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MOST IMPORTANT BARRIERS AND FACILITATORS REGARDING THE USE OF HEALTH TECHNOLOGY ASSESSMENT

Published online by Cambridge University Press:  15 June 2017

Kei Long Cheung ,
Silvia M.A.A. Evers ,
Hein de Vries  and
Mickaël Hiligsmann
Kei Long Cheung
Affiliation:
Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Maastricht University Department of Health Promotion, Care and Public Health Research Institute (CAPHRI), Maastricht Universitykl.cheung@maastrichtuniversity.nl
Silvia M.A.A. Evers
Affiliation:
Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Maastricht University Trimbos Institute, Netherlands Institute of Mental Health and Addiction
Hein de Vries
Affiliation:
Department of Health Promotion, Care and Public Health Research Institute (CAPHRI), Maastricht University
Mickaël Hiligsmann
Affiliation:
Department of Health Services Research, Care and Public Health Research Institute (CAPHRI), Maastricht University
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Abstract

Objectives: Several studies have reported multiple barriers to and facilitators for the uptake of health technology assessment (HTA) information by policy makers. This study elicited, using best-worst scaling (BWS), the most important barriers and facilitators and their relative weight in the use of HTA by policy makers.

Methods: Two BWS object case surveys (one for barriers, one for facilitators) were conducted among sixteen policy makers and thirty-three HTA experts in the Netherlands. A list of twenty-two barriers and nineteen facilitators was included. In each choice task, participants were asked to choose the most important and the least important barrier/facilitator from a set of five. We used Hierarchical Bayes modeling to generate the mean relative importance score (RIS) for each factor and a subgroup analysis was conducted to assess differences between policy makers and HTA experts.

Results: The five most important barriers (RIS > 6.00) were “no explicit framework for decision-making process,” “insufficient support by stakeholders,” “lack of support,” “limited generalizability,” and “absence of appropriate incentives.” The six most important facilitators were: “availability of explicit framework for decision making,” “sufficient support by stakeholders,” “appropriate incentives,” “sufficient quality,” “sufficient awareness,” and “sufficient support within the organization.” Overall, perceptions did not differ markedly between policy makers and HTA experts.

Conclusions: Our study suggests that barriers and facilitators related to “policy characteristics” and “organization and resources” were particularly important. It is important to stimulate a pulse at the national level to create an explicit framework for including HTA in the decision-making context.

Keywords

Health technology assessmentBarriersFacilitatorsBest-worst scalingPolicy makers
Type
Assessments
Information
International Journal of Technology Assessment in Health Care , Volume 33 , Issue 2 , 2017 , pp. 183 - 191
Copyright
Copyright © Cambridge University Press 2017 

