I have a few conflicts that are going to show up here. All right. I'm going to start with a case. It's a 58-year-old man with non-alcoholic fatty liver disease diagnosed with cirrhosis who's presenting to your hepatology clinic for initial evaluation. The questions that we will be asking are whether and how can you use the EMR to promote effective longitudinal care of this patient? How many in this room love their EMR? Four, five. Good. How should order sets be incorporated into management? What reminder systems should be or can be in place? What opportunities are there with the EMR and telehealth to monitor and prevent complications in patients with cirrhosis? I'm going to take a little side trip here. How many of you have used Waze? More than EMR? So for those of you who have not used Waze, this is a GPS navigation system that helps us outsmart traffic together. So this system, it has lots of information, for those of you who have used it, lots of data that's coming to you as you're using it about traffic jam, if there's police ahead of you, if there are accidents, a variety of information and data that are coming into Waze all the time. And actually, there are the drivers who are out there on the road. They are the source or sensors for this information. They're feeding in this information along with other drivers who are out there as well. And they are all in the context that you're in, which is on the road. And you can use this information in a timely manner to make decisions, real-time decisions as you go. When do I leave the house? Where do I turn? Where do I slow down? So all these decisions are being made based upon this flurry of data that are coming in to you in real-time. EMR has attributes that are similar to Waze. There are lots of users that are putting in information from different sources at a very fast speed. Healthcare professionals, even information from patients that's coming in. The trick is for us to be able to use that information to really guide decision-making in a timely, or a real-time manner. And in the next 17, 18 minutes, I will show you how EMR is helping us do that to provide these timely decisions and also provide effective longitudinal care for patients with liver disease. So let's go back to the patient, the non-alcoholic fatty liver disease patient with cirrhosis that we were seeing in our clinic for initial evaluation. So we as liver doctors are fairly good at the liver care that we provide initial evaluation, but also ongoing cirrhosis care across the whole spectrum of disease, compensated to decompensated and pre-post liver transplant. We're fairly good at it. But if you just step back a little, it's a myopic view because cirrhosis care continuum actually includes these early steps. Identification of patients with cirrhosis, linkage to liver care. And these steps, identification and linkage, are important. They're important because we miss patients with cirrhosis. They can be missed, and it's not that infrequent. And it's not infrequent, and it's also not inconsequential. So here are data from one of our studies, now from a few years ago, where we had pulled information on over 1,200 patients with confirmed liver cancer, HCC, who had evidence of cirrhosis at the time of HCC diagnosis. And we looked back. We looked back to see how many of these patients had their cirrhosis recognized or diagnosed during routine clinical care before their HCC diagnosis, and how many patients were not recognized to have cirrhosis prior to their HCC diagnosis. Twenty-five percent. One in four patients who had cirrhosis, were in care, did not have their cirrhosis diagnosed prior to HCC diagnosis. So this underdiagnosis had its consequences. Patients with unrecognized cirrhosis were 6.5 times, about seven times more likely to have advanced HCC at the time of diagnosis than those where the condition was diagnosed ahead of time. The good news, and that's where I come back to the EMR, the good news is that EMR can help us identify cirrhosis patients in a timely manner. So there are many, many studies that have looked at different algorithms, mostly combining diagnosis codes for cirrhosis. And what these studies collectively show, the patients who meet these algorithms based on diagnosis of cirrhosis, they have a very high likelihood of actually having cirrhosis. Even in cases where they do not have diagnosis of cirrhosis, meaning they are not recognized, and hence there is no diagnosis, still even in those cases, now with the noninvasive tests and markers that we have available, especially in patients who have chronic liver disease, they have fairly reasonable accuracy in identifying patients who have, or who are at high risk for advanced fibrosis or cirrhosis. So these are tools that we can and should be able to use to really help address these early steps in the cirrhosis care continuum. And this is what we did. In this study, which is more of a quality improvement project in the Houston VA hospital, we used these methods and used our EMR to identify patients with cirrhosis in the entire system. This was a quality improvement project. Our main focus was to link patients to liver care. So we were focusing on patients who had cirrhosis, but who also did not have any evidence of seeing us in the liver clinics, at least in the last few years prior to the data pull, so unlinked patients. And when we did this, we found 1,200 patients with cirrhosis who fit those criteria. This