A Theory of Waiting Time Reporting and Quality Signaling

Hospital report cards: Quality competition and patient selection

Hospital 'report cards' policies involve governments publishing information about hospital quality. Such policies often aim to improve hospital quality by stimulating competition between hospitals. Previous empirical literature lacks a comprehensive theoretical framework for analysing the effects of report cards. We model a report card policy in a market where two hospitals compete for patients on quality under regulated prices. The report card policy improves the accuracy of the quality signal observed by patients. Hospitals may improve their published quality scores by costly quality improvement or by selecting healthier patients to treat. We show that increasing information through report cards always increases quality and only sometimes induces selection. Report cards are more likely to increase patient welfare when quality scores are well risk-adjusted, where the cost of selecting patients is high, and the cost of increasing quality is low.

A note on the trade-off between waiting times and quality in a constrained hospital market

While aging population and technological innovation are expected to increase healthcare demand in the future, increase in healthcare spending is not likely to be sustainable in times of fiscal constraint. This might lead to a tightening of hospital capacity and, potentially, to higher patient waiting times. This paper studies waiting times and quality in a healthcare market where semi-altruistic hospitals operate at full capacity. We show that in this context a trade-off between waiting times and quality emerges which, if hospitals dislike patients to wait, decreases the incentive for the quality of care. We also show that, when hospitals operate at full capacity, standard waiting time policies involving targets and penalties (e.g., "Targets and Terror" in England) can meet the target at the expense of a lower quality of care, with relevant implications for the empirical evaluation of waiting time policy.

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.

Dynamic hospital competition under rationing by waiting times

We develop a dynamic model of hospital competition where (i) waiting times increase if demand exceeds supply; (ii) patients choose a hospital based in part on waiting times; and (iii) hospitals incur waiting time penalties. We show that, whereas policies based on penalties will lead to lower waiting times, policies that promote patient choice will instead lead to higher waiting times. These results are robust to different game-theoretic solution concepts, designs of the hospital penalty structure, and patient utility specifications. Furthermore, waiting time penalties are likely to be more effective in reducing waiting times if they are designed with a linear penalty structure, but the counterproductive effect of patient choice policies is smaller when penalties are convex. These conclusions are partly derived by calibration of our model based on waiting times and elasticities observed in the English NHS for a common treatment (cataract surgery).