How Learning Health Networks Improve Access, Insight, and Patient Success

The fastest way to improve healthcare is to stop treating clinics like isolated islands. I’m joined by Donna Murray, PhD, speech-language pathologist and quality improvement leader, to unpack learning health networks and why they’re becoming a serious force in hearing healthcare, tinnitus care, and patient-centered outcomes.

We get practical about what a learning health network actually is: a connected community of clinics and organizations where patients, families, clinicians, and researchers co-design what gets measured, collect real-world clinical data once, and then use it to improve decisions at the point of care. Donna explains how this shared data feedback loop helps break the silos that slow evidence-based practice, and why it can shorten the long lag between research findings and what happens in exam rooms.

We also dig into the messy reality of variability and complexity in conditions like tinnitus and autism. One broad label can hide many subtypes, which is why “what works” can look inconsistent across patients and across locations. Donna makes the case for building a common language, standardizing assessment and severity tracking, and using network-scale cohorts to spot what works for whom, not just what works on average.

If you care about clinical outcomes, quality improvement, real-world evidence, and a more collaborative culture of care, this conversation will give you a clear framework for what comes next. Subscribe to Hearing Matters, share this with a colleague, and leave a review with the one change you think would most improve tinnitus care.

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Transcript

Meet Donna Murray

Blaise M. Delfino, M.S. - HIS 0:06

Welcome back to the Hearing Matters Podcast, where we explore hearing technology, communication science, and the people and ideas shaping the future of hearing health care and hearing loss around the world. Before we kick things off, special thank you to our partners. CareCredit. Here today to help more people here tomorrow. Inventis. Inventis is innovation. LA Productions. Tell your story, share your brand, and fader plugs, the world's first custom adjustable earplugs. Welcome back to another episode of the Hearing Matters Podcast. I'm your founder and host, Blaise Delfino. And as a friendly reminder, this podcast is separate from my work at Starkey. Now, let's get into the conversation. You're tuned in to the Hearing Matters Podcast. I'm your host, Blaise Delfino, and joining me today is Donna Murray. Today we're going to talk all things learning health networks. And Donna, I'm not going to steal your thunder, but I just want to preface this episode in that we, of course, have connected because we are both colleagues and friends of Jeffrey Reagan, who is founder of the Tinnitus Learning Health Network in partnership with Dr. James Henry and yourself. So we're talking all things learning health networks. Donna, before we dive into what a learning health network is, you and I share similar backgrounds in speech language pathology. So tell us a little bit about yourself, your experience in communication sciences and disorders. And then we'll get into what learning health networks are.

SLP Roots And Career Pivot

Donna Murray, PhD 1:53

Sure. As you've stated, I'm clinically trained as a speech language pathologist and work as a clinician, a researcher, and program developer actually for about 20 years in the field. And I, you know, my trajectory, I think, is a little unusual in the fact that I worked in a diagnostic and treatment aspect of speech language pathology. But then in about 2004, after I finished my PhD in communication sciences, I joined the Cincinnati Children's Hospital as the co-director of their autism program there. And we were selected for a Robert Wood Johnson grant and using quality improvement science to think about how we could improve access to care and how you can implement that methodology to improve outcomes. And so that really sort of shifted my trajectory into quality improvement. Additionally, at that time, our clinic joined a network of other autism programs in the Autism Treatment Network, which was a clinical research network looking to improve the diagnostic process for autism and address and learn more about effective interventions. And we did a lot of publications, a lot of research, had numerous presentations. And then it was interesting because after I moved from Cincinnati Children's Hospital, I actually moved to Autism Speaks to lead that network. And we continued our publications, we continued research, but we were beginning to field questions from our funders, rightly so, but difficult questions around you know, you have these publications, you have these presentations, you've developed all kinds of resources. How do you know what you've done has had impact? Right? Fair, but oh, that that hurts, you know, that's that's a tough question. So I went back to my mentors in quality improvement since I had children's and I said, you know, it's interesting, we've done all of this work, our centers are doing quality improvement, but how do we think about this as a network in a larger scale? And that's where I learned about learning health networks. And at that point, we said, this is really what we should be doing in order to look at how quality improvement can then follow our efforts and try to look at outcomes at scale and over a long period of time. And so we in about 2015 or 2016 started transitioning our more traditional clinical research network to a learning health network.

