In this episode, we are joined by G.T. LaBorde, CEO of IllumiCare, to discuss their study on the correlation between the number of physicians during an inpatient hospitalization and the length of stay.
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Learn how to listen to The Hospital Finance Podcast® on your mobile device.Highlights of this episode include:
- Background on Illumicare’s study and what they were looking to find.
- Details on how the study’s data was collected and analyzed.
- How the number of physicians consults related to the patient’s length of stay.
- What does the study’s analysis mean for hospital’s and how they track physician consults?
- And more…
Mike Passanante: Hi, this is Mike Passanante. And welcome back to the award-winning Hospital Finance Podcast®.
IllumiCare recently released an analysis that looked at whether or not involving multiple physicians during an inpatient hospitalization prolongs the length of stay. To share the findings of this analysis, I’m joined by G.T. LaBorde, CEO of IllumiCare. G.T., welcome to the show.
G.T.: Thank you, Mike.
Mike: G.T., why don’t you start out by telling the audience a bit about IllumiCare and what you do there?
G.T.: Well, we work with health systems to collect both clinical and cost data in real time. So every order for every medication and lab and radiology test, along with every day updating the wholesale cost of these medicines, for example, to this health system so that we can tell physicians in real time what every one of those orders costs. And we can also– because we know every order, every provider in the hospital and what they order, and how they order and ultimately how sick those patients were with disease category they were in, we can also sort of profile the behavior and the practice patterns of these physicians to see which ones kind of spend the most or the most efficient and on the least efficient and so forth. And as a business, we have a way of– in real time kind of coaching them and nudging them to more efficient care.
Mike: Excellent. And obviously, as part of what you do, you get access to a lot of different data points, which I think led to the analysis that you did. So why don’t you tell us a little bit about that analysis and what specifically you were looking for?
G.T.: Very often, we think that getting a second opinion in medicine is a good thing. Right? That the more heads you can have, the more sort of clinical brains you can have on your case, the likelier you are going to have– I don’t know, a good outcome or to get the “right answer.” And in healthcare, what we see a lot of, particularly someone that stays in the hospital for several days or longer, is that you have these hand offs from one provider to another or the involvement of lots of different providers on the case, and that may be a hospitalist who may rotate off of their seven days on and hand off to a colleague who takes over in the middle of the case. It could be that that hospitalist might consult other specialists on the case. He might bring a nephrologist or an oncologist or whatever depending on the underlying diseases, and what you wind up with is a lot of cooks in the kitchen. And the question that we looked at was what’s the implication of all of those hand offs? Does that cause excess length of stay, and is there a relationship between the number of providers who are actively involved in a case in the length of stay of a patient?
Mike: And can you just briefly explain how you conducted the analysis?
G.T.: Sure. So what we did was we took a subset of our client base. We have hundreds of facilities that sent us data, but we looked at the facilities that we had really good data from– as to who the ordering providers was and what specialty those providers where in. We required that whatever hospitals we included in the analysis, we had to know who the provider was and know what type of provider that were on 75% or more of the orders of that health system. And then we kind of looked at those providers who were actively involved in a case, which we defined as someone who wrote an order for a medication or radiology test or a lab test. So there could be some providers that were consulted and might have offered an opinion, but if they didn’t actively order something, we didn’t even consider them as part of what we analyzed. And what we went about doing, then, with these thousands and thousands of inpatient admissions, is began to sort of analyze what are the effects of having multiple physicians on the case and does length of stay change the more you have? And so the first thing that we found– we took sort of a simple cut of the day and just simply said, “What’s the correlation between the number of unique providers who are actively involved in an inpatient stay and their length of stay?” And what we found is that once you pass three providers who are actively involved in your case, every additional provider added about 0.6 days to the length of stay. And the correlation there continued all the way up to 18 different providers. It’s almost a straight-line correlation between every additional provider that you have adds another 0.6 days to length of stay. So it was pretty amazing to see that tight of a correlation before we began risk-adjusting or any other methods.
Mike: Yeah, so the short story when you looked at the results was more doctors, longer length of stay, and I think the second point that you made in the study, which is maybe the natural go-to for a lot of people, is, “Hey, a sicker patient’s just going to require more consults. More doctors will be involved,” and so you have to adjust for risk. But you basically found that that was the same story. There was really no change there as well, isn’t that correct?
