The market for elective surgery: joint estimation of supply and demand

How do family doctors respond to reduced waiting times for cancer diagnosis in secondary care?

Reducing waiting times is a priority in public health systems. Efforts of healthcare providers to shorten waiting times could be negated if they simultaneously induce substantial increases in demand. However, separating out the effects of changes in supply and demand on waiting times requires an exogenous change in one element. We examine the impact of a pilot programme in some English hospitals to shorten waiting times for urgent diagnosis of suspected cancer on family doctors' referrals. We examine referrals from 6,666 family doctor partnerships to 145 hospitals between 1st April 2012 and 31st March 2019. Five hospitals piloted shorter waiting times initiatives in 2017. Using continuous difference-in-differences regression, we exploit the pilot as a 'supply shifter' to estimate the effect of waiting times on referral volumes for two suspected cancer types: bowel and lung. The proportion of referred patients breaching two-week waiting times targets for suspected bowel cancer fell by 3.9 percentage points in pilot hospitals in response to the policy, from a baseline of 4.8%. Family doctors exposed to the pilot increased their referrals (demand) by 10.8%. However, the pilot was not successful for lung cancer, with some evidence that waiting times increased, and a corresponding reduction in referrals of -10.5%. Family doctor referrals for suspected cancer are responsive at the margin to waiting times. Healthcare providers may struggle to achieve long-term reductions in waiting times if supply-side improvements are offset by increases in demand.

Effects of private health insurance on waiting time in public hospitals

The Australian government pays $6.7 billion per year in rebates to encourage Australians to purchase private health insurance (PHI) and an additional $6.1 billion to cover services provided in private hospitals. What is the justification for large government subsidies to a private industry when all Australians already have free coverage under Medicare? The government argues that more people buying PHI will relieve the burden on the public system and may reduce waiting times. However, the evidence supporting this is sparse. We use an instrumental variable approach to study the causal effects of higher PHI coverage in the area on waiting times in public hospitals in the same area. The instrument used is area-level average house prices, which correlate with average income and wealth, thus influencing the purchase of PHI due to tax incentives, but not directly affecting waiting times in public hospitals. We use 2014-2018 hospital admission and elective surgery waiting list data linked at the patient level from the Victorian Center for Data Linkage. These data cover all inpatient admissions in all hospitals in Victoria (both public and private hospitals) and those registered on the waiting list for elective surgeries in public hospitals in Victoria. We find that one percentage point increase in PHI coverage leads to about 0.34 days (or 0.5%) reduction in waiting times in public hospitals on average. The effects vary by surgical specialities and age groups. However, the practical significance of this effect is limited, if not negligible, despite its statistical significance. The small effect suggests that raising PHI coverage with the aim to taking the pressure off the public system is not an effective strategy in reducing waiting times in public hospitals. Alternative policies aiming at improving the efficiency of public hospitals and advancing equitable access to care should be a priority for policymakers.

Who responds to longer wait times? The effects of predicted emergency wait times on the health and volume of patients who present for care

Healthcare is often free at the point-of-care so that price does not deter patients. However, the dis-utility from waiting for care that often occurs could also lead to deterrence. I investigate responses in the volume and types of patients that demand emergency care when predicted waiting times quasi-randomly change. I leverage a discontinuity to compare emergency sites with similar predicted wait times but with different apparent wait times displayed to patients. I use impulse response functions estimated by local projections to estimate effects of predicted wait times on patient demand for care. An additional thirty minutes of predicted wait time results in 15% fewer waiting patients at urgent cares and 2% fewer waiting patients at emergency departments within three hours of display. Patients that stop using emergency care are also triaged as healthier. However, at very high predicted wait times, there are reductions in demand for all patients including sicker patients.

The effect of local hospital waiting times on GP referrals for suspected cancer

INTRODUCTION

Reducing waiting times is a major policy objective in publicly-funded healthcare systems. However, reductions in waiting times can produce a demand response, which may offset increases in capacity. Early detection and diagnosis of cancer is a policy focus in many OECD countries, but prolonged waiting periods for specialist confirmation of diagnosis could impede this goal. We examine whether urgent GP referrals for suspected cancer patients are responsive to local hospital waiting times.

