Cleveland, OH June 7ꟷ Reduce Costs and Downtime with

© AAMI 2019 www.aami.org

Reduce Costs and

Downtime with Artificial

Intelligence-Enabled

Predictive Maintenance

• Binseng Wang, ScD, CCE, fAIMBE, fACCE, BSI

• Puneet Pandit, MBA, President/CEO, Glassbeam

Cleveland, OH • June 7ꟷ10, 2019

Understand how AI/ML-enabled predictive maintenance of medical equipment saves costs and downtime


Abstract

Medical equipment maintenance has been mostly either

reactive or scheduled based on assumed wear-and-tear.

Both are costly and cause significant downtime, negatively

affecting patient safety and throughput, as well as providers’

revenue and efficiency. Most equipment now has

embedded sensors that continuously monitor the

performance of its critical parts; however, it has been very

challenging to leverage the machine data due to the large

amount, unstructured nature, and unclear predictive value.

The recent progress in AI/ML has reduced this challenge,

thus ushering in predictive maintenance already used in

other industries. Successful application cases will be

presented to validate this new approach.


Session Organization

ORDER SPEAKER TOPIC

1 B Wang Welcome

Maintenance Strategies

Review

Considerations on AI/ML

2 P Pandit AI/ML Applied to Imaging

Equipment

3 B Wang &

P Pandit

Questions and Answers

4 B Wang Session Closing


Binseng Wang, ScD

♦ Binseng Wang is a partner at BSI, a health technology consulting company located in Cornelius NC and also Director, Quality & Regulatory Affairs for Greenwood Marketing, LLC located in White Plains NY

♦ Previously, worked as – VP, Quality & Regulatory Affairs for Sundance Enterprises Inc. (a manufacturer of

devices for pressure ulcer prevention and treatment)

– Adjunct Professor with the Biomedical Eng. Program - Dept. Electrical Eng. &

Computer Science, Milwaukee School of Engineering (MSOE)

– VP, Quality & Regulatory Compliance for Aramark Healthcare Technologies (the

largest independent service organization in the US)

– VP, Quality Assurance &Regulatory Affairs for MEDIQ/PRN Life Support Services,

Inc. (the largest medical equipment company in the US)

♦ Earned a Doctor of Science degree from the Massachusetts Institute of Technology (MIT) and is certified as a Clinical Engineer and ISO 9001 Auditor.

♦ Elected fellow by the American College of Clinical Engineering (ACCE) and by the American Institute of Medical & Biological Engineering (AIMBE).

♦ Received the 2010 Association for the Advancement of Medical Instrumentation (AAMI) Clinical/Biomedical Engineering Achievement Award and the ACCE Lifetime Achievement award in 2015.

♦ Inducted into ACCE’s Clinical Engineering Hall of Fame in 2017.


Puneet Pandit, MBA

♦ Puneet is co-founder and CEO at Glassbeam, a machine data

analytics company focused on healthcare vertical to improve

patient care through advanced analytics on machine logs,

DICOM, HL7 and RIS data.

♦ Previously, worked as – Senior Director, Network Appliance, focused on capturing the $500 Million

Oracle-on-NetApp market

– Senior Manager, Ernst & Young, Strategic Advisory Services, focused on

advising global clients on growth strategies

– IT management consultant with Tata Unisys Ltd

♦ Earned MBA from University of Chicago, Booth School of

Business

♦ Received BE Electrical Engineering degree from Punjab

Engineering College (PEC), India


Review of Medical Equipment


♦ Reactive

– Corrective (repairs)

♦ Proactive

– Scheduled

– On-condition

– Predictive


Reactive Maintenance

♦ Advantages – No need for planning, do as needed

– Low cost, low waste

♦ Disadvantages – Downtime => revenue loss

– Pt safety (delayed or denied care)

– Unplanned resource drain

– Customer dissatisfaction

♦ Can never be totally eliminated but can be reduced


Scheduled Maintenance

♦ Advantages – Predictable resource drain

– May reduce failures and downtime

♦ Disadvantages – SPI: only able to detect hidden failures and potential

failures, not prevent them

– True PM: higher costs and downtime unless well timed (PM interval < MTBF)

– Some failures are still unpreventable => poor user perception

♦ Example – OEM recommended periodic checks and replacements

(often excessive due to liability concerns)


On-Condition Maintenance

♦ Advantages – Only performed as needed

• Low resource demand

• Lower downtime

♦ Disadvantages – Monitoring costs (labor & material) => justified

only occasionally

♦ Examples – Oil viscosity monitoring for large internal

combustion engines (earth moving machines and power generators)

– Aircraft turbine vibration monitoring


Predictive Maintenance

♦ Advantages – Only performed as needed

• Low resource demand

• Lower downtime

♦ Disadvantages – Need actionable information from equipment =>

investment in sensors and prediction software

♦ Example – Major industries: oil & gas, electric & water

utilities, steel production

– Puneet will present Glassbeam’s accomplishments with imaging equipment



♦ Natural intelligence: All the systems of control that are not man-made but present in biology. Besides animal or human brains function, it includes non-neural control in plants and protozoa, as well as distributed intelligence in colony species like ants.

