Amorphous sets of enormous amounts of data generated from different software applications are amassed in fragmented and increasingly indigestible compilations. Complex interfaces are needed to allow the exchange of data, which are shifted between systems in huge volumes. These overload hardware infrastructure capacity, driving costs while reducing usability. Often, “single solution” systems are adopted, which inherently are an amalgamation of subsystems, with many of the flaws of individual solutions connected by interfaces. Management of upgrades of systems entail considering adaptations to a host of associated components, while proprietary data storage limits access and transfer of data to new platforms. All the while, evaluation of the data is difficult and costly.
Experienced medical, genomics and imaging research experts and specialists in data analysis and IT-technology have integrated extraordinary medical knowhow and knowledge in secure handling of sensitive patient data to develop an innovative Platform for Data Collection, Retrieval and Evaluation in Digital Diagnostics and Research.
The novel platform resolves the fundamental predicaments of canonical, narrative-based, clinical documentation, information analysis and reporting. The outdated format of clinical items is replaced with digital objects that are linked with attributes and properties following uniform, standardized nomenclature. Storage in a searchable database enables algorithms to identify items and events of interest for interoperable analysis with other digital data sets or numerical data, e.g. laboratory results. Thus, limited data integration from various internal and external sources, costly migration of programs and complex adaptations of interfaces are averted, whilst the data remain in their original policy space. Searchability and algorithmic evaluation across large data pools enables a detailed, real-time personalized health status assessment and fully digital clinical decision support. Besides, the creation of defined cohorts allows comparisons over time and against a benchmark of care to optimize outcomes and safety.
The novel platform provides a bottom-up, domain knowledge-driven, clinical decision support system. Expediting digital integration of clinical, laboratory and genomic data - using ultrafast enterprise search engine technology (ESET) and software solutions - saves time and resources. In this project, the usual clinical data are extended by investigating genomic variants for their utility to predict disease course, undesired effects, and response to therapy. Inherently, these highly personalized diagnostic features are extendable to any clinical condition of choice.
The basis for full interoperability of information is provided by a noSQL database system that is inherently fully addressable by enterprise search engine technology (ESET). ESET indexes other data archiving systems to make them addressable, while avoiding the need for interfaces. This satisfies the prerequisites for data use across platforms in personalized medicine and systems medicine/biology without the need for interfaces. This solution integrates data held in different formats. ESET lays an index over the content and context and allows accessing data without copying or removing them from their policy spaces. The capacities of ESET has been potentiated by integrating a symbolic statistical program by project partner Iterata (Wolfram Mathematica, Wolfram Research, Champaigne IL, USA). A search engine with integrated Wolfram Mathematica (SIMA) delivers high-speed and capacity for across-the-board exploration and analysis of health data of individuals and patient populations.
Iterata Health Platform is implemented for the computation of genomic data. The advantages of this methodology are high data processing capacity and aptitude for counting and comparing events. Not only can pre-defined DNA sequences be sought and identified, but also alignment and haplotype analysis are possible. High-speed and high-throughput evaluations are supported, if necessary, on parallel devices. The possibility of capturing the raw data stream of the sequencing apparatus is evaluated and implemented. Equally, tabulations, complex cluster analysis and accessing existing gene databases are within the scope of the technology as well as seamless analysis with the clinical dataset and, thus, to incorporate the results of WGS into the routine CDSS. Likewise, other -omics data can be seamlessly integrated into the novel CDSS. More benefit can be expected from these modalities though, if they are measured longitudinally.