METHOD

We used annual counts of referrals from all 6,667 general practices to all 185 hospital Trusts in England between April 2012 and March 2018. Using a practice-level measure of local hospital waiting times based on breaches of the two-week maximum waiting time target, we examined the relationship between waiting times and urgent GP referrals for suspected cancer. To identify whether the relationship is driven by differences between practices or changes over time, we estimated three regression models: pooled linear regression, a between-practice estimator, and a within-practice estimator.

RESULTS

Ten percent higher rates of patients breaching the two-week wait target in local hospitals were associated with higher volumes of referrals in the pooled linear model (4.4%; CI 2.4% to 6.4%) and the between-practice estimator (12.0%; CI 5.5% to 18.5%). The relationship was not statistically significant using the within-practice estimator (1.0%; CI -0.4% to 2.5%).

CONCLUSION

The positive association between local hospital waiting times and GP demand for specialist diagnosis was caused by practices with higher levels of referrals facing longer local waiting times. Temporal changes in waiting times faced by individual practices were not related to changes in their referral volumes. GP referrals for diagnostic cancer services were not found to respond to waiting times in the short-term. In this setting, it may therefore be possible to reduce waiting times by increasing supply without consequently increasing demand.

The effect of hospital spending on waiting times

Long waiting times have been a persistent policy issue in the United Kingdom that the COVID-19 pandemic has exacerbated. This study analyses the causal effect of hospital spending on waiting times in England using a first-differences panel approach and an instrumental variable strategy to deal with residual concerns for endogeneity. We use data from 2014 to 2019 on waiting times from general practitioner referral to treatment (RTT) measured at the level of local purchasers (known as Clinical Commissioning Groups). We find that increases in hospital spending by local purchasers of 1% reduce median RTT waiting time for patients whose pathway ends with a hospital admission (admitted pathway) by 0.6 days but the effect is not statistically significant at 5% level (only at the 10% level). We also find that higher hospital spending does not affect the RTT waiting time for patients whose pathway ends with a specialist consultation (non-admitted pathway). Nor does higher spending have a statistically significant effect on the volume of elective activity for either pathway. Our findings suggest that higher spending is no guarantee of higher volumes and lower waiting times, and that additional mechanisms need to be put in place to ensure that increased spending benefits elective patients.

Provider supply and access to primary care

Resource-constrained delivery systems often have access issues, causing patients to wait a long time to see a provider. We develop theoretical and empirical models of wait times and apply them to primary care delivery by the U.S. Veterans Health Administration (VHA). Using instrumental variables to handle simultaneity issues, we estimate the effect of clinician supply on new patient wait times. We find that it has a sizable impact. A 10% increase in capacity reduces wait times by 2.1%. Wait times are also associated with clinician productivity, scheduling protocols, and patient access to alternative sources of care. The VHA has adopted our models to identify underserved areas as specified by the MISSION Act of 2018.

Public Interest in Knee Replacement Fell During the Onset of the COVID-19 Pandemic: A Google Trends Analysis

BACKGROUND

The COVID-19 pandemic significantly altered medical practice and public behavior in the USA. In spring of 2020, elective surgery including most joint replacement was suspended and much of the public asked to stay at home. As elective surgery resumes, it is unknown how the public will respond.

QUESTIONS/PURPOSES

We sought to describe public interest in knee replacement during the onset of the COVID-19 pandemic.

METHODS

Google Trends was used to obtain the daily number of searches for "knee replacement," "coronavirus," and "knee pain" from December 19, 2019, to May 14, 2020. The number is on a term-specific scale weighted to the highest number of daily searches for that term. Seven-day weighted averages were used to smooth the data.

RESULTS

The number of daily searches for "knee replacement" was stable until around March 8, 2020, after which it decreased through late March, plateauing at less than half the number of searches. At the same time, searches for "coronavirus" spiked. By early May, searches for "knee replacement" had not meaningfully increased, though at the end of the search period the slope turned positive and coronavirus searches decreased. Searches for "knee pain" initially followed a similar pattern to "knee replacement," though the decline was not as steep, and by late April searches for "knee pain" had meaningfully increased.

