Waiting lists, waiting times and admissions: an empirical analysis at hospital and general practice level

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times. Whereas previous empirical analyses have used data aggregated to area level, our analysis focuses on the supply responses of a single hospital and the demand responses of the GP practices it serves, and distinguishes between first outpatient visits, inpatient admissions, day-case treatment and emergency admissions. The results are plausible and in line with the theoretical model. For example: the demand from practices for first outpatient visits is negatively affected by waiting times and distance to the hospital. Increases in waiting times and waiting lists lead to increases in supply; the supply of elective inpatient admissions is affected negatively by current emergency admissions and positively by lagged waiting list and waiting time. We use the empirical results to investigate the dynamic responses to one off policy measures to reduce waiting times and lists by increasing supply.     

Unrepresentative, invalid and misleading: are waiting times for elective admission wrongly calculated?

THESIS: The UK Government Statistical Service reports the percentage of elective 'admissions' that took place in England within 3 months of a patient being added to NHS waiting lists. This percentage is calculated from cross-sectional data using the total number of elective episodes within a specified calendar period as denominator and the number of these enrolled on the waiting list less than 3 months previously as numerator. This approach assumes that NHS waiting lists are closed and stationary populations, and has been widely used by government and non-government researchers in the UK and elsewhere. ANTITHESIS: Little attention has been given to the bias introduced when waiting lists are neither stationary nor closed. This paper identifies four groups of patients which are excluded from the denominator used by the Government Statistical Service and criticises the established method of ignoring left and right censored observations. SYNTHESIS: We describe two alternative formulae that would give the same results as the Government Statistical Service method if waiting lists were closed and stationary, but that also give unbiased results when waiting lists are open and non-stationary. They require a limited amount of additional cross-sectional data to produce upper and lower estimates of the cumulative likelihood of admission among those listed. We recommend the production of unbiased estimates by applying period life-table techniques to a complete and consistent set of 'times since enrolment'.     

Modeling and Analysis of Multistate Access to Elective Surgery

In this paper, we attempt to determine whether delays in scheduling operation affect waiting time in a queue for elective surgery. We analyze the waiting-list management system in a Canadian hospital. We estimate the impact of scheduling delays by modeling access to treatment as a multistate process. We found that patients with any delay in scheduling surgery had longer waiting times than patients without delays. For certain sources of delays, the admission rate was 50–60% lower compared with the rate for admissions without a delay independent of urgency of surgical intervention. Our findings support a concern that waiting time for elective surgery is not simply determined by how many patients are on the waiting list, or by how urgently they need treatment, but also by the waiting list management practice.     

The role of economic evaluation in setting priorities for elective surgery

Problems with waiting lists have long affected the National Health Service. The priority given by clinicians to the elective surgery conditions usually found on waiting lists is low, but the publicity surrounding the waiting lists ensures that the priority accorded elective surgery in the political arena is much higher. Waiting list initiatives have provided additional resources for the purpose of reducing the number of patients waiting for elective surgery. It is suggested that economic evaluation should form one of a package of tools used by those setting priorities within elective surgery, but that the evidence provided by previously conducted economic evaluations of elective surgery is not of sufficient quality for purchasing authorities to use as a basis for priority setting.     

Impact of dental National Partnership Agreements on public dental service waiting lists in Queensland

Objective: Analyse the Queensland Dental Public Service waiting list from 2013 to 2015 while various funding agreements between the federal and state and territory governments were in place. Methods: Queensland Public Dental Service waiting list is open data and is updated monthly. This analysis reports on the changing number of people waiting for care and the percentage of people waiting beyond the reasonable period. Results: While the number of people waiting decreased when funding was specifically allocated to “blitz the dental public waiting list”, these have since increased back to pre‐blitz period numbers. The percentage now waiting beyond the reasonable period has decreased from 57% to 28% over the study period. Conclusions: While the ‘blitz’ was successful in reducing waiting list numbers, this was not sustained. The deferred federal funding to states/territories for dental services may have worsened the situation. Implications for Public Health: While an injection of funds to reduce the waiting list is important and has had an impact, to adequately address oral health will require not just continuing funding, but also a shift away from the current curative ‘downstream’ approach towards a health‐promotive ‘upstream’ approach. This will reduce not only the cost of treatment, but also waiting lists.     

