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DEVELOPMENT AND TEST OF A DECISION SUPPORT TOOL FOR HOSPITAL HEALTH TECHNOLOGY ASSESSMENT

Published online by Cambridge University Press:  12 October 2012

Laura Sampietro-Colom
Affiliation:
Directorate of Innovation, Hospital Clínic of Barcelonalsampiet@clinic.ub.es
Irene Morilla-Bachs
Affiliation:
Department of Innovation Management, Fundació Clínic per la Recerca Biomèdica
Santiago Gutierrez-Moreno
Affiliation:
Department of Innovation Management, Fundació Clínic per la Recerca Biomèdica
Pedro Gallo
Affiliation:
Department of Sociology and Organizational Analysis, University of Barcelona

Abstract

Objective: To develop and test a decision-support tool for prioritizing new competing Health Technologies (HTs) after their assessment using the mini-HTA approach.

Methods: A two layer value/risk tool was developed based on the mini-HTA. The first layer included 12 mini-HTA variables classified in two dimensions, namely value (safety, clinical benefit, patient impact, cost-effectiveness, quality of the evidence, innovativeness) and risk (staff, space and process of care impacts, incremental costs, net cost, investment effort). Weights given to these variables were obtained from a survey among decision-makers (at National/Regional level and hospital settings). A second layer included results from mini-HTA (scored as higher, equal or lower), which compares the performance of the new HT (in terms of the abovementioned 12 variables) with the available comparator. An algorithm combining the first (weights) and second (scores) layers was developed to obtain an overall score for each HT, which was then plotted in a value/risk matrix. The tool was tested using results from the mini-HTAs for three new HTs (Surgical Robot, Platelet Rich Plasma, Deep Brain Stimulation).

Results: No significant differences among decision-makers were observed as regards the weights given to the 12 variables, therefore, the median aggregate weights from decision-makers were introduced in the first layer. The dot plot resulting from the mini-HTA presented good power to visually discriminate between the assessed HTs.

Conclusion: The decision-support tool developed here makes possible a robust and straightforward comparison of different competing HTs. This facilitates hospital decision-makers deliberations on the prioritization of competing investments under fixed budgets.

