WEBVTT
00:00:08.800 --> 00:00:11.759
Welcome back to another episode of Theoretically Speaking.
00:00:11.919 --> 00:00:17.679
Every day, life scientist teams look at massive data sets to track how their therapies perform in the real world.
00:00:17.920 --> 00:00:21.199
But too often those spreadsheets only tell a fraction of the story.
00:00:21.440 --> 00:00:30.239
We can see that a patient stopped taking a medication or when they switched to treatments, but we're left guessing as to the human realities behind those numbers.
00:00:30.480 --> 00:00:38.960
In this episode, we sit down with Becky Hollenberg, senior RWD strategy lead at Nursela, for a deep dive into an incredibly valuable topic.
00:00:39.200 --> 00:00:48.399
Becky walks us through how breaking down the traditional walls between claims, EMRs, and payer data completely changes how we view the patient journey.
00:00:51.520 --> 00:00:53.520
Becky, thank you so much for your time today.
00:00:53.600 --> 00:00:55.439
I'm really excited to get into this discussion.
00:00:55.679 --> 00:00:57.920
Yeah, it was so great to speak with you today.
00:00:58.320 --> 00:01:06.879
Okay, so the pharmaceutical industry certainly isn't hurting for data right now, but having data and having the whole story are two different things.
00:01:07.040 --> 00:01:09.519
So I really wanted to start the discussion there.
00:01:09.920 --> 00:01:14.400
For pharma companies, there's more data available today than ever before.
00:01:14.560 --> 00:01:19.439
However, most real-world evidence captures only part of the patient journey.
00:01:19.599 --> 00:01:23.519
So where do these gaps exist and how can they be filled?
00:01:24.239 --> 00:01:32.640
Yeah, so most real-world evidence is generated from fragmented data sources that, like you said, are only capturing parts of the patient journey.
00:01:32.879 --> 00:01:37.519
So this can often answer who's receiving treatments, when did treatments occur?
00:01:37.680 --> 00:01:39.200
How much did treatments cost?
00:01:39.439 --> 00:01:42.480
But they struggle to answer kind of the why behind those questions.
00:01:42.640 --> 00:01:44.719
So why was therapy initiated?
00:01:44.879 --> 00:01:46.560
Why did patients discontinue?
00:01:46.719 --> 00:01:49.599
What clinical characteristics drove outcomes?
00:01:49.760 --> 00:01:52.480
How did payer restrictions impact access?
00:01:52.719 --> 00:01:56.560
And which patients never received therapy despite clinical need.
00:01:56.799 --> 00:02:00.719
So the root issue isn't the volume of the data, it's that it's fragmented.
00:02:00.879 --> 00:02:05.280
So sources capture isolated pieces of the journey that aren't connected.
00:02:05.519 --> 00:02:13.520
So our approach at Noristella is integrating those data sources to kind of create a complete picture of the why behind the patient journey.
00:02:13.599 --> 00:02:25.520
So that's linking the claims, the EMR, clinical notes, labs, and payer coverage and access data on the payer level and then rolling it up to whatever level the client needs.
00:02:26.080 --> 00:02:26.639
Got it.
00:02:26.800 --> 00:02:27.759
That totally makes sense.
00:02:27.840 --> 00:02:32.159
And it's clear that this integration is key to understanding the why behind the patient journey.
00:02:32.240 --> 00:02:34.960
And it isn't just an internal goal for pharma.
00:02:35.280 --> 00:02:40.960
There's also a lot of external pressures to deliver these deeper insights that are continuously mounting.
00:02:41.120 --> 00:02:50.319
So looking at that broader landscape, how are evidence expectations among payers, HTA bodies, and internal stakeholders?
00:02:50.879 --> 00:02:57.039
Yeah, so I think the why is becoming more central for external stakeholders and internal teams.
00:02:57.439 --> 00:03:06.080
They increasingly want evidence of real world effectiveness, appropriate patient identification, outcome drivers, and the impact of access restrictions.
