Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes

BackgroundDecision-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.MethodsEmpirical 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.ResultsAfter 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’.ConclusionsThe 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.

[1]  Clare McGrath,et al.  Framework for describing and classifying decision-making systems using technology assessment to determine the reimbursement of health technologies (fourth hurdle systems) , 2006, International Journal of Technology Assessment in Health Care.

[2]  D. Sharp,et al.  Why do managers allocate resources to workplace health promotion programmes in countries with national health coverage? , 2007, Health promotion international.

[3]  G. Chapman,et al.  Decision Making in Health Care: Theory, Psychology, and Applications , 2003 .

[4]  Hanna Toiviainen,et al.  Decision-making in priority setting for medicines--a review of empirical studies. , 2008, Health policy.

[5]  Steve Morgan,et al.  The Common Drug Review: a NICE start for Canada? , 2006, Health policy.

[6]  R. Baltussen,et al.  Priority setting of health interventions: the need for multi-criteria decision analysis , 2006, Cost effectiveness and resource allocation : C/E.

[7]  Christopher McCabe,et al.  Health Technology Funding Decision-Making Processes Around the World , 2011, PharmacoEconomics.

[8]  Jörg Dirmaier,et al.  Measurement of shared decision making - a review of instruments. , 2011, Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen.

[9]  Paul Hansen,et al.  Health technology prioritization: which criteria for prioritizing new technologies and what are their relative weights? , 2011, Health policy.

[10]  Stirling Bryan,et al.  Seeing the NICE side of cost-effectiveness analysis: a qualitative investigation of the use of CEA in NICE technology appraisals. , 2007, Health economics.

[11]  Reiner Leidl,et al.  A structured tool to analyse coverage decisions: development and feasibility test in the field of cancer screening and prevention. , 2011, Health policy.

[12]  Christopher McCabe,et al.  Role of centralized review processes for making reimbursement decisions on new health technologies in Europe , 2011, ClinicoEconomics and outcomes research : CEOR.

[13]  N. Daniels,et al.  The ethics of accountability in managed care reform. , 1998, Health affairs.

[14]  Neil Hawkins,et al.  Who does the numbers? The role of third-party technology assessment to inform health systems' decision-making about the funding of health technologies. , 2009, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[15]  Jing Jing Li,et al.  The Role of Value for Money in Public Insurance Coverage Decisions for Drugs in Australia: A Retrospective Analysis 1994-2004 , 2008, Medical decision making : an international journal of the Society for Medical Decision Making.

[16]  Wynne W. Chin,et al.  Handbook of Partial Least Squares , 2010 .

[17]  Zhiping Walter,et al.  Physician acceptance of information technologies: Role of perceived threat to professional autonomy , 2008, Decis. Support Syst..

[18]  John N Lavis,et al.  "It all depends": conceptualizing public involvement in the context of health technology assessment agencies. , 2010, Social science & medicine.

[19]  Panos Kanavos,et al.  The impact of health technology assessments: an international comparison , 2010 .

[20]  Marko Sarstedt,et al.  Structural modeling of heterogeneous data with partial least squares , 2010 .

[21]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[22]  Leonie Segal,et al.  Fixing the game: are between-silo differences in funding arrangements handicapping some interventions and giving others a head-start? , 2010, Health economics.

[23]  E. Emanuel,et al.  Principles for allocation of scarce medical interventions , 2009, The Lancet.

[24]  Nancy J Devlin,et al.  "Yes", "No" or "Yes, but"? Multinomial modelling of NICE decision-making. , 2006, Health policy.

[25]  Thomas Kahan,et al.  Initial effects of a reimbursement restriction to improve the cost-effectiveness of antihypertensive treatment. , 2010, Health policy.

[26]  Kerstin Liehr-Gobbers,et al.  Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach , 2010 .

[27]  Scott D. Grosse.  Economic evaluations of newborn screening , 2009 .

