Stratified Sample ◽  
The Impact

A waiting time survey (WTS) conducted in several clinics in Cape Town, South Africa provided recommendations on how to shorten waiting times (WT). A follow-up study was conducted to assess whether WT had reduced. Using a stratified sample of 22 clinics, a before and after study design assessed changes in WT. The WT was measured and perceptions of clinic managers were elicited, about the previous survey’s recommendations. The overall median WT decreased by 21 minutes (95%CI: 11.77- 30.23), a 28% decrease from the previous WTS. Although no specific factor was associated with decreases in WT, implementation of recommendations to reduce WT was 2.67 times (95%CI: 1.33-5.40) more likely amongst those who received written recommendations and 2.3 times (95%CI: 1.28- 4.19) more likely amongst managers with 5 or more years’ experience. The decrease in WT found demonstrates the utility of a WTS in busy urban clinics in developing country contexts. Experienced facility managers who timeously receive customised reports of their clinic’s performance are more likely to implement changes that positively impact on reducing WT.

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 EP.WE.844Two-week bowel cancer referrals during the COVID pandemic

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.

How Should Lungs Be Allocated for Transplant?

2018 ◽  
Vol 39 (02) ◽  
pp. 126-137 ◽  
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.

scholarly journals Exploring overcrowding trends in an inner city emergence department in the UK before and during COVID-19 epidemic

2021 ◽  
Author(s):  
J Panovska-Griffiths ◽  
J Ross ◽  
S Elkhodair ◽  
C Baxter-Derrington ◽  
C Laing ◽  
...  
Keyword(s):  
Waiting Time ◽  
Hospital Emergency ◽  
Number Of Patients ◽  
Before And After ◽  
Ed Overcrowding ◽  
University College London ◽  
Flow Through ◽  
Multifaceted Interventions ◽  
The Uk ◽  
The Impact

AbstractBackgroundWe compared impact of three pre-COVID-19 interventions and of the COVID-19 UK-epidemic and the first UK national lockdown on overcrowding within University College London Hospital Emergency Department (UCLH ED). The three interventions: target the influx of patients at ED (A), reduce the pressure on in-patients’ beds (B) and improve ED processes to improve the flow of patents out from ED (C).MethodsWe analysed the change in overcrowding metrics (daily attendances, the proportion of people leaving within four hours of arrival (four-hours target) and the reduction in overall waiting time) across three analysis. The first analysis used data 01/04/2017-31/12-2019 to calculate changes over a period of six months before and after the start of interventions A-C. The second and third analyses focused on evaluating the impact of the COVID-19 epidemic, comparing the first 10 months in 2020 and 2019, and of the first national lockdown (23/03/2020-31/05/2020).ResultsPre-COVID-19 all interventions led to small reductions in waiting time (17%, p<0.001 for A and C;9%, p=0.322 for B) but also to a small decrease in the number of patients leaving within four hours of arrival (6.6%,7.4%,6.2% respectively A-C,p<0.001).In presence of the COVID-19 pandemic, attendance and waiting time were reduced (40% and 8%;p<0.001), and the number of people leaving within four hours of arrival was increased (6%,p<0.001). During the first lockdown, there was 65% reduction in attendance, 22% reduction in waiting time and 8% increase in number of people leaving within 4 hours of arrival (p<0.001). Crucially, when the lockdown was lifted, there was an increase (6.5%,p<0.001) in the percentage of people leaving within four hours, together with a larger (12.5%,p<0.001) decrease in waiting time. This occurred despite the increase of 49.6%(p<0.001) in attendance after lockdown ended.ConclusionsThe mixed results pre-COVID-19 (significant improvements in waiting time with some interventions but not improvement in the four-hours target), may be due to a ‘spill-over effect’ where clogging up one part of the ED system affects other parts. Hence multifaceted interventions and a system-wide approach to improve the pathway of flow through the ED system is necessary.During 2020 and in presence of the COVID-19 epidemic, a shift in public behaviour with anxiety over attending hospitals and higher use of virtual consultations, led to notable drop in UCLH ED attendance and consequential curbing of overcrowding.Importantly, once the lockdown was lifted, although there was an increase in arrivals at UCLH ED, overcrowding metrics were reduced. Thus, the combination of shifted public behaviour and the restructuring changes during COVID-19 epidemic, maybe be able to curb future ED overcrowding, but longer timeframe analysis is required to confirm this.

scholarly journals Telehealth to support referral management in a universal health system: a before-and-after study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sabrina Dalbosco Gadenz ◽  
Josué Basso ◽  
Patrícia Roberta Berithe Pedrosa de Oliviera ◽  
Stephan Sperling ◽  
Marcus Vinicius Dutra Zuanazzi ◽  
...  
Keyword(s):  
Linear Models ◽  
Patient Flow ◽  
Waiting Times ◽  
Median Number ◽  
Waiting Lists ◽  
Referral Management ◽  
Before And After ◽  
The Impact ◽  
Universal Health ◽  
Before And After Study

