Skip to main content Accessibility help


  • Alina Brandes (a1), Larissa Schwarzkopf (a1) and Wolf H. Rogowski (a2)


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.



Hide All
1. Rogowski, WH. An economic theory of the fourth hurdle. Health Econ. 2013;22:600-610.
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.
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.
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.
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.
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).
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.
8. Mohr, PE, Tunis, SR. Access with evidence development - the US experience. Pharmacoeconomics. 2010;28:153-162.
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.
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.
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.
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.
13. Willison, DJ. Health services research and personal health information: Privacy concerns, new legislation and beyond. Can Med Assoc J. 1998;159:1378-1380.
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.
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.
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.
17. Deutsches Institut für Medizinische Dokumentation und Information (DIMDI). Operationen- und Prozedurenschlüssel Version 2013. 2013 [updated 2013]. (accessed July 8, 2013).
18. Kassenärztliche Bundesvereinigung. Einheitlicher Bewertungsmaßstab für ärztliche Leistungen. 2013 [updated 2013]. (accessed July 8, 2013).
19. Spitzenverband der gesetzlichen Krankenversicherungen. Hilfsmittelverzeichnis des GKV-Spitzenverbandes. 2007 [updated 2007]. (accessed July 8, 2013).
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.
21. Stausberg, J, Lehmann, N, Kaczmarek, D, Stein, M. Reliability of diagnoses coding with ICD-10. Int J Med Inform. 2008;77:50-57.
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.
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.
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.
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.


Type Description Title
Supplementary materials

Brandes supplementary material
Table S1

 Word (15 KB)
15 KB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed