Using Artificial Intelligence and Machine Learning for Healthcare Machines

Transcription

1 Using Artificial Intelligence and Machine Learning for Healthcare Machines April 10, 2018 Slide 1

2 Webinar Housekeeping Items All audio is set to mute Questions will be answered live after the end of the presentation For other webinar set up items and questions please use the chat feature All attendees will get slides with recording within 24 hours after the event Slide 2

3 Key Objectives for Today s Webinar Challenges Facing Healthcare Industry Desired Solutions & Potential Business Impact Glassbeam Introduction Glassbeam AI and ML for Healthcare Equipment Q&A Slide 3

4 Meet the Presenters Frank Beltré QDC Biomedical, Glassbeam Advisory Board Founder of QDC BioMedical, a leading healthcare industry consultancy Consultant at UCSF Medical helping reorganize clinical engineering operations 25+ years veteran of Healthcare industry with companies like Aramark, Sodexo, Medtronic, Toshiba Medical BBA from Northwood University, MBA from Strayer University, and from Walden University Mohammed Guller Principal Architect, Glassbeam Principal Architect and Data Scientist at Glassbeam Author of leading industry text book Big Data Analytics with Spark 20+ years veteran of Silicon Valley, founder of TrustRecs.com, and various positions at IBM and start-ups MBA from Univ of California, Berkeley, and a Master of Computers from RCC, Gujarat University, India. Slide 4

5 Key Challenges For Clinical and Bio Med Engineering in Healthcare Industry Lost productivity and revenues with machine downtime Lack of data science to track machine utilization Adhoc reporting on service metrics like MTTR, MTBF, FTR etc Slide 5

6 Revenue & Productivity Loss Due to Expensive Downtimes On average, an expensive imaging machine like MRI or CT Scanner will face an issue 6-8 times per year and will be down 5-6 hours on average each time $3.8M* Loss in Revenues Over 3 Years * For a site with 5 MRI and 5 CT Scanners, that has an average of 97% uptime (about 9 days of downtime per machine per year) and can get to a more reasonable metric of 99.5% uptime Slide 6

7 Machine Utilization Reporting an Art Not a Science Today Facility 1 Facility 2 Facility 3 Machine utilization differs widely between facilities and machine workload (procedure types) Archaic ways used today to track and report on utilization using scheduling and manual inputs No historical data used from actual machine usage to understand patterns and predict utilization Slide 7

8 Service Metrics are Incomplete Without Machine Data Inputs Mean Time Between Failures (MTBF) First Time to Repair (FTR) Key service metrics are driven primarily with outdated incomplete entries in CMMS systems Machine data is the real truth that could verify when a service event was detected and when it was resolved Trending and predicting these metrics over time by combining CMMS and machine data is the holy grail Slide 8

9 Tremendous Business Impact Can be Enabled With Right Analytics Foundation 96% to 99.5% Availability Reduce downtime from 12 days to less than 2 days per machine per year Sample Facility with 5 MRI and 5 CT Scanners $5.3M Revenues Recovered Over 3 Years Cost Savings Optimized support contracts and reduced expenditures on part replacements thru ML $1.3M Costs Savings Over 3 Years Cost Savings with optimized service contracts and parts replacement Revenues recovered by going from 96% to 99.5% uptime Slide 9

10 Key Objectives for Today s Webinar Challenges Facing Healthcare Industry Desired Solutions & Potential Business Impact Glassbeam Introduction Glassbeam Case Studies on AI and ML for Healthcare Equipment Q&A Slide 10

11 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 Slide 11

12 Machine Data Complexity Sweet Spot for Glassbeam Solution Multi-structured data, multiple formats, multiple mechanisms for transport call home, streams, batch file uploads (support bundles) EVENT LOGS CONFIGURATION LOGS PROCEDURE LOGS Slide 12

13 Glassbeam Allows Fast Conversion of Raw Machine Data into Powerful Insights Multi-structured logs Glassbeam Studio Glassbeam SCALAR* Glassbeam Apps 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 Slide 13 13

14 For AI/ML project Focus on 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 Slide 14

15 For CT Scanner Focus on CT Tube Failure as a Prediction 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 We identified 16 event types that have high correlation to tube failures Thousands of permutations possible when building decision tree logic on such problem statements perfect solution to have machines learn the pattern and build predictive models Slide 15

16 CT Tube Failure Final Model Built 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 Slide 16

17 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 Slide 17

18 Future Roadmap & Patent Filings Future Roadmap for ML Projects Predict utilization per machine or per facility based on past usage patterns Similar to CT Tube part failure, predict failures of other critical parts in a CT scanner Expand similar projects for other modalities such as MRI, Cathlabs, Ultrasound machines Patents Provisional patent filed for Machine Learning - PREDICTING MEDICAL IMAGING DEVICE FAILURE BASED ON OPERATIONAL LOG DATA This is in addition to 3 other patent filings (one fully granted, others are provisional) Slide 18

19 Q & A Type your questions in the right hand panel - we will answer in the order received. If we don t get to your question we will reach out personally after the webinar All attendees will get the slides with recording within 24 hours after the event Slide 19

20 Thank You and Call to Action Our inside sales team will follow up within the next two days answer any additional questions, schedule a demo in a deeper discovery session that is specific to your needs and strategy. Glassbeam, Inc sales@glassbeam.com New Harbor Research White Paper Released Today: Machine Data Analytics Drives Innovation in Healthcare Market Slide 20