Productive innovations in hospitals: an empirical research on the relation between technology and productivity in the Dutch hospital industry

Equity and efficiency of medical and health service system in China

BACKGROUND

Equity and efficiency are basic value dimensions to evaluate the effectiveness of China's medical and health service system (MHS) reform and development. Coordinated development of equity and efficiency is necessary to realize high-quality development of medical and health services. This study aims to evaluate the equity, efficiency, and combined efforts in coordinating the MHS during 1991-2020 reform.

METHODS

Data on China's MHS were obtained from the China Statistical Yearbook 1992-2021. Ratios of urban to rural residents' medical expenditure and number of medical professionals per 10,000 people were employed to evaluate MHS's equity. The data envelopment analysis-Malmquist model was employed to evaluate MHS's efficiency. We constructed a combined-efforts-in-coordination model to examine the coordination degree between equity and efficiency.

RESULTS

Equity of medical expenditure burden significantly improved from during 1991-2007. Urban residents' 1991 medical expenditure burden was 87.8% of that of rural residents, which increased to 100.1% in 2007. Urban areas' mean medical expenditure burden was 105.94% of that in rural areas during 1991-2007. The gap in equity of medical expenditure burden between urban and rural areas slowly widened after 2007, with urban areas' mean burden being 68.52% of that in rural areas during 2007-2020. Medical and health resources allocation shows an alarming inequity during this period, with mean number of medical professionals per 10,000 people in urban areas being 238.30% of that in rural areas. Efficiency experienced several fluctuations before 2008. Since 2008, efficiency was high (0.915) and remained stable, except in 2020. The combined-efforts-in-coordination score for medical expenditure burden was less than 0.2 for 80% of the years, while that for in medical and health resources was more than 0.5 for 99.67% of the years.

CONCLUSIONS

MHS inequity remains between urban and rural China, primarily because of disproportionate allocation of medical and health resources. The government should enhance rural medical professionals' salary and welfare and provide medical subsidies for rural residents to adjust resource allocation levels in urban and rural areas, control differences in medical expenditure burden between urban and rural residents to a reasonable range, and continuously improve urban and rural residents' equity level.

Using flexible regression models for calculating hospital's production functions

Background

The relative lack of flexibility of parametric models has led to the development of nonparametric regression techniques based on the family of generalized additive models. However, despite the potential advantages of using Generalized Additive Model (GAM) in practice many models have, until now, not been sufficiently explored in health economics problems.

It could be interesting to calculate a new flexible hospital production function by means of a GAM including interactions and to compare it with the classic model Cobb-Douglas in the prediction of the behavior of productive factors.

Method

The flexible model considered has been the AM including the beds-facultative interaction. The covariates “Hospital”, being a categorical variable and “Year” being a continuous variable, have also been included in the model. Based on the estimation of the model penalized thin plate splines will be used to represent smoothed functions. In this configuration, the smoothed parameters will be estimated via REML.

Results

Cobb-douglas model fits well for the production functions of the more general clinical and surgical services, while the GAM adjusts better in the case of more specialized medical services.

Conclusions

Generalized Additive Models are more flexible than parametric models, providing a better fit in the presence of non-linear relationships and thus allowing more accurate prediction values. The results of this study suggest that AM is a promising technique for the areas of research and application in health economics.

Concentrating Emergency Rooms: Penny-Wise and Pound-Foolish? An Empirical Research on Scale Economies and Chain Economies in Emergency Rooms in Dutch Hospitals

In this paper, we address the issue of whether it is economically advantageous to concentrate emergency rooms (ERs) in large hospitals. Besides identifying economies of scale of ERs, we also focus on chain economies. The latter term refers to the effects on a hospital's costs of ER patients who also need follow-up inpatient or outpatient hospital care. We show that, for each service examined, product-specific economies of scale prevail indicating that it would be beneficial for hospitals to increase ER services. However, this seems to be inconsistent with the overall diseconomies of scale for the hospital as a whole. This intuitively contradictory result is indicated as the economies of scale paradox. This scale paradox also explains why, in general, hospitals are too large. There are internal (departmental) pressures to expand certain services, such as ER, in order to benefit from the product-specific economies of scale. However, the financial burden of this expansion is borne by the hospital as a whole. The policy implications of the results are that concentrating ERs seems to be advantageous from a product-specific perspective, but is far less advantageous from the hospital perspective. © 2016 The Authors. Health Economics Published by John Wiley & Sons, Ltd.

