ADDING DATA ANALYSIS TO PREPARE FOR VBP You began connecting into the DSRIP program, and after signing an MPA, you began to earn DSRIP dollars. How would you describe what you began to earn?

Gail Speedy Mayeaux:  Part of the DSRIP financial model is that you were going to engage in transformation and at the end, you were going to receive grant dollars for that action of engagement. And because we were always going to get paid in arears, that’s when Mike and I started talking about, if we’re going to do this, we are going to adopt those things that are going to get us paid that essentially when that money comes in, it is unencumbered.

And we had many conversations, and I would ask Mike, “what are we going to do with this money?” And Mike would respond, “well, what’s the strategy?”

Michael Malick: We spent a lot of time understanding where we had come in terms of making the organization stable and strong; and what would best serve the mission? That’s what drove a lot of the discussions.

Gail Speedy Mayeaux:  Right from the beginning we both said: “This is unencumbered money we weren’t expecting, how do we invest this money in what we need to get ready for Value Based Payment? This was constant. This is new. This is unencumbered. This is found money, what does this organization need?” And so, what was your strategic reply?

Gail Speedy Mayeaux:  We began by funding OMAs and a Data Analyst.

So the first time we said, we recognize that we have a lot of burden on our providers and we need the nurses to help the providers more, but we can’t just squash that work down and lay it flat. So we need Office Medical Assistants (OMAs) to help with those things an OMA can do so the nurses can help the provider more.

And on the flip side we said, “We need better data.” The thing that drove us was that fear around understanding our data.

Michael Malick: In preparation for Value Based Payment and whatever that would bring, we determined we needed a Data Analyst, an IT person who could do data mining.

Gail Speedy Mayeaux:  One of the best things we did there was we hired a Data Analyst that was not from health care. She came from banking and is somebody who can look at data without any sort of bias. She didn’t have a bias as to whether a number was good or bad. What she cared about was it accurate? What would be examples of that “accurate data”? What types of data sets did she bring to you?

Gail Speedy Mayeaux: All Federally Qualified Health Centers need to report out on uniform data systems every year. We have a software overlay system called the Center for Primary Care Informatics (CPCI) that mines our data for us, and our Data Analyst came to me and said, “I ran the UDS and our colorectal cancer screenings dropped… and this is why…” and she had dug through and had realized that because the test name changed and we were ordering the colorectal cancer screenings diagnostic imagining differently, it was no longer connected in CPCI and it looked like we were no longer as compliant as we should have been.

And what was really key about that was the IT department started saying: “Wow, we need protocols when a lab name changes; when a diagnostic image changes, when an insurance field changes… you don’t go changing an HAR without letting us know so we can re map it.”

On the flip side, she has worked with our billing manager. She has every insurance we work with in the system.  They went through and categorized insurance by payer so that we got better data that showed we were even seeing more Medicaid patients than we thought we were doing. So as those kinds of things that having an analyst on staff really brought to the table.

And it was really interesting because the more we saw that she could do, the more we saw we needed additional help. And in the next year of funding, that’s what we did.



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