Generic waiting lists for routine spinal surgery

National Health Service Hospitals are under pressure to reduce waiting lists within the constraints of a limited infrastructure. We implemented two systems to reduce waiting times for elective non-complex spinal surgery. The first of these was the introduction of managed generic waiting lists for both initial outpatient appointments and subsequent surgery. Thereafter, the MRI booking system was integrated with outpatient review appointments. Times from referral to first outpatient appointment and from scan to outpatient review and time on waiting list for surgery were analysed before and after implementation of these changes. Despite constant unit capacity there was a global decrease in waiting times. Before introduction of the generic waiting list, 37% of listed patients waited for more than 9 months; this figure fell to zero. Time from scan to outpatient review was 185 days before integration, 30 days after. Changes of this sort demand a quorum of consultants who will accept each others' recommendations. The generic waiting list will have impact only when there are large disparities in waiting times for different consultants. Targets are met at the expense of continuity of care.     

A messy reality: an analysis of New Zealand's elective surgery scoring system via media sources, 2000–2006

Waiting lists for elective procedures are a characteristic feature of tax‐funded universal health systems. New Zealand has gained a reputation for its ‘booking system’ for waiting list management, introduced in the early‐1990s. The New Zealand system uses criteria to ‘score’ and then ‘book’ qualifying patients for surgery. This article aims to (i) describe key issues focused on by the media, (ii) identify local strategies and (iii) present evidence of variation. Newspaper sources were searched (2000–2006). A total of 1199 booking system stories were identified. Findings demonstrate, from a national system perspective, the extraordinarily difficult nature of maintaining overall control and coordination. Equity and national consistency are affected when hospitals respond to local pressure by reducing access to elective treatment. Findings suggest that central government probably needs to be closely involved in local‐level management and policy adjustments; that through the study period, the New Zealand system appears to have been largely out of the control of government; and that governments elsewhere may need to be cautious when considering developing similar systems. Developing and implementing scoring and booking systems may always be a ‘messy reality’ with unintended consequences and throwing regional differences in service management and access into stark relief. Copyright © 2012 John Wiley & Sons, Ltd.     

The waiting time for heart interventions: trends for percutaneous coronary interventions and cardiothoracic interventions

OBJECTIVE: (a) To describe trends in the number of heart interventions performed over time, (b) to determine the length of waiting lists for elective heart interventions in the Netherlands according to the monthly survey of the Supervisory Committee for Heart Interventions in the Netherlands [Begeleidingscommissie Hartinterventies Nederland (BHN)], (c) to compare the length of the waiting lists with existing standards, and (d) to determine the reliability of the waiting list survey. DESIGN: Prospective. METHOD: Data were obtained from the monthly waiting list survey of the 13 heart centres in the Netherlands (1 January 1999-30 November 2002) and from the intervention registry (1 January 1999-30 June 2001), which was complete for 10 centres. Both the survey and the maintenance of the registry are carried out by the Supervisory Committee for Heart Interventions in the Netherlands. RESULTS: (a) The number of percutaneous coronary interventions performed in the Netherlands has increased. The number of cardiothoracic interventions remained stable. (b) The number of patients waiting for a percutaneous coronary intervention is increasing by 16% per annum. In November 2002 there were 751 patients on the waiting list. The number of patients waiting for a cardiothoracic intervention increased by 20% per annum until August 2001 and since then there has been a decrease of 21% per annum. In November 2002, 1557 patients were on the waiting list. (c) The percentage of patients treated within existing standards has fallen to 78% for percutaneous coronary interventions and to 53% for cardiothoracic interventions. (d) The length of the waiting list and the waiting times obtained in the survey concurred with the data taken from the intervention registry. CONCLUSIONS: The length of the waiting list for heart interventions has increased and complies increasingly less with existing standards. The monthly waiting-list survey was a reliable method of determining the length of waiting lists for elective heart interventions.     

Towards Decision Support for Waiting Lists: An Operations Management View

This paper considers the phenomenon of waiting lists in a healthcare setting, which is characterised by limitations on the national expenditure, to explore the potentials of an operations management perspective. A reference framework for waiting list management is described, distinguishing different levels of planning in healthcare – national, regional, hospital and process – that each contributes to the existence of waiting lists through managerial decision making. In addition, different underlying mechanisms in demand and supply are distinguished, which together explain the development of waiting lists. It is our contention that within this framework a series of situation specific models should be designed to support communication and decision making. This is illustrated by the modelling of the demand for cataract treatment in a regional setting in the south-eastern part of the Netherlands. An input–output model was developed to support decisions regarding waiting lists. The model projects the demand for treatment at a regional level and makes it possible to evaluate waiting list impacts for different scenarios to meet this demand.     

