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Our ID pharmacist was spending about four to five hours a week. So 0.1, FTE, Reviewing those cases with the teams. Even the lab, every one of those specimens they had to look at and reject they'd have to stop what they were doing.

Log into the computer, make a note to the providers. And so we had put this huge non-healthcare like, non EHR infrastructure in place, but it was eating a huge amount of human capital.

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Hello everybody. I'm Jake Lancaster, an internal medicine physician and the chief medical information officer for Baptist Memorial healthcare based outta Memphis, Tennessee. And today I'm very excited to have Dr. Byers on to tell us about automation and how it's used to reduce their CDF rates. Dr.

Buyers, welcome to the program.

Well, thanks for having me, Jake.

So for the audience. Can you just give us a little bit of your background and tell us what you do?

Sure. I'm Dave Byers and I'm an infectious disease doctor by training and. I'm currently the senior medical director for infectious disease at Southern Ohio medical center, which is a small hospital, about 200 a little over 200 beds in Portsmouth, Ohio that serves south central Ohio and Northern Kentucky.

I grew up in Ohio and spent about nine years in the Navy. Did my residency and ID fellowship in. The Navy Bethesda and Walter Reed. And then both my wife and I since were from Ohio, looked for a way to come back. And I've been here at Southern Ohio about 10 years almost now. And have the privilege of working both in clinical I infectious disease, but also with the teams in infection prevention and microbial stewardship, sepsis, and wound care to take care of the patients here in Apalach.

Sure. Sure. So it's been a busy couple of years for infections of these docs. But it looks like you've managed to find some time to address another infection besides COVID.

Yeah. COVID has kind of been an all consuming thing as you say for the last couple of years, but we have tried it our best to not let that be the sole focused, of our quality and safety improvement projects.

And so. We really wanted to work hard at all of the areas of, of, infection measurement, especially as it relates to C difficile, things like catheters associated urinary tract infections, collapsy those kind of things. And so we actually had really kind of leaned into working on C diff a couple of years ago.

And, I think to better understand what we did, it's probably best to step back a little bit and understand. Like why C diffs a problem and why we picked to go about it the way we did. And so, for the members of our audience, there's about 400,000 or so recorded cases of C diff according to the CDC and about half of those are, kind of like hospital associated.

And so, the thing is, is that C diff carries a lot of morbidity and mortality, the most recent statistics. Like, 12 to 13,000 deaths, but that kind of understates, I think you would say agree that the morbidity, right. I mean, all of us have cases of, young, otherwise healthy patients that have, had, antibiotics they didn't necessarily need for, something they probably would've gotten over otherwise then had, multiple recurrent bouts of C diff.

And so our goal was both to improve our care around C diff, but also try to more appropriately identify who had C diff and get them treated. Earlier the better. And so part of that challenge of course, is that, at this point, diagnostic testing for C diffe really is PCR based. And what that does is of course it finds everybody who has the bacteria in their colon very well.

But of course we know that there, it does not differentiate who is colonized as opposed to who is infected. And the challenge of that of course, is, that, there are a fair number of patients that are colonized that are actually not symptomatic, not infected, do not have CDIP and trying to differentiate.

Who has colonization, like who just has it there versus who's sick with it was kind of really important because a, we wanna make sure that we only treat the people that actually need it, but also from a big picture, healthcare administration standpoint, C diff in the hospital is what's called a lab ID event.

So if you bring a patient in the hospital and you test them, Positive for C diff more than three days after they came in, of course it's attributed as a hospital acquired infection, even though they may very well be colonized and they probably had it when they came in. Right. And so to kind of understand how we got arrived at our journey, that was kind of the, problem.

Right? And so we knew a few years ago that we had to help better identify who was colonized as opposed to who was infected. And so we looked at the literature and we kind of. Okay. We realize that, people that, don't have diarrhea, don't have C diff that's been well published. Right. And so even if their toxins positive, and so, we tried to say, well, let's, look at that.

How do we start defining what is, who is infected versus who is not? And so we looked at things like, who has diarrhea based on the number of stools. How do we encourage people not to test people that they're giving laxative to for C diff, right? If you're causing diarrhea if it isn't actually diarrhea, when it gets to the lab, how do we stop that kind of testing?

And so that is a concept that is kind of how we wanted to arrive at our next step, which really was kind of like, what are our interventions gonna.

Yeah. So just to recap, you covered a lot of information there, but it's a problem that all health systems deal with is this differentiation between an active C diff infection versus an individual that is colonized with C diff that you may just happen to order the C diff test on I'm an internist.

I work in the hospital a lot. A lot of times we'll, have a patient that. Good report that they have diarrhea and will reflexively order a C diff test. When, it may just be that it's the coal that you've been given the patient over the last couple of days. And so, because I order that test and that patient is a colonizer of C diff it comes back as a hospital, acquired C diff case.