00:03:06.240 --> 00:03:26.400
And there is a real risk there because incomplete evidence can lead to mischaracterized patient populations, biased outcomes, missing access barriers, and value stories that are only telling half the pictures, in some cases, telling the wrong story that's not really capturing what's truly happening to the patient when you connect everything together.
00:03:26.639 --> 00:03:35.439
So, in short, data access is no longer the differentiator, the usability of the evidence and the applicability and generalizability of that evidence will be.
00:03:37.120 --> 00:03:45.759
And if usability and understanding the why is is the new baseline expectation, we have to look a lot closer at the raw materials we're working with.
00:03:45.919 --> 00:03:52.000
So we know things like claims and EMRs are powerful but limited when they're on their own.
00:03:52.400 --> 00:04:06.319
So wanting to break that down a bit, each data set, like I mentioned, claims, EMR data, labs, clinical notes, and policies and access data, they all have individual strength, but it doesn't tell the full patient story like you've mentioned previously.
00:04:06.479 --> 00:04:14.800
So how does connecting real-world data sources to build an integrated 360-degree um view of the patient journey work?
00:04:15.759 --> 00:04:21.199
Yeah, so it's each source is kind of answering a different question about the patient journey.
00:04:21.360 --> 00:04:25.759
So claims are gonna really help ground into what happened and when.
00:04:26.560 --> 00:04:34.399
EMR is gonna show more of that patient status and vital side of it, and lab values are gonna show what's happening biologically.
00:04:34.560 --> 00:04:43.600
Um the clinical notes is where we really see a lot of the depths unlocked in terms of things you wouldn't see typically in a structured data source.
00:04:43.759 --> 00:04:53.759
So that could be anything from disease severity to treatment decision making, treatment rationale, symptoms, clinical presentation that the patient's going through.
00:04:54.079 --> 00:05:07.279
And then the payer and policy data is going to understand what access barriers are shaping care and how do those play into those treatment decisions and the rationale that the physician is making.
00:05:07.839 --> 00:05:21.839
Individually, there are partial data sources, but the value comes from connecting them into one clinical record that has all the clinical lab and narrative access signals for one patient all combined.
00:05:22.319 --> 00:05:38.959
So that's going to lead to more accurate cohort definitions, better confounder adjustment, stronger outcomes capture, clearer access and barrier identification, and a more defensible value story because you're seeing more of the picture and more of the holistic patient journey.
00:05:39.279 --> 00:05:52.879
So if we look across a patient journey, for example, ICD 10s plus labs could show the confirmed diagnosis based on phenotypes and uh ICD 10 code.
00:05:53.120 --> 00:06:03.040
Notes will kind of unlock why a treatment was chosen or why a switch happened, whether it was an efficacy signal, a tolerability signal, or an access problem.
00:06:03.199 --> 00:06:08.560
Um, and then claims will, we can then track how that uh how that switch occurred.
00:06:08.720 --> 00:06:10.639
Did the patient get on therapy?
00:06:10.720 --> 00:06:17.199
Um, and then using labs in progress notes, we can see the outcomes of that and how did the patient respond to their treatment switch?
00:06:17.519 --> 00:06:18.000
Got it.
00:06:18.160 --> 00:06:24.319
And seeing how that comprehensive timeline comes together is really impressive, especially from a data science perspective.
00:06:24.480 --> 00:06:29.199
But I know its real power really lies in that commercial and clinical application, like you were mentioning.
00:06:29.360 --> 00:06:36.319
So when you unlock that 360-degree view, what does it actually mean for the people on the ground doing the research?
00:06:36.480 --> 00:06:46.399
How do different business functions, market access, HUR, medical affairs, medical comms team benefits from that 360-degree patient view?
00:06:46.959 --> 00:06:53.279
Yeah, so for market access teams, it's going to be focused on defending access against payer restrictions.