number is just a little bit lower than the number of patients who were in regular care with us. So the point here is that close to 50% of patients with cirrhosis in this healthcare system with equal access to healthcare were either not linked or suboptimally linked to hepatology care. So with this EMR-based infrastructure combined with care navigation and to the last time we pulled the data, we were able to arrange for liver clinic appointments for close to 700 of these patients, and we were able to see close to 400 of these individuals. So this 400 looks like a small percentage, but these 400 patients we would not have had seen if it were not for this program. Once patients are actually in our clinics, just like the patient in the example, EMR can be pretty useful for risk stratification and for prognosis. So I'm going to show you a few examples here. This is a recent study, a neat study, that followed close to 145 patients with compensated cirrhosis, used information that we routinely have available, followed them for about four to five years for development of incident decompensation. Now using information that was readily available, they combined two different scores. One is the ALBI score, which is a measure of liver function, with information from PIV4 score, which is this noninvasive measure of fibrosis. They combined them together to come up with a risk prediction model that predicted the development of decompensation. It did really well, the model. They were able to separate patients who were at low risk for decompensation from patients who were at a high risk for decompensation with a hazard ratio of seven. They were then able to validate this in two separate validation cohorts. One was a prospective and one was a retrospective cohort with very similar information. So the point here is that these readily available, readily accessible, cheap information is available in the EMR that can be combined and actually makes it a really attractive method to implement in our EMR to help risk stratify patients in a relatively cheap and quick manner. So here's another example, similar concept, but using risk for HCC as the outcome. So here, this study had over 23,000 patients with cirrhosis followed over time for development of liver cancer. Again, using very simple tests, age, gender, BMI, diabetes, and routine tests that we again have available that came up with the risk stratification model. And this risk stratification model helped separate patients into high risk, low risk, and medium risk for liver cancer. In the same study, extending this analysis, using the risk model-based strategy to determine who should undergo HCC screening, meaning that everyone, every patient got a risk score and in this hypothetical model, they were then entered into a screening program only when the risk of HCC was higher than a pre-specified cutoff. It performed better and had a higher net benefit compared to screening all strategy for HCC. So again, suggesting that these simple methods can be used because they're already there, the data are already there to leverage and help us take better care of our patients. Taking you back to my Waze example. So just as Waze helps us identify these issues, traffic jam, police, and accident, EMR uses the same big data to do the same thing. We can identify our patients, especially those at high risk but who are not seeing us. We can also predict or expect or anticipate problems. Who is going to get cirrhosis complications? Who is going to get, who is at high risk for getting HCC? It can also help us navigate as we tailor management for patients with cirrhosis. And here is one example, another example of order sets. So order sets in the management of cirrhosis. Order sets have been around for quite a while. They help us reinforce best practices. They help us guide the decisions at the right time. They've shown to improve patient outcomes across many different conditions. Most of these data, these effectiveness data on order set, they come from other conditions. But there are actually a few examples within cirrhosis. And I'm going to show you a few here. This study now from a few years ago implemented an order set for patients with GI bleeding who came to the emergency room. Most of these patients, all of them had cirrhosis. And the order set was a pretty standard order set. It had nursing orders, lab tests, consults including the GI consult, and orders for guideline recommended medications. And the outcome was whether they got the guideline recommended care. With this order set in place, there were differences in the pre versus post implementation time frame. But most of the processes, they improved over time. The only exception was EGD or upper endoscopy. The rates were high to begin with. Another example of using order sets or reminders for HCC screening. This is a more involved intervention, multi-component. There was a nursing staff based HCC screening protocol that relied on decision support system to manage the intervention. And there were built in alerts and reminders to remind when patients did not get HCC screening. So again, based on the EMR with case management around it, total of 355 patients went through the intervention. And the rates of HCC screening increased from 70% from before intervention to about 90% from after interventions. Again, showing that these order sets can help optimize care when they're used. Now moving into something which is more