CONCLUSION

Public interest in knee replacement, assessed through internet search queries, decreased during the onset of the COVID-19 pandemic. While interest in pain has returned, the continued decreased level of interest in surgery may represent a fear of surgery among the general public in the setting of COVID-19. Surgeons may wish to focus outreach and education efforts on the safety and efficacy of knee replacement.

New geographic model of care to manage the post-COVID-19 elective surgery aftershock in England: a retrospective observational study

OBJECTIVES

The suspension of elective surgery during the COVID-19 pandemic is unprecedented and has resulted in record volumes of patients waiting for operations. Novel approaches that maximise capacity and efficiency of surgical care are urgently required. This study applies Markov multiscale community detection (MMCD), an unsupervised graph-based clustering framework, to identify new surgical care models based on pooled waiting-lists delivered across an expanded network of surgical providers.

DESIGN

Retrospective observational study using Hospital Episode Statistics.

SETTING

Public and private hospitals providing surgical care to National Health Service (NHS) patients in England.

PARTICIPANTS

All adult patients resident in England undergoing NHS-funded planned surgical procedures between 1 April 2017 and 31 March 2018.

MAIN OUTCOME MEASURES

The identification of the most common planned surgical procedures in England (high-volume procedures (HVP)) and proportion of low, medium and high-risk patients undergoing each HVP. The mapping of hospitals providing surgical care onto optimised groupings based on patient usage data.

RESULTS

A total of 7 811 891 planned operations were identified in 4 284 925 adults during the 1-year period of our study. The 28 most common surgical procedures accounted for a combined 3 907 474 operations (50.0% of the total). 2 412 613 (61.7%) of these most common procedures involved 'low risk' patients. Patients travelled an average of 11.3 km for these procedures. Based on the data, MMCD partitioned England into 45, 16 and 7 mutually exclusive and collectively exhaustive natural surgical communities of increasing coarseness. The coarser partitions into 16 and seven surgical communities were shown to be associated with balanced supply and demand for surgical care within communities.

CONCLUSIONS

Pooled waiting-lists for low-risk elective procedures and patients across integrated, expanded natural surgical community networks have the potential to increase efficiency by innovatively flexing existing supply to better match demand.

Worth the wait: The impact of government funding on hospital emergency waiting times

In the absence of a price mechanism, emergency department waiting times act as a rationing device to equate demand for treatment with available supply. Sustained increases to demand stemming from population growth, aging populations, and rising comorbidities has caused waiting times internationally to rise. This has resulted in increased calls for higher funding from governments and commitments from both state and national governments to address excessive waiting times. This paper aims to determine the effectiveness of government funding for improving the median waiting times for treatment and the proportion of patients seen within clinically recommended waiting times. For this purpose, an econometric analysis was conducted on a panel of data on Victorian local health networks over the period 2015-2018. This is supplemented with a discussion of the alternative measures which governments might take to both address demand for emergency treatment, and also ensure that waiting time reductions can be maintained over the long-term.

Anatomy of a demand shock: Quantitative analysis of crowding in hospital emergency departments in Victoria, Australia during the 2009 influenza pandemic

Objective

An infectious disease outbreak such as the 2009 influenza pandemic is an unexpected demand shock to hospital emergency departments (EDs). We analysed changes in key performance metrics in (EDs) in Victoria during this pandemic to assess the impact of this demand shock.

Design and setting

Descriptive time-series analysis and longitudinal regression analysis of data from the Victorian Emergency Minimum Dataset (VEMD) using data from the 38 EDs that submit data to the state’s Department of Health and Human Services.

Main outcome measures

Daily number of presentations, influenza-like-illness (ILI) presentations, daily mean waiting time (time to first being seen by a doctor), daily number of patients who did-not-wait and daily number of access-blocked patients (admitted patients with length of stay >8 hours) at a system and hospital-level.

Results

During the influenza pandemic, mean waiting time increased by up to 25%, access block increased by 32% and did not wait presentations increased by 69% above pre-pandemic levels. The peaks of all three crowding variables corresponded approximately to the peak in admitted ILI presentations. Longitudinal fixed-effects regression analysis estimated positive and statistically significant associations between mean waiting times, did not wait presentations and access block and ILI presentations.