Defining Learning Health Networks

Blaise M. Delfino, M.S. - HIS 4:24

Well, Donna, I love the fact that we, of course, both share a passion for communication sciences. And you bring up Autism Speaks. I graduated with my bachelor's and master's from East Strasbourg University of Pennsylvania. And it was during my, I believe it was either my junior or senior year of undergrad, brought the first chapter to East Strasbourg University and loved, you know, leading that club in partnership with a lot of, you know, now my colleagues. And just so much to impact there. Now, the SLP background in me as it relates to, well, you have this resource kit and all of these resources to the point of the questions of, well, how do you know it's actually working? I'm sure your SLP brain was like, well, let me show you. I can, I can show you how it's working, but it's more of a grander scale, right? Like you're distributing these resources to other clinicians. And how do we know that there is appropriate, correct, and effective carryover? And that's, I believe, a great segue for you know what a learning health network is. Now, for someone who's never heard the term before, what is a learning health network in plain language?

Donna Murray, PhD 5:41

A learning health network actually was described first by the Institute of Medicine, which is now, I think, the National Academy of Medicine, as a learning health system. And the vision that they described was being able to bring together patients, families, clinicians, and researchers and using that shared information and data in order to drive clinical decision making to improve care and do that more quickly. A learning health network then takes that concept into multiple organizations. So it creates a community of organizations working collaboratively, breaking down those silos so that patients, families, researchers, clinicians are all then working collaboratively in order to improve care and bring the findings to practice more quickly.

Blaise M. Delfino, M.S. - HIS 6:32

What core problem in healthcare were LHNs originally designed to solve?

Donna Murray, PhD 6:39

It's really designed to accelerate bringing findings to practice and also to break down silos. As you know, many of us clinicians are busy, they're working, they're on the ground every day, researchers are busy, they're over here doing the research. The problem is the connection between the findings and research and getting that information to the clinician, the on-the-ground clinician, and also knowing and understanding better what's important to our patients and families. Are we prioritizing what they are finding to be the biggest barriers or challenges? And so we have these sort of, you know, family advisory committees, we have researchers, we have clinicians, and often they were sort of working in silos with some overlap, but not really collaboratively working together. So learning health networks want to create systems in which, from the very beginning of conceptualizing what we're working toward, we are working collaboratively with those families, patients, clinicians, and researchers. And also, when you embed doing this work within clinical practice, you are learning more about how this works in the real world. And so you can have a mechanism of connected sites where you can begin to evaluate what's working for whom and then share that knowledge more quickly, test that in another site and in order to then distribute and more quickly embed those practices in the clinical world.

Blaise M. Delfino, M.S. - HIS 8:09

And Donna, I am so fascinated by learning health networks. Of course, now I have learned a ton about them being connected with Jeffrey and Dr. Henry, and of course, you. I feel as though that they solve so many unique challenges. And we're not going to name all of them today, but you can't see the picture when you're in the frame, right? So, Donna, how that process really should look like is well, I'm working with a patient who presents with apraxia of speech or just had a stroke, and this is the evidence-based therapy that we implemented. Now I'm going to share that with the learning health network. Am I correct in saying that that's essentially how that ecosystem works within a learning health network, where of course you're implementing as a speech language pathologist or now audiologist with the Tenetus Learning Health Network, implementing evidence-based practice, working with that patient, and then sharing the results and the outcomes with the research team. And then there's this constant feedback loop. Am I correct in saying that or am I missing something?

