Transition database for rare diseases and its use for clinical documentation.

Patients with rare diseases commonly suffer from severe symptoms as well as chronic and sometimes life-threatening effects. Not only the rarity of the diseases but also the poor documentation of rare diseases often leads to an immense delay in diagnosis. One of the main problems here is the inadequate coding with common classifications such as the International Statistical Classification of Diseases and Related Health Problems. Instead, the ORPHAcode enables precise naming of the diseases. So far, just few approaches report in detail how the technical implementation of the ORPHAcode is done in clinical practice and for research. We present a concept and implementation of storing and mapping of ORPHAcodes. The Transition Database for Rare Diseases contains all the information of the Orphanet catalog and serves as the basis for documentation in the clinical information system as well as for monitoring Key Performance Indicators for rare diseases at the hospital. The five-step process (especially using open source tools and the DataVault 2.0 logic) for set-up the Transition Database allows the approach to be adapted to local conditions as well as to be extended for additional terminologies and ontologies.

Development of a Dashboard for Rare Diseases - A Technical Case Report.

About 30 million people suffer from a rare disease in Europe. Those affected face a variety of problems. These include the lack of information and difficult access to scientific knowledge for physicians. For a higher visibility of rare diseases and high-quality research, effective documentation and use of data are essential. The aim of this work is to optimize the processing, use and accessibility of data on rare diseases and thus increase the added value from existing information. While dashboards are already being used to visualize clinical data, it is unclear what requirements are prevalent for rare diseases and how these can be implemented with available development tools so that a highly accepted dashboard can be designed. For this purpose, based on an analysis of the current situation and a requirements analysis, a prototype dashboard for the visualization of up-to-date key figures on rare diseases was developed at the University Hospital Carl Gustav Carus in Dresden. The development was based on the user-centered design process in order to achieve a high-level user-friendliness. The requirements analysis identified parameters that stakeholders wanted to see, focusing primarily on statistical analyses. The dashboard handles the automated calculation of statistics as well as their preparation and provision. The evaluations showed the prototypical dashboard would be considered valuable and used by potential users. This work demonstrates that stakeholders are interested in access to prepared information and exemplifies a way to implement it. The dashboard can increase the usage of existing information in terms of a higher accessibility and thus improve the knowledge about rare diseases.

Adaption of the OMOP CDM for Rare Diseases.

The OMOP Common Data Model (OMOP CDM) is an option to store patient data and to use these in an international context. Up to now, rare diseases can only be partly described in OMOP CDM. Therefore, it is necessary to investigate which special features in the context of rare diseases (e.g. terminologies) have to be considered, how these can be included in OMOP CDM and how physicians can use the data. An interdisciplinary team developed (1) a Transition Database for Rare Diseases by mapping Orpha Code, Alpha ID, SNOMED, ICD-10-GM, ICD-10-WHO and OMOP-conform concepts; and (2) a Rare Diseases Dashboard for physicians of a German Center of Rare Diseases by using methods of user-centered design. This demonstrated how OMOP CDM can be flexibly extended for different medical issues by using independent tools for mappings and visualization. Thereby, the adaption of OMOP CDM allows for international collaboration, enables (distributed) analysis of patient data and thus it can improve the care of people with rare diseases.