Income and Technology as Drivers of Australian Healthcare Expenditures

The roles of income and technology as the major determinants of aggregate healthcare expenditure (HEXP) continue to interest economists and health policy researchers. Concepts and measures of medical technologies remain complex; however, income (on the demand side) and technology (on the supply side) are important drivers of HEXP. This paper presents analysis of Australia's HEXP, using time-series econometrics modeling techniques applied to 1971-2011 annual aggregate data. Our work fills two important gaps in the literature. First, we model the determinants of Australia's HEXP using the latest and longest available data series. Second, this novel study investigates several alternative technology proxies (input and output measures), including economy-wide research and development expenditures, hospital research expenditures, mortality rate, and two technology indexes based on medical devices. We then apply the residual component method and the technology proxy approach to quantify the technology effects on HEXP. Our empirical results suggest that Australian aggregate healthcare is a normal good and a technical necessity with the income elasticity estimates ranging from 0.51 to 0.97, depending on the model. The estimated technology effects on HEXP falling in the 0.30-0.35 range and mimicking those in the literature using the US data, reinforce the global spread of healthcare technology. Copyright © 2016 John Wiley & Sons, Ltd.

Balancing the health workforce: breaking down overall technical change into factor technical change for labour-an empirical application to the Dutch hospital industry

BACKGROUND

Well-trained, well-distributed and productive health workers are crucial for access to high-quality, cost-effective healthcare. Because neither a shortage nor a surplus of health workers is wanted, policymakers use workforce planning models to get information on future labour markets and adjust policies accordingly. A neglected topic of workforce planning models is productivity growth, which has an effect on future demand for labour. However, calculating productivity growth for specific types of input is not as straightforward as it seems. This study shows how to calculate factor technical change (FTC) for specific types of input.

METHODS

The paper first theoretically derives FTCs from technical change in a consistent manner. FTC differs from a ratio of output and input, in that it deals with the multi-input, multi-output character of the production process in the health sector. Furthermore, it takes into account substitution effects between different inputs. An application of the calculation of FTCs is given for the Dutch hospital industry for the period 2003-2011. A translog cost function is estimated and used to calculate technical change and FTC for individual inputs, especially specific labour inputs.

RESULTS

The results show that technical change increased by 2.8% per year in Dutch hospitals during 2003-2011. FTC differs amongst the various inputs. The FTC of nursing personnel increased by 3.2% per year, implying that fewer nurses were needed to let demand meet supply on the labour market. Sensitivity analyses show consistent results for the FTC of nurses.

CONCLUSIONS

Productivity growth, especially of individual outputs, is a neglected topic in workforce planning models. FTC is a productivity measure that is consistent with technical change and accounts for substitution effects. An application to the Dutch hospital industry shows that the FTC of nursing personnel outpaced technical change during 2003-2011. The optimal input mix changed, resulting in fewer nurses being needed to let demand meet supply on the labour market. Policymakers should consider using more detailed and specific data on the nature of technical change when forecasting the future demand for health workers.

Improving productive efficiency in hospitals: findings from a review of the international evidence

At present, health systems across Europe face the same challenges: a changing demographic profile, a rise in multi-morbidity and long-term conditions, increasing health care costs, large public debts and other legacies of an economic downturn. In light of these concerns, this article provides an overview of the international evidence on how to improve productive efficiency in secondary care settings. Updating and expanding upon a recent review of the literature by Hurst and Williams (2012), we set out evidence on potential interventions in the policy environment, hospital management, and operational processes. We conclude with five key lessons for policy makers and practitioners on how to improve productive efficiency within hospital settings, and identify several gaps in the existing evidence base.