The Real-Time Data Feedback Loop

Donna Murray, PhD 9:16

No, no, no. Uh actually, I think you're doing a great job of sort of stating that. And I would even go further to say, you know, often in the clinical world, clinical data, there's a ton of data collected. We don't always harness that, right? We don't harness that in an aggregated or group way, right? Same thing with research, there's a lot of data being collected, but that doesn't enter the medical record, because there's all kinds of issues with that data coming together. The concept behind a learning health network is pretty much what you were stating, which is it should be the concept is data in once, right? If we're collecting data as part of clinical practice, we can enter that data and be able to evaluate that data in more real time. What that means is if we can get that data back to the clinician, which often doesn't happen in traditional research, and I want to say traditional research is incredibly important. I this is really sort of another arm of that kind of research and testing that in the real world. And so if you can get data in, get that data back to the provider, that data can be used at the point of visit for that patient's care. That data then can be aggregated with other patients from that clinic, right, to look at how they're doing and then also contributed to the network so that sites can look at what's happening. Then, if we have really well described subpopulations or a population, then you can begin to see, you know what, for this type of patient presenting with this profile, what we're finding is that this intervention is working really well. Or hospital A or Clinic A is doing a great job with this kind of patient. Their outcomes look great. Hey, what are you doing? Then we share that and we test that in other sites to see if that's working with their clinical populations. So you see how you create this ecosystem of much more rapidly beginning to look at what is working and what's not working with certain patients. Because you can say, hey, we think we're doing a great job. Everybody, this is our step, this is our best practice. Look at this, we're implementing this guideline. But oh my gosh, did you ever notice patients who are presenting like this? Look at this. They're not improving, they're getting left behind. That's an intervention. And so that's when you bring in the quality improvement. We're going to test an intervention with this population and see if that works. So that's that ecosystem that we're talking about. It's just constantly being able to use that data to improve care at the time of medical visit. So that patient contributing data is helping their own care, but they're also helping those that those other patients that have a similar condition by contributing their data in a de-identified aggregate way.

Why Evidence Takes Years To Reach Care

Blaise M. Delfino, M.S. - HIS 11:54

I absolutely love this model. And looking at this through the lens of hearing healthcare, the importance of evidence-based practice, practicing at the top of your game. And not only, you know, of course, every year we have to obtain a certain amount of continuing education units, right? But reading the science, there's so much pseudoscience today. I mean, just within the past five years, you go on your phone, if you're scrolling through, it's like, did you know X, Y, and Z causes X, Y, and Z? And, you know, so many people are like, oh my gosh, I didn't know that. It's wild to me. And traditional research, incredible, so important. This is why, from a scientific standpoint, not only here in the States, but globally, we've been able to make some incredible scientific findings and advancements and enhancements throughout the entire healthcare system. But even looking at this through the audiology and hearing care professional lens. Now, when I was practicing full-time, of course, we were not a part of a learning health network. But what I will say, Don, is what we did, we would run these in-house mini studies, if you will. You know, it was never an IRB or anything of that. But what we were so interested in is okay, let's see how new patients do with the latest technology. We're going to practice at the top of our game in terms of high standard of care. I mean, video autoscopy, autoacoustic emissions, speech and noise testing. And then we would conduct the abbreviated profile hearing aid benefit. And what we would do is compare and contrast. Sometimes we would have an N of 30, which is a, it's not incredibly large sample size. But Donna, the results that we would see is oh wow, this specific technology when it's fit with real ear measurement, but also subjective outcomes, patients will see X, Y, and Z. And that's why when we first connected, I was so interested in this because how many not only clinicians can this help, but most importantly, how many patients can this can help? And we often hear that research can take 15 to 17 years to actually reach clinical practice. Why does that happen?

Donna Murray, PhD 14:25

You know, I think because of those silos, right? There's a lot of research happening in one place. But in order to access that information when it's published, you'd have to read the journal or find the article that relates to the condition you're talking about. Or maybe it happens at one or a few handful of hospitals involved in the study and they make the change. And then you have to wait till like, you know, you go to professional presentations and then you hear about it and then you come back. But then maybe you have questions about how to implement it. And so I think that there's a lot going on as far as how this relates. Or nine to talk about how sometimes research then takes place in sort of these pristine protocols and sort of lab environments. And we don't know necessarily how that intervention translates to clinical practice. Is it reimbursable? Are there time constraints? Does it represent the population that I serve? So I think that there's a lot of issues around, again, very needed, but the replication processes take a bit longer than when you embed that kind of data collection and evaluation in real-world clinical settings. Now, I will say the reverse. Learning health networks also are very supportive and can enhance and support traditional research, right? By identifying those gaps where further in-depth research needs to take place, where you do need those stringent protocols and you need to figure out what interventions are working. They also provide well-described populations or cohorts or subgroups that can be easily recruited for research. So I can say, as a part of a member of a learning health network, we did a lot of our daily work was in that ecosystem of the QI, but we also were involved in quite a bit of traditional research as well. So they do go hand in hand. But I will say that learning health networks were designed specifically to accelerate that implementation of findings into practice. Because think about it, you have a network of sites already connected. You're using aggregated data to look at what's working, where and for whom. And then you also then have a network of hospitals or clinics in which you can distribute findings very quickly because they're already connected. So that's really where I think this resolves and why we think both it takes so long for traditional research, and then, you know, what is it that learning health networks are set up to solve?

