**Transcript of Cyberseminar**

**Spotlight on Pain Management**

**Quality Improvement and Implementation Support to Improve use of Guideline Recommended Practices for Chronic Opioid Therapy**

**Presenter: Jodie A. Trafton, Ph.D.**

**March 4, 2014**

*This is an unedited transcript of this session. As such, it may contain omissions or errors due to sound quality or misinterpretation. For clarification or verification of any points in the transcript, please refer to the audio version posted at www.hsrd.research.va.gov/cyberseminars/catalog-archive.cfm or contact**robin.masheb@yale.edu**.*

Robin Masheb:This is Robin Masheb. I am the Director of Education at the Time Center and will be hosting a monthly pain call entitled Spotlight on Pain Management. Today’s session is Quality Improvement and Implementation Support to Improve use of Guideline Recommended Practices for Chronis Opioid Therapy. I would like to introduce our presenter for today, Dr. Jodie Trafton. Dr. Trafton is Director of the Program Evaluation and Resource Center for the Office of Mental Health Operations and an HSR&D and Substance Abuse Disorder QUERI researcher at the VA Palo Alto Health Care System. She was trained as a neurobiologist and health services researcher focusing on neurobiological and behavioral effects of opioids and treatment for pain and substance use disorders. 

We will be holding questions for the end of the talk. At the end of the hour, there will be a feedback form to fill out immediately following today’s session. Please stick around for a minute or two to complete this short form, as it is critically important to help us provide you with great programming. Dr. Bob Kerns, Director of the Prime Center will be on our call today, and he will be around to take any questions related to policy at the end of our session. And, now, I am going to turn this over to our presenter, Dr. Jodie Trafton.

Dr. Trafton:Hello. So, are the slides going to be up, or do I need to host those somehow.

Moderator:You should be seeing the slides right now, Jodie.

Dr. Trafton:Okay, I am not.

Moderator:Okay, I have got two screens. I can see them on both screens. What are you guys seeing on your screens?

Dr. Trafton:Something that look like, it is a black screen with something that looks like a little sun or...

Moderator:Oh, so it is still loading on your screen.

Dr. Kerns:But, it has stopped loading.

Moderator:Okay, for the audience, if you guys could give me, at the top of the screen there is like a little guy that looks like he is raising his hand, if you could give me agree if you see the slides, a disagree if you do not, I would very much appreciate it. I am just trying to figure out, okay, I am getting some feedback. The people can see the slides.

Dr. Trafton:Well, if everyone else can see them, I have them, I can run them separately.

Moderator:Okay, it is tough, but just let me know and I will move through them. It looks like everyone can see them except for you two. I feel terrible.

Dr. Trafton:That is okay. Technology is fun. So, as was mentioned, I am Jodie Trafton. I have, what I want to talk about today is not just specific to opiate therapy, but I wanted to talk a bit about ways of using data and information collected from research to help implement recommended practices from research, from guidelines, into clinical care and into quality improvement efforts. And, so what I am going to be talking about is how to think about, and we can switch to the second slide. I guess I can maybe do that. I do not know if that worked or not. About how to take, I am going to talk about a bunch of projects where we worked with the clinical practice guidelines for opiate therapy. And, to use the guideline itself to help develop variable definitions. So, clear definitions about, that could be coded in data terms to help understand whether or not guidelines were being implemented, what was being done, and identified potentially patient risks and recommendations for how to deliver care.

So, the thing I would emphasize is that while there are a large number of clinical practice guidelines that are available, they are written in standard English. They are not written in terms of standard data elements that are in either medical administration records or electronic medical record fields. And, what that means is that there is a large translation step that needs to happen between sort of what is written in text and what is found in clinical trials. And, what you can pull out of a medical record or understand from patient date in order to be able to use it in a variety of different ways. So, what I want to talk about here is one, just in general terms, some various methods that we use to try to convert the clinical practice guideline for opiate therapy into codable elements, into standard data elements that we could use for various purposes. And, then talk about a variety of different ways in which we took those definitions and worked them into implementation, decision support and quality improvement tools. So, things that we thought could behaviorally help to change clinical practice, guide clinical decision-making, and improve patient care.

Additionally, just because I have a slightly non-typical role, I am both a researcher and I work directly for VACO clinical operations, I wanted to talk about ways, also show examples of ways that HSR&D in query funded projects can be leveraged and incorporated directly into operation and policy initiatives. I get a lot of questions from people about how to do this, and I think some of these provide clear and actionable ways in which you can directly translate some of the very specific work that gets done in research into things that are really reaching clinicians and patients, reaching clinicians, in some cases patients, and definitely administrators and policy makers.