[28]  Douglas K. Martin,et al.  Priority setting: what constitutes success? A conceptual framework for successful priority setting , 2009, BMC health services research.

[29]  Steve Morgan,et al.  Centralized drug review processes: are they fair? , 2006, Social science & medicine.

[30]  E. Vigneau,et al.  Clustering of Variables Around Latent Components , 2003 .

[31]  Wei Wen Wu,et al.  Linking Bayesian networks and PLS path modeling for causal analysis , 2010, Expert Syst. Appl..

[32]  W. Rogowski,et al.  Clearing up the hazy road from bench to bedside: A framework for integrating the fourth hurdle into translational medicine , 2008, BMC health services research.

[33]  Corinna Sorenson,et al.  Use of comparative effectiveness research in drug coverage and pricing decisions: a six-country comparison. , 2010, Issue brief.

[34]  Bjørn Hofmann,et al.  Different methods for ethical analysis in health technology assessment: An empirical study , 2011, International Journal of Technology Assessment in Health Care.

[35]  Christian Derquenne,et al.  A modified PLS path modeling algorithm handling reflective categorical variables and a new model building strategy , 2007, Comput. Stat. Data Anal..

[36]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[37]  P. Dolan,et al.  Principles of justice in health care rationing , 2000, Journal of medical ethics.

[38]  M. Drummond,et al.  Inclusion of cost effectiveness in licensing requirements of new drugs : The fourth hurdle , 2017 .

[39]  D Kernick.  Decision making in health care , 2010 .

[40]  Partha Deb,et al.  The effect of job loss on overweight and drinking. , 2011, Journal of health economics.

[41]  Jing Jing Li,et al.  Using effectiveness and cost-effectiveness to make drug coverage decisions: a comparison of Britain, Australia, and Canada. , 2009, JAMA.

[42]  Bengt Jönsson,et al.  The costs and benefits of regulations for reimbursement of new drugs. , 2006, Health policy.

[43]  S. Erntoft,et al.  Pharmaceutical priority setting and the use of health economic evaluations: a systematic literature review. , 2011, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[44]  Michael Drummond,et al.  Comparison of anticancer drug coverage decisions in the United States and United Kingdom: does the evidence support the rhetoric? , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[45]  Wendy J. Ungar,et al.  Economic Evaluation in Child Health , 2010 .

[46]  C. Craig,et al.  Advances in international marketing , 1992 .

[47]  Douglas K. Martin,et al.  Evaluating priority setting success in healthcare: a pilot study , 2010, BMC health services research.

[48]  Veronica Wiley,et al.  Newborn screening. , 2008, Pathology.

[49]  A Rid,et al.  Justice and procedure: how does “accountability for reasonableness” result in fair limit-setting decisions? , 2008, Journal of Medical Ethics.

[50]  J. Luis Guasch,et al.  The Costs and Benefits of Regulation: Implications for Developing Countries , 1997 .

[51]  D. Menon,et al.  Role of patient and public participation in health technology assessment and coverage decisions , 2011, Expert review of pharmacoeconomics & outcomes research.

[52]  S. Grosse,et al.  The role of health technology assessment in coverage decisions on newborn screening , 2011, International Journal of Technology Assessment in Health Care.

[53]  Marko Sarstedt,et al.  PLS-SEM: Indeed a Silver Bullet , 2011 .

[54]  C. Mitton,et al.  Centralized drug review processes in Australia, Canada, New Zealand, and the United kingdom. , 2006, Health affairs.

[55]  David Parkin,et al.  Does NICE have a cost-effectiveness threshold and what other factors influence its decisions? A binary choice analysis. , 2004, Health economics.

[56]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[57]  Pietro Giorgio Lovaglio,et al.  Model building and estimation strategies for implementing the Balanced Scorecard in Health sector , 2011 .

[58]  Mita Giacomini,et al.  Bringing 'the public' into health technology assessment and coverage policy decisions: from principles to practice. , 2007, Health policy.