G.T.: You’re right. So the way that we risk-adjusted is that we said, “Okay, let’s not look at things so broadly. Let’s compare patients in the same DRG.” So as you and the audience probably knows, DRGs have been a 40, 50-year experiment by CMS to declassify patient admissions into ones that are roughly similar outcomes. And each one of those have a published geometrical mean length of stay. There are about 900 disease categories that those fall in. And so, we looked at the data and said, “Let’s only consider those DRGs for which we have at least 100 admissions in each one of the hospitals that we were doing analysis of,” so that we didn’t allow smaller numbers of admissions to kind of skew the analysis. And then, let’s compare patients within the DRG that had one provider on the case, and then two providers, and then three providers, and so on. So the question became, if you control for acuity by using DRG, is it still the case that the more providers you have, the longer you stay? Presumably, a patient in a DRG, say, heart failure without complications and comorbidities– within that category, there shouldn’t be a reason for one patient to have 5 providers and another to have 15. They’re roughly, similarly sick and without other comorbidities. So when we use that as a way of risk-adjusting, we found this correlation continued to exist. It didn’t really begin linearly until you exceeded five providers, but once you hit the sixth provider and on – and we had patients all the way out to 40 different providers – we saw the same linear relationship. So as you add another you are adding length of stay, even controlling for how sick patients were. We kind of looked at this a different way, too, Mike. We also considered, well, is it a function of how many other subspecialists you have on the case? So is it, presumably, really sick patients would have a nephrologist, and an oncologist, and multiple sub-specialties on the case and not just a hospitalist, or two, or three, or four hospitalists, for example, who are handing off to one another? And so that was another way to sort of control for how sick patients were by looking at the number of non-generalists, if you will. So we looked at hospitalists and we looked at how many other non-hospitalists were on the case, and we sort of said if you had five different providers but three of them were hospitalists, we’re only going to consider that two others, if you will, two subspecialists . And even when we controlled for the number of subspecialists on the case, we said okay, what about patients that have only one other subspecialist, or two other subspecialists, or three other subspecialists? It turns out, even up to seven other subspecialties, it’s still the case that the more of those you have, like if you have two oncologists or three oncologists on the case, it adds incrementally, linearly to your length of stay. So it’s a relationship that no matter how we tried to sort of explain it by patient acuity, the correlations still existed that it’s really a bad thing, if you will, all of the things being equal, to have physicians handing off to one another in the case. And it sort of makes sense to us. Every time a new provider comes on the case, it’s almost like a new detective coming to solve a crime on the same case. You start with one detective who sort of gets his theory of the case and examines all the evidence, what happened here, and formulates a hypothesis about it, is working through it and in the middle of that they stop and they hand it off to another person. Well, that second person then has to start from scratch almost, right? And maybe try and pick up, okay, what happened here, and what’s the evidence, and let me read all of the notes and form my own opinion, my own hypothesis of what happened here. And as soon as they get to it, you stop and start all over again. So these handoffs , one can hypothesize how inefficient they can be. And especially in this day and age, where a lot of physician notes are copied and pasted of a ton of information, has sort of lost some of the ease of kind of reviewing everything that’s happened so far and everybody else’s opinion that came before you. And that’s what we think kind of contributes to this effect of really adding to the length of stay.
Mike: And you also found that some providers just consult with other doctors more than others?
G.T.: Yeah, so when we thought about okay, well, what do you do about this? I mean, it’s interesting to know that this correlation exists, but is there any way to manage this? So if I’m a hospital CMO or CFO, how can I turn this into actionable information? And it turns out that you’re exactly right, that some physicians tend to over consult, if you want to think of it that way. They consult much more frequently than their peers. Even when you control for DRG and other kinds of patient factors. And we found huge variation in the propensity. We studied, specifically, hospitalists and asked how many other non-hospitalists do they tend to consult? And we only looked at hospitalists– I had a pretty large number of encounters that we were studying so that we could get a good feel for how frequently this happened and not allow one or two sort of “train wreck” patients to muddle the numbers. And what we found is, on average, hospitalist tended to consult four– about 4.3 other non-hospitalist per admission. But there were some hospitalist, in fact, the highest on the list averaged 11.8 non-hospitalist consults per admission. And we thought, “Wow, that’s crazy. That must be someone who barely made the cut of 20 admissions. Surely that’s an outlier in the data.” No, actually, that provider had seen 265 encounters included in that. So that average wasn’t just one patient skewing, or one or two patients skewing the data. That was across quite a number of admissions. And it just highlighted that this is something that is actionable, that you can begin to look at your providers and see their propensity to consult other specialists and other providers. And they probably have no idea. They may think either they’re practicing defensive medicine, or they’re “covering all the bases.” But it really does have a negative effect on length of stay. And we know that the longer you stay, it’s not just more expensive for the hospital, you increase the iatrogenic risk of patients potentially getting infections or other complications as you extend their length of stay really unnecessarily. And so it is something to begin to manage and to think about and to measure. Kind of this consult rate, the propensity of your generalist to consult other specialists in the hospital.
Mike: G. T., what do you think the implications of this data are going forward?
G.T.: I guarantee that– I have never met– I’ve been in the healthcare technology business for about 21 years now and measuring outcomes, and behaviors, and so forth. And I’ve never met a CFO or anyone kind of at the C-suite who even measures this, who could even tell me who are the doctors who tend to over-consult in your hospital. Generally, a CMO would know one or two kind of the worst offenders at that, but it’s not something that they manage. It’s not something that they measure or manage too. So we are kind of, as a result of this analysis, are starting to look at that for our own clients. And I’d encourage other people to see if they can measure it internally. And beginning to think about and have conversations with and try and drill down. For the people that are really the highest consulters, to try and understand why they do that and talk with them about it, and see if they can’t understand sort of the negative effects of that and manage that down because I think, ultimately, if we can kind of take those outliers and bring them back to the mean, we can not only be more financially efficient as a hospital by lowering length of stay overall, but provide more efficient– we know that efficient care is better care for patients. And so we can kind of help patient care at the same time.
Mike: If someone wanted to read this analysis or find out more about IllumiCare, where can they go?
G.T.: We’ve put this analysis on our website at illumicare.com. We’ve also– this is the second kind of cut of research that we did. The first one was looking at which provider types have the highest variation in spending, given the same patient type. So we also are asking folks– we’ve got three or four more research ideas. And on our site they can actually vote for the next e-report that we release. We’ve done some preliminary analysis to look at other things and are interested in, and we’ll continue kind of putting out this sort of research.
Mike: G.T. LaBorde, thanks for joining us today on the Hospital Finance Podcast.
G.T.: Michael, it’s been my pleasure. Thank you for doing this.
