Category Archives: For Clinicians

Clinical Trial Matching

Today, the pharmaceutical industry faces a time challenge since approval of innovative new drugs requires successful completion of clinical trials. Identifying relevant candidates for clinical trials is time-consuming and not targeted, e.g. by using TV and newspaper advertisements. Benefits for participants of clinical trials remain hidden during their participation, which results in relatively high dropout rates exposing many clinical trials at risk to fail before their completion.

Together with experts from the pharmaceutical industry, we created the Clinical Trial Matching app: an innovative data-driven way to identify and contact relevant candidates for clinical trials within seconds. We incorporated latest in-memory database technology to test inclusion and exclusion criteria for registered users. Search results are clustered accordingly to identified similarities and ranked compared to perfect matching candidate. Ultimately, selected candidate can be contacted directly to highlight the benefits from participating in the given clinical trial.

During the whole process, the personal identity of candidates is kept private. After conducting an informed consent interview with the personal medical doctor, she or he contacts the clinical research organization to acquire further details. As a result, identifying clinical trials participants can be performed for the first time within seconds saving a tremendous amount of preparation time. Furthermore, participants in clinical trials may benefit from access to latest medical innovations, i.e. access to an improved way of healthcare long before it is released to public access.

Cloud Services for Analysis of Genome Data

Alignment screenshotGenome data can be used to identify individual roots of certain diseases and to derive specific treatment decision. However, clinicians and medical experts only rarely incorporate genomic data due the required technical knowledge nowadays. We focus on providing tools and services for non-IT experts that enable them to process and analyze medical data, e.g. genome data, by themselves. Our services are provided as Software-as-a-Service (SaaS) cloud applications eliminating the need for local hardware resources. Test-drive our Cloud Services for Analysis of Genome Data today.

Workflow

The user logs into the personal account, which protects all personal data. After submitting raw genome sequence data, e.g. as FASTQ file, the algorithm for alignment and the reference genome are configured. The high-throughput processing of data is performed asynchronously, i.e. multiple samples can be submitted in parallel. After processing, results can be explored interactively. Thus, medical results from international research databases are combined to identify relevant mutations and diseases. Identified mutation sites of individual study participants are listed and sorted accordingly to their relevance for certain diseases. In addition, each mutation site can be investigated in a detailed way on various levels, e.g. nucleotide or amino acid base, using our Genome Browser. The comparison of genomic data from multiple patients or samples, is supported by our Cohort Analysis.

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Clinical Trials Search

Clinical Trials Search on iPad
To identify relevant participants for clinical trials, preconditions are formulated as long text document. With more than 30,000 active clinical trials worldwide, it is hard to keep pace with newly released trials. Thus, the identification of appropriate clinical trials is a time-consuming and manual task for clinicians today. While investigating genomic variants of a particular case, our cloud services automatically check all open clinical trials for relevant preconditions, such as age restrictions, affected genes, and pharmaceuticals. As a result, clinicians and researchers receive a list of ranked trials. Thus, only this very short list of possible trials needs to be checked for applicability. Since the extraction of entities is performed instantly, new clinical trials are directly included in the search once they are published.

Medical Knowledge Cockpit App

The Medical Knowledge Cockpit shows how in-memory database technology combines international research data with patient specifics to find most relevant details for the treatment of individuals in course of personalized medicine in real-time. The video provides you with a brief walk through of an interactive use of the Medical Knowledge Cockpit.

Experience our Medical Knowledge Cockpit yourself and experience the advantages of in-memory technology interactively.

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Oncolyzer Mobile App

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

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

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

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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