Conclusions

This pandemic event caused excess demand leading to increased waiting times, did-not-wait patients and access block. Increases in admitted patients were more strongly associated with crowding than non-admitted patients during the pandemic period, so policies to divert or mitigate low-complexity non-admitted patients are unlikely to be effective in reducing ED crowding.

Analysis of uncertainty in the surgical department: durations, requests and cancellations

Objective Analytical techniques are being implemented with increasing frequency to improve the management of surgical departments and to ensure that decisions are well informed. Often these analytical techniques rely on the validity of underlying statistical assumptions, including those around choice of distribution when modelling uncertainty. The aim of the present study was to determine a set of suitable statistical distributions and provide recommendations to assist hospital planning staff, based on three full years of historical data. Methods Statistical analysis was performed to determine the most appropriate distributions and models in a variety of surgical contexts. Data from 2013 to 2015 were collected from the surgical department at a large Australian public hospital. Results A log-normal distribution approximation of the total duration of surgeries in an operating room is appropriate when considering probability of overtime. Surgical requests can be modelled as a Poisson process with rate dependent on urgency and day of the week. Individual cancellations could be modelled as Bernoulli trials, with the probability of patient-, staff- and resource-based cancellations provided herein. Conclusions The analysis presented herein can be used to ensure that assumptions surrounding planning and scheduling in the surgical department are valid. Understanding the stochasticity in the surgical department may result in the implementation of more realistic decision models. What is known about the topic? Many surgical departments rely on crude estimates and general intuition to predict surgical duration, surgical requests (both elective and non-elective) and cancellations. What does this paper add? This paper describes how statistical analysis can be performed to validate common assumptions surrounding surgical uncertainty. The paper also provides a set of recommended distributions and associated parameters that can be used to model uncertainty in a large public hospital's surgical department. What are the implications for practitioners? The insights on surgical uncertainty provided here will prove valuable for administrative staff who want to incorporate uncertainty in their surgical planning and scheduling decisions.

Should I stay or should I go? Hospital emergency department waiting times and demand

In the absence of the price mechanism, hospital emergency departments rely on waiting times, alongside prioritisation mechanisms, to restrain demand and clear the market. This paper estimates by how much the number of treatments demanded is reduced by a higher waiting time. I use variation in waiting times for low-urgency patients caused by rare and resource-intensive high-urgency patients to estimate the relationship. I find that when waiting times are higher, more low-urgency patients are deterred from treatment and leave the hospital during the waiting period without being treated. The waiting time elasticity of demand for low-urgency patients is approximately -0.25 and is highest for the lowest-urgency patients.

The effects of immigration on NHS waiting times

This paper analyzes the effects of immigration on waiting times for the National Health Service (NHS) in England. Linking administrative records from Hospital Episode Statistics (2003-2012) with immigration data drawn from the UK Labour Force Survey, we find that immigration reduced waiting times for outpatient referrals and did not have significant effects on waiting times in accident and emergency departments (A&E) and elective care. The reduction in outpatient waiting times can be explained by the fact that immigration increases natives' internal mobility and that immigrants tend to be healthier than natives who move to different areas. Finally, we find evidence that immigration increased waiting times for outpatient referrals in more deprived areas outside of London. The increase in average waiting times in more deprived areas is concentrated in the years immediately following the 2004 EU enlargement and disappears in the medium term (e.g., 3-4 years).

The effect of waiting times on demand and supply for elective surgery: Evidence from Italy

Waiting times are a major policy concern in publicly funded health systems across OECD countries. Economists have argued that, in the presence of excess demand, waiting times act as nonmonetary prices to bring demand for and supply of health care in equilibrium. Using administrative data disaggregated by region and surgical procedure over 2010-2014 in Italy, we estimate demand and supply elasticities with respect to waiting times. We employ linear regression models with first differences and instrumental variables to deal with endogeneity of waiting times. We find that demand is inelastic to waiting times while supply is more elastic. Estimates of demand elasticity are between -0.15 to -0.24. Our results have implications on the effectiveness of policies aimed at increasing supply and their ability to reduce waiting times.