So, if we go to the next slide, I am going to, the elements that I am going to talk about are different ways in which we have used the chronic pain guideline definitions, the things that we translate, the data elements that we translated into creating specific tools that could be used. And, these are just examples, but they kind of demonstrate different ways in which, once you have those data codable elements, you can start to provide information to people at the place where they need it, in order to guide decision-making. So, the first example I am going to give is the first way that we tried to look at this, which was by developing clinician decision support systems. And, the idea with clinical decision support systems is that you want to bring patient personalized information about how to implement the guidelines. So, to the point of care so that a clinician can make decisions about a specific patient case. So, the idea here being you have a 200-page guideline that says generally how do you treat patients with a whole variety of different conditions, how do you take that and turn that into specific recommendations for an individual patient case. So, you need to have algorithms that help take that guideline, parse out how it relates to an individual and then present that to a clinician at the time they need in order to implement those clinical decisions. So, I will show you an example of a tool that we developed that does that. You have probably seen that opiate therapy and some of you are probably familiar with it already. 

Another challenge in terms of getting things in place is while clinical decision support systems can provide information, they do not necessarily provide outside motivation for following guidelines. A lot of times guidelines suggest things that are, that take some clinical effort or require clinicians to go the extra mile in order to implement, or have uncomfortable conversations with patients and that sort of thing. While you can tell people that they need to do it, that does not always necessarily motivate them to want to take those steps. So, one of the things that we have learned fairly quickly is that you, in addition to giving the clinician information, you have to create some priority for them doing things. So, you have to let their supervisors know, for example, that this is something that they should be paying attention to. You have to have experts talking to them. You have to have some sort of feedback about whether or not they are doing recommended clinical practices. So, quality improvement metrics is one method for trying to help show what is being done in clinical care. So, we developed a set of quality improvement metrics. The idea here is to develop measures that help at the system level identify, prioritize, address, and then also evaluate system-wide issues that are limiting use of guideline recommendations. The hope here is that you can help sites find their problems and make things easier for clinicians to do things, or help provide encouragement to the clinicians to do the things that they are not currently addressing. So, I will talk about how we developed those and how some of those are being used.

Another key area where definitions kind of feed in is in actually designing and developing electronic medical record systems. So, you know, electronic medical record systems are just software. They have to be, if you want them to work effectively and help support clinical decision-making and good patient care, they need to collect the right information and they need to present important information about the patient cases in a usable way to clinicians. So, understanding what guidelines are recommending clinicians can do and being able to, and understanding what is not in a clinical record can help you design better electronic medical records to collect and display information to guide high quality care. So, I will talk a little bit about how some this process set into EMR specifications.

Additionally, so in addition to specific clinical decision support system, where you are support one-on-one patient cases, there is also increasing focus on cohort-based care. So, team-based care for a specific patient cohorts, where the idea here is instead of just waiting for individual patients to come to you with a problem, you can potentially identify cohorts of people with a specific care need and help address those needs to a population rather than to individuals. So, there is a variety of examples that I will mention regarding panel management tools that have been made to help with opiate therapy, and these are tools that help with that cohort-based management and risk mitigation. And, they have been used very specifically by academic detailing to target and evaluate clinical education efforts. So, I will mention those, even those are not, I cannot take responsibility for them. They are fabulous work being done by others.

I also want to talk about how research and validation can be done using these definitions to help really identify the process/outcome relationships, test intervention strategies, and help define high-risk populations, so that we can better target and prioritize recommendations. So, sort of big picture of what I am going to through and I am just going to walk you through examples, and hopefully we will have some time to discuss these and also how to take research and get them included in these efforts more readily.

So, if we go to the next slide, I want to talk briefly about strategies for operationalizing clinical practice guidelines. So, how do you take a written practice guideline and convert it into codable data elements. And, I am going to talk about a general strategy. I will talk in a little bit more detail for individual examples how this is done, because you have to tweak it a little bit depending on the exact use. But, the general strategy we have used is to start with an overall analysis of the guideline. So, what we do here, what we did with the opiate therapy guideline is we sat down with the very large guideline, walked through it step-by-step and actually pulled out every single recommendation that was in the guideline. From that, we had, from all those recommendations, we could find the key concepts that required definition. So, what are, you could read, basically go through the recommendations and can highlight all of the different things that need defining. So, what is a liver disease that changes metabolism of methadone, for example. Or, what is a respiratory disorder that increases risk for respiratory depression on opiates. So, we basically parsed out the entire guideline, which I noted was fairly long and detailed, into those key recommendations. And, then sorted the recommendations to come up with a set of concepts that needed defining and key recommendations that we would want to consider, including in various data systems.

