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© 2017 Glassbeam, Inc. - Confidential & Proprietary
1
HTMA Texas Annual Meeting
January 2019
Machine Data Analytics & AI/ML Impact on
Healthcare Technology Management
© 2017 Glassbeam, Inc. - Confidential & Proprietary
2
Machine Data Analytics – Overview
Machine Data & Key Use Cases
AI/ML with Machine Data
Business Impact on HTM
Who Owns the Data
Q&A
Agenda
© 2017 Glassbeam, Inc. - Confidential & Proprietary
3
The Data Explosion – Multiple Eras Since mid 1970s
3
Machine Data (IoT) is growing at 50x growth rate of traditional business data Over 42% of World’s data by 2025 will be machine generated data
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Machine Data Will Transform Major Industries Over Next Decade
$63B in potential
business impact in
Healthcare vertical
from IoT Analytics
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Medical Machines That Generate Machine Data
Anesthesia Machine
Blood Gas Monitor
Defibrillator
MRI Machine Computed Tomography (CT) Machine
Infusion Pump
In Vitro Diagnostic Machine
Patient Monitoring Systems
Ventilator
X Ray Machine Robotic Surgery Machine
Ultrasound Equipment
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Basic Primer 101: Machine Data Comes in Two Categories
SENSOR DATA
Tip of the iceberg as an opportunity, most visible, structured, deterministic, pre-configured set of known attributes, relatively easy to report and analyze
LOG DATA
Massive amounts of data, hidden from normal view, unstructured, complex & messy formats, ideal for machine learning and predictions, requires specialized tools for analytics
Making sense of complex machine logs and combining with other data sources is a HUGE challenge in any IIoT analytics project
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Challenges in Mining Machine Log Data
Variety - Multi structured formats
Volume - TBs per day with multi year retention
Velocity – Streaming or every 5 min intervals or as errors happen
Veracity – data quality checks and consistency
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Applications Platform to Store Info Parser Engine Multi-structured logs
Best Practices Solution – 4 Steps from Raw Data to Machine Intelligence
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© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Machine Data Analytics – Overview
Machine Data & Key Use Cases
AI/ML with Machine Data
Business Impact on HTM
Who Owns the Data
Q&A
Agenda
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Top 5 Use Cases with Machine Data
1. Machine Utilization - number of Procedures per Machine, Per Facility, By Manufacturer Type
2. MRI Machine Health – Show when key triggers happen and send proactive alerts
3. CT Scanner Health – Show when key triggers happen and send proactive alerts
4. Environmental Sensors – Show when key triggers happen and send proactive alerts
5. Operator Usage & Analytics – Show which operators are doing what etc
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Multi Modality Multi Manufacturer Asset Utilization Dashboard
Track utilization and Uptime
Ensure inventory is being used optimally while
bubbling up critical issues to maximize uptime
Aggregate ALL assets in single portal
View data from multiple data sources,
modalities and frequencies; Drill down to
individual modalities
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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MRI Health Check Dashboard
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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CT Tube Health
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Environmental Sensing
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Usage & Analytics
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Machine Data Analytics – Overview
Machine Data & Key Use Cases
AI/ML with Machine Data
Business Impact on HTM
Who Owns the Data
Q&A
Agenda
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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• Identify a part likely to fail soon
• Preventively replace part
• Reduce downtime
Predictive Maintenance
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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• Identify a part likely to fail soon
• Preventively replace part
• Reduce downtime
Predictive Maintenance
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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• Identify abnormal sensor readings or environmental factor
• Take corrective action before severe damage
• Eliminate downtime
Real-time Maintenance
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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• Forecast usage
• Preemptively add capacity
• Prevent missed revenue opportunities
Capacity Planning
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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• Apply NLP techniques on event logs
• Diagnose problems using classification algorithms
• Identify relevant knowledge base articles
Problem Diagnostics
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Predict CT Tube Failure
Business Case is Strong
• Typical Tube costs anywhere from $80,000 to $150,000 or more
• Most expensive part in hard costs and soft costs in machine downtime
Hard problem to solve without AI/ML
• 50,000+ events logged every day by each system
• 2,500+ different types of warning and error events
• Identify events that are leading indicators of tube failures