00:06:53.439 --> 00:07:08.560
So that could be quantifying the impact of payer restrictions, identifying the unmet need populations, understanding drivers of abandonment, building a payer dossier grounded in clinical reality, uh, and informing contracting and access strategy.
00:07:08.800 --> 00:07:16.079
On the HOR side, it's going to mean sharper evidence that's more defensible to scrutiny from external uh stakeholders.
00:07:16.240 --> 00:07:39.279
So that could mean improved cohort definitions and identification, reducing misclassification bias, adjusting for disease severity within our cohort definitions, um, linking to real world outcomes and access pathways, more comparative effectiveness studies that are stronger with their evidence, and overall more credible economic evidence.
00:07:39.439 --> 00:07:44.720
Um from a medical affairs side, it's kind of combining the clinical and the access story.
00:07:44.879 --> 00:08:03.680
So understanding treatment rationale and unmet need, um, identifying if there are any gaps between guidelines and how physicians are actually practicing characterizing disease burden and informing evidence generation priorities so that they can equip their MSL teams to be able to speak to that.
00:08:03.920 --> 00:08:09.279
So the unifying point here is that all three work off of one unified value story.
00:08:09.360 --> 00:08:21.920
So with the same underlying data, even though the actions based on those takeaways might be different, the takeaways individually should be the same and kind of create that unified story.
00:08:22.639 --> 00:08:23.680
That completely makes sense.
00:08:23.759 --> 00:08:33.360
And it's fascinating to see how one unified value story can align with so many different departments and how they all work together from Yeah, market access to all the way through to medical fairs.
00:08:33.519 --> 00:08:39.440
And to ground this concept and to show what it looks like in practice, I wanted to look at a real life scenario.
00:08:39.679 --> 00:08:47.759
So, can you give us an example of a use case of how connected data can provide a more complete picture of a treatment journey?
00:08:48.159 --> 00:08:48.559
Yeah.
00:08:48.799 --> 00:08:56.159
So, for example, if we're looking at a treatment discontinuation that we see in a moderate to severe immunology patient.
00:08:56.399 --> 00:09:00.399
So if we look at claims alone, we might see that the patient discontinued therapy.
00:09:00.480 --> 00:09:04.480
They had one fill, they didn't refill it, but we don't really understand the reason why.
00:09:04.639 --> 00:09:06.720
And that's kind of all we're left with.
00:09:06.799 --> 00:09:08.720
There's not really any layer there.
00:09:08.960 --> 00:09:18.000
If we add on EMR data, we can understand based on lab values and the patient had an ER visit showing that their disease was worsening.
00:09:18.159 --> 00:09:21.840
Um, we can see that their discontinuation was linked to a flare.
00:09:22.159 --> 00:09:32.320
And so then we can kind of rule out that that's not due to poor adherence or the patient didn't pick up the drug, they actually had a real life outcome that led to that discontinuation.
00:09:32.559 --> 00:09:41.120
Um, within the clinical notes, we can then look and see the progress note, uh, and the physician noticed that the patient might have had an injection site reaction.
00:09:41.279 --> 00:09:49.600
So we can understand that that switch was clinically warranted and had a reason behind it that we weren't seeing within the structured data.
00:09:49.679 --> 00:09:57.279
Um, and then with the payer access data, we can see maybe the physician notes that they're going to switch the patient to a new therapy.
00:09:57.360 --> 00:10:00.559
Um, and then in the claims, we see that that therapy was denied.
00:10:00.639 --> 00:10:10.720
And when we layer in the access data there, we understand that that was denied because the uh patient didn't have the proper step therapy, allowing them for insurance to cover it.
00:10:10.879 --> 00:10:17.039
So we can see how the access barrier leads to the patient not getting on the switch therapy.
00:10:17.360 --> 00:10:39.679
So, in some, you know, each level layer of evidence as we layer on different pieces of this disparate data source, we see how this patient interaction can be reframed from a lost patient to something that can actually be an access barrier that could be defended against and kind of utilized by teams to then have more uh effective engagement.