Blaise M. Delfino, M.S. - HIS 17:35

I love the influence that a learning health network can have on traditional research. And we talk about acceleration of science and acceleration of evidence-based findings. If anything can accelerate that, it's absolutely that symbiotic relationship that a learning health network can have with traditional research. This is so exciting, Donna.

Donna Murray, PhD 18:02

Think about it, Blaze. I mean, when you get a grant, part of the big issue for funding is you know, finding those collaborative partners, identifying the patients for recruitment. When you really have a platform that is nimble enough then to be able to quickly identify the appropriate, you know, patients for that trial, right? And a mechanism in which we can already have partners in place and distribute those findings. So you're right. It's very much a nice ecosystem for both.

Managing Variability With Subgroups

Blaise M. Delfino, M.S. - HIS 18:34

Learning health networks have been around for quite some time and just also the importance that it has on clinicians practicing to that high standard of care and that cross-collaboration, but also intercollaboration between different hospital systems as well. And I know if we put ourselves in the patient's shoes and you go to a clinic that is part of a learning health network, you know that they are implementing this evidence-based practice, evidence-based findings, and there is that constant feedback loop. I know as a patient, I'll be like, oh wow, like you know your stuff. Like you really do.

Donna Murray, PhD 19:16

For our patients and families, that's incredibly important. And even though you may not have all the answers right away, and there is evidence often lacking, especially in conditions like tinnitus and autism, the fact that you're really trying to work together collaboratively to solve those problems is incredibly meaningful for those families and patients.

Blaise M. Delfino, M.S. - HIS 19:35

Absolutely. And of course, you know, we're not going to touch on the, you know, the back end stuff in terms of, you know, HIPAA compliance, data security, things of that nature. But that is a main priority of a learning health network. So I just want our audience to hear that. I want to dive in a little bit to variability and systems complexity. So in conditions like autism and tinnitus, there is significant variability. From a systems perspective, Donna, why does variability make improvement more difficult?

Donna Murray, PhD 20:09

Well, from a lot of angles. And I this is where, although I'm not a tinnitus expert specifically, you know, I leave that to Dr. Henry and Jeffrey, the content experts.

Blaise M. Delfino, M.S. - HIS 20:18

Me too, Donna. I do see humility, me too.

Donna Murray, PhD 20:24

I do see a lot of similarities in conditions like autism and tennitus. And so I, you know, I think it's incredibly important we have an umbrella term, if you will, in which the symptoms are very similar, right? Very specific around symptoms. But we know that there are likely different etiologies or causes of that condition. And we also know then that we have non-responders and responders to different interventions, right? Which is why it makes improvement or research incredibly difficult because the label alone, if you will, tetadus or autism, and you recruit and you do a research project, and you don't really get a significant finding. Well, what you may not be able to find is maybe it did help a subgroup, but they were only a small subgroup of that larger cohort. So we may miss actually those signals of improvement in that large. That's why it makes it so difficult when you have this kind of variability because it's not a one size fits all. It's, you know, it really depends on that patient profile. So part of the work that's really important in networks like this is doing a good job of describing that population so that we can begin to cluster those patients at local light. We call them sort of cohorts or subtypes or subgroups. And this is why it's really helpful for both improvement and traditional research, because you want to make sure that if you're testing something or you're looking at an intervention, or you're just tracking initially the interventions that these patients have tried, and then you begin to see that wow, for this subgroup, this works, or this subgroup, this definitely doesn't work. And that's incredibly important on a number of levels. One is when you have a condition as variable as tennitus and autism, a few things happen. One is we see a lot of disparities based on geographic location, that the care you get depends a lot on where you go because there's a lot of trial and error. And that's not really being negative, that's just the realities of when we don't have good evidence for what works for whom, there's a bit of trial and error, right? So there's lack of some standardization of care. And and Secondly, the cost, the frustration, the time to effective treatments is frustrating for families. And I will say, frustrating for clinicians. You know, I I can't tell you how many patients sat in front of me and said, we just want a roadmap. Just tell us, like, you know, our child just got this diagnosis. What is it that we should do? And, you know, what should we expect as far as outcomes? And we didn't have those answers, you know. So that's why I think in many cases we think, well, there's a lot of data because there's a lot of patients with these conditions. But when you start breaking these conditions down to subgroups, those numbers get smaller in a singular location or a singular clinic. And that's where the power of networks, because now you've got networks with a few of these types of patients. So then you begin to get bigger groups in order to effectively conduct research or look at improvements or gaps in care.

