The Oncolyzer project is an interdisciplinary cooperation with Charité — Universitätsmedizin Berlin and SAP started in 2011. It combines individual competences in software engineering, IT systems, and medicine and defines a major key in our strategy to provide real-time data analysis cloud services for clinicians and researchers. The objective of the Oncolyzer project is to improve the treatment process of patients suffering from cancer diseases by leveraging optimized IT-aided software components for clinicians and researchers. As a result, doctors are enabled to select best available cancer therapies much faster by having all relevant information at hand.
The ‘heart’ of this innovation builds the in-memory computing platform, which supports the combined processing of structured and unstructured medical data in real-time. Thus, it is enables the integration of heterogeneous data sources within the clinical environment without the need for long-running and complex Extract Transform Load (ETL) processes to unify data. The organizational changes in the healthcare sector require increasing support by proper IT-aided tools and processes. Data needs to be instantaneously available at any location a doctor requires the data — even worldwide. The immense increase of knowledge about cancer requires the detailed analysis of biological and genetic details acquired during diagnosis of cancer cells to address only harmful cells as the target of future treatments while keeping side effects at a minimum. Until recently, common therapies that were not individually targeted were applied during the treatment of cancer. Meanwhile, treatments for specific genetic mutations are available, which enable treatment of cancer based on individual genetic dispositions. Nowadays, data processing and analysis becomes a time-consuming challenge due to improved and more detailed diagnostic approaches, such as next-generation sequencing of tumor DNA.
In the near future, the tumor’s DNA of all cancer patients will be sequenced to support individualized patient-specific cancer therapies, which result in diagnostic medical data in amount of multiple terabytes. The analysis of these data required optimized software tools that enable graphical exploration of data, their real-time analysis, and the identification of therapy-relevant details to support clinical decision taking.
Features of the Hana Oncolyzer iPad Application
The Oncolyzer iPad application provides clinicians and researchers access to relevant patient data while in the secured network of the clinic’s campus. The in-memory technology builds the backend of the application performing relevant data processing and analyses. Selected features of the Oncolyzer application are described in the following.
Combined Search in Structured and Unstructured Data
Oncolyzer: Search in structured and unstructured data medical data
The Oncolyzer combines data from various data sources — structured and unstructured.
Structured data are, for example, biopsy results, size of tumor or tissue regions, blood concentration, etc. They are stored in a relational database format and can be accessed via defined attributes. Unstructured data are, for example, text documents, diagnosis, notes, etc. They are stored in text file and they neither consist of a predefined structure nor use standardized text paragraphs. As a result, unstructured data has the following drawback: typos, usage of pseudonyms, abbreviations, etc. However, the majority of clinical medical data is unstructured. Thus, it is important to analyze them in a systematic way and to extract relevant details in real-time. Further details about combined processing of structured and unstructured data can be found on the corresponding feature page.
Visualization of Patient Details for Personalized Medicine
Oncolyzer: Summary of patient’s anamnesis
All patient related information need to be available for decision taking by medical doctors. Individual specifics of patients need to be analyzed and evaluated to for personalized medicine. The Oncolyzer combines current as well as historic data of a selected patient on a single screen and performs automatically analysis of the available data, e.g. to highlight patient specifics compared to patients with similar diagnosis or anamnesis. Thus, characteristics and important differences are highlighted, i.e. medical doctors can use these additional information as indicator assessment of the individual reason for the disease and impact of the selected treatment. All information are visualized on an interactive time line, which enables clinicians to move back and forward through the patient’s anamnesis while having access to all relevant data with a single click.
Analytical Exploration of Patient Cohorts
Oncolyzer: Real-time analysis of individual patient cohorts using freely definable criteria
Analysis data of all patients or a patient cohort is a time-consuming and often manual task. The Oncolyzer app enables analysis of patient data on mobile devices, i.e. there is no longer a need for a desktop PC to perform the analysis. Furthermore, freely definable filters can be applied to explore patient details. Thus, the Oncolyzer app enables identification of individual patients, e.g. for participation in specific clinical studies to provide individualized treatment. The use of always up-to-date data bridges information gaps and supports fast analysis and evaluation of patient cohorts. Further details about analysis of patient cohorts can be found in the corresponding app description.
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