Budget Forecasting & User Security Apps

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Webinar Recap: Q2 Healthcare Industry User Group Meeting

Hi everyone,

Thanks for taking the time to join  @Charity_K_Wilson and @patrick_mcauliffe in presenting our Alteryx Webinar: Q2 Healthcare Industry User Group Meeting

We saw some very intriguing work to see how Alteryx can be used to give insight and predictions on forecasting insurance payments. Predicting organizational cash flow and payer reimbursement for provider practices has improved budget forecasting. We also saw how organizations can utilize the Alteryx Gallery to publish apps that help disseminate information throughout an organization with some considerations for user security and protected health information. 

We are happy to share the slide deck and webinar from today’s presentation. Please feel fill out the following survey with any feedback or questions you may have:

Survey Link:  https://www.surveymonkey.com/r/9L3WRWD

Thanks again to @NPT @MaddieJ @LaurenU for putting together another awesome presentation.

Some Announcements:

Wake up and Meet up: Healthcare Networking at Inspire  Wednesday, June 6 from 7:00AM-7:45AM

Thanks,

Alteryx User Group Leaders

[NPT]

I cannot thank @Charity_K_Wilson and @patrick_mcauliffe enough for being willing to present their work.  It's great to see the power of Alteryx in real-life use cases.  You two were amazing, thanks!

~ Nathan

[Charity_K_Wilson]

When we were finishing up the Webinar, a really neat question came in about the Linear Regression output, and how to interpret it.  I was having technical difficulties, and couldn't respond at the time.  So, now let me answer that question.

The first section of the question asked where I got the accuracy of the equation from.  At the bottom of the picture below, the adjusted R squared is circled in a red circle.  This metric tells me how accurate the equation is.  In this case, I would say that this equation can account for 88% of the variation that is seen in the Insurance Reimbursement rate.

The next question asked about how I understood what things were driving this equation.  This is a more complicated question with no easy answer.  I've sat thru way to many graduate level courses on statistics.  The two rectangles that I've highlighted are the data points that I look at.  The green one is called by my prof's the significance.  The smaller the number, and the more stars it gets, the better the significance.  Another word for significance is also consistency.  The more stars it has, the more consistent and reliable it is.  The second half of this is in the Orange box.  The estimate of the coefficient tells me how much it will move the needle on Insurance Reimbursement.  I could read this to say Diagnosis code 4 will decrease the Insurance Reimbursement rate by $110, and it is has a high significance, so I can be confident it will do that every single time.

Feel free to reach out if you need help. 

[Rawan]

Hello, I am not sure if it is only me, but I am struggling to hear the audio appropriately in the video provided. it breaks every few seconds.. is there any other way to watch it? I am very interested.

Thanks

Rawan

[MaddieJ]

Hey @Rawan, so sorry to hear that you're having trouble viewing. The audio is working fine on my end, but I'll send you the MP4 directly to see if that helps. :)

[Rawan]

Hello all,

Thanks so much for such a great subject, I am very interested in the workflows and slides used in this one, can you please direct me on how can I access them..

Thanks

Rawan