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USING CLAIMS DATA FOR EVIDENCE GENERATION IN MANAGED ENTRY AGREEMENTS

Part of: Special Collection - Methods

Published online by Cambridge University Press:  15 March 2016

Alina Brandes ,
Larissa Schwarzkopf  and
Wolf H. Rogowski
Alina Brandes
Affiliation:
Helmholtz Zentrum München; German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management
Larissa Schwarzkopf
Affiliation:
Helmholtz Zentrum München; German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management
Wolf H. Rogowski
Affiliation:
Department of Health Care Management, Institute of Public Health and Nursing Research, Health Sciences, University of Bremen, Germany; Helmholtz Zentrum München; German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Managementrogowski@uni-bremen.de
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Abstract

Objectives: This study assesses the use of routinely collected claims data for managed entry agreements (MEA) in the illustrative context of German statutory health insurance (SHI) funds.

Methods: Based on a nonsystematic literature review, the data needs of different MEA were identified. A value-based typology to classify MEA on the basis of these data needs was developed. The typology is oriented toward health outcomes and utilization and costs, key components of a new technology's value. For each MEA type, the suitability of claims data in establishing evidence of the novel technology's value in routine care was systematically assessed. Assessment criteria were data availability, completeness, timeliness, confidentiality, reliability, and validity.

Results: Claims data are better suited to MEA addressing uncertainty regarding the utilization and costs of a novel technology in routine care. In schemes where safety aspects or clinical effectiveness are assessed, the role of claims data is limited because clinical information is not included in sufficient detail.

Conclusions: The suitability of claims data depends on the source of uncertainty and, in consequence, the outcome measures chosen in the agreements. In all schemes, the validity of claims data should be judged with caution as data are collected for billing purposes. This framework may support manufacturers and payers in selecting the most suitable contract type and agreeing on contract conditions. More research is necessary to validate these results and to address remaining medical, economic, legal, and ethical questions of using claims data for MEA.

Keywords

Technological innovationDecision makingInsurancehealthreimbursementRisk sharingfinancial/economicsData collection/methods
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 32 , Issue 1-2 , 2016 , pp. 69 - 77
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