The ebb and flow of the NHS waiting list: how do recruitment and admission affect event‐based measures of the length of ‘time‐to‐admission’?

The likelihood of admission is reported in England as the percentage of elective episodes occurring within a certain time, for example, within three months of the date of enrollment on the waiting list. This event-based measure is calculated from cross-sectional data: the denominator is the number of elective episodes occurring in a specified calendar period, and the numerator is the number found to have enrolled on the waiting list less than three months previously. Now the number of elective episodes occurring within three months reflects the likelihood of admission and the numbers eligible to be admitted. If there is any increase in the likelihood of admission or in the number of people exposed to that likelihood then there will be an increase in the number of elective episodes found to have enrolled on the waiting list less than three months previously. Thus the numerator used by the Government Statistical Service accurately reflects conditions during the calendar period and within the enrollment cohorts of interest. The Government Statistical Service also needs a denominator so the episodes observed 0-2, 3-5, 6-8, 9-11 etc. months after enrollment are added as an indication of the number of people that could have been admitted within three months. This denominator implies that the number of people eligible for admission from the 3-5 month waiting time category is the same as the number surviving admission from the 0-2 month waiting time category but, during the period of interest, these two groups of people belong to cohorts that were recruited to the waiting list quite independently. As a result, this denominator will be too big if the number surviving to the end of one waiting time category is bigger than the number eligible for admission from the next and it will be too small if the number surviving to the end of one waiting time category is smaller than the number eligible for admission from the next. The event-based measure assumes that the waiting list is stationary and closed and only gives unbiased estimates under these conditions. This paper describes three alternative measures which recognize that the number of people recruited or admitted may vary from one quarter to the next. It uses Department of Health data to assess the size of the error if the event-based measure is used in these circumstances.     

A note on the dynamic interaction between waiting times and waiting lists

Waiting times are commonly used as a rationing device in health care and the public sector. We develop a stylised model, which predicts the dynamics of waiting times and waiting lists over time as a function of differing demand and supply parameters. We show that a path with decreasing waiting time and increasing waiting list over certain time intervals is a possible solution, which is consistent with some empirical evidence.     

EMERGENCY ADMISSIONS AND ELECTIVE SURGERY WAITING TIMES

An average patient waits between 2 and 3 months for an elective procedure in Australian public hospitals. Approximately 60% of all admissions occur through an emergency department, and bed competition from emergency admission provides one path by which waiting times for elective procedures may be lengthened. In this article, we investigated the extent to which public hospital waiting times are affected by the volume of emergency admissions and whether there is a differential impact by elective patient payment status. The latter has equity implications if the potential health cost associated with delayed treatment falls on public patients with lower ability to pay. Using annual data from public hospitals in the state of New South Wales, we found that, for a given available bed capacity, a one standard deviation increase in a hospital's emergency admissions lengthens waiting times by 19 days on average. However, paying (private) patients experience no delay overall. In fact, for some procedures, higher levels of emergency admissions are associated with lower private patient waiting times. Copyright © 2012 John Wiley & Sons, Ltd.     

The Impact of the COVID-19 Pandemic on the Uptake of Influenza Vaccine: UK-Wide Observational Study

BackgroundIn the face of the COVID-19 pandemic, the UK National Health Service (NHS) extended eligibility for influenza vaccination this season to approximately 32.4 million people (48.8% of the population). Knowing the intended uptake of the vaccine will inform supply and public health messaging to maximize vaccination.ObjectiveThe objective of this study was to measure the impact of the COVID-19 pandemic on the acceptance of influenza vaccination in the 2020-2021 season, specifically focusing on people who were previously eligible but routinely declined vaccination and newly eligible people.MethodsIntention to receive the influenza vaccine in 2020-2021 was asked of all registrants of the largest electronic personal health record in the NHS by a web-based questionnaire on July 31, 2020. Of those who were either newly or previously eligible but had not previously received an influenza vaccination, multivariable logistic regression and network diagrams were used to examine their reasons to undergo or decline vaccination.ResultsAmong 6641 respondents, 945 (14.2%) were previously eligible but were not vaccinated; of these, 536 (56.7%) intended to receive an influenza vaccination in 2020-2021, as did 466 (68.6%) of the newly eligible respondents. Intention to receive the influenza vaccine was associated with increased age, index of multiple deprivation quintile, and considering oneself to be at high risk from COVID-19. Among those who were eligible but not intending to be vaccinated in 2020-2021, 164/543 (30.2%) gave reasons based on misinformation. Of the previously unvaccinated health care workers, 47/96 (49%) stated they would decline vaccination in 2020-2021.ConclusionsIn this sample, COVID-19 has increased acceptance of influenza vaccination in previously eligible but unvaccinated people and has motivated substantial uptake in newly eligible people. This study is essential for informing resource planning and the need for effective messaging campaigns to address negative misconceptions, which is also necessary for COVID-19 vaccination programs.     