And so that's what you're really trying to reduce is those falsely identified hospital acquired C diff cases. So. You told us a little bit about, the patient population you're trying to exclude. You don't wanna have anybody that you know is not having diarrhea. Obviously get this test ordered because that would not identify an active C Diff case.

You don't want to screen people that are on a laxative. How else did you identify this patient population? And how else did you, use clinical decision support or automation to, make this process? A little bit more streamlined.

Yeah. So, it's interesting, I think that like all things in quality improvement, we started with a team, right?

So we got together a team, with our infection preventionists and our antimicrobial stewardship team and other kind of hospital leaders. And this was a few years ago and we said, we identified these characteristics of patients. We did not want to screen. And so we get leveraged the tools we had at the time, which was people, right?

And so our antimicrobial stewardship pharmacist. would Review every ordered C difficile test in the hospital and she would talk to the nurse and sometimes the provider involved and say, Hey, are they really actually having diarrhea? Hey, they're on Colace can we maybe stop that before we reassess whether or not we're gonna treat 'em or test them, excuse me.

And then, if someone got a specimen sent to the lab, cause of course, we, we wanna encourage people to get tested. If they have symptoms, have the disease. We want to identify people that have the disease and get them treated because that's how we help make them. better But we want to make sure we can kind of add some, screening in there.

And so we did that with people. And so when it got to the lab, the lab would actually assess it and say, Hey, this isn't really diarrhea. And they'd would put a look that would go into the computer log in, cancel it and say, Hey, this does not meet the criteria, cuz it is not really diarrhea kind of thing.

And what this really it did is it really did improve. Identification of who is colonized and who is actually infected, but it was a very big lift, right? Mm-hmm our ID pharmacist was spending about four to five hours a week. So 0.1, FTE reviewing those cases with the teams. And whenever they come in and of course, like all things in healthcare, it was never Monday at noon.

It was always Saturday at two in the afternoon kind of thing. And, we would batch these in an effort to try to make it more doable. But of course, that would delay testing in order to allow this batching. Go through this, just like twice a day kind of thing. But also even the lab, every one of those specimens they had to look at and reject they'd have to stop what they were doing.

Log into the computer, make a note to the providers. They understood that. Yes, we got the specimen, but no, we, didn't run it because it didn't, it wasn't actually, meeting the didn't look like diarrhea. Mm-hmm . And so with that in mind, you know, we had put this huge non-healthcare like, non EHR infrastructure in place, but it was eating a huge amount of human capital.

And so we got a team together again, and we said, okay, we believe that all this information is discreetly in the computer, right. Is a patient having diarrhea is a patient. on a laxative, those kind of things. And we believe we could leverage our EHR to help us make some of those decisions and help encourage people to make the right behavior.

We just had to get, the computer, to help us do that. And so the next step in our journey really was trying to get, the team back together. And this time, of course, We got our is team involved. And in addition to our stewardship team and our infection prevention team and our microbiology lab team, and we really talked about what are the challenges involved?

Right? So if we said, what would this system ideally do for us? Right. We would ideally like it to identify people who might need to be tested, right? The holy grail of healthcare it right. Is if it helps us make better decisions. And so if it can identify who needs to be tested earlier, That would be great.

That was the first piece of this. And then maybe if it could help us identify who doesn't need to be tested or who maybe we should stop and say, to Hey, maybe this person should think about it beforehand. And then finally, our previous setup, was all very human capital intensive. It didn't actually alert or flag anybody in the system we had to say, Hey, this person has C diff, please put them in isolation and make sure they get treated kind of thing.

It relied on, the lab to come back. in and You and I during rounds to see that and assess it and change our plan. And so we outlined what we thought was an ideal future state. And then we tried to see what we would have to do to get the system to do that. And so, what that really involved was.

Getting all pieces of your healthcare system and, or all your healthcare encounter and seeing where that data might live. And so of course, we know that whenever a patient comes into the urgent care or the ER or even the, floor, there's a nursing assessment, right. That nurse is gonna ask them about, are they having diary, if so, how frequently, that kind of thing, you and I are doing the same thing and in our review of systems.

And so that's discreet data, right? And so if a patient reports that they're having. Diarrhea when they come in. Well, gosh, that's a missed opportunity if we don't leverage the system to help us find those people and get 'em tested early. Right. Mm-hmm . And then if they meet that criteria, if they tell the nurse or whatever, when they're doing that intake Hey, I'm having, diarrhea for the last three or four days.

And that nurse clicks, yes, they're having diarrhea. Can we get the system to encourage them to test that person for C diff if it's appropriate. And so that was kind of the first piece that was, really making sure. We could pull those data elements out and encourage appropriate testing.

