Introduction of ICD-11 in Germany: Seizing opportunities together.

With the new ICD-11 developed by the WHO and translated into German for use in Germany by the Federal Institute for Drugs and Medical Devices, the German healthcare system is facing a change that is more than a simple change of a medical coding system. The ICD-11 modernises the coding system, for example, by including new, separate health conditions such as sleep-wake disorders and conditions related to sexual health, thus making the corresponding diseases more visible. The ICD-11 is more precise than the ICD-10: it allows cross-referencing between diagnoses, symptoms, functionality and locations and partially abandons the strict hierarchy of the ICD-10.

Ten years of AMNOG from an oncological perspective: new horizons and continuing expansion.

Purpose: 2021 marks the tenth anniversary of the AMNOG process and brought with it a new German administration-two good reasons to take stock of where we stand today, what has been achieved so far, and how the path of early benefit assessments in Germany should continue.

Results: From the perspective of manufacturers of cancer drugs, the AMNOG process, as a constantly evolving system, has for the most part proved itself-which does not mean that there is no longer room for improvement. Significant improvements have been achieved in the area of early consultation of medical societies regarding the selection of the appropriate comparator therapy as well as in the reimbursement of biomarker diagnostic tests in the outpatient sector.

Use of algorithms for identifying patients in a German claims database: learnings from a lung cancer case.

Background: The analysis of statutory health insurance (SHI) data is a little-used approach for understanding treatment and care as well as resource use of lung cancer (LC) patients in Germany. The aims of this observational, retrospective, longitudinal analysis of structured data were to analyze the healthcare situation of LC patients in Germany based on routine data from SHI funds, to develop an algorithm that sheds light on LC types (non-small cell / NSCLC vs. small cell / SCLC), and to gain new knowledge to improve needs-based care.