Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-25T06:01:55.906Z Has data issue: false hasContentIssue false

DECISION-COMPONENTS OF NICE'S TECHNOLOGY APPRAISALS ASSESSMENT FRAMEWORK

Published online by Cambridge University Press:  10 April 2018

Joost de Folter
Affiliation:
National Institute for Health and Care Excellence (NICE)[email protected]; [email protected]
Mark Trusheim
Affiliation:
Massachusetts Institute of Technology (MIT)
Pall Jonsson
Affiliation:
National Institute for Health and Care Excellence (NICE)[email protected]
Sarah Garner
Affiliation:
National Institute for Health and Care Excellence (NICE)

Abstract

Objectives: Value assessment frameworks have gained prominence recently in the context of U.S. healthcare. Such frameworks set out a series of factors that are considered in funding decisions. The UK's National Institute of Health and Care Excellence (NICE) is an established health technology assessment (HTA) agency. We present a novel application of text analysis that characterizes NICE's Technology Appraisals in the context of the newer assessment frameworks and present the results in a visual way.

Methods: A total of 243 documents of NICE's medicines guidance from 2007 to 2016 were analyzed. Text analysis was used to identify a hierarchical set of decision factors considered in the assessments. The frequency of decision factors stated in the documents was determined and their association with terms related to uncertainty. The results were incorporated into visual representations of hierarchical factors.

Results: We identified 125 decision factors, and hierarchically grouped these into eight domains: Clinical Effectiveness, Cost Effectiveness, Condition, Current Practice, Clinical Need, New Treatment, Studies, and Other Factors. Textual analysis showed all domains appeared consistently in the guidance documents. Many factors were commonly associated with terms relating to uncertainty. A series of visual representations was created.

Conclusions: This study reveals the complexity and consistency of NICE's decision-making processes and demonstrates that cost effectiveness is not the only decision-criteria. The study highlights the importance of processes and methodology that can take both quantitative and qualitative information into account. Visualizations can help effectively communicate this complex information during the decision-making process and subsequently to stakeholders.

Type
Assessment
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Doshi, JA, Willke, RJ. Advancing high-quality value assessments of health care interventions. Value Health. 2017;20:181-184.Google Scholar
2. Westrich, K. Current landscape: Value assessment frameworks. Washington DC: National Pharmaceutical Council; 2016.Google Scholar
3. Sorenson, C, Lavezzari, G, Daniel, G, et al. Advancing value assessment in the United States: A multistakeholder perspective. Value Health. 2017;20:299-307.Google Scholar
4. Oortwijn, W, Sampietro-Colom, L, Habens, F. Developments in value frameworks to inform the allocation of healthcare resources. Int J Technol Assess Health Care. 2017;33:1-7.Google Scholar
5. Angelis, A, Kanavos, P. Multiple Criteria Decision Analysis (MCDA) for evaluating new medicines in health technology assessment and beyond: The advance value framework. Soc Sci Med. 2017;188:137-156.Google Scholar
6. Kristensen, FB, Lampe, K, Chase, DL, et al. Practical tools and methods for health technology assessment in Europe: Structures, methodologies, and tools developed by the European Network for Health Technology Assessment, EUnetHTA. Int J Technol Assess Health Care. 2009;25 (Suppl 2):1-8.Google Scholar
7. EUnetHTA. HTA Core Model Version 3.0. 2016 [updated January 25, 2016]. http://www.htacoremodel.info (accessed February 26, 2018).Google Scholar
8. NICE. Guide to the processes of technology appraisal. London: National Institute for Health and Care Excellence; 2014.Google Scholar
9. NICE. Guide to the methods of technology appraisal. London: National Institute for Health and Care Excellence; 2008.Google Scholar
10. NICE. Guide to the methods of technology appraisal. London: National Institute for Health and Care Excellence; 2013.Google Scholar
11. NICE. Social value judgements: Principles for the development of NICE guidance, Second edition. London: National Institute for Health and Care Excellence; 2008.Google Scholar
12. Rawlins, M, Barnett, D, Stevens, A. NICE's approach to decision-making. Pharmacoeconomics. 2009;70:346-349.Google Scholar
13. Byron, SK, Crabb, N, George, E, Marlow, M, Newland, A. The health technology assessment of companion diagnostics: Experience of NICE. Clin Cancer Res. 2014;20:1469-1476.Google Scholar
14. Mason, A, Drummond, M, Ramsey, S, Campbell, J, Raisch, D. Comparison of anticancer drug coverage decisions in the United States and United Kingdom: Does the evidence support the rhetoric? J Clin Oncol. 2010;28:3234-3238.CrossRefGoogle ScholarPubMed
15. Sorenson, C, Drummond, M, Chalkidou, K. Comparative effectiveness research: The experience of the National Institute for Health and Clinical Excellence. J Clin Oncol. 2012;30:4267-4274.Google Scholar
16. Nicod, E, Kanavos, P. Scientific and Social value judgments for orphan drugs in health technology assessment. Int J Technol Assess. 2016;32:218-232.CrossRefGoogle ScholarPubMed
17. Manning, C, Raghavan, P, Schütze, H. An introduction to information retrieval. Cambridge, England: Cambridge University Press; 2009.Google Scholar
Supplementary material: PDF

de Folter et al. supplementary material

Table S1

Download de Folter et al. supplementary material(PDF)
PDF 129.5 KB
Supplementary material: PDF

de Folter et al. supplementary material

Table S2

Download de Folter et al. supplementary material(PDF)
PDF 538.6 KB
Supplementary material: PDF

de Folter et al. supplementary material

Table S3

Download de Folter et al. supplementary material(PDF)
PDF 217.6 KB