And if you decide that you're gonna test someone, the next piece of course is that, is there a way we can. Encourage people to consider these other things before they send the test. Right? Making sure they're not on stool, softeners, those kind of things. And then of course, if you're gonna order a C difficile test and the person that's on antibiotics, can we maybe say, Hey, these things are a little bit incongruous to say, I'm gonna think about C diff, but I'm gonna keep giving them antibiotics and can we encourage you to stop those?

And so we looked at the way to do that. And then finally, once we identified who had C diff, is there a way we could make the system notify or put a flag on someone. For that. And so once we were able to get those teams together we were able to make our system do that with the help of the folks in is here, but also our EHR professional services division was able to help us, with some of the kind of more nuanced Meditech specific build to really able to, leverage the system, to help us make these intervention.

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That's all very nice. So it sounds like you had to get a bunch of different people to come together and agree on the process and the solutions, what sort of resistance did you find along the way?

Well, I mean, I think that's the, one of the really unique and interesting things about working here at Southern Ohio medical center is that, there has generally been an acceptance that if something is, good for the organization, it's good for the patients that people are generally on board.

But also , the real thing is, was just, we're having a kind of a dialogue with the nursing staff that actually this is gonna make your life easier, as opposed to having the antimicrobial stewardship nurse call you later in the day and say, Hey, does this person only have diarrhea?

It that you're gonna do the clinical decision support in the order. And so it's gonna save you time and it's gonna save you phone calls later. And then as the physicians, it's one of those things that it was little conversation about. This is gonna help you identify earlier, because again, we're trying to shift the screening.

Like we've now had people earlier who has a disease. Help you find earlier who does have the disease that you can treat them appropriately, but also we're not taking away your autonomy as a physician. You can override the clinical decision support. I feel like that's important, right. You know, as a, physician in there's always some nuance to healthcare.

And so we wanna make sure we encourage people. To do the right thing. We wanna leverage the EHR to help them do what we believe is the right thing, but always allow account for some nuance in healthcare. And so we also made it clear that they could override it if they felt like the clinical decision support was not appropriate.

But actually with that, just the ability to do that. We've had really very good. Buy-in

so low override rights you would say, or.

We have very low override rates and, I think the real win has been that as we looked at this intervention we had low pushback, but we also noted that we got more buy-in as we were able to talk to people about the benefits we were seeing.

Right. And so what we really saw was that , for our early detection, right? So by virtue of the nurse doing her. So they're they're screening in their intake and saying, yes, this person has diarrhea. Yes. That was here on admission. We're gonna test for it. We noted a significant shift in our community onset, meaning diagnosed before three days of admission, as opposed to a hospital onset diagnosed.

After three days of admission, we noticed about a 25, 20 9% reduction in that hospital acquired that shift towards earlier identification. So, again, we, saw people saw that it was helping us find people earlier, helping them treat them earlier. And then even from our lab colleagues, they.

Had about a 30% reduction in the number of specimens they had to evaluate and then cancel, which saved them about three to five minutes per time, which was really a big win for them. And so, again, as it was clear that this was really making an impact and actually almost streamlining a process that before it revolved a lot of phone calls.

It really, the buy-in was very easy.

So really you didn't reduce overall testing. You just shifted testing. Patients that are greater than three days in the hospital to testing them earlier in their disease course.

So we were cause that was never a goal, right? So anytime, you don't want to ever, like, my goal is not to miss who has C diff and it's not to, push the numbers away from healthcare associated.

Infections arbitrarily or our goal is to identify who had that infection earlier so that we can help them be treated earlier. And so actually what we found was is that our total number of ordered tests actually increased because they, again, the triggers from the EHR input. Was saying, Hey, this person may be indicated for C diff testing.

And so our total number of tests ordered increased significantly though. Whenever you go through actually the follow on and collection, many of those patients, as you know, when they report diarrhea, they come into the hospital, they don't really have diarrhea. And so The actual number collected and get to the lab still was significantly less, but we are identifying significantly more.

And the benefit of that of course, is, is that we are testing those patients earlier. And so from a hospital perspective, of course we're identifying them earlier, but from a patient perspective, we're helping them get treated earlier.

Nice. And you already mentioned that you were able to reduce. A certain percentage of hospital acquired C diff cases.

What was that percentage again?

So we actually, I just checked this morning. We are at a 29% reduction in hospital acquired C diff as opposed to where we were before. We were able to, with the help of our consultants, help us build a dashboard that has been incredibly useful with this, whereby we actually have tracked our pre implementation numbers and compared it to the now more than six months post implementation.

And we are actually staying relatively stable in the 25 to 30% range. Over this past six months in the reduction of hospital acquired C difficile.