Blaise M. Delfino, M.S. - HIS 23:37

Well, even when you're talking about autism and just the scope of speech language pathology is quite vast. I'm not going to go through all of it, but I mean, you have cognition, voice, you have speech, you have language. And underneath speech, of course, is that neuromuscular process. Then you'll have articulation and fluency. And there are some cases, I'm sure, that you might be working on pediatric feeding. And that's where that subgroup under autism might include. Or is it echolalia? And then as we turn to tinnitus, those subgroups could be well, patients who report their tinnitus being mild or moderate, or this specific patient is saying that they hear their tinnitus as crickets in the ear. So to your point, it's not that one size fits all model. And I really do appreciate that segmentation because my personal opinion is that my op-ed, if you will, is that like you can get to the root cause, I feel quicker when you do have those segmentations. So it's more representative of the patient you might be working with. Am I correct in saying that? You're exactly correct.

Donna Murray, PhD 24:54

You're exactly correct. Right. Because what we have found, I'm only going to be able to assume it and send this, but I can say it as an autism, is that there are a lot of sort of non-responders. And that is both to behavioral interventions or medication. So we just need to do a better job of understanding, you know, and we might have very different mechanisms in place for the etiology or the cause of that condition or set of symptoms that then label as autism. And it's quite a spectrum. And it sounds to me, from what I've learned from my colleagues, is that tetis is very, very similar. We don't know if it's, you know, like autotoxicity, if it's some other neurological, is it secondary to this, like not only just the cause, but then how those symptoms present, right? Or, like you said, the level of severity that patient experiences, you know, what kind of barrier that creates for them in everyday life. So those aspects are incredibly important when determining what type of intervention should be in place.

Blaise M. Delfino, M.S. - HIS 25:55

So, Donna, you've emphasized the importance of real-world data. How is that different from traditional research samples? Because traditional research samples, I mean, you have your sample size and then you have different groups, like kind of bring us through this, just for my own understanding, too.

Donna Murray, PhD 26:17

Right. In traditional research, typically when you are in recruitment, what we tend to find is that most, not all, but most research lacks often a great deal of diversity. It that is more representative of our true clinical population, number one. And it also takes place under strict protocols for very good reasons, right? That's what traditional research does. It's very specific, very specific patients, very specific protocols, which is incredibly important work. The importance of real world intervention is that it takes place in the clinic. So a couple of things happen. The realities of implementing that practice or that intervention in the real world setting is important because there are issues around time constraints and reimbursement constraints. And does this meet your population that you serve daily, which typically is much more diverse than what we see frequently in traditional research? So I think it's incredibly important that we learn how we translate that. You know, a lot of times you'll read an article about how this was done in research, but then you have to kind of think about how do then I take this and how do I implement this? Where in learning health networks and these clinical settings, you're doing that in real time. And not only that, but like you may learn that this works in hospital A with this population, but when you try this in hospital B, it sort of works. But these are some modifications that need to happen for this particular setting and the constraints of their system and their clinical processes and their population. But you get to do that in real time and read your data. So that's really why I think having this research embedded or this work embedded into clinical settings is such an important step.