We then took those key concepts and stepped back and looked at what we had for usable data. So, we did a review of the available electronic medical record and administrative data to find what data elements there were that could potentially feed into defining these concepts. So, we looked at what variables are available and tried, started filtering those down to a potential list that could help us define key concepts. One thing I note is that this, while we obviously had to do this once up front, this is an iterative process, and requires at least annual updating for all of these data elements. So, it is one definite challenge in turning research products into operations products, is that research is typically, has time-limited funding. And, development of tools required the ongoing maintenance, so figuring out how to make that translation can be a challenge. Also emphasizes that not only are the electronic medical record data elements not static, but typically, the recommendations are not static either, that they get updated as well. And, so all of these tools need, and all of these definitions need some way of being updated and managed over time. 

We then took the guideline elements that we had identified and the potential data elements that we could use to define them, and worked, put those together to create sort of putative definitions for review. We then took these to the guideline authors, very clinical experts, and we tried to get a diversity of perspective across areas that we thought would be important. So, for opiate therapy we got pain specialists, addiction specialists, primary care clinicians, nurses and pharmacologists to review all of the definitions that were being created. And, what we had them to do is sit down with the potential definitions and actually filter specifics for us. So, we gave people, for example, lists of diagnosis codes and said which ones of these would in fact be ones that you would worry about. Which of these medications are medications that could have potential interactions with a specific opiate, for example, and used those to create very specific definitions. So, the trick here I note is that a given panel of experts typically does not agree 100%, so on any measure definitions, so you have to come up with some method for finalizing your list. If you have a group that works well together, consensus in discussions typically work fine. And, we did a lot of that for our measures. So, we just piece out as much as we could and then get the expert group together to make final decisions on the pieces that they did not agree on. But, we have also seen other systems of voting, but voting on definitions, on sort of iterative parsing with ongoing feedback from a group and even sort of algorithmic ways of finalizing details. We have always worked with very agreeable people, so we have not had trouble with a consensus, but there are other methods for doing that if need be.

We also emphasize once we get those definitions, we then would operationalize them into tools. At that point, we would use those tools to get feedback from the target audience. So, give them examples of the definitions, ask them to report back any potential errors, and then we do very specific, targeted testing of the definitions, tasking clinicians to do comparative chart review of individual patient cases and the definitions to make sure that they appear to agree in concept. And, we did very specific user validation on a patient-by-patient basis. Wanted to make sure that we were pulling the data correctly, and too, to make sure that the data that was being pulled was being interpreted correctly in whatever measure we were using. And, lastly, we do additional validation looking at associations between the concepts that we created and the expected outcomes that, or other associations that the guideline would predict would be there. Because, if they do not, if they are not actually predictive of the risk, for example, or the benefits that they were supposed to be associated, it may be because our definitions are not accurate. So, this is a general process and I will got through details about how we did some of that.

So, I am going to shift now, next slide, to talking specifically about the clinical decision support system that we created. The intended use, as I mentioned, of clinical decision support system was to help with individual patient case review and treatment planning for patients with chronic pain being considered for opiate therapy. So, our decision support system was to help not just with patients who are on opiate therapy, but for patients who might be appropriate for opiate therapy or for whom a clinician might want to consider opiate therapy. And, the decision support provides both reminders and summarized and personalize information to help facilitate and guide good care decisions. I note that this was a project that has been a long time in the making. It was funded originally in 2003 by a Substance Use Disorder QUERI, and we were funded to develop this system to usability testing and in single in-clinic pilot testing of the system. We built that, had successful pilot tests and at that point there were IT changes that were required that stopped us from implementing it further, sort of stalled this project for a while. But, we relatively recently got funding from the National Center for Patient Safety and they go to update the system and study some expanded pilots of the system. I will note that in that time we also updated the system to be updated 2010 guideline, and added a large number of elements to expand the focus of the system to look not only at opiate therapy, but other pain management options that a clinician probably should be considering, at least in conjunction with opiate therapy or instead of opiate therapy.

