machine data analytics & ai/ml impact on healthcare ... · 8/16/2018 · top 5 use cases with...

© 2017 Glassbeam, Inc. - Confidential & Proprietary 1

CEAI Conference

Oakbrook Terrace, IL

August 16, 2018

Machine Data Analytics & AI/ML Impact on

Healthcare Technology Management


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


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


Machine Data Will Transform Major Industries Over Next Decade

$63B in potential

business impact in

Healthcare vertical

from IoT Analytics


Medical Machines That Generate Machine Data

Anesthesia Machine

Blood Gas Monitor

Defibrillator

MRI MachineComputed Tomography(CT) Machine

Infusion Pump

In Vitro Diagnostic Machine

Patient Monitoring Systems

Ventilator

X Ray MachineRobotic Surgery Machine

Ultrasound Equipment


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


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


Transforming Machine Data is a Huge ChallengeOver 70% Time and Cost Spent Before Any Meaningful Analytics

TEXT LOGS

XML

JSON

CSV

Sensor data

Average 6 Months Duration with Significant Waste in Iterative Processes

Data indexing & Cubing

Target Schema Design

Extract, transform, load (ETL)

Complex Parsing scripts

Source Data

Modeling

PROGRAMMERS DBA BUSINESS / DATA ANALYSTS PROJECT MANAGER

PREDICTIVE MAINTENANCE

PRODUCT INTELLIGENCE

NEW REVENUE MODELS


Glassbeam SPL™ & SCALAR™Glassbeam Studio ™

1 DATA ENGINEER

Glassbeam Solves This Challenge With 25x Faster Time-To-Value

Elapsed Time of Less Than 1 Week Providing 25x faster time-to-value

Data indexing

&

Cubing

Targ

et Sche

ma

Design

Extract, transform,

load (ETL)

Complex Parsing

scripts

Source Data

Modeling

PROGRAMMERS

D

BA

BUSINESS / DATA ANALYSTS PROJECT MANAGER



REVENUE GENERATION



NEW REVENUE MODELS

TEXT LOGS

XML

JSON

CSV

Sensor data

Semiotic Parsing Language (SPL) and SCALAR platform are patented inventions of GlassbeamGlassbeam Studio is visual parsing and ETL tool to automate SPL creation


Glassbeam AppsGlassbeam SCALAR*Glassbeam StudioMulti-structured logs

Four Step Process from Raw Data to Machine Intelligence

10

Ingests raw logs from install base

Log files are converted using SPL*

Data is organized and meaning is extracted

Out-of-the-box and custom apps using dashboards

* SPL (Semiotic Parsing Language) and SCALAR are patented technology inventions of Glassbeam


About Glassbeam

We help product manufacturers and operators

make sense of complex machine data leveraging

our proven cloud based platform and applications

Key customers Recognized leader in IoT Analytics






Who Owns the Data

Q&A

Agenda


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


Machine Utilization & Types of Procedure Analysis

Exam Begin Timestamp

System Identifier

Protocol Identifier

Exam Identifier

Exam End Timestamp

Lookup

1

2

3

4

5


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


Magnet Supervision – Derive Health Check for MRI Scanners

GE Magnet Supervision

Siemens Magnet Supervision

Philips Magnet Supervision


MRI Health Check Dashboard


CT Scanner Health & Related Analytics

Aborts

Tube Spits

Errors

Temperatures

mAs = mA * Scan Time * No. Of Scans

1

2

3

4

5


CT Tube Health


Environmental Sensing


Usage & Analytics

Exam Timestamp

DICOM Lookup

Exam Identifier

Mode

Probe1

2

3

4

5


Usage & Analytics






Who Owns the Data

Q&A

Agenda


• Identify a part likely to fail soon

• Preventively replace part

• Reduce downtime

Predictive Maintenance


• Identify abnormal sensor readings or environmental factor

• Take corrective action before severe damage

• Eliminate downtime

Real-time Maintenance


• Forecast usage

• Preemptively add capacity

• Prevent missed revenue opportunities

Capacity Planning


• Apply NLP techniques on event logs

• Diagnose problems using classification algorithms

• Identify relevant knowledge base articles

Problem Diagnostics


ML Use Case – CT Scanner Modality

Complex sophisticated machines

generating log data on various attributes

• X-ray tube, Scan counts, Tube temperature,

Water/Air temperature, Gantry temperature, Dose,

Arcs, Aborts, etc.

Rich ”Big Data” repository in the Cloud for CT

Scanners

• 18 Billion events and growing

• 100 Million events per day

• 50,000 events per system per day


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


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


Anomaly Detection – Another ML Use Case

Key Sensor Readings Extracted from Logs

1. Air outlet temperature2. Air inlet temperature3. Water outlet temperature4. Water inlet temperature 5. Room temperature6. External WCS glycol temperature7. DMS temperature8. Tube temperature9. Room humidity10. Fanspeed11. Waterflow12. Airflow13. 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






Who Owns the Data

Q&A

Agenda


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

“

”* Key Assumptions:

- For a site with 5 MRI and 5 CT Scanners, that has an average of 98% uptime (about 6 days of downtime per machine per year)

- Target machine uptime to get to a more reasonable metric of 99.5% uptime

- Operating parameters for each facility is assumed at 10 hours per day and 6 days per week

- 1 procedure per hour @ $2K per procedure

500*Additional Procedures

Per YearBusiness

Impact with Machine Data

Analytics


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)

* Key Assumptions:

- For a site with 5 MRI and 5 CT Scanners, that has an average of 98% uptime (about 9 days of downtime per machine per year)

- Target machine uptime to get to a more reasonable metric of 99.5% uptime

- Operating parameters for each facility is assumed at 10 hours per day and 6 days per week

- 4 hours per PM with 4 PMs per machine per year

Before After


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


Tremendous Business Impact Can be Enabled With Right Analytics Foundation

Reduce downtime from

12 days to less than 2

days per machine per

year

97% to 99.5%

Availability $5.3MRevenues Recovered

Over 3 Years

Optimized support

contracts and reduced

expenditures on part

replacements thru ML

Cost Savings$1.3MCosts Savings

Over 3 Years

Sample Facility with 5 MRI and 5 CT Scanners

Cost Savings with optimized parts

procurement costs and more

efficient service contracts

Revenues recovered by going

from 97% to 99.5% uptime

Summary – Business Impact






Who Owns the Data

Q&A

Agenda


Who Owns The Data

40

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


What do OEMs say?

41

“ 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 ”


What do ISOs say?

42

“ 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 ”


What do Providers Say?

43

“ 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 ”


Glassbeam Viewpoint – A Short Lesson from Data Center Industry

44

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






Who Owns the Data

Q&A

Agenda

machine data analytics & ai/ml impact on healthcare ... · 8/16/2018 · top 5 use cases with...

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