00:10:40.000 --> 00:10:44.240
Yeah, and like you said, help to engage and solve for future barriers down the line.
00:10:44.320 --> 00:10:49.360
Um so hopefully we can avoid use that information to sort of educate different policies and procedures.
00:10:49.600 --> 00:10:50.799
Great, amazing Becky.
00:10:50.879 --> 00:10:51.840
Thank you so much for your time.
00:10:51.919 --> 00:10:56.399
I've learned a lot from our conversation, and I'm sure our audience will as well.
00:10:56.960 --> 00:10:57.840
Thank you.
00:10:58.320 --> 00:11:02.639
It's clear that looking at the patient journey through a single lens doesn't cut it anymore.
00:11:02.879 --> 00:11:14.480
When teams can look at a single cohesive narrative, one that respects both clinical realities and pair complexities, it completely changes the game for how therapies are valued, defended, and delivered.
00:11:14.720 --> 00:11:19.120
A huge thank you to Becky Hollenberg from Nostella for sharing her expertise with us today.
00:11:19.279 --> 00:11:22.720
And to our listeners, thank you for tuning in to Theoretically Speaking.
00:11:22.879 --> 00:11:29.840
If you found value in today's episode, please take a moment to subscribe, rate us, and leave a review on your favorite podcast platform.
00:11:30.000 --> 00:11:31.039
Until next time.
00:00:08.800 --> 00:00:11.759
Welcome back to another episode of Theoretically Speaking.
00:00:11.919 --> 00:00:17.679
Every day, life scientist teams look at massive data sets to track how their therapies perform in the real world.
00:00:17.920 --> 00:00:21.199
But too often those spreadsheets only tell a fraction of the story.
00:00:21.440 --> 00:00:30.239
We can see that a patient stopped taking a medication or when they switched to treatments, but we're left guessing as to the human realities behind those numbers.
00:00:30.480 --> 00:00:38.960
In this episode, we sit down with Becky Hollenberg, senior RWD strategy lead at Nursela, for a deep dive into an incredibly valuable topic.
00:00:39.200 --> 00:00:48.399
Becky walks us through how breaking down the traditional walls between claims, EMRs, and payer data completely changes how we view the patient journey.
00:00:51.520 --> 00:00:53.520
Becky, thank you so much for your time today.
00:00:53.600 --> 00:00:55.439
I'm really excited to get into this discussion.
00:00:55.679 --> 00:00:57.920
Yeah, it was so great to speak with you today.
00:00:58.320 --> 00:01:06.879
Okay, so the pharmaceutical industry certainly isn't hurting for data right now, but having data and having the whole story are two different things.
00:01:07.040 --> 00:01:09.519
So I really wanted to start the discussion there.
00:01:09.920 --> 00:01:14.400
For pharma companies, there's more data available today than ever before.
00:01:14.560 --> 00:01:19.439
However, most real-world evidence captures only part of the patient journey.
00:01:19.599 --> 00:01:23.519
So where do these gaps exist and how can they be filled?
00:01:24.239 --> 00:01:32.640
Yeah, so most real-world evidence is generated from fragmented data sources that, like you said, are only capturing parts of the patient journey.
00:01:32.879 --> 00:01:37.519
So this can often answer who's receiving treatments, when did treatments occur?
00:01:37.680 --> 00:01:39.200
How much did treatments cost?
00:01:39.439 --> 00:01:42.480
But they struggle to answer kind of the why behind those questions.
00:01:42.640 --> 00:01:44.719
So why was therapy initiated?
00:01:44.879 --> 00:01:46.560
Why did patients discontinue?
00:01:46.719 --> 00:01:49.599
What clinical characteristics drove outcomes?
00:01:49.760 --> 00:01:52.480
How did payer restrictions impact access?
00:01:52.719 --> 00:01:56.560
And which patients never received therapy despite clinical need.
