Machine Data Analytics & AI/ML Impact on Healthcare ... Education… · Top 5 Use Cases with...

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HTMA Texas Annual Meeting

January 2019

Machine Data Analytics & AI/ML Impact on

Healthcare Technology Management


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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


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The Data Explosion – Multiple Eras Since mid 1970s

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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


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Machine Data Will Transform Major Industries Over Next Decade

$63B in potential

business impact in

Healthcare vertical

from IoT Analytics


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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


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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


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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


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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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Who Owns the Data

Q&A

Agenda


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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


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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


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MRI Health Check Dashboard


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CT Tube Health


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Environmental Sensing


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Usage & Analytics


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Who Owns the Data

Q&A

Agenda


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• Identify a part likely to fail soon

• Preventively replace part

• Reduce downtime

Predictive Maintenance


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• Identify a part likely to fail soon

• Preventively replace part

• Reduce downtime

Predictive Maintenance


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• Identify abnormal sensor readings or environmental factor

• Take corrective action before severe damage

• Eliminate downtime

Real-time Maintenance


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• Forecast usage

• Preemptively add capacity

• Prevent missed revenue opportunities

Capacity Planning


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• Apply NLP techniques on event logs

• Diagnose problems using classification algorithms

• Identify relevant knowledge base articles

Problem Diagnostics


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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


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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


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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


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Who Owns the Data

Q&A

Agenda


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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


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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


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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


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Who Owns the Data

Q&A

Agenda


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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


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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 ”


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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 ”


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What do Providers Say?

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“ 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 ”


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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


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Who Owns the Data

Q&A

Agenda


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Machine Data Analytics & AI/ML Impact on Healthcare ... Education… · Top 5 Use Cases with...

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