Self-regulation in hospital waiting lists

There is evidence that hospital waiting lists in the UK are resistant to shortening because reductions in length generate increases in referrals. We explored this concept by examining outpatient data for eight specialties in a large hospital centre over 17 months. Correlation coefficients were calculated by regressing waiting list density (numbers waiting more than 26 weeks) against referral rate. In three of the eight specialties, with the longest waiting lists, referral rates were significantly related, after one month's delay, to waiting list density (P < 0.01)--dermatology, R=0.68; ear-nose-throat R=0.78; trauma/orthopaedics (R=0.64). These were the three with the longest lists. These results help to explain why initiatives to shorten waiting lists are commonly ineffective in the long term.     

Equity in waiting times for two surgical specialties: a case study at a hospital in the North West of England

BACKGROUND: Reducing inequalities in access to the National Health Service is a key government priority. This study investigates the extent to which equitable access is achieved in one routinely administered hospital waiting list system. METHODS: Using hospital episode statistics for one hospital in the North West of England, a retrospective study of waiting times to surgery was undertaken for two surgical specialties (Orthopaedics and Ophthalmology). Participants were 4306 waiting list patients (elective, first episodes) living within Health Authority boundaries, treated within the two specialties between 1 April 2000 and 31 March 2001. Multiple logistic regression analysis of the relationship between waiting times and age, gender, ethnicity and deprivation status was undertaken. Main outcome measures were length of waiting time between being referred to the waiting list and treatment, and odds ratios for being associated with longer than median waiting times by age, gender, ethnicity and deprivation. RESULTS: For ophthalmology, the median waiting time was 140 days. After adjustment, older people had an odds ratio (OR) of 1.64 (95 per cent confidence interval (CI) 1.42-1.89), patients in deprived areas an OR of 1.29 (95 per cent CI 1.08-1.55) and men an OR of 0.79 (95 per cent CI 0.68-0.92) for having longer than median waiting times. No significant association was found between waiting times and ethnicity. For orthopaedics, the median waiting time was 129 days, and there were no significant differences in waiting times according to age, gender, ethnicity or deprivation. CONCLUSION: Routine waiting list systems are not always delivered equitably. For one specialty, female, older and deprived patients were significantly more likely to experience longer than average waits. Potential explanations and implications for policy-makers are considered.     

Priority for Elderly Persons With Behavioral and Psychological Symptoms of Dementia on Waiting Lists for Placement in Nursing Homes in Japan: Do Nursing Homes Change Priorities Based on Their Own Guidelines?

ObjectivesIn Japan, individuals seeking to be placed in nursing homes under the public long term care insurance program are subject to long waiting lists. Applicants are evaluated according to their needs for nursing home placement and assigned to homes based on their relative priority. The aim of the present study was to examine differences between the admission guidelines used by nursing homes and their attitudes regarding the priority for admitting elderly persons with behavioral and psychological symptoms of dementia (BPSD) from their waiting lists.DesignThis study was conducted using a cross-sectional study design.Setting/ParticipantsTwo hundred and eight different facilities provided sets of completed questionnaires and copies of their placement guidelines.MeasurementsThe managing director or social worker at each facility provided self-reports of whether they would increase the priority of applicants with BPSD and whether they would admit applicants who require treatment for BPSD. Each facility's placement guidelines were also collected.ResultsMost evaluation guidelines (65.9%) considered an applicant's BPSD as a condition that required nursing home placement; however, only 16.8% of the respondents actually increased the placement priority of applicants with BPSD. Some respondents refused to admit applicants who require treatment for BPSD but did not explicitly state in their guidelines that the priority of applicants with BPSD would be reduced because of the facility's inability to address BPSD.ConclusionThe present study revealed differences between nursing homes' official guidelines and their attitudes toward the priority of admitting elderly persons with BPSD from their waiting lists. Future studies should explore the factors that influence the placement of individuals with BPSD into nursing homes.     