A Theory of Waiting Time Reporting and Quality Signaling

We develop a theoretical model to study a policy that publicly reports hospital waiting times. We characterize two effects of such a policy: the 'competition effect' that drives hospitals to compete for patients by increasing service rates and reducing waiting times and the 'signaling effect' that allows patients to distinguish a high-quality hospital from a low-quality one. While for a low-quality hospital both effects help reduce waiting time, for a high-quality hospital, they act in opposite directions. We show that the competition effect will outweigh the signaling effect for the high-quality hospital, and consequently, both hospitals' waiting times will be reduced by the introduction of the policy. This result holds in a policy environment where maximum waiting time targets are not binding. Copyright © 2015 John Wiley & Sons, Ltd.

Waiting time prioritisation: Evidence from England

A number of OECD countries have introduced waiting time prioritisation policies which give explicit priority to severely ill patients with high marginal disutility of waiting. There is however little empirical evidence on how patients are actually prioritised. We exploit a unique opportunity to investigate this issue using a large national dataset with accurate measures of severity on nearly 400,000 patients. We link data from a national patient-reported outcome measures survey to administrative data on all patients waiting for a publicly funded hip and knee replacement in England during the years 2009-14. We find that patients suffering the most severe pain and immobility have shorter waits than those suffering the least, by about 24% for hip replacement and 11% for knee replacement, and that the association is approximately linear. These differentials are more closely associated with pain than immobility, and are larger in hospitals with longer average waiting times. These result suggests that doctors prioritise patients according to severity even when no formal prioritisation policy is in place and average waiting times are short.

Hospital sector choice and support for public hospital care in New Zealand: Results from a labeled discrete choice survey

This study uses a discrete choice experiment (DCE) to measure patients' preferences for public and private hospital care in New Zealand. A labeled DCE was administered to 583 members of the general public, with the choice between a public and private hospital for a non-urgent surgery. The results suggest that cost of surgery, waiting times for surgery, option to select a surgeon, convenience, and conditions of the hospital ward are important considerations for patients. The most important determinant of hospital choice was whether it was a public or private hospital, with respondents far more likely to choose a public hospital than a private hospital. The results have implications for government policy toward using private hospitals to clear waiting lists in public hospitals, with these results suggesting the public might not be indifferent to policies that treat private hospitals as substitutes for public hospitals.

Testing the bed-blocking hypothesis: does nursing and care home supply reduce delayed hospital discharges?

Hospital bed-blocking occurs when hospital patients are ready to be discharged to a nursing home, but no place is available, so that hospital care acts as a more costly substitute for long-term care. We investigate the extent to which greater supply of nursing home beds or lower prices can reduce hospital bed-blocking using a new Local Authority (LA) level administrative data from England on hospital delayed discharges in 2009-2013. The results suggest that delayed discharges respond to the availability of care home beds, but the effect is modest: an increase in care home beds by 10% (250 additional beds per LA) would reduce social care delayed discharges by about 6-9%. We also find strong evidence of spillover effects across LAs: more care home beds or fewer patients aged over 65 years in nearby LAs are associated with fewer delayed discharges.

Waiting time and socioeconomic status--an individual-level analysis

Waiting time is a rationing mechanism that is used in publicly funded healthcare systems. From an equity viewpoint, it is regarded as preferable to co-payments. However, long waits are an indication of poor quality of service. To our knowledge, this analysis is the first to benefit from individual-level data from administrative registers to investigate the relationship between waiting time, income, and education. Furthermore, it makes use of an extensive set of medical information that serves as indicators of patient need. Differences in waiting time by socioeconomic status are detected. For men, there is a statistically highly significant negative association between income and waiting time, driven by men in the highest income group, which constitutes 12% of all men. More educated women, that is, those having an education above compulsory schooling, experience lower waiting time than their fellow sisters with the lowest level of education.