So, we used the process that I described to try to create definitions that would help us filter the electronic medical record data for clinical use. So, the idea here was we needed to find data elements that mapped onto those guideline concepts within a time range that was clinically relevant, bring those into the system, defined in specific ways. And, then we needed to create an algorithm that would generate individual recommendations based on the patient’s existing data. So, we took those concepts, filtered them through a decision algorithm to provide very, very specific recommendations about how to deliver care for those patients. And, then we created an interface to provide clinicians with summarized information about that patient as well as recommendations to reduce risk and increase clinical effectiveness of opiate therapy right around the time of their clinic visit.

So, I will show you what this looks like. And, actually let us just go down two slides to the screenshot on slide eight. This is what the clinical decision support tool looks like on its front page, and what you can see is how those data elements have been used. So, if you look in the upper right-hand box, there is a set of cautions. These are basically data that was pulled out of the clinical record that the guidelines said cause greater risk for adverse events related to opiate therapy. So, these are basically variable definitions that create alerts for this system. All of those alerts are going to be explained further in the data on the left, so you will see if you look on the right side of the screen there is a set of clinical alerts that describe what all of those cautions are. If you can drill down on those then they will give very specific information about how to address all of those risks related to the alerts. The system also takes the data that we pulled, the guideline-related information and summarizes it into a large set of data tables. This filters out essentially your electronic medical record data into things that you actually need to consider when you are considering opiate therapy. And, this includes everything from what is the history of opiate prescribing in that patient, what are their clinical diagnoses that are relevant to pain management and opiate therapies, what has their pain score been over the last however many years. Have they done urine drug screening, and what other treatments and medications for pain has the patient received historically. So, that summarizes all that information, makes it easy for a clinician to take a look at. And, then on the right you can see what are detailed recommendations that are generated based upon the filtered data that we pulled from the electronic medical records. 

And, one of the things that we note is that the system is supposed to provide information to guide clinical decision-making. It does not make decisions for clinicians. So, what is provided here, if you go to the next slide is patient recommendations are actually detailed information about how to implement various options that a clinician may decide. And, it also includes in some cases indications or contraindications for making various decisions. So, you can see here in this if you choose, that if you decide you want to start a long-acting morphine, it is long-acting morphine in this patient, it will give you the dosing schedule, titration schedule and key elements about, in terms of patient education that needs to be delivered. So, you could choose as many options as you want, basically, to get information about each. Additionally, under clinical alerts, it explains what you find in the cautions and exactly how to respond to that clinically. 

So, as I mentioned, we did a large amount of testing with the system. One of the main things we found in doing in-clinic testing was that while clinicians were, found it very, very useful in general, it got used mostly by clinicians who are already doing the best job with opiate therapy. So, it was the clinicians who were already doing good practice around opiate therapy who found this to be a very helpful tool for speeding up their practice. But, we found that people who were maybe not doing or following clinical practice guidelines as much as we would like, were not spending much time looking at it and frankly, were not that interested. And, the feedback we got from them was that they had other clinical priorities, and while this would be helpful if this was something they wanted to do, they needed, unless their supervisor told them that this was something that they wanted them to focus on, they probably would not spend a lot of time doing. So, based on that we decided we needed to create other incentives for quality improvement and use that to guide development of quality improvement metrics.

So, here the focus instead of being on specific individual, clinical decision making, was on defining very key recommendations that could be indicators of system problems in delivering care. So, what we did was we took all of those identified recommendations and tried to cluster them down into conceptual concepts that would be managed similarly from a clinical practice perspective. And, because we obviously could not include every element in a set of quality improvement metrics, or they would not be very helpful for helping prioritize process improvement efforts, we tried to pick one key recommendation in each of the clusters of practice recommendations that we thought could be well-coded. So, could be looked at effectively, and what is particularly important for improving care practice. So, we filtered down the guideline into a set of conceptual domains and then tried to pick one or two measures in each of those domains to focus on and to measure systematically. 

So, here we developed metrics that we calculated at the system level. The goal here was to motivate system-wide change, not to try to identify individual patients or individual providers for targeted intervention. The hope was that using these metrics could help sites identify local weaknesses. It could help them search for other sites that are doing a better job than them, as a way to help identify best practice sites and learn new ways that they could do things. And, we worked to try to connect people up with better practice sites. And, then we wanted them to be able to monitor their progress over time as they made local efforts to try to improve care delivery. And, the hope was that these measures would help providers to both attend to pain and opiate management, and highlight where they were and were not making good use of practices. The other hope was that these would help administrators identify places in which they had resource gaps or training gaps that they could help fill to make it easier for providers to follow guidelines, or make it possible to deliver the types of care the recommendations required.