00:01:56.799 --> 00:02:00.719
So the root issue isn't the volume of the data, it's that it's fragmented.
00:02:00.879 --> 00:02:05.280
So sources capture isolated pieces of the journey that aren't connected.
00:02:05.519 --> 00:02:13.520
So our approach at Noristella is integrating those data sources to kind of create a complete picture of the why behind the patient journey.
00:02:13.599 --> 00:02:25.520
So that's linking the claims, the EMR, clinical notes, labs, and payer coverage and access data on the payer level and then rolling it up to whatever level the client needs.
00:02:26.080 --> 00:02:26.639
Got it.
00:02:26.800 --> 00:02:27.759
That totally makes sense.
00:02:27.840 --> 00:02:32.159
And it's clear that this integration is key to understanding the why behind the patient journey.
00:02:32.240 --> 00:02:34.960
And it isn't just an internal goal for pharma.
00:02:35.280 --> 00:02:40.960
There's also a lot of external pressures to deliver these deeper insights that are continuously mounting.
00:02:41.120 --> 00:02:50.319
So looking at that broader landscape, how are evidence expectations among payers, HTA bodies, and internal stakeholders?
00:02:50.879 --> 00:02:57.039
Yeah, so I think the why is becoming more central for external stakeholders and internal teams.
00:02:57.439 --> 00:03:06.080
They increasingly want evidence of real world effectiveness, appropriate patient identification, outcome drivers, and the impact of access restrictions.
00:03:06.240 --> 00:03:26.400
And there is a real risk there because incomplete evidence can lead to mischaracterized patient populations, biased outcomes, missing access barriers, and value stories that are only telling half the pictures, in some cases, telling the wrong story that's not really capturing what's truly happening to the patient when you connect everything together.
00:03:26.639 --> 00:03:35.439
So, in short, data access is no longer the differentiator, the usability of the evidence and the applicability and generalizability of that evidence will be.
00:03:37.120 --> 00:03:45.759
And if usability and understanding the why is is the new baseline expectation, we have to look a lot closer at the raw materials we're working with.
00:03:45.919 --> 00:03:52.000
So we know things like claims and EMRs are powerful but limited when they're on their own.
00:03:52.400 --> 00:04:06.319
So wanting to break that down a bit, each data set, like I mentioned, claims, EMR data, labs, clinical notes, and policies and access data, they all have individual strength, but it doesn't tell the full patient story like you've mentioned previously.
00:04:06.479 --> 00:04:14.800
So how does connecting real-world data sources to build an integrated 360-degree um view of the patient journey work?
00:04:15.759 --> 00:04:21.199
Yeah, so it's each source is kind of answering a different question about the patient journey.
00:04:21.360 --> 00:04:25.759
So claims are gonna really help ground into what happened and when.
00:04:26.560 --> 00:04:34.399
EMR is gonna show more of that patient status and vital side of it, and lab values are gonna show what's happening biologically.
00:04:34.560 --> 00:04:43.600
Um the clinical notes is where we really see a lot of the depths unlocked in terms of things you wouldn't see typically in a structured data source.
00:04:43.759 --> 00:04:53.759
So that could be anything from disease severity to treatment decision making, treatment rationale, symptoms, clinical presentation that the patient's going through.
00:04:54.079 --> 00:05:07.279
And then the payer and policy data is going to understand what access barriers are shaping care and how do those play into those treatment decisions and the rationale that the physician is making.
00:05:07.839 --> 00:05:21.839
Individually, there are partial data sources, but the value comes from connecting them into one clinical record that has all the clinical lab and narrative access signals for one patient all combined.
00:05:22.319 --> 00:05:38.959
So that's going to lead to more accurate cohort definitions, better confounder adjustment, stronger outcomes capture, clearer access and barrier identification, and a more defensible value story because you're seeing more of the picture and more of the holistic patient journey.