High-profile investigations into hospital safety problems in England did not prompt patients to switch providers

Amid international concerns about health care safety and quality, there has been an escalation of investigations by health care regulators into adverse events. England has a powerful central health care regulator, the Care Quality Commission, which conducts occasional high-profile investigations into major lapses in quality at individual hospitals. The results have sometimes garnered considerable attention from the news media, but it is not known what effect the investigations have had on patients' behavior. We analyzed trends in admission for discretionary (nonemergency) care at three hospitals that were subject to high-profile investigations by the Healthcare Commission (the predecessor to the Care Quality Commission) between 2006 and 2009. We found that investigations had no impact on utilization for two of the hospitals; in the third hospital, there were significant declines in inpatient admissions, outpatient surgeries, and in numbers of patients coming for their first appointment, but the effects disappeared six months after publication of the investigation report. Thus, the publication and dissemination of highly critical reports by a health care regulator does not appear to have resulted in patients' sustained avoidance of the hospitals that were investigated. Our findings reinforce other evaluations: Reporting designed to affect providers' reputations is likely to spur more improvement in quality and safety than relying on patients to choose their providers based on quality and safety reports, and simplistic assumptions regarding the power of information to drive patient choices are unrealistic.

The effect of waiting time and distance on hospital choice for English cataract patients

This paper applies latent-class multinomial logit models to the choice of hospital for cataract operations in the UK NHS. We concentrate on the effects of travel time and waiting time and especially on estimating the waiting time elasticity of demand. Models including hospital fixed effects rely on changes over time in waiting time to indentify coefficients. We show how using a latent-class multinomial logit model characterises the unobserved heterogeneity in GP practices' choice behaviour and affects the estimated elasticities of travel time and waiting time. The models estimate waiting time elasticities of demand of approximately -0.1, comparable with previous waiting time-demand models. For the average waiting time elasticity, the simple multinomial logit models are good approximations of the latent-class logit results.

Waiting-time targets in the healthcare sector: how long are we waiting?

Waiting-time targets are used by policy makers to monitor providers' performance. Such targets are based on the distribution of the patients on the list. We compare and link such distribution with the distribution of waiting time of patients treated, as opposed to on the list, which is a better measure of total disutility from waiting (although can only be calculated retrospectively). We show that the latter can be calculated from the former, and vice versa. We also show that, depending on how the hazard rate varies with time waited, the proportion of patients on the list waiting more than x periods can be higher or lower than the proportion of patients treated waiting more than x periods. However, empirically we find that the proportion of patients waiting on the list more than x months is smaller than our estimate of the proportion of patients treated waiting more than x months.

Waiting times and socioeconomic status among elderly Europeans: evidence from SHARE

Waiting times for specialist consultation and non-emergency surgery are often considered an equitable rationing mechanism in the public healthcare sector, because access to care is not based on socioeconomic status. This study tests empirically this claim using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). The sample includes nine European countries: Austria, Denmark, France, Germany, Greece, Italy, the Netherlands, Spain and Sweden. For specialist consultation, we find that individuals with high education experience a reduction in waiting times of 68% in Spain, 67% in Italy and 34% in France (compared with individuals with low education). Individuals with intermediate education report a waiting-time reduction of 74% in Greece (compared with individuals with low education). There is also evidence of a negative and significant association between education and waiting times for non-emergency surgery in Denmark, the Netherlands and Sweden. High education reduces waits by 66, 32 and 48%, respectively. We also find income effects, although generally modest. An increase in income of 10 000 Euro reduces waiting times for specialist consultation by 8% in Germany and waiting times for non-emergency surgery by 26% in Greece. Surprisingly, an increase in income of 10 000 Euro increases waits by 11% in Sweden.

Assessing hospital competition when prices don't matter to patients: the use of time-elasticities

Health care reforms in several European countries provide health insurers with incentives and tools to become prudent purchasers of health care. The potential success of this strategy crucially depends on insurers' bargaining leverage vis-à-vis health care providers. An important determinant of insurers' bargaining power is the willingness of consumers to consider alternative providers. In this paper we examine to what extent consumers are willing to switch hospitals when they are fully covered for hospital services, which is typical for many European countries. Since prices do not matter to these patients, we estimate time-elasticities to assess hospital substitutability. Using data from a large Dutch health insurer on non-emergency neurosurgical outpatient hospital visits in 2003, we estimate a conditional logit model of patient hospital choice taking both patient heterogeneity and hospital characteristics into account. We use the parameter estimates to simulate the demand effect of an artificial increase in travel time by 10% for every patient, holding all other hospital attributes constant. Overall, the resulting point estimates of hospitals' time-elasticities are fairly high, although variation is substantial (-2.6 to -1.4). Sensitivity tests reveal that these estimates are very robust and differ significantly across individual hospitals. This implies that all hospitals in our study sample have at least one close substitute which is an important precondition for effective hospital competition.

