[MUSIC] Welcome back, in this example where we formulate a data question with an online tool, we're going to use the HCUPNet web interface. HCUPNet is a tool developed by the Agency for Healthcare Research and Quality, focused on the project of healthcare cost and utilization. It has one central website where it blends data from State and Federal data providers. And through that web based query tool you can export to Excel or CSV and use smaller data sets both to blend into a wider data set, or drill down into small results from a broader data set. So it's really kind of a synthesis of the prior two examples, I hope you enjoy. Welcome back, we're now going to demonstrate HCUPNet healthcare cost and utilization projects website that allows you to formulate data queries against a broad range of EHR impatient, EUD or emergency department, or other population health care cost data. There are two types of general analysis you can do. You can create your own more detailed analysis or get quick statistics tables. In this demo, we're going to create a analysis. And we're going to talk a little bit about the kinds of data you can get in this in conjunction with the steps that we've discussed previously. There are four main categories, and this really can be aligned with our notion of picking our source data. These are the sources you can get. You can get inpatient, emergency department, ambulatory surgery or community, community-based data that's organized along lines of regions in the United States. For this demo we're going to pick community. And we'll pick the option, we have either a single year or we go an aggregate year range. We'll pick a single year and we'll pick Maryland for demonstrating. Then let’s view the state instead of by counties which is many and might not have a lot of general meaning will do bigger aggregations into regions. And now we can pick what type of analysis. We can use either diagnostic procedure, there is quality indicators or just all stays which is a more of a broad range result. Let's pick a diagnostic procedure and pick of those, we have some different options how granular we want to get. I'm going to pick the major diagnostic categories because they are a fairly broad range, but they'll give enough detail to be meaningful, but not so much detail to be kind of overwhelming. And I'm going to pick, let's say diseases and disorders of the circulatory system. And let's create that analysis and see what we get. So here, we have a table, a list of columns of pre-aggregated data. This did not give us all the source data, every row, every hospital admission, but rather pre-aggregated it into summary data for us. We chose various filters on that aggregation, and it gives us what we chose up here, a community inpatient, state in 2015, major diagnostic categories, diseases and disorders of the circulatory system and by region, okay? So here's state total, this is a rate of discharges per 100,000, that's an aggregate calculated field. That is a derived element from the counting of data. And here's an age, sex, adjusted rate of discharges. Note that we can go back and modify some of our filter by these choices on the sides here. For example, we can change that instead of the counts, we can change that to the sum, or in addition, we can add the sum and the number of days. We rehit this Submit Request. Now we've added not just the rate of discharge, but the mean, the average length of stay. Again, an aggregate calculated average stay, and length of stay data here. We can modify the years, we can change that detail. We can also add or compare other states, and this provides kind of a helpful trend data analysis of some very specific diagnostic trends within a state. Note, also we have the ability to, change the output of this data. For example, we can open up an Excel or a comma separated value file of this data, which could be useful to joining it or comparing it to other data sets.