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References

REFERENCES

1. Park, AL, McDaid, D, Weiser, P, et al. Examining the cost effectiveness of interventions to promote the physical health of people with mental health problems: A systematic review. BMC Public Health. 2013;13:787. PubMed PMID: 23988266. Pubmed Central PMCID: 3765875.CrossRefGoogle ScholarPubMed
2. Nicod, E, Kanavos, P. Commonalities and differences in HTA outcomes: A comparative analysis of five countries and implications for coverage decisions. Health Policy. 2012;108:167–177.CrossRefGoogle ScholarPubMed
3. Drummond, MF, Schwartz, JS, Jönsson, B, et al. Key principles for the improved conduct of health technology assessments for resource allocation decisions. Int J Technol Assess Health Care. 2008;24:244–258.CrossRefGoogle ScholarPubMed
4. van Velden, ME, Severens, JL, Novak, A. Economic evaluations of healthcare programmes and decision making. Pharmacoeconomics. 2005;23:1075–1082.CrossRefGoogle ScholarPubMed
5. Oliver, K, Innvar, S, Lorenc, T, et al. A systematic review of barriers to and facilitators of the use of evidence by policymakers. BMC Health Serv Res. 2014;14:2. PubMed PMID: 24383766. Pubmed Central PMCID: 3909454.CrossRefGoogle ScholarPubMed
6. Macintyre, S, Chalmers, I, Horton, R, et al. Using evidence to inform health policy: Case study. BMJ. 2001;322:222.CrossRefGoogle ScholarPubMed
7. Garrido, MV. Health technology assessment and health policy-making in Europe: Current status, challenges and potential. Denmark: WHO Regional Office Europe; 2008.Google Scholar
8. Drummond, M. Economic evaluation in health care: Is it really useful or are we just kidding ourselves? Aust Econ Rev. 2004;37:3–11.CrossRefGoogle Scholar
9. Neumann, PJ. Why don't Americans use cost-effectiveness analysis. Am J Manag Care. 2004;10:308–312.Google ScholarPubMed
10. Neumann, PJ, Sullivan, SD. Economic Evaluation in the US. Pharmacoeconomics. 2006;24:1163–1168.CrossRefGoogle ScholarPubMed
11. Prosser, LA, Koplan, JP, Neumann, PJ, et al. Barriers to using cost-effectiveness analysis in managed care decision making. Am J Manag Care. 2000;6:173–179.Google ScholarPubMed
12. Mühlbacher, AC, Kaczynski, A, Zweifel, P, et al. Experimental measurement of preferences in health and healthcare using best-worst scaling: An overview. Health Econ Rev. 2015;6 (1):1–14.Google Scholar
13. Cheung, KL, Wijnen, BF, Hollin, IL, et al. Using best–worst scaling to investigate preferences in health care. Pharmacoeconomics. 2016;34:1195–1209.CrossRefGoogle ScholarPubMed
14. Finn, A, Louviere, JJ. Determining the appropriate response to evidence of public concern: The case of food safety. J Public Policy Mark. 1992:12–25.CrossRefGoogle Scholar
15. Train, KE. Discrete choice methods with simulation. Cambridge, UK: Cambridge University Press; 2009.Google Scholar
16. Flynn, TN, Louviere, JJ, Peters, TJ, et al. Best–worst scaling: What it can do for health care research and how to do it. J Health Econ. 2007;26:171–189.CrossRefGoogle Scholar
17. Marley, AA, Louviere, JJ. Some probabilistic models of best, worst, and best–worst choices. J Math Psychol. 2005;49:464–480.CrossRefGoogle Scholar
18. Merlo, G, Page, K, Ratcliffe, J, et al. Bridging the gap: Exploring the barriers to using economic evidence in healthcare decision making and strategies for improving uptake. Appl Health Econ Health Policy. 2014;13:303–309.CrossRefGoogle Scholar
19. van Gool, MK, Gallego, G, Haas, M, et al. Economic evidence at the local level. Pharmacoeconomics. 2007;25:1055–1062.CrossRefGoogle ScholarPubMed
20. Williams, I, Bryan, S. Understanding the limited impact of economic evaluation in health care resource allocation: A conceptual framework. Health Policy. 2007;80:135–143.CrossRefGoogle ScholarPubMed
21. Huić, M, Nachtnebel, A, Zechmeister, I, et al. Collaboration In health technology assessment (EU net HTA joint action, 2010–2012): Four case studies. Int J Technol Assess Health Care. 2013;29:323–330.CrossRefGoogle Scholar
22. Drummond, M, Weatherly, H. Implementing the findings of health technology assessments. Int J Technol Assess Health Care. 2000;16:1–12.CrossRefGoogle ScholarPubMed
23. Hivon, M, Lehoux, P, Denis, J-L, et al. Use of health technology assessment in decision making: Coresponsibility of users and producers? Int J Technol Assess Health Care. 2005;21:268–275.CrossRefGoogle ScholarPubMed
24. Stakes, H, Rius, ME, Espinas, JA. EUR-ASSESS project subgroup report on dissemination and impact. Int J Technol Assess Health Care. 1997;13:220–286.Google Scholar
25. Brousselle, A, Lessard, C. Economic evaluation to inform health care decision-making: Promise, pitfalls and a proposal for an alternative path. Soc Sci Med. 2011;72:832–839.CrossRefGoogle Scholar
26. Hoffmann, C, Stoykova, BA, Nixon, J, et al. Do health‐care decision makers find economic evaluations useful? The findings of focus group research in UK health authorities. Value Health. 2002;5:71–78.CrossRefGoogle ScholarPubMed
27. Johnson, RM. Understanding HB: An intuitive approach. Sequim, WA: Sawtooth Software Inc; 2000.Google Scholar
28. Orme, B. Hierarchical Bayes: Why all the attention? Quirk's Mark Res Rev. 2000;14:16–63.Google Scholar
29.Sawtooth Software. Identifying ‘bad’ respondents: Fit Statistic and Identifying Random Responders. 2016. https://www.sawtoothsoftware.com/help/issues/ssiweb/online_help/hid_web_maxdiff_badrespondents.htm (accessed January 27, 2016).Google Scholar
30. Ehlers, L, Jensen, MB. Attitudes and barriers toward mini-HTA in the Danish municipalities. Int J Technol Assess Health Care. 2012;28:271–277.CrossRefGoogle ScholarPubMed
31. Oortwijn, W, Broos, P, Vondeling, H, et al. Mapping of health technology assessment in selected countries. Int J Technol Assess Health Care. 2013;29:424–434.CrossRefGoogle ScholarPubMed
32. Cheung, KL, Evers, SM, Hiligsmann, M, et al. Understanding the stakeholders’ intention to use economic decision-support tools: A cross-sectional study with the Tobacco Return on Investment tool. Health Policy. 2016;120:46–54.CrossRefGoogle ScholarPubMed
33. de Vries, H, Mudde, A, Leijs, I, et al. The European Smoking prevention Framework Approach (EFSA): An example of integral prevention. Health Educ Res. 2003;18:611–626.CrossRefGoogle ScholarPubMed
34. de Vries, H, Eggers, SM, Bolman, C. The role of action planning and plan enactment for smoking cessation. BMC Public Health. 2013;13: 393.CrossRefGoogle ScholarPubMed
35. De Vries, H, Eggers, SM, Jinabhai, C, et al. Adolescents’ beliefs about forced sex in KwaZulu-Natal, South Africa. Arch Sex Behav. 2014;43: 1–9.CrossRefGoogle ScholarPubMed
36. Hoffmann, C, von der Schulenburg, J-MG. The influence of economic evaluation studies on decision making.: A European survey. Health Policy. 2000;52:179–192.CrossRefGoogle ScholarPubMed
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