Technology diffusion in hospitals: a log odds random effects regression model

This study identifies the factors that affect the diffusion of hospital innovations. We apply a log odds random effects regression model on hospital micro data. We introduce the concept of clustering innovations and the application of a log odds random effects regression model to describe the diffusion of technologies. We distinguish a number of determinants, such as service, physician, and environmental, financial and organizational characteristics of the 60 Dutch hospitals in our sample. On the basis of this data set on Dutch general hospitals over the period 1995-2002, we conclude that there is a relation between a number of determinants and the diffusion of innovations underlining conclusions from earlier research. Positive effects were found on the basis of the size of the hospitals, competition and a hospital's commitment to innovation. It appears that if a policy is developed to further diffuse innovations, the external effects of demand and market competition need to be examined, which would de facto lead to an efficient use of technology. For the individual hospital, instituting an innovations office appears to be the most prudent course of action.

Understanding hospital performance: the role of network ties and patterns of competition

BACKGROUND

To improve efficiency and quality, a number of policies have recently been implemented to increase competition and cooperation within the health systems of many countries. We theorize how hospital performance, measured as productivity, is contingent upon network embeddedness, the extent to which a hospital is involved in a network of interconnected interorganizational relationships.

PURPOSE

The aim of this study was to explore the effects on hospital productivity resulting from both collaborative network ties and competitive relationships between providers.

METHODOLOGY

We used panel data collected between 2003 and 2007 from 35 hospitals in Abruzzo, one of the most populated regions of central Italy. We used secondary data of hospital activities regarding both clinical and administrative aspects. For each year, we examined the intensity of interhospital competition and the unique position each provider has within a larger network of relationships with other hospitals. Other idiosyncratic organizational characteristics were examined as well.

FINDINGS

Our results show that hospital productivity is negatively related to the degree of competition that a hospital faces and positively related to the degree with which hospitals establish collaborative relationships. We also found that the negative impact on hospital productivity due to competition was lessened when hospitals were more likely to create cooperative network ties.

PRACTICE IMPLICATIONS

Because interhospital collaboration and competition are related to hospital productivity, they should constitute a core element in the strategic planning of a hospital's operation. Health administrators should implement policies that favor collaborative network ties at the regional level and mitigate interorganizational rivalries when establishing collaborative relationships with local competitors.

Electronic medical records and cost efficiency in hospital medical-surgical units

This study examines the impact of electronic medical records (EMRs) on cost efficiency in hospital medical-surgical units. Using panel data on California hospitals from 1998 to 2007, we employed stochastic frontier analysis (SFA) to estimate the relationships between EMR implementation and the cost inefficiency of medical-surgical units. We categorized EMR implementation into three stages based on the level of sophistication. We also examined the effects of specific EMR systems on cost inefficiency. Our SFA models addressed potential bias from unobserved heterogeneity and heteroskedasticity. EMR Stages 1 and 2, nursing documentation, electronic medication administration records, and clinical decision support were associated with significantly higher inefficiency.

Governance and performance: the performance of Dutch hospitals explained by governance characteristics

This paper describes the efficiency of Dutch hospitals using the Data Envelopment Analysis (DEA) method with bootstrapping. In particular, the analysis focuses on accounting for cost inefficiency measures on the part of hospital corporate governance. We use bootstrap techniques, as introduced by Simar and Wilson (J. Econom. 136(1):31–64, 2007), in order to obtain more efficient estimates of the effects of governance on the efficiency. The results show that part of the cost efficiency can be explained with governance. In particular we find that a higher remuneration of the board as well as a higher remuneration of the supervisory board does not implicate better performance.