Unrestricted © Siemens Healthcare GmbH, 2016
Optimized Service Delivery for Medical Systems
What to get out of IoT and Process Data
Dr. Mirko Appel November 2016
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Siemens Healthineers
2016-02-20Page 3 HC xxx – xxx
Dr. Mirko Appel | HC SV CS SLM DGA AS
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> €1 bnR&D spent
73countries
with direct presence
>209,000patients
every hour2
Biggestsupplier
of medtechinfrastructure
Worldmarketleader
in most businesses
> 45,000employees
1,474invention
disclosuresin 2015
> 70%of critical clinical
decisions are influenced by the
type of technology we provide1
~ €13 bnrevenue
Access for
1.08 bnpeople
in developing countries2
As your partner, we offer expertise and resources for your specific needs
1 AdvaMedDX, “A Policy Primer on Diagnostics”, June 2011, page 3
2 Siemens AG, “Sustainable healthcare strategy - Indicators in fiscal 2014”, page 3-4
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Outcomes
Costs
Future
1956CLINISTIX − dry chemistry testing for glucose in urine
E.v. BehringW. C. Röntgen
1901 Nobel prize winners (Physics + Medicine)
1896 Industrially manufac-tured X-ray appliance for medical diagnostics
2011First integrated, simultaneous whole-body MRI and PET system
1998First Siemens track-based laboratory automation system
1967First real-time ultrasound scanner
1975First Siemens CT scanner
1983First Siemens MRI scanner
2008Robotic-assisted angiography system
2009Multi-modality 3D imaging network
2008 Digital radio-graphysystem with wireless flat-panel detector
2012Wireless transducers for ultrasound
2014“Free breathing”CT scanning with powerful dual X-ray sources and two detectors
Molecular DX
Digital Health Services
Enterprise Services
Advanced Therapies
1999First intuitive medical IT platform from Siemens
2001First PET/CT system from Siemens
2006Diagnostic analyzer integrating four tech-nologiesin one system
2005First Dual Source CT scanner
1957Fully automated discrete chemistry analyzer for whole blood or serum
1982First acridiniumester based chemilumin-escenceimmuno-assays
2015Wide-angle image acquisition breast tomosynthesis– Mammomat® Inspiration
2015First Twin RoboticX-ray scanner for enhanced patientcare and productivity
Built on over 120 years of dedication to innovation,we have a long history in enabling healthcare providers …
2016-02-20Page 5 HC xxx – xxx
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Ultrasound
We enable real-time access to decision-critical information
Advanced Therapies
We enable advanced therapeutic procedures
• Cardiology• Interventional Radiology• Radiation Oncology• Surgery
*Incubated within Business Function Strategy & InnovationImage courtesy Diagnostic Imaging: CMRR, Minneapolis, MGH, BostonImage courtesy Advanced Therapies: IHU Strasbourg, France
... offering the broadest and deepest portfolio
Diagnostic Imaging
We help achieve highest diagnostic quality and efficiency
• Computed Tomography• Magnetic Resonance• Molecular Imaging• Radiography & Fluoroscopy• Imaging IT
Laboratory Diagnostics
We enable clinical and workflow excellence in the lab
• Chemistry, Automation & Immunoassay• Hemostasis, Hematology
& Specialty Business
• Molecular Diagnostics*
Point of Care
We provide critical patient information in-office and at the bedside
Services
We help achieve best institutional performance
• Customer Services• Digital Health Services• Enterprise Services & Solutions
Outcomes
Costs • Blood Gas• Diabetes• Urinalysis
• Cardiology• Radiology• Obstetrics & Gynecology
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Siemens Healthineers Customer Care Program
The products/features and/or service offerings (here mentioned) are not commercially available in all countries and/or for all modalities. If the services are not marketed in countries due to regulatory or other reasons, the service offering cannot be guaranteed. Please contact your local Siemens organization for further details.
Management Services
Consulting and partnership for economically sustainable healthcare delivery business
User Services
Enabling users with expertise and efficiency in the long run
IT Services
Delivering comprehensive and professional IT services to optimally run healthcare delivery
UserServices
ManagementServices
ITServices
TechnischeServicesSystemServices
Siemens Remote Service
LifeNet
System Services
Proactively ensuring that medical systems operate at peak performance
Siemens
Integrated
Service
Management
Siemens
Utilization
Management
Education
Management
Check
End-to-end
Automation
Workflow
Solutions
Business Management
Healthcare IT Care
Education
Product /
Clinical
Training
Remote
Application
Services
Optimize
CARECRADLE
Efficiency
Consulting
System Care and Repair
Siemens
Shared
Services
Siemens
Protect
Plans
Siemens
Guardian
Program
Evolve
Program
Guardian
Program
incl.