cutting edge, a new innovation in the way we deliver healthcare. And actually it's going to change the way we deliver healthcare in the future, telemedicine. So telemedicine is this delivery of a service, of healthcare service from a distance using either telecommunication or virtual technology to provide the healthcare outside of the traditional face-to-face interaction. The three different types that generally are talked about teleconsultation, televisits, and telemonitoring. And there are actually examples, again, early preliminary examples from cirrhosis for each of those. And I'm going to share some of that with you. So for teleconsultation, a scan echo program, which is a VA Veterans Affairs based program, is a good example of teleconsultation. How does it happen? Patient cases, they're discussed through videoconferencing between the consultants and primary care providers from multiple sites. It can happen with multiple sites at the same time. But the recommendations for patient care, they're given in real time to the primary care providers. This really helps cut down the access issues. Patients don't have to travel from far to be able to see the specialist, and that is the key advantage. But now data are coming out suggesting that patient outcomes and processes of care are actually not much different with the teleconsultation as compared to the traditional face-to-face consults or care, liver care that we are used to. So this study in hepatology recently showed that teleconsultation was associated with improved overall survival compared to patients who did not have any visits with the liver provider or liver clinic. The outcomes were better, but also getting recommended care was better for patients who were seen in teleconsultation. There were higher rates of ATC surveillance, higher rates of variceal surveillance, again, showing that this method improves the care not just from the processes, but also the outcomes of care. So the whole link is improved with it. This is another innovative intervention. It's based on this telemonitoring platform, which relies on a 4G tablet. It has a wireless device. It monitors blood pressure, heart rate, run by a telemonitoring team, which is a nurse-led team. And it allowed the care providers to monitor from a distance patient signs and symptoms. These are all patients with decompensated liver disease with ascites, hepatic encephalopathy, and variceal bleeding. And with that system in place, it's a pilot study and preliminary results, but the rate of rehospitalization at 90 days was reduced from 33% in the standard care group to 0% in the intervention group. Again, very, very exciting and promising data. We need to wait and see more of that, but it is promising. Despite the promise that it holds, telemedicine, as we stand right now, has challenges. One challenge is the reimbursement. It remains an ongoing issue. Payment varies for private payers. Medicare reimburses for only one of the three elements, which is the video consultation. And that is also only for individuals that are in designated health professional shortage areas or who are enrolled in chronic care management programs. Relatively limited scope at this time. Beyond just the reimbursement, there are concerns that maybe quality can be limited or it might not be as optimal. The data that I showed you would suggest that that is not the case. And most interventions are single arm. We really need to wait for larger and better studies, but I think these issues will be addressed in the short term. One other exciting thing that I want to share with you that is another example of how EMR is being leveraged, and I have to say AASLD has really been leading this effort of maximizing the potential for EMR in providing care for patients with cirrhosis. Cirrhosis Quality Collaborative, it's a multi-site quality improvement initiative that is funded by AASLD. And it will host a platform, a shared platform, that will input information from the healthcare professionals and the patients. And it will triangulate that information with data that will come directly from the EMR and using that information for us to be able to understand and improve quality that we provide to patients with liver disease. And that is the way to the future with the era that we are facing. So I'm going to summarize the electronic medical records, despite the enthusiasm that we saw here, especially when they are in the integrated healthcare system. They offer an unprecedented opportunity to monitor and improve long-term care of patients with cirrhosis. There are, of course, ways that we can further enhance the potential of EMR. And I think the EMR that allows better sharing of data, better display of data, creating models that may not be too sensitive to having perfect data elements, relying on patients for patient-reported outcomes, they matter a lot. And using data to inform decisions in real time. And that is some of where CQC is going to be very helpful for us to understand how to do this all together. One other key point that I want to leave you with is telemedicine, despite the issues, despite the fact that we need to have defined specific interventions and ways to implement it, they do fill a key gap in the chronic care management of patients with cirrhosis. And they're here to stay. So with that, thank you very much. Thank you.

electronic medical records

cirrhosis patients

EMR algorithms

telemedicine

patient outcomes