Hey everyone. I had to the great opportunity to talk with Doctor Martin Pusic, who’s responsible for the “Healthcare by the numbers: Populations, Systems, and Clinically Integrated Data”. He’s also the Director of the Division of Learning Analytics for the Institute for Innovations in Medical Education at NYU School of Medicine. We had a long talk on how clinicians’ daily practices can be improved with machine-learning.
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 first part, we talked about how data analytics can improve clinicians’ daily practices.
RD: Hello Martin, could you tell us a little more about the “Healthcare by the Numbers” project?
MP: With the “Healthcare by the numbers: Populations, Systems, and Clinically Integrated Data” project we’re leading, we wanted to create a curriculum that would teach physicians to become data literate. We want to teach physicians how to analyze data so that in 20 years’ time they will be able to adapt their practices to what the healthcare system will need at that point. You may know that New York has a large SPARCS dataset containing information on every hospital admission in the state. There are around 30 fields of information ranging from patient zip code, gender, to length of stay and the physician info, etc. There are around 2.4 million admissions a year and therefore 2.4 million rows in this database. It’s a wonderful example of the “Big Data” that is available.
"The ultimate goal is to give doctors the tools and skills necessary to care for not just an individual patient, but for an entire population of patients"
We ask our students to work on this database for two reasons:
It's interesting for data literacy: how do they handle spreadsheets, how do they generate analyses and graphics.
This database shows the granularity of the data currently available. For example, it allows everyone to see every surgery that has been performed in the state and by whom. It lets you benchmark yourself and assess your own performance in relation to other physicians.
The ultimate goal is to give doctors the tools and skills necessary to care for not just an individual patient, but for an entire population of patients by leveraging insights derived from system-wide clinical performance measures & outcomes.
RD: As a doctor, you are trained to be an autonomous decision-maker and highly attuned to your patients on a human level. How do you reconcile this with the ‘blackbox’, impersonally derived insights produced by machine learning?
MP: I think that a core machine-learning principle is that data is ‘king’ and the one who has the best data wins. To me, a physician is an advocate for his or her patients. I am an advocate for the 72-years-old woman in room 7 who is having difficulty with chest pain. But I am also an advocate for the ~1,500 other 72-years-old women who came into the emergency department in the last 4 years with this condition.
It’s an epidemiologic approach that lets us help each and every one of them. I’m indeed deeply convinced we are going to be responsible across patients potentially as much as we are individually. And the best way to do so is to gain insights into how best to deal with that one patient by thinking across patients and that’s where data-mining techniques are going to be helpful for us. Especially as we have more and more digital substrate with ubiquity of electronic healthcare records and databases.
Thank you for reading! We’ll be back soon with Dr. Pusic on how a proper healthcare data analytics tool should look like. Meanwhile, go and have a look at our healthcare industry webpage for more details on what we do.