TubeGuard
Guardian
Program
incl.
ImageGuard
Siemens IT Care PlanSiemens Performance
Plans
Evolve
Program
Education Plans PEP Connect
Admin
Plus
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Together with SAS, we built up technology, processes and organization to implement business analytics
Service Process
Data
Data Storage and Aggregation
Consistent Analytical Toolset
Data Sources
Business AnalyticsPlatform
Grow & safeguard service business Increase operational efficiency Improve decision speed and quality
What will happen next? How
will trends continue? What
scenarios are likely?
Example: Prediction of
system failures
Why is it happening? What
are conclusions from
associations, clusters,
trends?
Example: Commonly
exchanged service parts
Where are patterns,
anomalies, or dependencies
in the data?
Example: Peaks of error
patterns across fleet
How do we do things
better? What is the best
decision for a complex
problem?
Example: Smarter exchange
of service parts
Business AnalyticsCompetence Groups & Key Users
Service Business Processes
InstalledBaseData
LogisticsData …
Installed Base
Data Mining Statistical AnalysisPredictive Modeling & Forecasting
Optimization
Data Exploration & Integration
DI S
tud
ioV
isu
al A
nal
ytic
s
Data Analysis
Ente
rpri
se G
uid
eEn
terp
rise
Min
er
Results Presentation & Distribution
Vis
ual
An
alyt
ics
Web
Rep
ort
Stu
dio
Dr. Mirko Appel | HC SV CS SLM DGA AS
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132 countries
in
different parts on stock in Memphis
40,000over
35,000different parts on stock in Frankfurt
over
10,000different parts on stock in Singapore
over
14,000different parts on stock in Brussel
over
300customers are supplied with service parts
over
Service part delivery within 24 hours for about 98% of all orders.
Global availability of service parts
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Service Manager monitors
the process and analyzes
the performance and
efficency
Monitoring & optimization
Returned parts aresent back for analysis in factory and previous service orders are analyzed for unused parts
Return appraisal
System is up and running
again after Customer
Service Engineer replaces
several parts at once
Fix on site
Service Operation
Supporter orders service
parts based on available
information & experience
Clarification and parts ordering
Evidence-based service parts usage –reduction of service parts tourism & consumption
1) NDF: No defect found | 2) TS: Troubleshooting
Return appraisal and troubleshooting data:
Part A: NDF1) 7% TS2): 19%Part B: NDF 52% TS: 43%Part C: NDF 20% TS: 4%
System is down & Customer calls. System sends error logs; symptoms often unclear
System down
Did I order the right service parts
Did I replace the right service parts
Is my process running efficiently
Data Driven Solution Approach
Collect, aggregate, and evaluate return appraisal and
troubleshooting information and provide it across
service delivery chain
Analyze parts consumption patterns and provide to PLM
Business Impact due to smarter part
exchange – most parts are included in service
contracts!
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Service Operation Supporter
Did I order the right service parts?
Reconsider ordering of service parts with high NDF1) & TS2) rate
Customer Service Engineer
Did I replace the right service parts?
Exchange first service parts with lower NDF rate in case multiple parts have to be exchanged
Evidence-based service parts usageAnalytical results integration in Visual Analytics & SAP
Service Manager
Is my process running efficiently?
Analyze tree map by identifying service parts with high NDF-rate and high consumption
1) NDF: No defect found | 2) TS: Troubleshooting
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Customer Service Engineer receives decision support on mobile front end on-site
Evidence-based service part exchange
Customer Service Engineers (CSEs) receive NDF1) and TS2)
rates for selected service parts
Based on the provided statistical information CSEs decides which parts should be ordered and which parts should really be replaced
1) NDF: No defect found | 2) TS: Troubleshooting
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Dashboards enable Service Managers to optimize service process efficiency
Process performance optimization
Multiple dashboards provide in-depth information and role-specific views on service parts statistics.
Data is updated on a daily basis and enables the service managers to take corrective actions in a timely manner
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Dashboards enable Service Managers to optimize service process through evidence-based data
SERVICE PART X20058%3000€
MATERIAL TEXT:QUANTITY_CONSUMEDNDF_RATE_WORLD[%]SERVICE_PARTS_PRICE
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Dashboards enable Service Managers to optimize service process through country benchmarking
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Predictive Service – Provide decision support to service operations process
5 to 10 TB of Historical Data Service Experience
Pre
cisi
on
Capture Rate
LOG
Files
Service
Data
Component failure coming up?
