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Over the past decade, many OECD countries have introduced new policies to tackle excessive waiting times for elective surgery with some success. However, in the wake of the recent economic downturn and severe pressures on public budgets, waiting times times may rise again, and it is important to understand which policies work.  In addition, the European Union has introduced new regulations to allow patients to seek care in other member states, if there are long delays in treatment.   This book provides a framework to understand why there are waiting lists for elective surgery in some OECD countries and not in others. It also describes how waiting times are measured in OECD countries, which differ widely, and makes recommendations for best practice. Finally, it reviews different policy approaches to tackling excessive waiting times. Some countries have introduced guarantees to patients that they will not wait too long for treatment. These policies work only if they are accompanied by sanctions on health providers to ensure the guarantee is met or if they allow greater choice of health-care providers including the private sector. Many countries have also introduced policies to expand supply of surgical services, but these policies have generally not succeeded in the long-term in bringing down waiting times. Given the increasing demand for elective surgery, some countries have experimented with policies to improve priorisation of who is entitled to elective surgery. These policies are promising, but difficult to implement.

Background It is globally agreed that a well-designed health system deliver timely and convenient access to health services for all patients. Many interventions aiming to reduce waiting times have been implemented in Chinese public tertiary hospitals to improve patients’ satisfaction. However, few were well-documented, and the effects were rarely measured with robust methods. Methods We conducted a longitudinal study of the length of waiting times in a public tertiary hospital in Southern China which developed comprehensive data collection systems. Around an average of 60,000 outpatients and 70,000 prescribed outpatients per month were targeted for the study during Oct 2014-February 2017. We analyzed longitudinal time series data using a segmented linear regression model to assess changes in levels and trends of waiting times before and after the introduction of waiting time reduction interventions. Pearson correlation analysis was conducted to indicate the strength of association between waiting times and patient satisfactions. The statistical significance level was set at 0 . 05. Results The monthly average length of waiting time decreased 3 . 49 min ( P  = 0 . 003) for consultations and 8 . 70 min ( P  = 0 . 02) for filling prescriptions in the corresponding month when respective interventions were introduced. The trend shifted from baseline slight increasing to afterwards significant decreasing for filling prescriptions ( P  =0.003). There was a significant negative correlation between waiting time of filling prescriptions and outpatient satisfaction towards pharmacy services ( r  = −0 . 71, P  = 0 . 004). Conclusions The interventions aimed at reducing waiting time and raising patient satisfaction in Fujian Provincial Hospital are effective. A long-lasting reduction effect on waiting time for filling prescriptions was observed because of carefully designed continuous efforts, rather than a one-time campaign, and with appropriate incentives implemented by a taskforce authorized by the hospital managers. This case provides a model of carrying out continuous quality improvement and optimizing management process with the support of relevant evidence.

Background The results of liver transplantation are excellent, with survival rates of over 90 and 80% at 1 and 5 years, respectively. The success of liver transplantation has led to an increase in the indications for liver transplantation. Generally, priorities are given to cirrhotic patients with a high Model for End-Stage Liver Disease (MELD) score on the principle of the sickest first and to patients with hepatocellular carcinoma (HCC) on the principle of priority points according to the size and number of nodules of HCC. These criteria can lead to a ‘competition’ on the waiting list between the above patients and those who are cirrhotic and have an intermediate MELD score or with life-threatening liver diseases not well described by the MELD score. For this latter group of patients, ‘MELD exception’ points can be arbitrarily given. Discussion The management of patients on the waiting list is of prime importance to avoid death and drop out from the waiting list as well as to improve post-transplant survival rates. For the more severe cases who may swiftly access liver transplantation, it is essential to rapidly determine whether liver transplantation is indeed indicated, and to organise a fast workup ahead of this. It is also essential to identify the ideal timing for liver transplantation in order to minimise mortality rates. For patients with HCC, a bridge therapy is frequently required to avoid progression of HCC and to maintain patients within the criteria of liver transplantation as well as to reduce the risk of post-transplant recurrence of HCC. For patients with cirrhosis and intermediate MELD score, waiting time can exceed 1 year; therefore, regular follow-up and management are essential to maintain the patient alive on the waiting list and to achieve a good survival after liver transplantation. Conclusion There is a diversity of patients on the waiting list for transplantation and equity should be preserved between those with cirrhosis of high and intermediate severity and those with HCC. The management of patients on the waiting list is an essential component of the success of liver transplantation.