And, so again, the metric developed in that development, in validation of these metrics was funded by the Substance Use Disorder QUERI, and once these were created, or once the measures were created they were translated into a dashboard, which is available on VSFC. And, they are maintained currently by the National Center for Patient Safety and Office of Mental Health Operations in collaboration. So, they have been translated from research developed and validated measures into a national quality improvement tool. So, I just discussed what we do there.

So, next slide is slide twelve that describes the opiate prescribing practice metrics. And, these are the large domains that we decided to focus on. So, first we decided to look on just the propensity to prescribe, to what extent, how likely as a patient are you to get an opiate if you come into a specific medical center. And, there is wide variation in that across our system. Side effects management, there is a large number of things that providers should do to improve side effect management. We decided to focus on provision of a bowel regimen since that is something that pretty much applies to every patient on opiate therapy. We looked at serious adverse events, so to what extent are patients having documented adverse events following an opiate prescription. We looked for potentially dangerous drug interactions, so over-prescribing of acetaminophen or combined opiate and sedative prescribing there. We looked at ways of minimizing misuse risk, so encouraging mental health assessment and getting patients with substance use disorder on opiate therapy into substance use disorder treatment. We looked at appropriate follow-ups, whether patients getting prescription would get into care in a timely, or have an additional visit, a follow-up visit in a timely manner. We put a lot of emphasis on making sure that other non-opiate therapy options were available and being used in these patients, so looking at psycho-social treatments, other recommended pharmacotherapies for pain, use of rehabilitation medicine and complementary and alternative treatments. And, then also looking at some, at pharmacy variables like whether or not medication management and pharmacy reconciliation was happening, and whether or not absolutely contraindicated opiates were being prescribed, such as high dose initiations of opiates. And, we also had a focus on an appropriate use of lab tests, here focusing on urine drug screening as a key example.

With these metrics we decided to break them out into hierarchical patient subgroups based on how they were prescribed. So, whether or not they were getting long-acting opiates, short-acting opiates and whether those were chronic or acute use. So, more or less than 90 days’ supply in a fiscal year. And, we note that the top two groups are really what the guideline addresses, but we felt that acute short-acting opiates and Tramadol also potentially create risks and were the population that could eventually become chronic users. And, so we wanted to provide information on use of these practices in those groups as well, so that people could get a sense of more holistically what was being done in their practice. 

So, if you go to the next slide, as I said, there is a dashboard now available. Its intended use is not as sort of any sort of carrot or stick performance measurement system, but it is a way to help sites identify problem areas, design and evaluate local quality improvements efforts, identify model programs and share best practices. So, this is the link you can get to that dashboard. And, this is what it looks like. You can see, you can pick your facilities, your comparisons, which metrics you want to look at, which patient populations you are interested in and what time window you want to look at, and drill down on any of these measures to get detailed information about performance and the patient population involved in each of these measures at each time point, at each facility. So, lots of information there that we hope sites will use to help identify and focus. And, I note we are now in discussions about how to support use of this dashboard by working with sites to come up with trainings on how to look at the data, how to interpret the data, how to design quality improvement projects and do local evaluations, basically focusing around the action planning.

So, another example, if we go to the next slide, I mentioned that electronic medical records need to be appropriately designed in order to help support care. And, really optimizing design of a electronic medical record requires that you understand what information needs to be taken in, in order to document care needs and plan and monitor treatment. You also have to know what needs to be presented to a clinician when in order to get that information out appropriately. So, as an example of how this process can feed into that, I note that Medicare, Medicaid developed a set of meaningful use criteria. It is what they called in that outline the standard types of information that an electronic medical record should collect and make available to clinicians to meaningfully support care delivery. Well, we did... we helped supply a workgroup that was working with CMS to develop meaningful use criteria around opiate therapy, with the data elements that we designed for that decision support system and the quality improvement metrics, to help them design a set of meaningful, of potential meaningful use criteria, to help support good opiate prescribing practices.

And, if you go to the next slide, what we did was using those codable elements, we helped them identify important guideline concepts that were not captured in electronic medical records. And, helped them propose addition of specific structured data elements that should become a standard component of electronic medical records, so things such as pain-related functioning or whether or not the patient has had informed consent or an opiate use agreement put in place. We used it to propose standard alerts and the data use that should drive them in electronic medical records, and those were taken from a lot of the work that we did in defining the cautions for the Athena opiate therapy system. And, then we also proposed, the group proposed standard quality metrics that should be included, along with electronic medical record systems to guide facilities in identifying care gaps. And, those were taken fairly directly from the quality improvement metrics that we created. And, those were put together as part of a solicited proposal for meaningful use criteria to help reduce adverse events related to opiate therapy.

