Efficiency in chronic illness care coordination: public-private collaboration models vs. traditional management

Abstract

Background: The aim of this paper is to analyze the differences in the coordination of chronic illness care between the different public hospital management models coexisting in the Spanish region of Madrid (25 hospitals) during the period 2013-2017.

Methods: The performance of hospitals might be affected by the characteristics of the population they serve and, therefore, this information should be taken into account when estimating efficiency measures. For this purpose, we apply the nonparametric Data Envelopment Analysis (DEA) conditioned to some contextual variables and adapted to a dynamic framework, so that we can assess hospitals during a five-year period. The outputs considered are preventable hospitalizations, readmissions for heart failure and readmissions for chronic obstructive pulmonary disease, whereas the inputs considered are the number of beds, personnel (physicians and other healthcare professionals) and total expenditure on goods and services.

Results: The results suggest that the level of efficiency demonstrated by the public-private collaboration models of hospital management is higher than traditionally managed hospitals throughout the analyzed period. Nevertheless, we notice that efficiency differences among hospitals are significantly reduced when contextual factors were taken into account.

Conclusions: Hospitals managed under public-private collaboration models are more efficient than those under traditional management in terms of chronic illness care coordination, being this difference attributable to more agile and flexible management under the collaborative models.

Keywords: Care coordination; Chronic disease management; Data envelopment analysis; Efficiency; Hospital management.

Fig. 1

Density distribution of efficiency scores by category of management (unconditional model)

Fig. 2

Density distribution of efficiency scores by category of management (conditional model)

Fig. 3

Evolution of unconditional efficiency scores for different types of management (2013–2017)

Fig. 4

Evolution of conditional efficiency scores for different types of management (2013–2017)

Fig. 5

Marginal effect of each contextual variable on the ratios Q(x,y|z,t). a Population > 65. b Morbidity index. c Time

Fig. 6

Effect of time and contextual variables on hospital performance: frontier shifts. a Population > 65 and time. b Morbidity index and time