1. Rogowski, WH. An economic theory of the fourth hurdle. Health Econ. 2013;22:600-610.Google Scholar
2. Hutton, J, Trueman, P, Henshall, C. Coverage with evidence development: An examination of conceptual and policy issues. Int J Technol Assess Health Care. 2007;23:425-432.Google Scholar
3. Adamski, J, Godman, B, Ofierska-Sujkowska, G, et al. Risk sharing arrangements for pharmaceuticals: Potential considerations and recommendations for European payers. BMC Health Serv Res. 2010;10:153.Google Scholar
4. Stafinski, T, McCabe, CJ, Menon, D. Funding the unfundable: Mechanisms for managing uncertainty in decisions on the introduction of new and innovative technologies into healthcare systems. Pharmacoeconomics. 2010;28:113-142.Google Scholar
5. Garrison, LP Jr, Towse, A, Briggs, A, et al. Performance-based risk-sharing arrangements-good practices for design, implementation, and evaluation: Report of the ISPOR Good Practices for Performance-Based Risk-Sharing Arrangements Task Force. Value Health. 2013;16:703-719.Google Scholar
6. Fünftes Buch Sozialgesetzbuch - Gesetzliche Krankenversicherung - (Artikel 1 des Gesetzes vom 20. Dezember 1988, BGBl. I S. 2477), das durch Artikel 3 des Gesetzes vom 22. Dezember 2011 (BGBl. I S. 3057) geändert worden ist. (1988).Google Scholar
7. Briggs, A, Ritchie, K, Fenwick, E, Chalkidou, K, Littlejohns, P. Access with evidence development in the UK: Past experience, current initiatives and future potential. Pharmacoeconomics. 2010;28:163-170.CrossRefGoogle ScholarPubMed
8. Mohr, PE, Tunis, SR. Access with evidence development - the US experience. Pharmacoeconomics. 2010;28:153-162.Google Scholar
9. Berger, ML, Mamdani, M, Atkins, D, Johnson, ML. Good research practices for comparative effectiveness research: Defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force. Value Health. 2009;12:1044-1052.Google Scholar
10. Schneeweiss, S, Avorn, J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58:323-337.Google Scholar
11. Johnson, ML, Crown, W, Martin, BC, Dormuth, CR, Siebert, U. Good research practices for comparative effectiveness research: Analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: The ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report–Part III. Value Health. 2009;12:1062-1073.Google Scholar
12. Garrison, LP, Neumann, PJ, Erickson, P, Marshall, D, Mullins, CD. Using real-world data for coverage and payment decisions: The ISPOR Real-World Data Task Force Report. Value Health. 2007;10:326-335.CrossRefGoogle ScholarPubMed
13. Willison, DJ. Health services research and personal health information: Privacy concerns, new legislation and beyond. Can Med Assoc J. 1998;159:1378-1380.Google Scholar
14. Carlson, JJ, Sullivan, SD, Garrison, LP, Neumann, PJ, Veenstra, DL. Linking payment to health outcomes: A taxonomy and examination of performance-based reimbursement schemes between healthcare payers and manufacturers. Health Policy. 2010;96:179-190.Google Scholar
15. Walker, S, Sculpher, M, Claxton, K, Palmer, S. Coverage with evidence development, only in research, risk sharing, or patient access scheme? A framework for coverage decisions. Value Health. 2012;15:570-579.Google Scholar
16. Kuepper-Nybelen, J, Hellmich, M, Abbas, S, et al. Association of long-term adherence to evidence-based combination drug therapy after acute myocardial infarction with all-cause mortality. A prospective cohort study based on claims data. Eur J Clin Pharmacol. 2012;68:1451-1460.Google Scholar
17. Deutsches Institut für Medizinische Dokumentation und Information (DIMDI). Operationen- und Prozedurenschlüssel Version 2013. 2013 [updated 2013]. http://www.dimdi.de/static/de/klassi/ops/kodesuche/onlinefassungen/opshtml2013/index.htm (accessed July 8, 2013).Google Scholar
18. Kassenärztliche Bundesvereinigung. Einheitlicher Bewertungsmaßstab für ärztliche Leistungen. 2013 [updated 2013]. http://www.kbv.de/ebm2013/EBMGesamt.htm (accessed July 8, 2013).Google Scholar
19. Spitzenverband der gesetzlichen Krankenversicherungen. Hilfsmittelverzeichnis des GKV-Spitzenverbandes. 2007 [updated 2007]. https://hilfsmittel.gkv-spitzenverband.de/home.action (accessed July 8, 2013).Google Scholar
20. Schwarzkopf, L, Menn, P, Leidl, R, et al. Excess costs of dementia disorders and the role of age and gender - An analysis of German health and long-term care insurance claims data. BMC Health Serv Res. 2012;12:165.CrossRefGoogle ScholarPubMed
21. Stausberg, J, Lehmann, N, Kaczmarek, D, Stein, M. Reliability of diagnoses coding with ICD-10. Int J Med Inform. 2008;77:50-57.Google Scholar
22. Erler, A, Beyer, M, Muth, C, Gerlach, FM, Brennecke, R. Garbage in - Garbage out? Validitat von Abrechnungsdiagnosen in hausarztlichen Praxen [Garbage in - garbage out? Validity of coded diagnoses from GP claims records]. Gesundheitswesen. 2009;71:823-831.Google Scholar
23. García Rodríguez, LA, Pérez Gutthann, S. Use of the UK General Practice Research Database for pharmacoepidemiology. Br J Clin Pharmacol. 1998;45:419-425.Google Scholar
24. Hennessy, S, Bilker, WB, Weber, A, Strom, BL. Descriptive analyses of the integrity of a US Medicaid claims database. Pharmacoepidemiol Drug Saf. 2003;12:103-111.Google Scholar
25. O'Malley, SP, Selby, WS, Jordan, E. A successful practical application of coverage with evidence development in Australia: Medical Services Advisory Committee interim funding and the PillCam Capsule Endoscopy Register. Int J Technol Assess Health Care. 2009;25:290-296.Google Scholar
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