Gaining Time – Real-time Analysis of Big Medical Data

Gaining Time – Real-time Analysis of Big Medical Data

Prof. Dr. Hasso PlattnerChairman of the Supervisory Board, SAP AG and

Professor, Hasso Plattner Institute

Growing Data Volumes in Diverse Healthcare Systems

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PubMed biomedical article database23+ Mil. articles

Clinical trialsCurrently more than 30,000 recruiting on ClinicalTrials.gov

Cancer patient records160,000 at NCT Heidelberg

Clinical information management systemsOften more than 50 GB

Human proteome160 Mil. data points (2.4 GB) per sample3.7 TB raw proteome data in ProteomicsDB

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Prescription data1.5 Bil. records from 10,000 doctors and 10 Mil. Patients (100 GB)

Human genome/biological data800 MB per full genome15 PB+ in databases of leading institutes

Medical sensor dataScan of a single organ in 1s creates 10GB of raw data

Innovation in Medicine can be Driven Using a Design Thinking Approach

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HumanFactors

BusinessFactors

TechnicalFactors

Clinicians

Researchers Administration &Operations Staff

Desirability Viability Feasibility

Clinical

Research

SAP HANA

Patients & Consumers Payers

Providers

Care Circles

Only a Collaborative Effort can beViable From a Business Perspective


Pharma

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SAP HANA is the Technology Enabler for This Vision

Advances in Hardware• Multi-core Architectures,

e.g. 16 CPUs x 10 Cores on Each Node

• Scaling Across Servers,e.g. 100 Nodes x 160 Cores

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A

• 64 bit Address Space – 12TB in Current Servers

• 25GB/s Data Throughput• Cost-Performance Ratio

Improving


Advances in Software

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CompressionMulti-CoreParallelization

Federation Complex Algorithms

No aggregatetables

ReducedFootprint

More Than Just a Faster Database, SAP HANAis a Revolutionary Computing Platform

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Selected SAP HANA Usage Scenarios

SAP HANA

CliniciansDecision Support

ResearchersPersonalized medicine Prescription

Analysis

Healthcare AdministrationOptimized Operations

Patient Management (IS-H) Analytics

Medical Explorer

Medical Knowledge Cockpit

Proteome Diagnostics

Genomics for Personalized Medicine

Genome Variant AnalysisFor personalized/preventative medicine

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Full human genome is 3.2 billion characters long

Researchers want to identify and chart amount of variation in one gene across a population

With SAP HANA, researchers can compare genetic variants of diseased & healthy cohorts in real-time

Using SAP HANA, Stanford has seen “spectacular” findings: Type 2 diabetes disease risk is very different across populations

"We have been thrilled to work with SAP and HPI on a collaboration to accelerate DNA sequence analysis. In our pilot projects, we are seeing dramatic speedups in computing on human genome variation data from many samples. We are dreaming of what will soon be possible as we integrate phenotype, genomics, proteomics, and exposome data to empower complex trait mapping using millions of health records.”

- Professor Carlos D. Bustamante at the Stanford University School of Medicine

Multi-Core ParallelizationAnalysis on 125 variants in 629 people in parallel; was not possible before

Research

Multi-CoreParallelization

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Proteome-based Cancer Diagnostics Platform for Researchers and Clinicians

Intuitive interface for complex analysis pipeline

Diagnosis can be done by analysing proteome “fingerprint” from just one drop of blood

Proteome analysis yields very large data sets (160Mil data points/sample)

Researchers can model a detection pipeline interactively on SAP HANA

Researchers can manipulate the detection pipeline interactively

Minimally invasive diagnostics made possible by large scale studies

Fingerprint recognitionon high resolution data now possible

Research

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ProteomicsDBwww.proteomicsdb.org








Medical ExplorerCancer patient treatment and research

Unified access to multiple formerly disjoint data sources

Oncologists need to find the best treatment option for patients Find patients eligible for clinical trials

Clinical records and inclusion criteria are very complex

Clinical data from different sources is combined in one SAP HANA system

Doctors can filter patient cohorts based on any clinical attribute Patients eligible for clinical trials can be found in seconds

Clinic

“In the future we would like to use SAP HANA at every diagnostic and therapeutic step in the fight against cancer as every cancer is different and can vary immensely from one patient to the next.“

- Prof. Dr. Christof von Kalle, Head of National Center for Tumor Diseases Heidelberg, Germany18

Flexible Analyticson historical datat

Medical Knowledge CockpitRelevant scientific findings at a glance

Unified access to structured and unstructured data sources

Search for affected genes in distributed and heterogeneous data sources

Immediate exploration of relevant information, such as Gene descriptions, Molecular impact and related pathways, Scientific publications, and Suitable clinical trials.

No manual search for hours or days –SAP HANA translates manual searching into interactive finding

Automatic clinical trial matching using HANA text analysis features

Clinic

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Patient Management (IS-H) AnalyticsReal-time analysis of hospital patient management data

Medical Controllers need to check occupancy for different wards frequently

Current systems too slow for real-time analysis no what-if scenarios possible

HANA made sub-second response times possible

New analytical applications can now help drive cost-savings and more efficient resource allocation

Flexible analysis – no need for materialized aggregates

Admin

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Prescription Data AnalysisUnderstanding the who, where, and what of drug prescriptions

Which is prescribed e.g. for migraine?

Specialists might prescribe different drugs than general practitioners

SAP HANA cloud system holds 1.5 Bil. Prescription records for around 10 Mil. patients and 10,000 doctors

Data can be explored and visualized interactively with SAP Lumira in seconds

Answers in 1 sec. instead of 1 hour

Intuitive analysis using data graphics

"SAP Health Data on Demand reduces the time it takes to analyze our more than 1.5 bn data records from 1 hour to 1 second. As a result, we are able to offer our customers new online services, establish a new business model and generate additional revenue.”

- Franz-Xaver Thalmeir, Managing Director, Medimed GmbH

Admin

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Speedups achievedPatient Management (IS-H) Analytics 50x (55 seconds 800 milliseconds)Virtual Patient Platform 5000x (4 hours 2-3 seconds)Prescription analysis 3600x (1 hour 1 second)DNA Sequence Alignment 17x (85 hours 5 hours)Proteome-based Cancer Diagnostics 22x (15 minutes 40 seconds)

New usage scenariosMedical Explorer Genome AnalysisClinical Trial Matching ProteomicsDBGenome Browser Biological Pathway AnalysisLarge Patient Cohort Analysis HANA Data Scientist

Genome Data Processing and Pipeline Modeling

Healthcare Projects on SAP HANAHANA helps gain time and enables completely new scenarios

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Demo

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The Power of Multidisciplinary Teams

SAP: Global Software Vendor and Expert for Enterprise Technologies World-Wide

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Hasso Plattner Institute: Academic Research Institute for IT Systems Engineering

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Carlos Bustamante Lab: Leading Stanford Lab On Human Population Genomics and Global Health

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Charité – Universitätsmedizin Berlin: One of the largest university hospitals in Europe

+National Center for Tumor Diseases Heidelberg (NCT): One of the leading institutions for cancer research and patient care

Join Us!

Design Thinking Teams

You

Only Strong Partners Build Strong Co-Operative Success Stories

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New Ways of Real-Time CollaborativePersonal Medicine

Thank you!

Medical

Explorer