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Data Driven Decision Making Presented by Benjamin Larson Informatics Specialist Atlantic Health System Founder: Analytics4all.org Turning Data Into Decisions: Analytics • Analytics: Discovering and communicating meaningful patterns in data • Tools include visualization, dashboarding, data mining (machine learning) • The stated goal is often to develop a predictive model • “Essentially, all models are wrong, but some are useful,” George Box HTM and Analytics • Many HTM professionals live in the world of descriptive analytics ▫ Static reporting, pivot tables • The next logical step for many is the use of visualizations or even dashboards. • The eventual goal should be to move towards predictive or even prescriptive analytics ▫ Machine Learning It All Begins with Data • Where to find data? ▫ ▫ ▫ ▫ Your CMMS (Computerized Medical Maintenance Software) Purchasing department PACs administrator 3rd party parts suppliers • Clean your data. Remove duplicates, fix errors, get data into a workable format. Data Exploration: Visualization Data Visualization allows you to quickly explore your data Simple color bars and graphs let you see areas of interest Used extensively in descriptive analytics to allow end users to quickly absorb vast amounts of information Basic visualization is easy to learn and can be accomplished with software you already have (Excel) More advanced tools (Qlik and Tablaeu) provide more dynamic capabilities while still remaining affordable Data visualization can be used to create dynamic dashboards which give you an instant understanding of your operation. Data Visualization: Excel Visualization(Tablaeu): PM Work Load Balancing Dashboarding (Qlik) Machine Learning (Data Mining) • Predictive Analytics • The purpose of a predictive model is to provide you with the best guess for an outcome • Two main types of Machine Learning: ▫ Supervised: Data set includes a known outcomes (Decision Trees, Neural Nets, Regression) ▫ Unsupervised: No known outcomes, so we look for patterns (Clustering, Association) Supervised Machine Learning • Used to create predictive models: predicting the value of a home ▫ Machine Learning algorithms can easily work with 100’s of variables to return results much more accurately than humans • There are plenty of methods, each with their own pros and cons • The only way to find the best method is through trial and error “Who you gonna call”? – Deciding who to send out on a call K-Nearest Neighbor How the Model Works Using the data given, the algorithm creates a model When a new call comes in, you can feed that information into the model Example: A call comes in for an Innova Cath Lab where the table won’t lock (a Service Type 2). The available tier one tech is Wilie Coyote. If I feed this information into the model, I will get a result like this: [0.25, 0.75] This tells me there is a 75% chance of Tier 2 needing to be called in. At 75%, do I decide it just makes sense to send Tier 2 in the first place. Clustering – Unsupervised Machine Learning • Clustering is a form of machine learning that works by grouping like items • Used widely by dating websites and recommender algorithms • Next I will show how I used it to review ultrasound repair data • For this, I used Python (Free) and Excel (which most people already have access to) Clustering – Ultrasound Repair Data High Work Order / No Problem Found Cluster This clustering high repair numbers on IE33’s was uncharacteristic. How was it done? I extracted 2 years worth of Ultrasound repair work orders from my CMMS I focused on easily quantifiable data – number of work orders, average time spent per repair, parts costs, travel time I ran the data through the short program I wrote to my left. After the program was complete, I examined the data to see what information I could extract from it I have a complete walk through of this on my website https://analytics4all.org Go to Python > Kmeans Cluster Part 1 and 2 Association – Market Basket Analysis • Used by retailers to determine if an item is being purchased based on other items purchased • This can be used by HTM departments to make smarter buying plans ▫ Get all parts in one order ▫ Save on shipping ▫ Equipment downtime is shortened as multiple orders do not have to be placed • In the following example, I will be using XLMiner – an add-on for Microsoft Excel Example: Ventilators – Parts used in last 500 repairs Data Set Sample: Market Basket Notice the strong relationship between power supplies and batteries Wrap-up • Data collection and cleaning are where it all begins ▫ Make friends in other departments – ask for data to be sent to you in CSV or Text form • Data Visualization is your friend – time spent learning some basic dataviz techniques will pay off in the end • Machine Learning – data mining methods can churn up information you might otherwise miss ▫ More complex though – results are easily misinterpreted ▫ Make friends with your BI department Questions?