The effect of quality and socio-demographic variables on efficiency measures in primary health care

Abstract

This paper aims to extend the literature on measuring efficiency in primary health care by considering the influence of quality indicators and environmental variables conjointly in a case study. In particular, environmental variables are represented by patients' characteristics and quality indicators are based on technical aspects. In order to deal with both aspects, different extensions of data envelopment analysis (DEA) methodology are applied. Specifically, we use weight restrictions to ensure that the efficiency scores assigned to the evaluated units take quality data into account, and a four-stage model to identify which exogenous variables have impact on performance as well as to compute efficiency scores that incorporate this information explicitly. The results provide evidence in support of the importance of including information about both aspects in the analysis so that the efficiency measures obtained can be interpreted as an accurate reflection of performance.

Primary Care Efficiency Measurement Using Data Envelopment Analysis: A Systematic Review

“…visits performed, examinations provided). Several studies defined outputs in terms of PC quality indicators [21,26,30,[40][41][42]57]. This may overcome the drawbacks of basing efficiency models on PC activities, such as the inability to take account of the quality of care delivered, therefore possibly rewarding PC DMUs that produce more activities than other DMUs just because they are operating at a lower quality level.…”

“…mix of labour or capital resources; five DEA models considered both single and disaggregated measures of inputs (see additional file 1). Inputs were measured in physical units in either total working hours or working hours by skill level [23,36,38,40,41,47,56], or in absolute number of individuals by skill level [21,22,25,29,32,33,51,53]. Only four studies divided their input mix into two categories (i.e.…”

“…focussing on the extent to which elements such as scale of operations or providers' practice style affect productivity in PC delivery) a VRS approach is advisable. A VRS approach was opted in 12 efficiency analyses [21,22,25,24,28,30,32,39,43,41,50,53] -see Table 1. For example, Rabetti and Freitas [43] adopted a VRS DEA model under the assumptions that any variation in the size of DMUs (municipalities) under scrutiny might interfere in the production scale of service and results (provision of hypertension care services).…”

“…This may overcome the drawbacks of basing efficiency models on PC activities, such as the inability to take account of the quality of care delivered, therefore possibly rewarding PC DMUs that produce more activities than other DMUs just because they are operating at a lower quality level. A set of variables for which the outputs included both activity levels and quality measures was adopted in five DEA applications [21,43,52,53,59].…”

“…Largely, DEA has been used to measure the relative efficiency of a wide range of activities provided by different PC centers or districts within national/regional PC systems [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. In a large number of studies DEA has also been used to compare PC providers in delivering specific activities (e.g.…”