If you go to the next slide, I am going to try to go quickly through these, so we have time for discussion. I mentioned as well that you can take these same data elements and use them to build not individualized patients or system-wide tools, but rather panel management tools. And, the goal here is to identify high risk patient cohorts for case review and augmentation or modification of treatment. And, here instead of, the point is to find either patient cohorts that are clustered around risk, or a specific clinician, who might be providing the treatments or a given patient setting, so say a PACT team, and pulling their data together to help clinicians review and address specific targeted risk factors or identified places where they are not adhering to guidelines. And, then these can be used by administration to help identify providers or clinics or patient clusters where they need process improvements or they need more training or they need more resources. It can also be used by the clinicians themselves to go in and intervene on that entire cluster of patient cases.

So, some examples of where this has been done. The Academic Detailing Program and the Opiate Safety Initiative have both developed panel management tools to help with improving opiate guideline adherence. So, Academic Detailing has developed a facility tool with provider-level metrics that allow you to drill down to specific patients. They, I note they also found that they really, in order to get attention to these, they needed to set facility goals for improvement and do some targeted quality improvement efforts. And, then they worked with individual clinicians to train them how to use that panel management tool and also educate them on evidence-based practice change. So, they did a really great job of sort of combining a tool and specific targeted education and quality improvement focus together, to make some very, very specific and targeted change in clinical practice at their sites. So, really cool stuff. I note that the Opiate Safety Initiative is a national initiative that is taking a similar path, and they develop provider-level lists of high risk cohorts, but they are, that VISNs are working on to review and develop strategies to address high risk prescribing in those areas. And, I would say, so I note that we helped collaborate with both of these groups to suggest potential measures and share codable data elements that they could use in their own local initiative. And, some of those have been used fairly directly.

Lastly, and probably most interestingly to researchers out there, we would emphasize that like make measures by, through group consensus, they are most useful when they are strongly validated and when we actually shown that those measures have expected relationships to outcomes and other related concepts. So, typically, you can take that straight out of the guidelines. So, the guidelines usually tell you not just what is important to pay attention to, but what those things should be associated with or predict. So, they tell you for example that this might be a risk of a specific adverse outcome or a better patient outcome, or a need for specific clinical intervention. And, you can actually, if you have data, can go in and look at whether those expected associations exist. So, some of the stuff, work that we have done around this, is we have examined whether some of our defined concepts have expected relationships to outcomes. 

If you go to slide twenty-two, here are just some examples of the multilevel models we did, taking the concepts from the quality metrics as well as data elements from the patient cautions list, to look at those patients and facility and guideline treatment receipts, predictors of adverse events in these populations. So, this looks at predictors of suicide attempts in patients receiving chronic, short-acting opiates across the VA, at the VA’s 141 healthcare systems. We looked at patient predictors at the individual level, treatment receipts, so receipt of guideline recommended practices at the individual level, and then facility performance on, in delivering guideline recommended practices at the facility level. And, what you can see is there is many of the expected predictors of increased risk of suicide attempts per the guidelines, things like having a substance use disorder, having a mood disorder in the medical record, having traumatic brain injury, all predict greater risk for a suicide attempt, not hugely surprising. Potentially, not unexpected, but potentially confusingly, the receipts of a lot of the guideline recommended practices were associated with greater risk at the individual level, suggesting that providers in general are more likely to be more cautious and follow guidelines in more detail when the patients have risk factors or are at high risk, suggesting that the providers are reasonably good at figuring out who they need to spend more time with. So, for example, patients that got a mental health assessment were more like to commit suicide. Patients that had more rehabilitative treatments that providers decided to urine drug screen were at substantially greater risk for a suicide attempt. Not that we think these things cause suicide attempts, but that providers are reasonably good at targeting them. 

At the facility level we found that when patients received their opiates at a facility that did urine drug screens on a greater proportion of the patient population, they were at lower risk for a suicide attempt. So, this suggests at the facility level, if the facility, facilities that use urine drug screening for risk mitigation strategies seem to have, patients that go there have lower rates of suicide attempts, or lower risk for suicide attempts. We did a similar analysis in patients with long-acting opiate prescriptions. You can see very similar patterns of patient predictors, both with patient risk factors and treatment receipts. At the facility level, we found with long-acting opiates facilities that more reliably followed up patients in a timely manner, that did drug screening, and that avoided sedative co-prescription had lower risk of suicide attempts. 

