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Logo of bmcmidmBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Medical Informatics and Decision Making
 
BMC Med Inform Decis Mak. 2012; 12: 83.
Published online Aug 2, 2012. doi:  10.1186/1472-6947-12-83
PMCID: PMC3444310
Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes
Katharina E Fischercorresponding author1,2
1Helmholtz Zentrum München, German Research Center for Environmental Health, Institute for Health Economics and Health Care Management, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
2University of Hamburg, Hamburg Center for Health Economics, Esplanade 36, 20354, Hamburg, Germany
corresponding authorCorresponding author.
Katharina E Fischer: katharina.fischer/at/wiso.uni-hamburg.de
Received October 24, 2011; Accepted July 28, 2012.
Abstract
Background
Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare.
Methods
Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM.
Results
After modification by dropping two indicators that showed poor measures in the measurement models’ quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of ‘transparency’, ‘participation’, ‘scientific rigour’ and ‘reasonableness’.
Conclusions
The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there are links between several non-observable constructs. PLS-PM was compatible in accounting for the complexity of coverage decisions to obtain a more realistic perspective for empirical analysis. The model specification can be used for hypothesis testing by using larger sample sizes and for data in the full domain of health technologies.
Keywords: PLS, Structural equation modelling, Quantitative research, Feasibility study, Model evaluation, Non-parametric, Fourth hurdle, Reimbursement, Neonatal, Europe
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