• Estimate a function that maps events to potential failures
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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CT Tube Failure – Model with 90% Precision on 40% Recall Rate
ML Algorithm: Gradient Boosted Trees
• Recall: 40%
• Precision: 90%
• Next Steps:
o Refine model with more data
o Operationalize and deploy in
production
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Anomaly Detection – Another ML Use Case
Key Sensor Readings Extracted from Logs
1. Air outlet temperature 2. Air inlet temperature 3. Water outlet temperature 4. Water inlet temperature 5. Room temperature 6. External WCS glycol temperature 7. DMS temperature 8. Tube temperature 9. Room humidity 10. Fanspeed 11. Waterflow 12. Airflow 13. Fanspeed-Airflow ratio
• ML model identifies threshold limits (lower and upper bounds) and alerts when limits are crossed
• ML model also able to correlate multiple attributes and detect abnormal combinations
© 2017 Glassbeam, Inc. - Confidential & Proprietary
25
Machine Data Analytics – Overview
Machine Data & Key Use Cases
AI/ML with Machine Data
Business Impact on HTM
Who Owns the Data
Q&A
Agenda
© 2017 Glassbeam, Inc. - Confidential & Proprietary
26
Revenue & Productivity Improvement Over 3 Years
$3M* Additional Revenues
Over 3 Years
On average, an expensive imaging
machine like MRI or CT Scanner will
face an issue 8-10 times per year and
will be down 6-8 hours each time
equating to about 62 hours average
downtime per machine per year
“
”
500* Additional Procedures
Per Year Recover more than 70% of downtime
hours per year
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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All Now Possible Through Smart Maintenance
100% = 784 Hours Per Year*
80%
20%
20%
20%
60%
Unplanned downtime • Corrective Maintenance • Reactive trouble shooting
Planned downtime • PMs - Preventive Maintenance
Planned Smart Maintenance with Analytics • Proactive Alerts with Rules • Predictive Notifications with AI/ML • Prescriptive Recommendations with KB
Planned Preventive Maintenance (PMs)
Unplanned Reactive Maintenance (Escalations)
Before After
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Measurable Business Impact Through KPIs
MTTR
MTBF
FTFR
Parts Costs
Data driven trouble shooting & root cause analysis
More proactive and predictive maintenance per machine
Pre-flight check list assembled before Engineer goes on site
Advance notice on parts procurement with Smart Maintenance
Mean Time to Resolution
Mean Time Between Failures
First Time Fix Ratio
Parts Replacement Costs
© 2017 Glassbeam, Inc. - Confidential & Proprietary
29
Machine Data Analytics – Overview
Machine Data & Key Use Cases
AI/ML with Machine Data
Business Impact on HTM
Who Owns the Data
Q&A
Agenda
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Who Owns The Data
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Three key constituents with potential
“say” on this topic
OEMs
ISOs
Providers
5-6 large global OEMs
About 500+ ISOs across NA
About 5,000+ Providers Across NA
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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What do OEMs say?
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“ We absolutely own the data because we own the software generating this data ”
“ We rather not own this data since we also want to see other OEM’s machine data ”
“ We will open only a part of this data for public access, and not disclose deep dive
machine log data since that is our secret sauce and core IP ”
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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What do ISOs say?
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“ We need access to this machine data, error codes, and knowledge base to better
service our customers ”
“ I can reverse engineer most of the meaning in these logs – no problem! ”
“ Let’s team up as a group or consortium of ISOs and put pressure on OEMs to open
up these logs and related knowledge and standards ”
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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What do Providers Say?
33
“ Oh, we own this data. We paid for these machines and we have a right to get to
anything that is coming out of these machines ”
“ I can get to this data only with service keys or when the systems are under contract
– let me check with the OEMs ”
“ What would I do with this data if there is no way to decode the error codes and
other encrypted log data – so not useful for me to go down this path ”
© 2017 Glassbeam, Inc. - Confidential & Proprietary
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Glassbeam Viewpoint – A Short Lesson from Data Center Industry
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1980s – 1990s – 2000s
Data Center Market
2010s - 2020s
Healthcare Market
• Large manufacturers dominated the industry for longest time
• IBM in Compute; EMC in Storage
• Customers demanded open standards
• Software innovation happened
• Rest is history
• Large manufacturers have been dominating the industry for longest time
• GE, Siemens, Phillips, Canon, Hitachi etc
• Customers WILL demand open standards
• Software innovation WILL happen
• Rest WILL be history
© 2017 Glassbeam, Inc. - Confidential & Proprietary
35
Machine Data Analytics – Overview
Machine Data & Key Use Cases
AI/ML with Machine Data
Business Impact on HTM
Who Owns the Data
Q&A
Agenda
© 2017 Glassbeam, Inc. - Confidential & Proprietary
36