Background Throughout the world, emergency departments (ED) are characterized by overcrowding and excessive waiting times. Furthermore, the related delays significantly increase patient mortality and make inefficient use of resources to the detriment of the satisfaction of employees and patients. In this work, lean thinking is applied to the ED of Cardarelli Hospital of Naples with the aim of increasing patient flow, improving the processes that contribute to facilitating the flow of patients through the various stages of medical treatment and eliminating all bottlenecks (queue) as well as all activities that generate waste. Methods This project was performed at National Hospital A.O.R.N. A. Cardarelli of Naples. The historical times of access to the ED were analysed from January 2015 to June 2015, for a total of 16,563 records. Subsequently, starting in November 2015, corrective actions were implemented according to the Lean Approach. Data collected after the introduced improvements were collected from April 2016 to June 2016 and compared to those collected during the starting period. Results The results acquired before application of the Lean Thinking strategy illustrated the as-is process with its drawbacks. An analysis of the non-added value activities was performed to identify the procedures that need to be improved. After implementation of the corrective actions, we observed a positive increase in the performance of the ED, quantified as percentages of hospitalized patients according to triage codes and waiting times. Conclusion This work demonstrates the applicability of Lean Thinking to ED processes and its effectiveness in terms of increasing the efficiency of services and reducing waste (waiting times).

INTERPRETATION: Exact wait times for urgent and emergent surgery can be measured using Canada's administrative data. Only one-third of patients received surgery within the safe time frame (24 h). Wait times varied according to hospital and physician factors; however, hospital factors had a larger impact.

Long waiting time for deceased donor kidney transplant is inevitable due to the scarcity of donor, resulting in highlighting the importance of waiting time care. We analyzed the Korean Network for Organ Sharing (KONOS) database to assess the impact of waiting time on post-transplant survival outcomes and investigate risk factors for mortality by waiting time based on a complete enumeration survey in Korea. We analyzed all persons aged over 18 years in deceased donor kidney transplant cases enrolled in the Korean Network for Organ Sharing (KONOS) database from January 2000 to January 2015. The primary end point was all-cause mortality after enrollment. Of the 24,296 wait-listed subjects on dialysis, 5,255 patients received kidney transplants from deceased donors, with a median waiting time of 4.5 years. Longer waiting times had distinct deleterious effects on overall survival after transplantation. While waiting for a transplant, patients with diabetes were more likely to die before transplantation (HR 1.515, 95% CI 1.388-1.653, p<0.001). Age was another significant risk factor for mortality. Only 56% of people aged 65 years survived after 10 years of waiting, whereas 86% of people aged 35 years survived after 10 years. Moreover, women on the waiting list were more likely to live longer than men on the list. More attention should be focused on patients with a higher risk of mortality while waiting for a deceased donor kidney transplant, such as patients with diabetes, those of advanced age, and those who are male.

Long waiting time for deceased donor kidney transplant is inevitable due to the scarcity of donor, resulting in highlighting the importance of waiting time care. We analyzed the Korean Network for Organ Sharing (KONOS) database to assess the impact of waiting time on post-transplant survival outcomes and investigate risk factors for mortality by waiting time based on a complete enumeration survey in Korea. We analyzed all persons aged over 18 years in deceased donor kidney transplant cases enrolled in the Korean Network for Organ Sharing (KONOS) database from January 2000 to January 2015. The primary end point was all-cause mortality after enrollment. Of the 24,296 wait-listed subjects on dialysis, 5,255 patients received kidney transplants from deceased donors, with a median waiting time of 4.5 years. Longer waiting times had distinct deleterious effects on overall survival after transplantation. While waiting for a transplant, patients with diabetes were more likely to die before transplantation (HR 1.515, 95% CI 1.388-1.653, p<0.001). Age was another significant risk factor for mortality. Only 56% of people aged 65 years survived after 10 years of waiting, whereas 86% of people aged 35 years survived after 10 years. Moreover, women on the waiting list were more likely to live longer than men on the list. More attention should be focused on patients with a higher risk of mortality while waiting for a deceased donor kidney transplant, such as patients with diabetes, those of advanced age, and those who are male.