Standardizing Care With Common Language

Blaise M. Delfino, M.S. - HIS 28:13

Donna, I'm so fascinating. And representation is so important for science and just overall research. And the word that came to mind was to be able to duplicate what you've read in research as it relates to maybe it's some evidence-based practice that you as an SLP want to implement. You have this evidence-based practice, whether you're an SLP, and maybe you're not able to duplicate that with that specific kiddo or that client. And that's probably where that clinical frustration can come in as well. That constant feedback loop gives you greater variability while implementing evidence-based. So building a common language and language is a code in which ideas are shared kind of makes sense here with the Learning Health Network. Why is building a common language and standardized process foundational before any predictive work can happen?

Donna Murray, PhD 29:19

I'll give you an example from the autism world and then think about how that translates. So when we first started our network, when it was still the clinical and research network, there were not really good standards or assessments for autism. And there really was a little bit of a lack of what how we were sort of defining and for whom we were putting into this autism under this autism label. So it became very important for us that we begin to think about, you know, what qualifies as autism. What are some good assessments? What needed to be assessed in order to define? And then we began to think about how we could standardize that process so that we all understood the assessments that were used, the checklists that were used, the discussions, the severity levels, and how we defined that, right? That's important because when we first did an evaluation of our sites, people who felt this is way back in the day when there's sort of an Asperger label and an autism label, and we found that was highly variable by location. So we knew it really wasn't greatly differential in the actual diagnostic process. But so that was one example of where we were saying we need to think about making sure that we are defining this population in a way that is very similar. I think that's very true in Tenetus, too. So that if you begin to build those profiles and you begin to work as a network to think about this is what we think is best practice as far as identification. This is how we're going to rate or rank or discuss levels of severity. You know, at least you begin to build a common language in which everyone can begin to understand how you're classifying, because otherwise you have that variability by location, and it's very difficult then to track that data in the aggregate or in the large network. So the standardization, I think, becomes very important. Also, as far as if every site is approaching something very differently, then that's another variability built in. But if you begin to standardize, even if it's consensus-based before it's evidence-based, at least you could begin to look at what's working and not working, right? And make modifications from there. In our center, we started looking at adherence to guidelines as one of our first initiatives. And we determined that anything under 80% was considered a chaotic system. And that really resonated with our clinicians that if all of our clinicians within our centers were doing something different, and then all of our centers within the network were doing something different, that really was a chaotic system. So we started looking at adherence to guidelines as one of our first steps. Now, those guidelines could be modified and changed. Also, sometimes a guideline isn't meeting your patient's needs, and you might need to veer from that guideline. But at least you have a standard, a starting place, and you can begin to note for whom that's working and not working. So that may be a long answer to your question of why it's so important, but it's incredibly important to begin to think about how you describe your population in a way that is common.

Avoiding Burnout While Measuring Outcomes

Blaise M. Delfino, M.S. - HIS 32:31

Well, even just putting my private practice hat on right now, when you have a standardized process internally in your practice and you're training a new hearing care professional, I always would say transfer of information, process of duplication, which pretty much defines a learning health network. So when I was training Megan, who took over my position at my family's practice in 2022, my goal was to download everything that I know and you know, evidence-based practice in terms of working with these patients. And it was so cool to see, for lack of a better term, Donna, because after six months, and you know, when she was seeing patients on her own solo, I'll never forget the first patient she fit. She was like, Yep, so-and-so, they're moving forward with technology. And I was standing outside the door listening to her talk track. Now, she had created her own, you know, talk track to fit her own character story and voice, but the core foundation was a standardized process that we created at the practice. And what that yielded was incredible patient outcomes, which is what we're talking about today. So I wanted to share my own real-world sort of mini, mini learning health network, but but the standardization in private practice is so important. Donna, we know that there are more individuals with hearing loss than there are hearing care professionals. So we have to meet these patients where they're at. I'm a firm believer that if we connect on the standardized process, we can go further faster. And you talk about, you know, clinician reality and operational feasibility. Clinicians are incredibly busy and they're often overwhelmed. I remember getting to the office 6:30, 7 a.m., prepping for the day, seeing patients all day. Then I would switch the floor and vacuum at the end. You know, I just being a private practice owner, you wear so many hats and you do that pretty much seven days a week. I loved it. I loved it, I loved it. But I mean, you're busy and you're overwhelmed. How do you build a network that you know collects that meaningful data without increasing burnout?

