Healthcare @ SAP Innovation Center Potsdam Dominik Bertram 28. November 2013.

Healthcare @ SAP Innovation Center Potsdam

Dominik Bertram28. November 2013

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IS-H Analytics - Real-time analysis of hospital patient management dataAndrew McCormick-Smith, Christian Heller, Spyros Antonopoulos

“This would change the way I do my job.” - Charité University Hospital Berlin “ ”

Reports in < 1 second with in-memory, old system 55s

Challenge Current patient management systems are too slow for real-

time analysis, making what-if planning impossible

Solution Re-implemented a selection of IS-H reports in HANA Results delivered in sub-second response time New analytical applications can now help drive cost-savings

and more efficient resource allocation

Benefits Cost savings for hospitals Improved patient experience

Flexible, real-time analysis – no need for materialized aggregates

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Medical ExplorerDominik Bertram, Massimiliano Marcon, Gennadi Rabinovitch, Matthias Steinbrecher

Challenge Integrate multiple sources of patient data, including both text (e.g.

doctor’s letters) and structured data (e.g. tumor registry) Pick out patients whose treatment history satisfies very complex

criteria Compare metrics like survival times, quality of life, treatment

response across different patient cohorts

Achievements Generic medical data model makes it easy to combine data from

many different sources Intuitive web UI supports analysis of patient cohorts based on

customizable attributes Solution will go live at the National Center for Tumor Diseases

(NCT) in Heidelberg in late 2013 / early 2014

Unified access to multiple formerly disjoint data sources

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ProteomicsDBJoos-Hendrik Boese, Lars Butzmann, Dave Schikora

Collaboration project with HANA Platform Core Team Walldorf and Technische Universität München

Goals Provide a public data repository for storing and sharing proteomics

experiment data Support rapid calculations across the entire database

Achievements Went live on https://www.proteomicsdb.org on June 11th 2013 Currently holds over 3.5 TB of proteomics data covering over 90% of the

human proteome

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Proteome-based Cancer DiagnosticsJoos-Hendrik Boese, Siegfried Gessulat, Christopher Ozdoba

Challenge Proteome data from blood samples could provide minimally invasive

early-stage cancer diagnostics Very large data sets (160Mio data points/sample) need to be analyzed

to detect disease patterns and do diagnostics

Solution Implemented complete analysis pipeline for proteome-data in SAP

HANA Built an analysis modeling tool that allows researchers to manipulate

the detection pipeline interactively

Benefits Large studies now feasible to identify complex disease patterns Intuitive interaction with data for researchers and doctors

Intuitive interface for complex analysis pipeline

Large scale studieson high resolution data now possible

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Virtual Patient PlatformMorten Ernebjerg, Valentin Flunkert, and Marcus Krug

Challenge

• The Virtual Patient model is a predictive computer model which captures how a human cells works; it contains almost 2500 differential equations

• Virtual Patient simulations could offer a revolutionary way of finding the best treatment for each cancer patient….

• …but the processing time and data volume previously made large-scale use impossible.

Achievements

• Optimized model solver to run 5000 times faster - large-scale simulations can be done in hours instead of weeks

• Built a comprehensive data model in HANA for quick analysis

• Created web-applications for doctors and scientists to run simulations and analyze the results on HANA

5000x faster simulations

Real-time analysisof results data now possible