And, it is similar if you go to the next slide, similar analyses predicting overdose. And, this is opiate sedative or acetaminophen overdose combined, I believe. And, again, very similar predictions with patient, key patient characteristics like have a substance use disorder or having a traumatic brain injury, or even having a medical frailty increasing risk for overdose. And, the medical frailty is sort of a combined category looking at liver, kidney and respiratory problems or chronic liver, kidney or respiratory problems. Again, treatment received, people who are getting more careful treatment tended to be at higher risk of overdose, and urine drug screening at the facility tended to appear to reduce, or was associated with reduced risk. And, this is the same, looking at overdose, the next one is looking at overdose in the long-acting opiate prescribed population. Again, sort of validating that same pattern of relationships between patients, of treatment receipts and facility, and consistent facility use of practices.

So, the key take homes I wanted to bring together were that first, that encoding guidelines really provides a large number of opportunities to help take those guideline recommendations and turn them into quality improvement, or implementation tools and strategies, that without, that given the big focus on data driven decision making, both at the clinical level, the administrative level, the operational level. Efforts to figure out ways of making, of taking clinical recommendations and making them concrete in terms of data, can be very, very helpful for providing information resources in a variety of different ways to people who need that information in order to make, to change their behavior and change their care decisions. So, I think one of the things that I would, that I think would be very, very helpful is if the research and operations community can find, can come up with new standardized and efficient ways of incorporating this data defining elements into guideline development. Another thing I wanted to point out is that variable and measure development, which is part of almost every research project has very important operational value to VA and other healthcare organizations. And, so I think researchers have a tendency to think of just publishing what their overall findings, there are lots and lots of ways that a lot of the work that goes into that can help have clinical impact or operation impact. And, so figuring out ways of sharing research developed data definitions and databases with operations may help increase the clinical impact of HSR&D research.

And, lastly, I would say that there is a large number of existing VA platforms that provide opportunity for pretty flexible development of tools to help facilitate quality improvement in good clinical practice. And, there has been very little research done in the impact of these tools and the factors that moderate their effects on clinical practice. And, I think research interest or help in doing some of that evaluation and research would be highly encouraged.

So, with that I would love to take questions or have some discussion.

Moderator:Okay. Fantastic. We do have a couple of questions. I am assuming you still cannot see these slides on the screen yet, Jodie?

Dr. Trafton:No.

Moderator:Okay. That is fine, that is fine. The first couple of questions are actually about the dashboard that you referred to on slide fourteen. The link on the slide is not active, and it looks like it is connected to a secure site. Is this available for general access, or how does one get access to that?

Dr. Trafton:It is available for general access. I will note the server has been going up and down for the last week, so it may be down at the moment. But, it will work generally. The way to get it is if you go to the main VSSC site, which I believe is vssc.med.va.gov. I might have got that wrong, but if you go the main VSSC website, it is available, and then go to the user acceptance bar, which is on the left upper right-hand corner of the screen on the main VSSC page and then go to mental health, you will find the dashboard under user acceptance in that mental health section. I can send it again, but I suspect the dashboard like worked yesterday, it should work again. I am sorry about the server problems. But, it is available to everyone, or anyone who has a VA login. If you are coming from outside VA, it will not work. If you are coming from your VA login, you should be able to get into it directly.

Moderator:Okay. We did just get another quick question here. Can we receive these instructions to log in by email?

Dr. Trafton:Absolutely. I can send them to you, if we can...

Moderator:Yeah, if you just want to send something over to me, I can send that over to everyone who attended today’s session.

Dr. Trafton:Okay, I will do that.

Moderator:Sounds good. Okay. The next question here, so the dashboard prioritizes who is a provider’s empaneled Veterans should be considered for early chart review and in person reassessment?

Dr. Trafton:So, the dashboard, the VSSC dashboard, it is really focused not on the provider, but rather on the facility as it whole. So, it does not actually drill down to the clinician or the patient. What we were hoping here was to get operational and administrative support for clinicians, rather than focusing on what individual clinicians are doing. So, it really does not drill down. It looks at a system level. It is really helpful for identifying places, for example, that may not have enough physical therapy available, that are not doing pharmacy reconciliations on a regular basis, have some barriers to urine drug screening, for example. So, it is designed more to look at system level problems rather than individual providers. There are other panel management tools, as I said, the Opiate Safety Initiative and there are some, VISN 21 and VISN 22 have developed some systems, part of the Academic Detailing Program. But, this one is much more a high level quality improvement tool.