Very nice. Yeah, I I was going to ask about what data you're tracking, tell me about the dashboard and what all y'all incorporate on.

it

Yeah. So, thanks for asking.

because actually really it's one of the things we're kind of most excited about in the sense that like, data is power, right? You don't know what you don't know until you can track it and measure it. And so we decided that we wanted to long term improve patient outcomes, right.

We wanted to improve C difficile care identification and treatment But we knew that that was almost kind of a lagging indicator, right? And so we, we had a huge amount of human capital involved in this previous, we talked very manual process and so we wanted to measure the percentage of time the lab was actually getting those specimens and having to stop what they were doing.

Go put a reason on the computer to cancel because that was a huge amount of time waste for them. And so we tracked the number of specimens that got to the lab that a lab tech later had to cancel. Hmm. And we, again, we saw that reduction about a third, and so that has also stayed stable over the last, kind of six months.

And so that's been a significant win for our microbiology lab team. We are tracking the total number of orders. And so that's one of the things I wanted to see is, again, I want to as a quality and safety person, we wanna make sure that, you know, if we're seeing a reduction in something, it's not because we're not looking right.

And so it was very important to me that we were able to identify a reduction in our healthcare associated C diff. But thereby of course, we're trying to find more early C diff community onset, but I wanted to make sure we weren't like having a decrease in testing as a cause for that. And so we've actually been tracking our weekly testing orders.

And again, those numbers, as I mentioned before, have actually gone up. Because again, we're identifying more people who maybe should be tested when they come in and that has again stayed very stable over the last six months. And so we, with each passing month, as those numbers have.

Really stayed in that same kind of bandwidth range of about one third reduction in, lab time lost , about 25 to 30% reduction in hospital acquired C diff and still remaining significantly higher in total number of tests ordered. I feel like this has been a lasting, and durable intervention.

Yeah. And, One thing we have not talked about, but you mentioned that 29, 30% reduction what does that translate to as far as I guess reduction in penalties, for these hospital acquired infections.

Well, it's one of those things that the penalties would be obviously related to, like every hospital would be affected differently in our hospital.

We are still within the window, the first, you know, kind of where we went live with this in January. So we haven't fully closed out our year yet to see how that's gonna make our impact. But I will say that, for the folks listening out there, we actually, our standardized infection ratio for C diff was actually good.

We were actually better than expected. We undertook this project. because We felt like we could do better and we could leverage the EHR to make those interventions. And so we won't know what, how it's gonna impact our final numbers as yet. Cause it hasn't closed out for the year. But like I said, I don't anticipate we will see a penalty, but if you think about, and in general, the hospital, that talks about.

Significant penalties and or thousands of dollars per cases that they hit. Because of course it hits on, value-based purchasing and hospital acquired condition reduction programs. It actually, it's kind of cuz it shows up in a couple of those payment programs doing something like this that you can.

Rather predictably decrease your hospital, acquired C diff numbers by purely finding people and treating them earlier. It's a win for everyone. This is one of those interventions in healthcare where everybody feels good about it, right? Because the patient in wins because we identified the problem they had and we treated them earlier.

The hospital wins because of course it made a streamlined, a process that was very cumbersome and the hospital and the hospital administration wins, of course, because we've taken a process, which they are negatively impacted on scoreboards and payment and everything else. And we have reduced that number.

No, no, I think this is tremendous. I certainly learned a lot, in just about 30 minutes, I think I'm going to a meeting where one of the items that is reported out is the number of hospital acquired C diff infections at our weekly patient safety huddle. So, this information is, is pertinent.

Yeah. To me personally, because of, I think I can take some of the things that you just mentioned and hopefully adapt 'em here, but I think every system in the country. Deals with this and having this kind of programmatic automated approach will do a lot of good. Well, thanks again. Thanks so much for coming on and sharing your experience.

I'd love to hear more in the future about how successful the program has been or anything else y'all do related to automation. Any closing comments or final words for the audience?

Yeah, I just one of those things, again, I wanna thank you for kind of allowing the opportunity to talk to your audience.

And I just wanna kind of say. I think this project was successful because we got the team together. Right. And I think that anytime any intervention like this, it's all about putting the team together. And I think. We were successful because we internally put a team together that included, all the stakeholders.

But also included, in this case, our EHR vendor with their specific expertise, we involved their, Meditech professional services in this case, which help us kind of do some things with the system that, maybe made this project a little bit more streamlined and more effective than I think we would've been if we tried to do it ourselves.

But it was really, truly about getting that team together that made all the difference in the world for us. And I think it served as kind of. A foundational proof of concept for us to look at other diagnostic stewardship interventions. Now we've been able to do it with this, and now we're kind of honestly looking at ways we can kind of apply this same logic or clinical decision support applications to other pieces of our EHR.

Oh yeah, no, certainly lots of opportunities, MRA, et cetera, where this could, potentially be a solution. Well, thanks again. And thank you everybody for listening.

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