Hospital productivity bias when not adjusting for cost heterogeneity: The case of Spain

Abstract

This research quantifies the bias caused in hospital productivity measurements when cost heterogeneity is not considered. A multi-output stochastic cost frontier under a normalised translog specification is used to approximate the structure of technology of a sample of public general hospitals in Spain during the period 2002–2009. To control for observable heterogeneity in costs, a set of variables related to hospital characteristics are included in the cost frontier specification (i.e., hospital complexity, degree of specialisation, availability of outpatient clinics, variety of high-technology equipment available, teaching activity and quality of care), whereas unobservable heterogeneity is accounted for by means of individual dummy variables. A measure of hospitals’ cost efficiency is first obtained, and the analysis is then completed by measuring and decomposing the total factor productivity index (TFP-I) change. Findings reveal that controlling for heterogeneity decreases total productivity from an annual average rate of 0.028% to 1.330%, mainly driven by the negative contribution of the cost efficiency change component. Hence, a bias of 1.303 percentage points in the overall TFP-I is found as consequence of not controlling for heterogeneity. In addition to this, if heterogeneity factors are not accounted for, the mean cost efficiency index during the period analysed is 0.730, figure that increases up to 0.974 if heterogeneity is considered. Hence, the omission of heterogeneity leads to a bias of 24.4 percentage points in the mean cost efficiency. Therefore, not adjusting for heterogeneity in costs gives rise to distorted measurements of hospital productivity, as well as distortions in the contribution of each of its components, which may lead to the adoption of inadequate policies and decisions on resource allocation.

Supporting efficiency improvement in public health systems: a rapid evidence synthesis

“…Studies included in the review consistently identified the use of benchmarking and data use as key enablers of efficiency improvement. Local networking and benchmarking can promote collaboration by identifying high-performing and low-performing facilities, however consideration to variations in cost and complexity between individual services was advised [53,66,67]. Peer benchmarking was proposed as an enabler for improvement through the identification of approaches which Establish a centralised efficiency improvement unit to support system-wide improvement processes Hassanian, 2017 [2] Lavoie-Tremblay et al 2012 [35] Address reducing duplication and maximising asset utilisation at a whole-of-system level Pencheon, 2015 [36] Tsai et al 2017 [37] Ensure timely, transparent performance reporting for improvement initiatives Tataw, 2014 [38] Alatawi, Niessen & Khan 2020 [27] Concurrently improving efficiency, quality and value Establish system-wide policy for balancing expenditure, quality and value are achieving positive outcomes for sharing with similar settings elsewhere [25,26,68].…”