The impact of the COVID-19 pandemic on the use of diagnostic imaging examinations in the Brazilian unified healthcare system (SUS)

ObjectivesTo assess the impact of the COVID-19 pandemic on the volumes of use of diagnostic imaging examinations in the Brazilian Unified Health System (SUS), the only healthcare provider for approximately 160 million people.MethodsWe collected the monthly numbers of diagnostic imaging examinations in the years 2019, 2020, and 2021 from a database provided by SUS. Data were collected by specific type of examination across different imaging modalities, both for the outpatient (elective and emergency) and inpatient settings.ResultsThere was a large reduction in the annual volume of almost all types of diagnostic imaging examinations in SUS in 2020, compared to 2019. Decreases were generally greater among outpatients than in the hospital setting, in which the annual volume of use of most modalities was similar or even higher in 2021 than in the pre-pandemic period. Computed tomography (CT) was the only modality for which use increased in 2020 compared to 2019. In contrast to other types of examinations, the use of chest CT was much higher in both 2020 and 2021 than in the preceding years. The relative changes in diagnostic imaging use in SUS started around March-April 2020, when the pandemic began to get worse in Brazil, and tended to correlate to COVID-19 incidence in Brazil over the following months.ConclusionsThe COVID-19 pandemic had a large impact on the use of diagnostic imaging examinations in the SUS. Policies and actions are needed to alleviate the resulting potential adverse health effects and to optimize the use of diagnostic tests in the future.     

Surveying the Cost-Effectiveness of the 20 Procedures with the Largest Public Health Services Waiting Lists in Ireland: Implications for Ireland’s Cost-Effectiveness Threshold

Objectives

To survey the cost effectiveness of procedures with the largest waiting lists in the Irish public health system to inform a reconsideration of Ireland’s current cost-effectiveness threshold of €45,000/quality-adjusted life-year (QALY).

Trouble in store: some thoughts on the management of waiting lists

Abstract This paper examines the day to day organisation and management of surgical waiting lists. A review of the literature on waiting lists identifies the main perspectives on waiting lists and two of these, the queue and the mortlake, are examined. This paper provides a critical analysis of these two theoretical outlooks and considers the explanatory power of each. It draws upon the sociology of work to understand the process of waiting list management, focusing particularly on the role of low level bureaucrats within the hospital setting. Using qualitative case study data on the day to day practices and street level organisation of waiting lists it is possible to identify inadequacies in our perception of waiting lists, of what they are and how they work. In the light of this the paper reconsiders the waiting list phenomenon and finally a new metaphor is proposed, that of the 'store' or shop which helps to explain some of the troubles with National Health Service waiting lists.     

Why we are wasting time in the operating theatre?

Objectives To determine reasons for delay during elective operating lists and suggest solutions. Design Prospective observational study. Setting A large under‐graduate teaching hospital. Participants Fifty‐five consecutive women undergoing elective gynaecological surgery under general anaesthesia. Interventions Every time point of individual patient's passage through the operating theatre (patients sent for, arrival in the anaesthetic room, general anaesthetic commenced, transfer to the operating theatre, surgery started, surgery completed, anaesthetic reversed, patient taken to recovery area) was documented. Main outcome measures Time intervals between the various time points with particular reference to wait by the anaesthetist and surgeon between cases. Results We monitored 55 operations carried out during 22 operating lists. Apart from the surgery itself (median 81 min per procedure), the longest interval was the time taken to get patients into the anaesthetic room from the ward (median 20 min). Although patients waited a median of 10 min before the start of anaesthesia, if the first procedure on the list was excluded, the anaesthetist was waiting for the patient to arrive in the anaesthetic room in 13/30 (43%) cases, wasting a median of 7 min per case. The surgeon had to wait a median of 22.5 min between operations. Conclusions Considerable operating theatre time is wasted while patients are transferred to and from the operating theatre resulting in both anaesthetists and surgeons having to wait between patients in a high proportion of cases, averaging 1 h during a 4 h operating list. Surgery could be made more time efficient by ensuring that patients arrive in the operating theatre complex early enough (to reduce time wasted for anaesthetists and surgeons), and by having two anaesthetists available at the end of surgery, one to reverse the anaesthetic while the other starts the next induction (to reduce time waste for the surgeon), coupled to adequate recovery area capacity. Copyright © 2008 John Wiley & Sons, Ltd.