To help move to a more standardized system with more consistent and predictable outcomes, we have identified three areas of care delivery that need a systematic approach—an analytic system, a deployment system, and a content system.
Thank you Steve Based on feedback from people like yourselves, we have designed a short analogy that compares Data Warehousing to Shopping. We use this analogy to highlight, in a non-technical way, the differences between the three models Steve just described.
Let’s start by looking at the Enterprise Shopping Model displayed on the screen. Notice that it is well organized and highly structured. Next I am going to put up on the screen a list of items you need to get at the store. I want you to map those items to the Enterprise Shopping Model. While doing that imagine you get a call from your significant other asking you to also get the following items. Okay, now that you have attempted the exercise let’s take a quick poll. Describe your experience with that exercise Worked great Frustrating Stopped trying I can see that many of you were frustrated even though the model was designed to have everything you needed and to make creating a shopping list easier. It lacked flexibility and it could not be easily adjusted to address the new requirement of making non-food purchases.
This shopping approach is much like the Enterprise Data Model which works very well in industries like retail and banking where what you need to capture is much more standardized and stable over time. Why this model breaks down in healthcare is because Medical knowledge is always expanding and changing so it is impossible to anticipate what the new data will look like and how it could fit into a model. Additionally, concepts like Length of Stay or Readmission Rates may have different definitions
Now let’s look at the Dimensional Shopping Model. In this model, instead of a shopping list, we have specific recipes that we need to create which are Chocolate Chip Cookies, a Cake and Apple Pie. Prior to the webinar you were sent a link to a short video that depicted this approach. Let’s take a quick poll, Did you watch the video prior to the start of the webinar?
For those of you that were not able to watch the video, I will high spot what was depicted. So we start with our shopper getting a call from the school board to bring cookies to the board meeting. She goes to the grocery store to get exactly what she needs to make the cookies, 2 cups of shortening, 4 cups of flour, 4 eggs and so on. Now imagine our shopper returning home to bake the cookies and getting a second call requesting she bring a cake too. So back to the store she goes to purchase many of the same ingredients. She returns home to get yet another call and the video ends as the exasperated shopper heads to the store for the third time.
So this Dimensional Shopping Trip started out fine for our shopper, getting just exactly what she needed but as soon as she added another recipe and another recipe she was making redundant trips to the store.
This shopping approach is like the Dimensional Data Model which starts out really great with a couple impressive point solutions for a few departments. But as the demand for analytics grows the model starts to become a mass of redundant data feeds from the source systems to multiple applications. So for organization’s that are looking for an enterprise data strategy verses a few point solutions, they would quickly become encumbered by the Dimensional Data Model. Spending all their time designing trips to the store.
Now let’s explore our final model, the Adaptive Shopping model. In this exercise you are given a shopping card with a simple structure that allows you to indicate the store you are shopping and the items you need. Now using the same shopping list we saw earlier, imagine how you would fill out your Adaptive Shopping Model. Likely you selected a store you were familiar with and then you started organizing the items in a way the reflected the layout of the store. Now when we add new items to the shopping list, if they don’t fit within your first shopping list you can just take another card and so one.
Now let’s think about bringing all those items back to your house. Could you still make the recipes? You can and you can do it without all the redundant trips to the store.
This shopping approach is like Catalyst’s Adaptive Data model. The cards represented source systems that are brought into the Data Warehouse and these are going to match the transactional system exactly so it doesn’t take a lot of effort to bring that data into the warehouse. There is no manipulating or forcing the data into a model. Additionally, you have all the data you need for the analytic applications you know you know and for the requests that will come in the future. You can build all of these without having to go back out to the source for more data. This approach works really well in healthcare because the data requirements are typically not well understood at the outset. With the Adaptive approach you can build incrementally adding source systems as you need them.
Time for another poll question. Which shopping approach would you prefer? Enterprise Dimensional Late-Binding
Now no analogy works perfectly, but hopefully it will help you remember the characteristics and differences between each model.
A final poll question, Did this analogy help you better understand the differences between the three models? Yes No I already understood the differences
What is the best Healthcare Data Warehouse Model for Your Organization?
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Comparing Healthcare Data Warehouse Approaches:
A Deep-dive Evaluation of the Three Major Methodologies
Healthcare Analytics Goal
Why have an EDW?
●It is a means to a greater end
●It exists to improve:
1. The effectiveness of care delivery (and safety)
2. The efficiency of care delivery (e.g. workflow)
3. Reduce Mean Time To Improvement (MTTI)
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Three Systems of Care Delivery