Abstract Background Management of patient flow within a healthcare network, allowing equitable and qualified access to healthcare, is a major challenge for universal health systems. Implementation of telehealth strategies to support referral management has been shown to increase primary care resolution and to promote coordination of care. The objective of this study was to assess the impact of telehealth strategies on waiting lists and waiting times for specialized care in Brazil. Methods Before-and-after study with measures obtained between January 2019 and February 2020. Baseline measurements of waiting lists were obtained immediately before the implementation of a remotely operated referral management system. Post-interventional measurements were obtained monthly, up to six months after the beginning of operation. Data was extracted from the database of the project. General linear models were applied to assess interaction of locality and time over number of cases on waiting lists and waiting times. Results At baseline, the median number of cases on waiting lists ranged from 2961 to 12,305 cases. Reductions of the number of cases on waiting lists after six months of operation were observed in all localities. The magnitude of the reduction ranged from 54.67 to 88.97 %. Interaction of time measurements was statistically significant from the second month onward. Median waiting times ranged from 159 to 241 days at baseline. After six months, there was a decrease of 100 and 114 waiting days in two localities, respectively, with reduction of waiting times only for high-risk cases in the third locality. Conclusions Adoption of telehealth strategies resulted in the reduction of number of cases on waiting lists. Results were consistent across localities, suggesting that telehealth interventions are viable in diverse settings.

scholarly journals The impact of the two-week wait rule on the diagnosis and management of bladder cancer in a single UK institution

2010 ◽  
Vol 92 (1) ◽  
pp. 46-50 ◽  
Author(s):  
Christopher Blick ◽  
David Bailey ◽  
Neil Haldar ◽  
Amarjit Bdesha ◽  
John Kelleher ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Waiting Times ◽  
Newly Diagnosed ◽  
Multidisciplinary Team Meeting ◽  
Significant Delay ◽  
Patient Pathway ◽  
Before And After ◽  
Routine Basis ◽  
Treatment Data ◽  
The Impact

INTRODUCTION The objective of this study was to investigate the impact of the 2-week wait rule on patient waiting times for the diagnosis and treatment of bladder cancer. PATIENTS AND METHODS Data reporting the waiting times from diagnosis to treatment for 100 consecutive patients newly diagnosed with bladder cancer immediately before and after the implementation of the 2-week wait rule were compared. The data were collected both prospectively and retrospectively from cancer multidisciplinary team meeting files and patient records. Various steps of the patient pathway were analysed including waiting times from referral to consultation as well as time to investigation and first treatment. Data were also analysed based upon tumour stage/grade and whether referrals were made on an urgent or routine basis. RESULTS One hundred newly diagnosed patients with bladder cancer in each group covered a period of 4–5 years (1997–2001 and 2001–2006). Following the introduction of the 2-week wait rule, there was a 47.6% reduction in the time from referral to first consultation with a specialist (42 days vs 22 days; P < 0.001). The time between first investigation and treatment has not reduced significantly. We also found that, despite the introduction of the 2-week wait rule, only 42% of the patients were diagnosed with bladder cancer using this pathway. Patients referred as ‘routine’ waited longer to be seen in hospital although there was no significant delay in receiving treatment. CONCLUSIONS The introduction of the 2-week wait rule has significantly reduced the time patients with bladder cancer wait for their first consultation with a specialist. However, there is no significant change in the time between first consultation and treatment.

scholarly journals The Value of Thoracic Computed Tomography Scans in Clinical Diagnosis: A Prospective Study

2006 ◽  
Vol 13 (6) ◽  
pp. 311-316 ◽  
Author(s):  
Mark O Turner ◽  
John R Mayo ◽  
Nestor L Müller ◽  
Michael Schulzer ◽  
J Mark FitzGerald
Keyword(s):  
Computed Tomography ◽  
Ct Scan ◽  
Clinical Diagnosis ◽  
Waiting Times ◽  
Tertiary Care ◽  
Ct Scans ◽  
Thoracic Ct ◽  
Chest Disease ◽  
Before And After ◽  
The Impact

BACKGROUND: Computed tomography (CT) scans are used extensively to investigate chest disease because of their cross-sectional perspective and superior contrast resolution compared with chest radiographs. These advantages lead to a more accurate imaging assessment of thoracic disease. The actual use and evaluation of the clinical impact of thoracic CT has not been assessed since scanners became widely available.OBJECTIVE: To identify patterns of utilization, waiting times and the impact of CT scan results on clinical diagnoses.DESIGN: A before and after survey of physicians who had ordered thoracic CT scans.SETTING: Vancouver General Hospital – a tertiary care teaching centre in Vancouver, British Columbia.SUBJECTS: Physicians who had ordered CT scans.INTERVENTION: Physicians completed a standard questionnaire before and after the CT scan result was available.MEASUREMENTS: Changes in the clinical diagnosis, estimates of the probabilities for the diagnosis both before and after the CT scan, and waiting times.RESULTS: Four hundred fifty-four thoracic CT cases had completed questionnaires, of whom 80% were outpatients. A change in diagnosis was made in 48% of cases (25% with a normal CT scan and 23% with CT scan findings that indicated a different diagnosis). The largest change in probability scores for the clinical diagnosis before and after the CT scan was 43.9% for normal scans, while it was 36.3% for a different diagnosis and 26.3% for the same diagnosis. High-priority scans were associated with decreased waiting time (−7.89 days for each unit increase in priority).CONCLUSIONS: The CT scan results were associated with a change in diagnosis in 48% of cases. Normal scans constituted 25% of the total and had the greatest impact scores. Waiting times were highly correlated with increased urgency of the presenting problem.

scholarly journals P042 Five-year review of a tertiary paediatric sleep service for complex patients

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.

scholarly journals Managing Queues with Heterogeneous Servers

2011 ◽  
Vol 48 (2) ◽  
pp. 435-452 ◽  
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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