Background Most data on mortality and prognostic factors of universal healthcare waiting lists come from North America, Australasia, and Europe, with little information from South America. We aimed to determine the relationship between medical center-specific waiting time and waiting list mortality in Chile. Method Using data from all new patients listed in medical specialist waitlists for non-prioritized health problems from 2008 to 2015 in three geographically distant regions of Chile, we constructed hierarchical multivariate survival models to predict mortality risk at two years after registration for each medical center. Kendall rank correlation analysis was used to measure the association between medical center-specific mortality hazard ratio and waiting times. Result There were 987,497 patients waiting for care at 77 medical centers, including 33,546 (3.40%) who died within two years after registration. Male gender (hazard ratio [HR] = 1.17, 95% confidence interval [CI] 1.1–1.24), older age (HR = 2.88, 95% CI 2.72–3.05), urban residence (HR = 1.19, 95% CI 1.09–1.31), tertiary care (HR = 2.2, 95% CI 2.14–2.26), oncology (HR = 3.57, 95% CI 3.4–3.76), and hematology (HR = 1.6, 95% CI 1.49–1.73) were associated with higher risk of mortality at each medical center with large region-to-region variations. There was a statistically significant association between waiting time variability and death (Z = 2.16, P  = 0.0308). Conclusion Patient wait time for non-prioritized health conditions was associated with increased mortality in Chilean hospitals.

The Model for End-Stage Liver Disease (MELD) score has been successfully used to prioritize patients on the United States liver transplant waiting list since its adoption in 2002. The United Network for Organ Sharing (UNOS)/Organ Procurement Transplantation Network (OPTN) allocation policy has evolved over the years, and notable recent changes include Share 35, inclusion of serum sodium in the MELD score, and a 'delay and cap' policy for hepatocellular carcinoma (HCC) patients. We explored the potential of a registrant's change in 30-day MELD scores ([DELTA]MELD.sub.30) to improve allocation both before and after these policy changes. Current MELD and [DELTA]MELD.sub.30 were evaluated using cause-specific hazards models for waitlist dropout based on US liver transplant registrants added to the waitlist between 06/30/2003 and 6/30/2013. Two composite scores were constructed and then evaluated on UNOS data spanning the current policy era (01/02/2016 to 09/07/2018). Predictive accuracy was evaluated using the C-index for model discrimination and by comparing observed and predicted waitlist dropout probabilities for model calibration. After the change to MELD-Na, increased dropout associated with [DELTA]MELD.sub.30 jumps is no longer evident at MELD scores below 30. However, the adoption of Share 35 has potentially resulted in discrepancies in waitlist dropout for patients with sharp MELD increases at higher MELD scores. Use of the [DELTA]MELD.sub.30 to add additional points or serve as a potential tiebreaker for patients with rapid deterioration may extend the benefit of Share 35 to better include those in most critical need.

Background Long waiting times are associated with public community outpatient health services. This trial aimed to determine if a new model of care based on evidence-based strategies that improved patient flow in two small pilot trials could be used to reduce waiting time across a variety of services. The key principle of the Specific Timely Appointments for Triage (STAT) model is that patients are booked directly into protected assessment appointments and triage is combined with initial management as an alternative to a waiting list and triage system. Methods A stepped wedge cluster randomised controlled trial was conducted between October 2015 and March 2017, involving 3116 patients at eight sites across a major Australian metropolitan health network. Results The intervention reduced waiting time to first appointment by 33.8% (IRR = 0.663, 95% CI 0.516 to 0.852, P  = 0.001). Median waiting time decreased from a median of 42 days (IQR 19 to 86) in the control period to a median of 24 days (IQR 13 to 48) in the intervention period. A substantial reduction in variability was also noted. The model did not impact on most secondary outcomes, including time to second appointment, likelihood of discharge by 12 weeks and number of appointments provided, but was associated with a small increase in the rate of missed appointments. Conclusions Broad-scale implementation of a model of access and triage that combined triage with initial management and actively managed the relationship between supply and demand achieved substantial reductions in waiting time without adversely impacting on other aspects of care. The reductions in waiting time are likely to have been driven, primarily, by substantial reductions for those patients previously considered low priority. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12615001016527 registration date: 29/09/2015.