Healthcare Q&A with Bill Frist & Sam Weinstein
This healthcare Q&A revolves around a conversation between SpecialtyCare CEO Sam Weinstein and former Tennessee Senator Bill Frist. The healthcare Q&A follows an in-depth interview of Bill Frist and discussions around healthcare and hospital management, the government’s role in healthcare and medicine, as well as the importance around data collection.
During this healthcare Q&A, topics ranged from patient-reported outcomes, data validation, and the importance of the Net Promoter Score to reducing costs in healthcare, artificial intelligence, and how machine learning can affect healthcare.
Following along on the theme of value, how important are patient-reported outcomes as compared to provider reported outcomes? And should we be in the business, so to speak, of following up on the patients in parallel with the other providers to help validate the work that we’re doing in the operating room?
Yeah, it’s a great question, and it’s one that everybody struggles with. And I mentioned Net Promoter Score, and I think the … Does everybody know what Net Promoter Score is? It’s commonly used, but it boils down, and there’s basically three question you use. It was developed out of Harvard, and I used it, every one of my companies, but I also used it in a personal relation. But it’s basically, would you recommend this service, this person, to someone else? And the ranking of that, and uses a mathematics to calculate it. I think that that is a pretty good bottom line to use.
And I do think that you need to talk to both. I think that you need to … You have the provider, you have the patient. We all know that patient rating scores, you can jimmy them, you can play with them, and therefore I think you need an integrated model of being able to go to both in looking at outcomes. We do that, again, I can pick any of the companies, but Teladoc, because we’ll do 6,000 consults today, doctor patient interactions, and I basically decided you need a combination. We need to do follow up, even though it’s not in that phone call with both the provider as well as the patient, and then get an amalgamated or a combined score. And it gets rid of most of the rough edges, but I think you need to do both.
Thank you, Dr. Weinstein, and Senator Frist, as a Nashvillian, or a transplant at least, thank you to your family for all of the things that you do for our community, et cetera. As it relates to data, Meaningful Use One, Meaningful Use Two, and what was Meaningful Use Three, was all geared towards [inaudible 00:02:16], and really one data source for everybody to use to know, or to move forward and to reduce costs. How far away from that are we? We’ve missed some targets. And do you think it’s actually possible, with the competitiveness within the different health systems that we have in sharing data?
Yeah, the whole Meaningful Use, the name was just changed yesterday, or two days ago. What’s it changed to? I forgot.
I was hoping you’d know. I forgot.
Yeah, it was just yesterday, I was talking to …
I’ll get you to wait, and it’s going to change again.
Yeah. So data, you don’t want to overstate the use of data, and I do a lot in the artificial intelligence world, and machine learning, and what’s a deep think, which is sort of the third category of that. Artificial intelligence is the big set of data, and then machine learning is a subset within that, and the deep think, the deep work is within that. So it’s a easy way to think about it when you hear these words coming through. But all of it depends on the data and the use of data, and raw data, if you’re really good, becomes valuable, but today we’re not quite there. So the raw data and the artificial intelligence of being able to sift out good data versus bad data, we’re not there yet. We’re there in certain areas like Google Maps, or using Waze to get over here today, but in the health care, it’s just early on.
So now we have to go back and say, “Well, the data has to be curated in some way, because we’re not smart enough to take the raw data.” So ten years from now, the curation’s probably not going to be as important, because the machines will be able to figure out what’s good data, what’s bad data, and use certain filters, and it’ll be a great time. But in the meantime, we have to figure out what is meaningful, what is not meaningful. How should the data be reported?
In a world that’s very competitive, in this fragmented world that we have, and a chaotic world, in health care, use of data, because it’s so valuable, we say, it becomes very proprietary. “I’m not going to share my data with anybody.” It applies in the hospital system. It applies physician to physician, and obviously it means you don’t do cloud sourcing. You don’t have the exponential power in being able to learn from 200 people in a room. You just have everybody holding on to their data.
So the Meaningful Use right now, obviously, has not been terribly effective. The concept of being able to have more transparency, more accountability to the benefit of individual patients, is a good one. We’ve tried beating people with sticks. We’ve tried giving some carrots. It’s going to get easier with this use of artificial intelligence coming in.
So that’s the way I look at it. Physicians, from a physician’s standpoint, productivity has fallen way, way, way off with the approach that Meaningful Use, where it’s mainly filling out forms. Use the quality example of too much, there’s a backlash to that now, which I think is very important. As time comes along, it’ll get a lot easier, but clearly the use of data has value, proprietary, the sharing of that data, means you’re giving up something. And people are very scared of that.
The power of it, again, is what, like this coming Aspire that I built, because 25,000 people out, learn a lot from that data that are being taken care of. I can develop the analytics if they’re this many people. The problem in setting up a system, I can’t wrap teams of people around every one of you, because it would … I’d go bankrupt and [inaudible 00:06:16] would go bankrupt, because these are intensive. For every one of you who are going to die within six months, I want to wrap five people around you. Until I had the analytics to figure out who in this room is the table that I need to focus on, my system was not there. The only reason I can do Aspire today is I have the data and the analytics to be able to predict, in a team of 40,000 people, which 25 I need to set up this intensive system.
Five years ago, I didn’t have that data, and I didn’t have the transparency, and I would have to set that system up for everybody, and the model is not doable.