Type
METHODS
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

1., AVALIA-T. Herramienta de Priorización. Observación. http://pritectools.es/Controlador/documentosAction.php (accessed November 5, 2010).Google Scholar
2., AVALIA-T. Herramienta de Priorización. Obsoletas. http://pritectools.es/Controlador/Obsoleta/obsoletaAction.php?idHerramienta=2 (accessed November 5, 2010).Google Scholar
3.Berghoff, WJ, Pietrzak, WS, Rhodes, RD. Platelet-rich plasma application during closure following total knee arthroplasty. Orthopedics. 2006;29:590598.Google ScholarPubMed
4.Briones, E, Loscertales, M, Pérez Lozano, MJ, en nombre del Grupo GANT. Proyecto GANT: Metodología de desarrollo y estudio preliminar. Sevilla: Agencia de Evaluación de Tecnologías Sanitarias de Andalucía; 1999.Google Scholar
5.Centre for Reviews and Dissemination. Systematic reviews: CRD's guidance for undertaking reviews in health care. York: University of York; 2009.Google Scholar
6.Cicchetti, A, Marchetti, M, Di Bidino, R, Corio, M. Hospital based health technology assessment world-wide survey. Hospital Based Health Technology Assessment HTAi Sub-interest Group; 2008.Google Scholar
7.Ehlers, L, Vestergaard, M, Kidholm, K, et al.Doing mini-health technology assessments in hospitals: A new concept of decision support in health care? Int J Technol Assess Health Care. 2006;22:295301.CrossRefGoogle ScholarPubMed
8.Everts, PA, Devilee, RJ, MC, Brown, et al.Platelet gel and fibrin sealant reduce allogeneic blood transfusions in total knee arthroplasty. Acta Anaesth Scand. 2006;50:593599.CrossRefGoogle ScholarPubMed
9.Everts, P, Devilee, R, Oosterbos, CJ, et al.Autologous platelet gel and fibrin sealant enhance the efficacy of total knee arthroplasty: Improved range of motion, decreased length of stay and a reduced incidence of arthrofibrosis. Knee Surg Sports Traumatol Arthrosc. 2007;15:888894.CrossRefGoogle Scholar
10.Gafni, A, Torrance, G. Risk attitude and time preference in health. Manage Sci. 1984;30:440451.CrossRefGoogle Scholar
11.Gardner, MJ, Demetrakopoulos, D, Klepchick, P, Mooar, P. The efficacy of autologous platelet gel in pain control and blood loss in total knee arthroplasty. Int Orthopaed. 2007;31:309313.CrossRefGoogle ScholarPubMed
12.Kean, R. Values and preferences in neo-classical environmental economics. http://www.russellkeat.net/research/marketboundaries/keat_valuesprefs.pdf (accessed November 11, 2010).Google Scholar
13.Kidholm, K, Ehlers, L, Korsbek, L, Kjarby, R, Beck, M. Assessment of the quality of mini-HTA. Int J Technol Assess Health Care. 2009;25:4248.CrossRefGoogle ScholarPubMed
14.Maeso Martínez, S, Reza Goyanes, M, Blasco Amaro, JA, Guerra Rodríguez, M. Effectiveness of the surgery realized by means of Da Vinci surgical system. UETS 2007/4. Madrid: Agencia Laín Entralgo; 2009.Google Scholar
15.Mamana, JP. Technology evaluation in the hospital setting: Starting from the bottom up. Hosp Med Staff. 1979;8:79.Google ScholarPubMed
16.Millenson, LJ, Slizewski, E. How do hospital executives spell technology assessment? “P-l-a-n-n-i-n-g.”. Health Manage Q. 1986;:4–8.Google Scholar
17.National Collaborating Center for Chronic Conditions. Parkinson's Disease: National clinical guideline for diagnosis and management in primary and secondary care. London: Royal College of Physicians; 2006.Google Scholar
18.Nielsen, CP, Funch, TM, Kristensen, FB. Health technology assessment: Research trends and future priorities in Europe. J Health Serv Res Policy. 2011;16 (Suppl 2):615.CrossRefGoogle ScholarPubMed
19.Peerbooms, JC, de Wolf, GS, Colaris, JW, et al.No positive effect of autologous platelet gel after total knee arthroplasty. Acta Orthop. 2009;80:557562.CrossRefGoogle ScholarPubMed
20.Sampietro-Colom, L, Espallargues, M, Rodriguez, E, et al.Wide social participation in prioritizing patients on waiting lists for joint replacement: A conjoint analysis. Med Decis Making. 2008;28:554566.CrossRefGoogle ScholarPubMed
21.SIGN. Critical appraisal: Notes and checklists. http://www.sign.ac.uk/methodology/checklists.html (accessed November 5, 2010).Google Scholar
22.Sloane, EB. Using a decision support system tool for healthcare technology assessments. IEEE Eng Med Biol Mag. 2004;23:4255.CrossRefGoogle ScholarPubMed
23.Velasco Garrido, M, Borlum Kristensen, F, Palmhoj Nielsen, C, Busse, R. Health technology assessment and health policy-making in Europe. Current status, challenges and potential EUnetHTA and European Observatory book on HTA and policy-making. 2008. ISBN 978 92 890 4293 2.Google Scholar
24.Weaver, FM, Follett, K, Stern, M, et al.Bilateral deep brain stimulation vs best medical therapy for patients with advanced Parkinson disease: A randomized controlled trial. JAMA. 2009;301:6373.CrossRefGoogle ScholarPubMed
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Supplementary Figure 1: Correlation matrix. A clear cluster pattern is observed for the value variables (safety, clinical benefit, cost-effectiveness and quality of evidence) pointing out to agreement in the weights given by stakeholders to these variables

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