Does hospital competition save lives? Evidence from the English NHS patient choice reforms

This paper examines whether or not hospital competition in a market with fixed reimbursement prices can prompt improvements in clinical quality. In January 2006, the British Government introduced a major extension of their market-based reforms to the English National Health Service. From January 2006 onwards, every patient in England could choose their hospital for secondary care and hospitals had to compete with each other to attract patients to secure their revenue. One of the central aims of this policy was to create financial incentives for providers to improve their clinical performance. This paper assesses whether this aim has been achieved and competition led to improvements in quality. For our estimation, we exploit the fact that choice-based reforms will create sharper financial incentives for hospitals in markets where choice is geographically feasible and that prior to 2006, in the absence of patient choice, hospitals had no direct financial incentive to improve performance in order to attract more patients. We use a modified difference-in-difference estimator to analyze whether quality improved more quickly in more competitive markets after the government introduced its new wave of market-based reforms. Using AMI mortality as a quality indicator, we find that mortality fell more quickly (i.e. quality improved) for patients living in more competitive markets after the introduction of hospital competition in January 2006. Our results suggest that hospital competition in markets with fixed prices can lead to improvements in clinical quality

Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

Is there any Substitution Between Medical Services and Over-the-Counter Medications in the Case of the Common Cold? -Analysis Based on an Original Survey-.

This article examines the choice of health care in Japan for patients suffering from the common cold.HEALTH ; JAPAN

Public bus transport demand elasticities in India

A number of static and dynamic specifications of a log linear demand function for public transport are estimated using aggregate panel data for 22 Indian states over the period 1990 to 2001. Demand has been defined as total passenger kilometers to capture actual market transactions, while the regressors include public transit fare, per capita income, service quality, and other demographic and social variables. In all cases, transit demand is significant and inelastic to the fare. Service quality is the most significant policy variable. Finally, social and demographic variables highlight the complex nature of public bus transit demand in India.Demand Elasticities, Dynamic Panel Data, Bus Transport, India

Towards panel data specifications of efficiency measures for English acute hospitals

This paper reports work undertaken for the Department of Health to explore different approaches of measuring hospital efficiency. The emphasis throughout is on developing adjusted cost-efficiency measures in line with NHS Trusts performance objectives. Previous work described the derivation of three residual-based cost indices (CCI, 2CCI and 3CCI), each with increasing adjustment in terms of case mix, factor prices and environmental factors for a single year’s data (1995/6) (Söderlund & van der Merwe, 1999). This study explores further options based on the previous work by: (1) supplementing hospital level with specialty level data; (2) studying a 4-year panel from 1994/5 to 1997/8; (3) estimating models with non-symmetric error terms and including Trust-specific effects when measuring inefficiency. Although the paper argues that panel data models may have certain advantages over cross-sectional ones, the results suggest that data pooling across years provide robust parameter estimates. Longitudinal fixed effect models may however be useful to construct efficiency indices while stochastic frontier models have the advantage of taking account of random noise. Specialty level models proved inferior to whole hospital estimations. The paper argues that the degree of variation between hospitals in terms of efficiency is not that great and scope for efficiency enhancement is primarily attainable by optimising capacity and activity levels in the long run. Increased activity levels may however have adverse consequences such as increased hospital infection rates, poorer quality of care and a lack of capacity to deal with emergency demand. The paper argues that the Department of Health might consider a shift from the adjusted cost index approach used in this normative benchmarking framework to the more conventional efficiency analysis approach using a total cost function, and more flexible functional forms, allowing for a more defensible interpretation of the residuals as inefficiency.efficiency

Quantifying the effects of modelling choices on hospital efficiency measures: A meta-regression analysis

It has often been argued that the results of efficiency analyses in health care are influenced by the modelling choices made by the researchers involved. In this paper we use meta-regression analysis in an attempt to quantify the degree to which modelling factors influence efficiency estimates. The data set is derived from 253 estimated models reported in 95 empirical analyses of hospital efficiency in the 22-year period from 1987 to 2008. A meta-regression model is used to investigate the degree to which differences in mean efficiency estimates can be explained by factors such as: sample size; dimension (number of variables); parametric versus non-parametric method; returns to scale (RTS) assumptions; functional form; error distributional form; input versus output orientation; cost versus technical efficiency measure; and cross-sectional versus panel data. Sample size, dimension and RTS are found to have statistically significant effects at the 1% level. Sample size has a negative (and diminishing) effect on efficiency; dimension has a positive (and diminishing) effect; while the imposition of constant returns to scale has a negative effect. These results can be used in improving the policy relevance of the empirical results produced by hospital efficiency studies.

Hospital production in a national health service: the physician's dilemma

There is a paucity of literature concerning the relation between the resource utilization decisions of the salaried hospital based physician and patient outcomes in a national health service. The purpose of our study is to model and test hospital production where the major decision makers are physicians. We view the output of the hospital as a distribution function over final health states of the patient. Our model contains a utility function for physicians whose arguments include the expected final health status of the patient and a pressure function which reflects the resource allocation and hospital financing policy of the Portuguese Health Ministry. Two sets of first order conditions derived from the theoretical model are estimated within a simultaneous equations framework using data consisting of inpatient discharges for the most frequent non-obstetric DRG during the 1992-1999 time period. We find evidence that budget setting methods and the possession of a third party payer outside of the NHS are important predictors for use of the resource in question. Moreover, we find that use of the resource is important in predicting the final health status of the patient.