Donna Murray, PhD 36:03

That's a great question. And one I get a lot from clinicians. And what I would say is that this is data that you would be collecting, right? If we are doing our job and we bring together patients and clinicians and researchers together, we want to pick measures that are relevant clinically, right? And that also tell us how we're doing clinically. So if the goal would be is that you embed and use data that would be collected as part of your regular clinical routine, then that data is harnessed for evaluation, both descriptive outcomes and evaluation of the interventions. And I think that initially people say, oh gosh, this is just one more thing I have to do. But I would say you are doing this. Your clinic is probably talking about how you improve access. You're probably talking about what interventions work for whom. We're creating a think tank. And if we do this correctly, it just brings you together with like-minded folks working and rolling in the same direction. So that I would hope that the Learning Health Network is a value add to make your job, I don't say, if not easier, at least more targeted and robust, and not a drain on your time. Because when you get that data back and you can begin to identify where to prioritize your efforts, what gaps need to be closed, what patients are being left behind, where you need to replicate and do more of, then you're targeting that limited time in really productive ways.

Blaise M. Delfino, M.S. - HIS 37:47

Alan Watts has a quote where he says, find a job that will pay you to play. And that always struck with me. The reason I'm bringing that up, Donna, is because I feel as though there's so many SLPs and hearing care professionals. You're passionate about patient care, you're passionate about patient outcomes, and you're passionate about collecting data to help patients, as we say here at Hearing Matters, hear life's story. You want to help improve the science. And I think there's also a potential reframe that could happen with you know, Tinnitus Learning Health Network bringing this to the industry really as that leader. And I'm excited because I truly do believe that clinicians will have that reframe and see that reframe and see that, oh, okay, I'm still collecting this data. Now let me input it. It might take me an extra five to 10 minutes, but the outcomes, it not only allows me to become a better clinician, but I will be able to serve more patients. And this is helping more individuals. And just like I was sharing earlier, how we would run our own little in-house, you know, field studies, if you will. We conducted what was called the abbreviated profile of hearing aid benefit. And what we would do, Donna, is we would have hundreds of those, and then we would, you know, average those out. And I could with confidence say, Mr. and Mrs. Smith, who is new to our practice, we know that new patients to technology, to hearing technology, uh, when they go through our standardized process, which is best practice, we know that they're going to hear X percent better in noise. Their outcomes are going to look like X, Y, and Z. So I was always that, you know, that I don't want to call myself a data nerd, but here's the thing: data and numbers tell a story. And if you're in hearing health care and you're working with, you know, patients who present with tinnitus, first and foremost, please get trained in tinnitus management. And second, please review and collect that data because numbers tell a story. If you have a high return rate as a hearing care professional, there's data in there that maybe you should, you know, maybe record your case presentation or something of that nature. But I'm sensitive to your time here, Donna. You know, how do you ensure then that the data collection remains relevant and useful then to those providing care? Because okay, I'm inputting this data. Cool, it's in there. Well, then what? How are they gonna then utilize that to provide care?

What True Co-Design Looks Like

Donna Murray, PhD 40:33

Exactly. I think the infrastructure of a learning health network really addresses that. So the co-design of having patients, families, clinicians, and researchers working together, determining what are the outcomes that would be meaningful for this population. As data comes in, we learn. It's iterating on the data. That's why you're right. Data tells a story. You may find that you you shift. And let me give you a quick example or at least refine. Our families were, when we looked at the autism network, really wanted us to address challenging behavior. Our clinicians were nervous about that because it's multifaceted, it happens a lot in the community, and schools have a big part, and they didn't know as medical, these are physicians and as medical providers, how they would actually be engaged in that. And our families said, look, you ask us what really prevented us from fully engaging in school, medical care, the community. This is important. So we looked at our data and our behavior checklist, and what we found was that the major many of the patients that exhibited the most challenging behavior had co-occurring ADHD, anxiety, and irritability. And our physicians were like, oh, I can do that, right? So working together, we decided if we targeted that population, how would that then play out as far as how behaviors were, if we were seeing a reduction in reported significant behavior? So all of that goes to say is it's not set in stone. The data does tell a story. We started in one way, and then we continued to refine that based on our findings, on what was important to our patients and families and our clinicians. They felt much more confident in now screening for those co-occurring conditions because they knew that the outcome for those children tended to be more challenging in many ways for that population. So they were able to screen earlier, they were able to intervene earlier. So that's how you keep that relevant, that you continue to evaluate that data and you iterate on that data based on what it's telling you.