Third degree waiting time discrimination: optimal allocation of a public sector healthcare treatment under rationing by waiting

In many public healthcare systems treatments are rationed by waiting time. We examine the optimal allocation of a fixed supply of a given treatment between different groups of patients. Even in the absence of any distributional aims, welfare is increased by third degree waiting time discrimination: setting different waiting times for different groups waiting for the same treatment. Because waiting time imposes dead weight losses on patients, lower waiting times should be offered to groups with higher marginal waiting time costs and with less elastic demand for the treatment.

Do waiting times reduce hospital costs?

Using a sample of 137 hospitals over the period 1998-2002 in the English National Health Service, we estimate the elasticity of hospital costs with respect to waiting times. Our cross-sectional and panel-data results suggest that at the sample mean (103 days), waiting times have no significant effect on hospitals' costs or, at most, a positive one. If significant, the elasticity of cost with respect to waiting time from our cross-sectional estimates is in the range 0.4-1. The elasticity is still positive but lower in our fixed-effects specifications (0.2-0.4). In all specifications, the effect of waiting time on cost is non-linear, suggesting a U-shaped relationship between hospital costs and waiting times. However, the level of waiting time which minimises total costs is always below ten days.

Rationing the public provision of healthcare in the presence of private supplements: evidence from the Italian NHS

In this paper we assess the relative effectiveness of user charges and administrative waiting times as a tool for rationing public healthcare in Italy. We measure demand elasticities by estimating a simultaneous equation model of GP primary care visits, public specialist consultations and private specialist consultations, as if they were part of an incomplete system of demand. We find that for public specialist consultations, own price elasticity of demand is about -0.3, while elasticity to administrative waiting time is about -.04. No substitution exists between the demand for public and private specialists, so that user charges act as a net deterrent for over-consumption. The public provision of healthcare does not induce the wealthy to opt out. Moreover our evidence suggests that user charges and waiting lists do not serve redistributive purposes.

Ramsey waits: allocating public health service resources when there is rationing by waiting

The optimal allocation of a public health care budget across treatments must take account of the way in which care is rationed within treatments since this will affect their marginal value. We investigate the optimal allocation rules for public health care systems where user charges are fixed and care is rationed by waiting. The optimal waiting time is higher for treatments with demands more elastic to waiting time, higher costs, lower charges, smaller marginal welfare loss from waiting by treated patients, and smaller marginal welfare losses from under-consumption of care. The results hold for a wide range of welfarist and non-welfarist objective functions and for systems in which there is also a private health care sector. They imply that allocation rules based purely on cost effectiveness ratios are suboptimal because they assume that there is no rationing within treatments.

Explaining trends in concentration of healthcare commissioning in the English NHS

In recent years there have been marked changes in organisational structures and budgetary arrangements in the English National Health Service, potentially altering the relationships between purchasers (primary care organisations (PCOs) and general practices) and hospitals. We show that elective admissions from PCOs and practices became significantly more concentrated across hospitals between 1997/98 and 2002/03. There was a reduction in the average number of hospitals used by PCOs (16.7-14.2), an increase in the average share of admissions accounted for by the main hospital (49-69%), and an increase in the average Herfindahl index (0.35-0.55). About half the increase in concentration arose from the increase in the number of purchasing organisations as 100 health authorities were replaced by 303 primary care trusts. Most of the remainder was probably due to hospital mergers. Fundholding general practices that held budgets for elective admissions had less concentrated admission patterns than non-fundholders whose admissions were paid for by their PCO. Around 1/10th of the increase in concentration at practice level was due to the abolition of fundholding in April 1999. Our results have implications for the effects of the recent reintroduction of fundholding and the halving of the number of PCOs.