Predictive Events and Decision Support
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Business Analytics Platform
Predictive ServiceHow do we build a predictive model?
day1 2 3 4 5 6 7 8 9
Data mining and
modeling for
specific system
component
Identify common failure patterns
Prediction model
applied in daily
scoring process
Model containing rules topredict future failures
Scan Start CTCT_IBY_349 ==> ABC_2999Scan Abort CT
HV_Drops > 4 ==> CT_DEF_3065
Scan Start CTXYZ_3028 ==> CT_XYZ_3044Scan Abort CT
Dr. Mirko Appel | HC SV CS SLM DGA AS
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2016-06-29 07:23:16 **_SCU921 LOG:
cran_a 0.0, rao_a 0.0, sid_a
2016-06-29 07:23:19 **_SCU924 LOG:
origin tbl, A 0.00, B 0.00
2016-06-29 07:23:19 **_SCU825 LOG:
Movemon
2016-06-29 07:24:02 **_ACU1158
LOG: fluoro footswitch pressed
2016-06-29 07:24:09 **_ACU1162
LOG, VID async param change
2016-06-29 07:25:45 **_ACU1153
LOG, Xray end of xray
2016-06-29 07:26:06 **_SYC38(Log)
Internal information
2016-06-29 07:27:11 **_KRC0Robotic
Stand Developer Info
2016-06-29 07:27:15 **_ANG255ANG
diagnostic info
Medical Systems
Worldwide base of installed systems provides technical data for service purposes
Secure connectivity through certified remote service infrastructure
System Log Data
System event-logs files, containing many different kinds of events, 20.000 to 100.000 lines per day and system
Predictive ServiceData assets
Service Notifications
Service process-data, telling us when system failures occur and which parts have been replaced
Dr. Mirko Appel | HC SV CS SLM DGA AS
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• We have built a feature-vector for every day, containing 7.000 attributes from the raw data.
• Good separation between healthy and faulty systems on the very day a failure occurs
• Separation becomes tougher the earlier we try to predict failure
Predictive ServiceVisualization of the logfile-feature-space
Day o
f system failu
re3
0 d
ays befo
re system failu
re
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Predictive Service – Methodology 1Classical Data-Mining on the logfile-feature-space
• After visualization, we build models using classical data-mining methods such as logistic regression, decision tree or support vector machines.
• Models show good performance on common KPIs, e. g. lift, ROC/ AUC, etc.!
• BUT: performance requirements in service delivery process are more demanding:
• False alarms are very bad and therefore cause high penalties.
• We need alarms in the right time frame (several days before the failure), not too late and not too early.
• Thus: Applicability is limited, works for selected components and needs manual tuning
Capture Rate: How many failures do we predict?
Precision: How many alarms are correct?t
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Predictive Service Methodology 2 – Prediction models based on system physics and expert knowledge
Example Use Case
• Magnetic resonance imaging systems require a coldhead, which is cooled by liquid helium.
• Coldhead failures usually result in the helium to “boil off”.
• To prevent this, “Magnet Monitor” provides predictions of cold head failures on a daily basis.
• Impact:
downtime reduction no helium loss
less effort for troubleshooting and cold head exchange
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Predictive Service Methodology 2Model Generation & Hypothesis Verification
Model Generation
• Expert input provides thresholds for sensor-signals
• “Blocking conditions“ are defined to filter the original alarms in order to reduce false alarm rate
• Initial validation of generated alarms on historical data (hypothesis verification)
• Secondary validation of generated alarms on live data, while not actually using the alarms in service process (pilot phase)
• Final model captures more than 90% of cold head failures and generates no false alarms
Decreasing helium pressure in the compressor is one alert trigger
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Predictive Service Methodology 2Complete process chain
Generate Reporting
import service datamatch alerts toservice data
compute report tables
send to Visual Analytics
Model Scoring
import sensordata
data cleaningfix missing data
data aggregation;generate ABT
Calculate blocking conditions
send alerts toservice center & reporting
calculateAlerts (Expert Parameters)
evaluateperformance
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Initial predictive service applications for selected components online
Magnetic Resonance Tomography:
• Monitoring and failure prediction of “Cold Heads“ ensuring helium cooling
• Extends average coldhead lifetime in system –from time based to condition based
• Avoidance of helium loss and express service delivery
Angiography Systems:
• Monitoring & failure prediction of selected x-ray tubes
• Patterns identify critical tubes, experts review based on additional, not yet modelled parameters
• Tubes covered by service contract are replaced within one week in average
• Reduction of failure probability during intervention
Siemens Healthineers
Hospital/ Laboratory
!Analytics Infrastructure Scoring of prediction models against
log file data
Siemens
Remote
Service
Infrastructure
Notification toservice center in caseof model hit
Dr. Mirko Appel | HC SV CS SLM DGA AS
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Dr. Mirko AppelHead of Analytical Services
Services, Customer Services
Hartmannstr. 1691054 Erlangen
Germany
Phone: +49 (9131) 84-8257Mobile: +49 (1522) 2786903E-Mail: [email protected]
Contact