Dr Pusic co-leads the "Healthcare by the numbers: Populations, Systems, and Clinically Integrated Data" three-year long program of education for students that is based on the real clinical data of practices.
We felt like he was the right guy to talk with about Healthcare and Data, which limits they are faced with, and how they can partner to increase patients’ final care. In this second part, we talked about what any healthcare data-savvy person expects from a proper Data Analytics tool.
MP: To me, Data Analytics’ main issue is its “blackbox” algorithms.
I hate it when I try to explain a neural network, and how we train it and nobody really understands what is happening inside that box. However good the output is, the process is totally opposite to the way physicians think of themselves in terms of approaching a problem. Indeed, where we add value as physicians is understanding the mechanism of diseases. Our job is going deeper and deeper and understanding health in greater depth. So the more blackbox the algorithm is, the more difficult the culture mismatch is.
The main barrier is that we present things as closed loops, waiting for the machine to spit out an answer. Over and over again, in medical informatics, those sort of decision-support things that don't involve physicians in the loop are not going to be trusted. We are meant to be critical of the information that comes in and carefully decide when to integrate that information. That’s why we need ‘white box’ data analytic tools that could easily onboard many clinicians.
What's inside the box ?
MP: Sure, a high level of creativity can be communicated through graphical visualization. I made it clear that Data Analytics in healthcare is all about generating trust for providers. So, getting a visualization that is trustworthy and that lets us get an understanding of how variable x relates to outcome y is our dream.
“We need tools that are visual
but also don’t restrict the connections that can be made.”
That’s why, in our “Healthcare by the numbers” project, we encourage our students to use the SPARCS database to understand how a patient’s background impacts their health outcomes. But we also have them focus on how they communicate that in order to advocate on behalf of their patients. They have to explain why this thing is related to that. We are not prescriptive of how we do that. Of course, being able to show how the algorithms work is a huge plus.
MP: Using SAS or writing a program is difficult. You need a degree to know how to do this and we, clinicians, spend our time learning about our healthcare subjects. So most of us use spreadsheets, even if our skills are inconsistent. Some providers won’t touch one (spreadsheet), others are very good with them. We need tools that are visual but also don’t restrict the connections that can be made. We are trying to maximize freedom of insight in the face of health IT systems that are often not interoperable. Today, people use mostly static spreadsheets. But we can see the day where data models will enable people to make connections between various Electronic Health Records. When this day arrives, we’ll need tools that are collaborative enough to span different health IT systems.
MP: Let’s consider an example. In research, we have a long tradition of creating clinical decision rules. We use many techniques that, in some ways, are used in data-mining to determine which clusters of patients behave which way. Let’s be frank, it takes us years to develop a clinical decision rule. But once the data rule is built, things go much faster. Today, it unfortunately still takes us a long time to make sense out of data.
“We can see the day where data models will enable people
to make connections between various Electronic Health Records.”
It’s all about collaboration (and looking cool)
Researchers, IT guys are going to spend a great deal of time to gather, clean and aggregate datasets. So we clinicians are not going to use SPARCS datasets to make decisions at the point of care, rather we’ll make use of a compilation of tools that together help augment clinical decisions. There is not one tool for one problem. Whatever tools are available to you; they need to work in concert with each other to provide context at the population level as well as inform our view of the individual patient’s needs.
Thank you for reading! We’ll be back soon with Martin to see how the future of healthcare with Data Analytics looks like. Meanwhile, go and have a look at our healthcare industry webpage for more details on what we do.
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