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This document discusses how clinical engineering departments can utilize everyday tools like Excel, XLMiner and Tableau to analyze untapped data from their computerized maintenance management systems (CMMS). Specific techniques demonstrated include pivot tables, market basket analysis, clustering, and dual-axis and other visualizations. The analysis can provide insights into equipment reliability issues, common part replacements, and user training needs to optimize clinical engineering operations and resources.
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- 1. UTILIZING EVERYDAY TOOLSTO OPTIMIZE DATA Benjamin Larson Informatics Specialist Atlantic Health System
- 2. #PSLS2015Innovations & InsightsLeadership Summit
- 3. #PSLS2015Innovations & InsightsLeadership Summit MY FAVORITE DEFINITION
- 4. #PSLS2015Innovations & InsightsLeadership Summit WHAT IS BIG DATA? • The collective concepts surrounding the challenges and benefits of working with very large data sets. • Challenges for Big Data: • Collection • Storage • Data Curation (active management of data) • Unstructured Data • Benefits of Big Data: • Inferential Statistics (removes uncertainty) • Enhanced Predictive Modeling • Fast, Agile Decision Support • Analytics: Discovering and communicating meaningful patterns found in data
- 5. #PSLS2015Innovations & InsightsLeadership Summit • Barriers • Automated Data Collection (building automation, clinical alarm analysis) • Expensive BI tools • Time Consuming • Requires employees with highly specialized education • Advanced Mathematics • Statistical Programming • Needed to interpret results “Given enough data, everything is statistically significant”– Douglas Merrill CLINICAL ENGINEERING AND BIG DATA
- 6. #PSLS2015Innovations & InsightsLeadership Summit • Most Clinical Engineering Departments are sitting on a wealth of untapped data • Actionable data can be found in your Computerized Maintenance Management System (CMMS) • Data can be used as part of a Performance Improvement Initiative (LEAN, Six Sigma) • Realistic Analytics • Easy to act on • Affordable (Excel, XLMiner, Tableau) WHY EVEN BOTHER???
- 7. #PSLS2015Innovations & InsightsLeadership Summit • Pose a Question • Gather Data • Prepare and Clean Data • Choose Your Tool / Technique • Summarize and Visualize Data • Analyze and Interpret Results STEPS TO ANALYZING DATA
- 8. #PSLS2015Innovations & InsightsLeadership Summit PIVOT TABLES FOR DATA DISCOVERY
- 9. #PSLS2015Innovations & InsightsLeadership Summit DEVICE RELIABILITY • MMC uses Brand A • CMC, NMC, OMC use Brand B • Popular belief was that Brand A was the most unreliable • Data proved the opposite was true
- 10. #PSLS2015Innovations & InsightsLeadership Summit DIG DOWN TO DEVICE LEVEL 20 1412 = 1.4% 1.4% of Total Pump Inventory Comprised 12.7% Of All Repairs.
- 11. #PSLS2015Innovations & InsightsLeadership Summit MORE DEVICE LEVEL DISCOVERY 61 37 + 27 125 So at ½ hour a repair you have 62.5 hours. That is over 8 days spent on these 3 machines alone.
- 12. #PSLS2015Innovations & InsightsLeadership Summit • Excel Add On • Statistical Data Mining • Limited, But Easy to Learn • I will demonstrate two of the more popular techniques: Association (Market Basket Analysis) and Clustering XLMINER
- 13. #PSLS2015Innovations & InsightsLeadership Summit • Used by Retailers to Determine if an Item Being Purchased Based on Other Items Purchased • Ventilators - Parts used in the last 500 repairs • Data Set Sample: MARKET BASKET ANALYSIS
- 14. #PSLS2015Innovations & InsightsLeadership Summit MARKET BASKET(XLMINER) Lift Ratio Determine How Much More Likely C Will Be Used if A is Used Confidence Shows Strength of the Rule
- 15. #PSLS2015Innovations & InsightsLeadership Summit MARKET BASKET
- 16. #PSLS2015Innovations & InsightsLeadership Summit • This Information Can Be Used to Put Together a Smarter Buying Plan • All parts one order • Save money on shipping • Improved discounts for volume shopping • Equipment downtime is shortened as multiple orders do not have to be placed MARKET BASKET
- 17. #PSLS2015Innovations & InsightsLeadership Summit • Market Segmentation • Microsoft used it when trying to break into server world • Hardcore UNIX • LINUX Dabblers • Market Followers • Financially Driven • LINUX Enthusiasts So how can we use clustering? CLUSTERING
- 18. #PSLS2015Innovations & InsightsLeadership Summit • Focused on Diagnostic Ultrasound • Queried 3 Years of Repair Work Order Data • Focused on following variables (WO Count, Labor Minutes, Labor Cost, No Problem Found Count, Parts Cost, Travel Cost) • Performed a K-Means Cluster Analysis CLUSTERING EXAMPLE (XL MINER)
- 19. #PSLS2015Innovations & InsightsLeadership Summit K-MEANS CLUSTERING We randomly place 3 Centroids Then using vectors, we group the data points based on their distance relationships to the centroids These groupings become our clusters.
