Importance of considering quality indicators in primary healthcare. Application of a two-stage cluster analysis.

  • Dr. Ana Luisa Godoy Caballero Faculty of Business,
  • Dr. Luis Regino Murillo Zamorano Faculty of Business.

Abstract

The aim of this paper is to evaluate the efficiency and quality of primary healthcare in
Extremadura (Spain), assessing at the same time the importance and influence of the quality
indicators in the performance of the health units. This analysis considers a series of quality
indicators that may affect the efficiency and activity levels of a series of primary care centres.
We build different synthetic indices of quantitative output; output adjusted by quality; input,
and costs, applying Principal Component Analysis. Using those indices we run several two‐stage
cluster analyses. In a first analysis, the output of the health system is obtained from a strictly
quantitative point of view and compared to the levels of inputs and costs. In a second analysis,
we include an output adjusted by quality to perform such a comparison. The health units in
which the region is organised can be clustered in four levels of efficiency and activity: efficient‐
active, efficient‐inactive, inefficient‐active and inefficient-inactive. The comparison of both
analyses highlights the importance of considering qualitative indicators as they substantially
influence the efficiency and activity levels of the different primary healthcare centres

Author Biographies

Dr. Ana Luisa Godoy Caballero, Faculty of Business,

Department of Economics. 
Finance and Tourism. University of Extremadura, 10071, Cáceres, Spain

Dr. Luis Regino Murillo Zamorano, Faculty of Business.

Department of Economics.
University of Extremadura, 06006, Badajoz, Spain

References

Amado D, Dyson R (2008) On comparing the performance primary care providers. European
Journal of Operational Research 185 (3), 1469-1482.
Amado D, Dyson R (2009) Exploring the use of DEA for formative evaluation in primary diabetes
care: an application to compare English practices. Journal of Operational Research 60 (11),
1469-1482.
Ball G, Hall D (1965) ISODATA, a novel method of data anlysis and pattern classification.
Technical report NTIS AD 699616. Stanford Research Institute, Stanford, CA.
Chilingerian J, Sherman H (1997) DEA and primary care physicians report cards: Deriving
preferred practice cones from managed care service concepts and operating strategies. Annual
Operational Research 73, 36-66
Cordero JM, Crespo E, Murillo-Zamorano, LR (2014) The effect of quality and sociodemographic variables on efficiency measures in primary health care. The European Journal of
Health Economics 15 (3), 289-302.
Goñi S (1999) An analysis of the effectiveness of the Spanish primary health care teams. Health
Policy 48 (2), 107-117.
Greenly GE, Hooley GJ, Rudd JM (2005) Market orientation in a multiple stakeholder orientation
context: implications for marketing capabilities and assets. Journal of Business Research 58 (11),
1483-1494
Jain KJ (2010) Data clustering: 50 years beyond K-means. Pattern Recognistion Letters 31 (8),
651-666.
Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Inc.
Jobson J (1992) Applied Multivariate Data Analysis. Volume II: Categorical and Multivariate
Methods. New York. Editorial Springer-Verlag. I.S.B.N.: 0-387-97804-6.
Klecka WR (1980) Discriminant analysis. Sage, 19.
Lloyd S (1982) Least squares quantization in PCM. IEEE Transactions on Information Theory 28
(2), 129–137.
López-Sánchez JA, Santos-Vijande ML (2015) How value creation and relationship quality
coalignment affects a firms’s performance: An empirical analysis. Journal of Marketing Channels
22 (3), 214-230.
MacQueen J (1967) Some methods for classification and analysis of multivariate observations.
In: Fifth Berkeley Symposium on Mathematics. Statistics and Probability. University of California
Press, 281–297.
Milligan GW, Cooper MC (1987) Methodology review: Clustering methods. Applied
Psychoclogical Measurement 11 (4), 329-354.
Murillo-Zamorano LR, Petraglia C (2011) Technical efficiency in Primary Health Care: Does
quality matter? The European Journal of Health Economics 12 (2), 115-125.
Murillo-Zamorano LR, Vega J, Corrales N, Fernández Y, de Miguel FJ, Morillo J, Nogales L,
Sánchez M (2011) APEX08. Sistema de Información de la Atención Primaria en la Comunidad
Autónoma de Extremadura, 2008. Consejería de Sanidad y Consumo, Junta de Extremadura.
Murillo and Vega eds. ISBN:978-84-6147-541-4.
Puig-Junoy J, Ortún V (2004) Cost efficiency in primary care contracting: A stochastic frontier
cost function approach. Health Economics 13 (12), 1149-1165.
Punj G, Stewart DW (1983) Cluster analysis in marketing research: Review and suggestions for
application. Journal of Marketing Research 134-148.
Salinas-Jiménez J, Smith, PC (1996) Data Envelopment Analysis applied to quality in primary
health care. Annals of Operational Research 67 (1), 141-161.
Stock RM, Zacharias NA (2011) Patterns and performance outcomes of innovation
orientation. Journal of Academic Marketing Science 39 (6), 870-888.
Ward JHJr (1963) Hierarchical grouping to optimize an objective function. Journal of the
American Statistical Association 58 (301) pp. 236-244.
Yang F, Sun T, Zhang C (2009) An efficient hybrid data clustering method based on K-harmonic
means and particle swarm optimization. Experts Systems with Applications 36 (6), 9847-9852.
Published
2018-11-30