[Development of an electronic death certificate for Germany]. / Entwicklung einer elektronischen Todesbescheinigung für Deutschland.

The certification of causes of death by physicians as well as further processing by local health offices, registrar's offices, statistical offices, and other public authorities are conducted in a paper-based way and regulated individually by the laws of the different states of Germany.The concept of a nationally standardized electronic death certificate (eTB), enabling a digitalized processing chain and timely and improved mortality statistic, is presented.Starting with a nationally agreed upon data set, aligned with WHO requirements, an electronic death certificate pilot will be developed and tested in different regions. Synergies resulting from digitalization of the public administration and of the health system will be harnessed.Data collected electronically in the testing phase will be processed with the electronic coding system Iris. Effects on data quality of national mortality statistics will be investigated through multicausal analysis, which will be compared to results from other countries, and through comparisons with data from the existing paper-based process. For the first time, a national multicausal analysis of causes of death will be conceptualized in order to visualize effects of the aging and multimorbid population in national mortality statistics for consideration by healthcare politics and research.Results and lessons learned from the pilot can serve as the basis for national implementation of an electronic death certificate in Germany.

[Electronic coding of death certificates]. / Elektronische Kodierung von Todesbescheinigungen.

About half of all German death certificates are processed electronically by regional statistical offices to select the underlying cause of death in accordance with the instructions from the World Health Organization. This paper illustrates electronic coding and its importance for cause of death statistics.The electronic coding kernel MUSE was added a few years ago to the international coding system Iris, which is maintained by the Iris Core Group.A new module assigns, as far as possible, ICD-10 codes to medical terms documented in death certificates. It takes into account syntactical specifics of the German language. In addition, automatic text correction is implemented. Unrecognised text parts are highlighted and coded manually.Despite these efforts, improvement of data quality is the greatest challenge of German cause-of-death statistics. All involved stakeholders (physicians, local health authorities, and regional statistical offices) can cope with this task by a common effort.The process of electronic coding provides valuable hints for improving the quality of death certificates. In future, the coding system could generate feedback to local health authorities indicating medical documentation problems.

[Cause-of-death statistics and ICD, quo vadis?] / Todesursachenstatistik und ICD, quo vadis?

The International Statistical Classification of Diseases and Related Health Problems (ICD) is the worldwide binding standard for generating underlying cause-of-death statistics. OBJECTIVES: What are the effects of former revisions of the ICD on underlying cause-of-death statistics and which opportunities and challenges are becoming apparent in a possible transition process from ICD-10 to ICD-11?This article presents the calculation of the exploitation grade of ICD-9 and ICD-10 in the German cause-of-death statistics and quality of documentation. Approximately 67,000 anonymized German death certificates are processed by Iris/MUSE and official German cause-of-death statistics are analyzed.In addition to substantial changes in the exploitation grade in the transition from ICD-9 to ICD-10, regional effects become visible. The rate of so-called "ill-defined" conditions exceeds 10%.Despite substantial improvement of ICD revisions there are long-known deficits in the coroner's inquest, filling death certificates and quality of coding. To make better use of the ICD as a methodological framework for mortality statistics and health reporting in Germany, the following measures are necessary: 1. General use of Iris/MUSE, 2. Establishing multiple underlying cause-of-death statistics, 3. Introduction of an electronic death certificate, 4. Improvement of the medical assessment of cause of death.Within short time the WHO will release the 11th revision of the ICD that will provide additional opportunities for the development of underlying cause-of-death statistics and their use in science, public health and politics. A coordinated effort including participants in the process and users is necessary to meet the related challenges.

Machine learning approaches for phenotype-genotype mapping: predicting heterozygous mutations in the CYP21B gene from steroid profiles.

OBJECTIVE: Non-linear relations between multiple biochemical parameters are the basis for the diagnosis of many diseases. Traditional linear analytical methods are not reliable predictors. Novel nonlinear techniques are increasingly used to improve the diagnostic accuracy of automated data interpretation. This has been exemplified in particular for the classification and diagnostic prediction of cancers based on expression profiling data. Our objective was to predict the genotype from complex biochemical data by comparing the performance of experienced clinicians to traditional linear analysis, and to novel non-linear analytical methods. DESIGN AND METHODS: As a model, we used a well-defined set of interconnected data consisting of unstimulated serum levels of steroid intermediates assessed in 54 subjects heterozygous for a mutation of the 21-hydroxylase gene (CYP21B) and in 43 healthy controls. RESULTS: The genetic alteration was predicted from the pattern of steroid levels with an accuracy of 39% by clinicians and of 64% by linear analysis. In contrast, non-linear analysis, such as self-organizing artificial neural networks, support vector machines, and nearest neighbour classifiers, allowed for higher accuracy up to 83%. CONCLUSIONS: The successful application of these non-linear adaptive methods to capture specific biochemical problems may have generalized implications for biochemical testing in many areas. Nonlinear analytical techniques such as neural networks, support vector machines, and nearest neighbour classifiers may serve as an important adjunct to the decision process of a human investigator not 'trained' in a specific complex clinical or laboratory setting and may aid them to classify the problem more directly.