Blaise M. Delfino, M.S. - HIS 42:43

Donna, you've shared that learning health networks should be driven by the issues identified by clinicians and patients. So what does true co-design look like in practice?

Staying Passionate Through Your Career

Donna Murray, PhD 42:56

True co-design, I can tell you what it's not because I was told this by a family member. So it was a learning for us. You know, I think typically often we feel like we're engaging patients and families when we've designed something and then we pull them in to we present it to them and then we get their feedback, right? And what we heard, and I think what other learning health networks learn, is that feels like you want to stamp of approval on something already designed. And that co-production starts from the very beginning, starts from the inception of the idea that they want to be at the table from the very beginning, determining what's important to be addressed and then how that should be addressed. So it's really backing up and working together, and it's working with families so that they understand the constraints of the medical system, and then working with medical providers to understand how to take family input. So it's really something that we continue to work towards because in our systems now, often those clinical providers' voices tend to be a little bit louder sometimes. So we have to really work on patients advocating for themselves and working together so that then we have good listening from our clinicians and our families learning back about the constraints, the realities of clinical work. So it's really been a challenging and lovely trajectory and journey for us and learning how you move from advising or endorsing to true co-design. And that is a skill set that I think on all sides we all need to continue to work together to learn.

Blaise M. Delfino, M.S. - HIS 44:41

And as we said earlier, you cannot see the picture when you're in the frame. So that co-design and collaboration only will help strengthen that learning health network. Donna, when the Tynetis Learning Health Network model succeeds, what does the culture of care feel like five years from now?

Donna Murray, PhD 45:02

I hope it feels more collaborative. That we are all coming together. We can begin to talk about what we feel is going well, where are those gaps and challenges, we can engage our patients in a way that helps us better understand this population and its multiple subtypes. And that we are, in many ways, sort of untangling the mysteries of what works for whom. And that we are not trying to do this in silos, but that we can accelerate improving care for these patients by working together.

Final Takeaways And Sign-Off

Blaise M. Delfino, M.S. - HIS 45:38

Donna, thank you so much for joining me on the Hearing Matters podcast today. Just kind of one left field question, if you will. We have a lot of hearing care professionals that tune in, and now even more speech. Language pathologist, because we're more than just a hearing aid or hearing technology show. We are communication sciences. You've helped so many patients throughout your career. What advice would you give to current and or new professionals who are SLPs or hearing care professionals to maintain their passion for the field as they move throughout their career?

Donna Murray, PhD 46:17

That's a great question, Blaze. I think when you have a science like ours that's so vast and can go in many different ways, a couple of things. One is I think we all have to be better at data-driven decision making that we do have, especially in sciences like ours, where we have wide population that we serve and a lot of variability within those populations. I would love to see us working together much in the same similar kinds of ways in which we are all learning from each other and getting better at how do we collect and evaluate that data and evaluate our programs and interventions. So I think it's important, you know, we're using this in a concept for a condition. We're using this, you know, we've talked about autism, we talked about tentatives, it's been used in many other conditions. And I will say that many learning health networks work together because much of what we do is very similar. So we learn from each other, which I think is also important. But I'd love to see hearing specialists and speech pathologists learning more and being more thoughtful about how they collect that clinical data that they're collecting anyway, and how to harness that data to make good decisions, both with their patients and aggregates at that scale.

Blaise M. Delfino, M.S. - HIS 47:37

You're tuned in to the Hearing Matters podcast. Today we spoke with Dr. Donna Murray. She sits on the board for the Tenetus Learning Health Network, which was founded by Jeffrey Reagan and in partnership with Dr. James Henry. Until next time, hear life's story.