Price Changes in Regulated Healthcare Markets: Do Public Hospitals Respond and How?

This paper examines the behaviour of public hospitals in response to the average payment incentives created by price changes for patients classified in different Diagnosis Related Groups (DRGs). Using panel data on public hospitals located within the Italian region of Emilia-Romagna, we test whether a one-year increase in DRG prices induced public hospitals to increase their volume of activity, and whether a potential response is associated with changes in waiting times and/or length of stay. We find that public hospitals reacted to the policy change by increasing the number of patients with surgical treatments. This effect was smaller in the two years after the policy change than in later years, and for providers with a lower excess capacity in the pre-policy period, whereas it did not vary significantly across hospitals according to their degree of financial and administrative autonomy. For patients with medical DRGs, instead, there appeared to be no effect on inpatient volumes. Our estimates also suggest that an increase in DRG prices either decreased or had no impact on the proportion of patients waiting more than six months. Finally, we find no evidence of a significant effect on patients’ average length of stay

Should I wait or should I go? Travelling versus waiting for better healthcare

We study patient mobility in the Italian National Health System, using patient-episode level data on elective Percutaneous Transluminal Coronary Angioplasty procedures over the years 2008-2011. We examine how patients’ choice of the hospital is affected by changes in waiting times and clinical quality within hospitals over time. We estimate mixed-logit specifications and show the importance of jointly controlling for time-invariant and time varying clinical quality to identify the effect of waiting times. Conversely, failure to capture variations in clinical quality over time does not affect the estimate of the discouraging effect of travel distance. We provide evidence that patients are responsive to changes in waiting times and clinical quality: average demand elasticity with respect to own waiting times and mortality is estimated to be – 0.17 and – 1.38, respectively. Patients’ personal characteristics significantly influence how they trade off distance and waiting times with quality of care. We find a higher Willigness-To-Wait and Willingness-to-Travel to seek higher quality care for patients in the younger age groups and who are more severely ill. The results convey important policy implications for highly regulated healthcare markets

Autonomy and performance in the public sector : The experience of English NHS hospitals

Since 2004, English NHS hospitals have been given the opportunity to acquire a more autonomous status known as a Foundation Trust (FT), whereby regulations and restrictions over financial, management and organisational matters were reduced in order to create incentives to deliver higher quality services in the most efficient way. Using difference-indifference models, we test whether achieving greater autonomy (FT status) improved hospital performance, as proxied by measures of financial management, quality of care and staff satisfaction. Results provide little evidence that the FT policy per se has made any difference to the performance of hospitals in most of these domains. Our findings have implications for health policy and inform the trend towards granting greater autonomy to public sector organisations

How do hospital‐specialty characteristics influence health system responsiveness? An empirical evaluation of in‐patient care in the Italian region of Emilia‐Romagna

Studies of health system responsiveness mostly focus on the demand side by investigating the association between sociodemographic characteristics of patients and their reported level of responsiveness. However, little is known about the influence of supply‐side factors. This paper addresses that research gap by analysing the role of hospital‐specialty characteristics in explaining variations in patients' evaluation of responsiveness from a sample of about 38,700 in‐patients treated in public hospitals within the Italian Region of Emilia‐Romagna. The analysis is carried out by adopting a 2‐step procedure. First, we use patients' self‐reported data to derive 5 measures of responsiveness at the hospital‐specialty level. By estimating a generalised ordered probit model, we are able to correct for variations in individual reporting behaviour due to the health status of patients and their experience of being in pain. Second, we run cross‐sectional regressions to investigate the association between patients' responsiveness and potential supply‐side drivers, including waiting times, staff workload, the level of spending on non‐clinical facilities, the level of spending on staff education and training, and the proportion of staff expenditure between nursing and administrative staff. Results suggest that responsiveness is to some extent influenced by the supply‐side drivers considere

Quality in Nursing Homes

In developed countries, the role of public authorities as financing bodies and regulators of the long-term care sector is pervasive and calls for well-planned and informed policy actions. Poor quality in nursing homes has been a recurrent concern at least since the eighties and has triggered a heated policy and scholarly debate. The economic literature on nursing home quality has thoroughly investigated the impact of regulatory interventions and of market characteristics on an array of input-, process- and outcome-based quality measures. Most existing studies refer to the US context, even though important insights can be drawn also from the smaller set of works that covers European countries. The major contribution to the empirical analysis of the nursing home industry is represented by the introduction of important methodological advances applying rigorous policy evaluation techniques with the purpose of properly identifying the causal effects of interest. The use of up-to-date econometric methods that, in most cases, exploit policy shocks and longitudinal data has given researchers the possibility to achieve a causal identification of the impact of a wide range of policy initiatives, including the introduction of nurse staffing thresholds, price regulation, and public reporting of quality indicators. This has permitted to overcome part of the contradictory evidence highlighted by the strand of works based on more descriptive evidence. Possible lines for future research can be identified in further exploration of the consequences of policy interventions in terms of equity and accessibility to nursing home care

Ипотечное жилищное кредитование

Several studies of health system responsiveness focus on the demand-side by investigating the association between socio-demographic characteristics of patients and their reported level of responsiveness. However, little is known about the influence of supply-side factors. This paper addresses that research gap by analysing the role of hospital-specialty characteristics in explaining variations in patients’ evaluation of responsiveness from a sample of about 38,700 in-patients treated in public hospitals within the Italian Region of Emilia-Romagna. The analysis is carried out by adopting a two-step procedure. First, we use patients’ self-reported data to derive five measures of responsiveness at the hospital-specialty level. By estimating a generalised ordered probit model, we are able to correct for variations in individual reporting behaviour due to the health status of patients and their experience of being in pain. Secondly, we run cross-sectional regressions in order to investigate the association between patients’ responsiveness and potential supply-side drivers, including waiting times, staff workload, the level of spending on non-clinical facilities, the level of spending on staff education and training, and the proportion of staff expenditure between nursing and administrative staff. Results suggest that responsiveness is to some extent influenced by the supply-side drivers considered

How do hospital-specialty characteristics influence health system responsiveness? An empirical evaluation of in-patient care in the Italian Region of Emilia-Romagna

Studies of health system responsiveness mostly focus on the demand side by investigating the association between sociodemographic characteristics of patients and their reported level of responsiveness. However, little is known about the influence of supply-side factors. This paper addresses that research gap by analysing the role of hospital-specialty characteristics in explaining variations in patients' evaluation of responsiveness from a sample of about 38,700 in-patients treated in public hospitals within the Italian Region of Emilia-Romagna. The analysis is carried out by adopting a 2-step procedure. First, we use patients' self-reported data to derive 5 measures of responsiveness at the hospital-specialty level. By estimating a generalised ordered probit model, we are able to correct for variations in individual reporting behaviour due to the health status of patients and their experience of being in pain. Second, we run cross-sectional regressions to investigate the association between patients' responsiveness and potential supply-side drivers, including waiting times, staff workload, the level of spending on non-clinical facilities, the level of spending on staff education and training, and the proportion of staff expenditure between nursing and administrative staff. Results suggest that responsiveness is to some extent influenced by the supply-side drivers considered