Moderator:Okay, great. Thank you. The next question here, if the facility has a system or a culture of UDS as standard of care, having had UDS was associated with lower risk of suicide?

Dr. Trafton:Yes. Yeah, so facilities that had, once you are factoring in, like controlling for patients , or not really controlling for, but including patient characteristics and facility characteristics, risk of suicide attempts and opiate overdose was lower if your facility was using urine drug screening more systematically. So, sites where a higher proportion of patients were getting urine drug screens, those individual patients had lower risk.

Moderator:Okay, great. Thank you. The next question here, how do you handle concurrent drugs such as benzoes and Zolpidem?

Dr. Trafton:How do we handle them? In our metrics, we look for overlapping days supplied. If that is what the question is. We discourage concomitant or overlapping prescriptions between opiates, benzoes and Zolpidem in general.

Moderator:Okay, great. Thank you. 

Dr. Trafton:And, I guess the guideline would recommends that if and when you have to do that, you should have more, you should, the patients need closer follow up and additional work. I also note that there are efforts under way to make overdose, opiate overdose education in the locks and distribution programs available across VA. So, we are hoping that very shortly, it should be easy for clinicians to provide patients that might be on high risk combinations or have other high risk combinations of drugs. We’ll lock down training in how to use it in the case of overdose.

Moderator:Okay, great. Thank you. The next question here, how is Athena OT further piloting being considered and what intent of and how is the adherence report being integrated with the PDM dashboard?

Dr. Trafton:Okay. So the Athena Opiate Therapy piloting right now is being conducted in the San Francisco primary care clinics and the Portland VA primary care clinics. So, there is an ongoing implementation occurring there that we are evaluating, impact on care delivery, on at that moment. So, Amanda Midboe is leading those efforts currently. At the same time, we are working on ways of making Athena OT more transportable from a technology perspective, so making it easier for other sites to implement. And, we anticipate sooner, or hopefully in the next year or so that we will be able to make it available to other sites that are interested in implementing it. So, I cannot see the rest of the question. And, how is the adherence report? So, the dashboard here being integrated with the Opiate Safety Initiative dashboard. Is that the question? So, what we have done there is we actually worked with them. We shared data definitions and helped with the development of the measures included in the Opiate Safety Initiative. So, we are working together to target the same issues essentially with our various metrics. So, the quality improvement metrics overlap highly with the panel management and dashboards that the Opiate Safety Initiative has created, in hopes that we can encourage both the system level and panel management approached to addressing the same issues.

Moderator:Okay, fantastic. And, we just received another request in for the link to the handouts, and I just sent that out to everyone there. So, hopefully, that should be a live link, you all can click on that to get the handouts. Or, when we send out the archive notice you can use that link to get to the handouts there. That actually is all of the questions that received in here. Bob, I am not sure if you are still on the call. If you have anything you would want to ask or comments you want to make, while you are doing that, I am going to put up the feedback form here so we can get that going at the same time.

Dr. Kerns:I actually am here. I am going to rush off. I do not have any additional questions. Jodie and I interact quite a bit about this. I really want to show my appreciation to Jodie for her excellent presentation, for Robin and Heidi for pulling all this together. And, to those of you who are participating today, I only want to reinforce your interest in this topic. I hope some of you, maybe many of you are involved in policy and practice decisions at your local facility, maybe involved in, for example, the national Opioid Safety Initiative or other efforts to promote organizational change in pain management. I hope you took good notes and will track down these slides. I think these, the work of Jodie and her colleagues at PERC, among the work of several other people in the operations and research domain, are really quite important and foundational to our organization efforts. And, I think you have a lot, we all have a lot to learn by paying attention to these efforts. So, please follow up this meeting today with further interactions with me, with Robin, Heidi and even Jodie is, as much as she can participate, to make sure that we bring this work to bear on our organizational change efforts. So, thanks everybody.

Robin Masheb:Thanks, Bob. Thanks, Heidi. And, especially thank you to you, Jodie, for preparing and presenting. We very much appreciate it. Just one more reminder to hold on another minute or two for the feedback form. Our next cyberseminar will be Tuesday, April 1, by Dr. Marianne Matthias. We will be sending registration information out to everyone around the 15th of the month. I want to thank everyone for joining us at this HSR&D cyber seminar. And, we hope to see you at a future session.

Moderator:Thank you everyone.

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