Adapted form Bath Univ. Computer Science http://www.cs.bath.ac.uk/~jjb/web/uni.html

IEEE Spectrum Apr 2019

http://www.cs.bath.ac.uk/~jjb/web/uni.html



♦ Artificial intelligence (aka extended intelligence or collective intelligence): "the study and design of intelligent agents, i.e., a system that perceives its environment and takes actions which maximizes its chances of success

♦ Machine learning: “… algorithms that use statistics to find patterns in massive amounts of data”

Adapted from Science Daily https://www.sciencedaily.com/terms/artificial_intelligence.htm

Adapter from Technology Review https://www.technologyreview.com/s/612437/what-is-machine-learning-we-drew-you-another-flowchart/

https://www.sciencedaily.com/terms/artificial_intelligence.htm

https://www.technologyreview.com/s/612437/what-is-machine-learning-we-drew-you-another-flowchart/


















AI: Hype or Reality

♦ Some claim AI is “Industry 4.0” and will accomplish more than any Homo Sapiens, including in arts.

♦ However, so far many promises have not become reality. IEEE Spectrum April 2019: IBM Watson, Heal Thyself

IEEE Spectrum April 2019

Fortune February 2019


Session Organization

ORDER SPEAKER TOPIC

1 B Wang Welcome


Review


2 P Pandit AI/ML Applied to Imaging

Equipment

3 B Wang &

P Pandit

Questions and Answers

4 B Wang Session Closing


AI/ML is a Reality — 3 Factors

15

Business Use Cases

and Dramatic Shift in

Industry Structures

Driving Unmet Need

for AI/ML Applications

Data Cloud Tools

Connected machines

and Industrial Internet

of Things

Scale up or down

based on demand

Open source software

and unprecedented

innovation


The Machine Data Explosion

16

2012 2020

Mainframe/Mini

Era PC/Client

Era

Internet

Era

Virtual

Era

Transactional Data

Human

Files

Social

Interactions

Machine

Generated Data

(Internet of Things

(IoT) Data

Volume

Terabyte

Petabyte

Exabyte

Zettabyte

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


Medical Machine Examples

Anesthesia

Machine

MRI

Machine

Computed

Tomography

(CT) Machine

X Ray

Machine Robotic Surgery

Machine

In Vitro Diagnostic

Machine

Blood Gas

Monitor Infusion Pump

Ultrasound

Equipment Patient Monitoring

Systems

Ventilator

Defibrillator


Challenges in Log Data Mining

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


ML Example: CT Tube Failures

♦ Business Case is Strong

– Expensive costs per Tube ~$100K+

– Expensive downtime costs ~2-3 days

♦ Hard problem to solve without AI/ML

– 50,000+ events per day per system

– 2,500+ warning and error events

– Identification of leading indicators

– Building logic / function


CT Tube Failure Dashboard

0

5

10

15

20

25

30

35

40

45

21-Mar

22-Mar

23-Mar

24-Mar

25-Mar

26-Mar

27-Mar

28-Mar

29-Mar

30-Mar

31-Mar

1-Apr

2-Apr

3-Apr

4-Apr

5-Apr

6-Apr

7-Apr

8-Apr

9-Apr

10-Apr

11-Apr

12-Apr

13-Apr

14-Apr

15-Apr

16-Apr

17-Apr

18-Apr

19-Apr

20-Apr

Tube Failure

Prediction vs Actual Failure

Prediction Actual


ML Example: Anomaly Detection

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. DMS temperature

7. Tube temperature

8. Room humidity

9. Fanspeed

10.Waterflow

11.Airflow

12.Fanspeed-Airflow ratio


Anomaly Detection Dashboard

♦ ML Alert

– Abnormal count and

frequency of Tube spits

♦ Action Taken

– Proactive maintenance by

Engineer before regular PM

♦ Business Impact

– Cost savings by extending

Tube life



♦ ML Alert

– Collimator Move and Home

errors over time

♦ Action Taken

– Proactive notification led to a

motor replacement during a PM

♦ Business Impact

– Avoided unplanned downtime



♦ ML Alert

– Issues with Gantry cooling, image

artifacts and Forward Error

Corrections (FEC)

♦ Action Taken

– Filter cleaning exercise brought

the system to normal

♦ Business Impact

– Avoided image artifacting, repeat

exams and eventual failure of

multiple components


Business Impact

Planned Maintenance with Analytics

Proactive Alerts with Rules

Predictive Notifications with AI/ML

Prescriptive Recommendations with KB

Planned downtime (PMs)

Unplanned downtime (Escalations)

80%

20%

20%

20%

60%

Unplanned downtime

• Corrective Maintenance

• Reactive trouble shooting

Planned downtime

• Preventive Maintenance

Before After

Shift Towards Smart Maintenance™

Move Unplanned Downtime to Planned Maintenance Windows


Measurable Business Impact

MTTR

Mean Time to

Resolution

Data driven trouble

shooting & root cause

analysis

FTFR

First Time Fix

Ratio

Pre-flight check list

assembled before

Engineer goes on site

MTBF

Mean Time

Between

Failures

More proactive and

predictive maintenance

per machine


Revenue & Productivity Impact

$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, average of 98% uptime (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 Year Recover more

than 70% of

downtime

hours per year


Questions & Answers


♦ Please remember to complete and turn

in your session evaluation form.

♦ Please contact us if you have any

questions, comments or suggestions – Binseng Wang

[email protected]

– Puneet Pandit

[email protected]

Thank You!

mailto:[email protected]

mailto:[email protected]

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Cleveland, OH June 7ꟷ Reduce Costs and Downtime with