This is the second part of my interview with Doctor Martin Pusic. In part one of the talk, he explained how data analytics can improve clinicians’ daily practices. Today, he defines how a proper data analytics tool should be designed.
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.
RD: What are some of the barriers you have seen in your experience with doctors in terms of opening their eyes to the analytic side of things?
MP: To me, data analytics’ main issue is its “black-box” 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 black-box 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?
RD: You have an academic interest in cognition, is there a cognitive aspect to why doctors seem to have difficulty embracing data analysis and its toolset? Are they not visual enough?
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.
RD: What types of tools are comfortable for doctors to exercise their intuition and creativity rather than taking a prescriptive approach?
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.
RD: Timing is key in healthcare and doctors must be accustomed to making quick decisions. It often takes a long time for insight to be distilled from raw data. How do you deal with that?
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 and 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.