- 20. #PSLS2015Innovations & InsightsLeadership Summit CLUSTER(SAMPLE OF SET) Full set contains 279 Machines
- 21. #PSLS2015Innovations & InsightsLeadership Summit CLUSTER 4 (HIGH WO AND NPF) A Review of Work Order History Pointed to User Error End User Refresher Training Would Reduce These Repair Calls.
- 22. #PSLS2015Innovations & InsightsLeadership Summit DUAL AXIS CHARTS •Allow visual comparison of large data sets. •Easy to create using Tableau •Interactive – allow for quick drill down
- 23. #PSLS2015Innovations & InsightsLeadership Summit DUAL AXIS CHARTS (TABLEAU) Aneroid Sphygmomanometer 885 Inspections vs 47 Repairs Scheduled Inspections Deemed Unnecessary
- 24. #PSLS2015Innovations & InsightsLeadership Summit • Communication: • Share your findings • Get your information into the hands of decision makers • Keep people informed SUMMARIZE AND VISUALIZE DATA
- 25. #PSLS2015Innovations & InsightsLeadership Summit SIMPLE VISUALIZATION (CRYSTAL REPORTS) Used to Keep Staff Informed Can Be Emailed Out Daily Snapshot of How We Stand
- 26. #PSLS2015Innovations & InsightsLeadership Summit VISUALIZATION (EXCEL) Work Order Load Beyond Useful Life
- 27. #PSLS2015Innovations & InsightsLeadership Summit VISUALIZATION (TABLEAU) Coverage Area (2010) Coverage Area (2015)
- 28. #PSLS2015Innovations & InsightsLeadership Summit VISUALIZATION (EXCEL) Equipment Acquired Across System Off-Site Equipment Inventory Growth by Year AHOP, 2282, 55% CMC, -33, - 1% MMC, 963, 23% NMC, 499, 12% OMC, 361, 9% NET EQUIPMENT ADDED IN PAST 12 MONTHS
- 29. #PSLS2015Innovations & InsightsLeadership Summit
Editor's Notes
- #3 Hadoop – SAS – mongoDB – tableau – data mining - BIG DATA These words are very popular right now. It’s everywhere you look. Television ads, web ads, I even saw it on the sides of taxi cabs in London. Everyone is talking about, but the real question is “What is it?”
- #5 Big Data is a phrase coined to describe a multi-disciplined approach to the analysis of large data sets. Coined by Francis X. Diebold, an economist at the University of Pennsylvania, Big Data has many businesses searching for talent and many universities scrambling to find a way to produce this talent. Big Data comes with its own set of challenges. How do we collect all of this data? Where do we store it? Who is going to care for and maintain this data? How do we discern meaning from the flood of unstructured data. It does have many benefits of course, otherwise no one would bother with it. The first is that it removes the uncertainty that is inherent in inferential statistics. If you think back to your college STATs course, inferential statistics dealt with the problems that arise when you try to find meaning in data samples. If you are running for mayor in a city with 100,000 registered voters and you poll 5000 random people, you can use inferential statistics to build a confidence interval that will give you a good idea of how the vote is going to turn out (within a 5 to 10 % percent margin of error at least). Well, imagine you could poll 99,000 of those 100,000 voters. With that kind of data, you margin of error disappears. Inferential statistics are no longer needed. That is Big Data. Walmart doesn’t have to guess what the hottest selling items are based on a sampling of stores, they have the actual data. And they are analyzing it as close to real time as is possible. Having such a wealth of data, and such a level of confidence in it, you can developed enhanced prediction models and use those models for fast, agile, decision making.
- #6 Big Data for Clinical Engineering is still in its developing stage. Most big data applications I see are more on the clinical (alarm management) and plant operations (building automation). The biggest issue is that most CED departments simply do not produce the amounts of data that would generally qualify them for Big Data. But that is okay because Big Data analytics are time consuming. They require hefty investments in expensive BI tools and require employees with highly specialized and advanced educations. For right now, I believe most CED should focus on descriptive analytics
- #7 Most CED departments are sitting on a wealth of untapped data they aren’t using. A typical CMMS system contains plenty of actionable data simply waiting to be discovered. If you don’t analyze the data, you cannot act on it. Once data is discovered it can used as part of a performance improvement initiative. Especially data driven ones like LEAN and Six Sigma. Data not analyzed cannot be acted on Without data decisions made with emotions and personal prejudices.
- #8 Okay, so we know we want to use analytics, but the big question is “How do we do it?” Well, no matter how simple or complex your project is, data analysis can always be broken down into the following steps: Pose a Question Gather Data Prepare and Clean Data Choose Your Tool / Technique Summarize and Visualize Data Analyze and Interpret Results