00:05:39.279 --> 00:05:52.879
So if we look across a patient journey, for example, ICD 10s plus labs could show the confirmed diagnosis based on phenotypes and uh ICD 10 code.
00:05:53.120 --> 00:06:03.040
Notes will kind of unlock why a treatment was chosen or why a switch happened, whether it was an efficacy signal, a tolerability signal, or an access problem.
00:06:03.199 --> 00:06:08.560
Um, and then claims will, we can then track how that uh how that switch occurred.
00:06:08.720 --> 00:06:10.639
Did the patient get on therapy?
00:06:10.720 --> 00:06:17.199
Um, and then using labs in progress notes, we can see the outcomes of that and how did the patient respond to their treatment switch?
00:06:17.519 --> 00:06:18.000
Got it.
00:06:18.160 --> 00:06:24.319
And seeing how that comprehensive timeline comes together is really impressive, especially from a data science perspective.
00:06:24.480 --> 00:06:29.199
But I know its real power really lies in that commercial and clinical application, like you were mentioning.
00:06:29.360 --> 00:06:36.319
So when you unlock that 360-degree view, what does it actually mean for the people on the ground doing the research?
00:06:36.480 --> 00:06:46.399
How do different business functions, market access, HUR, medical affairs, medical comms team benefits from that 360-degree patient view?
00:06:46.959 --> 00:06:53.279
Yeah, so for market access teams, it's going to be focused on defending access against payer restrictions.
00:06:53.439 --> 00:07:08.560
So that could be quantifying the impact of payer restrictions, identifying the unmet need populations, understanding drivers of abandonment, building a payer dossier grounded in clinical reality, uh, and informing contracting and access strategy.
00:07:08.800 --> 00:07:16.079
On the HOR side, it's going to mean sharper evidence that's more defensible to scrutiny from external uh stakeholders.
00:07:16.240 --> 00:07:39.279
So that could mean improved cohort definitions and identification, reducing misclassification bias, adjusting for disease severity within our cohort definitions, um, linking to real world outcomes and access pathways, more comparative effectiveness studies that are stronger with their evidence, and overall more credible economic evidence.
00:07:39.439 --> 00:07:44.720
Um from a medical affairs side, it's kind of combining the clinical and the access story.
00:07:44.879 --> 00:08:03.680
So understanding treatment rationale and unmet need, um, identifying if there are any gaps between guidelines and how physicians are actually practicing characterizing disease burden and informing evidence generation priorities so that they can equip their MSL teams to be able to speak to that.
00:08:03.920 --> 00:08:09.279
So the unifying point here is that all three work off of one unified value story.
00:08:09.360 --> 00:08:21.920
So with the same underlying data, even though the actions based on those takeaways might be different, the takeaways individually should be the same and kind of create that unified story.
00:08:22.639 --> 00:08:23.680
That completely makes sense.
00:08:23.759 --> 00:08:33.360
And it's fascinating to see how one unified value story can align with so many different departments and how they all work together from Yeah, market access to all the way through to medical fairs.
00:08:33.519 --> 00:08:39.440
And to ground this concept and to show what it looks like in practice, I wanted to look at a real life scenario.
00:08:39.679 --> 00:08:47.759
So, can you give us an example of a use case of how connected data can provide a more complete picture of a treatment journey?
00:08:48.159 --> 00:08:48.559
Yeah.
00:08:48.799 --> 00:08:56.159
So, for example, if we're looking at a treatment discontinuation that we see in a moderate to severe immunology patient.
00:08:56.399 --> 00:09:00.399
So if we look at claims alone, we might see that the patient discontinued therapy.
00:09:00.480 --> 00:09:04.480
They had one fill, they didn't refill it, but we don't really understand the reason why.
00:09:04.639 --> 00:09:06.720
And that's kind of all we're left with.
00:09:06.799 --> 00:09:08.720
There's not really any layer there.
00:09:08.960 --> 00:09:18.000
If we add on EMR data, we can understand based on lab values and the patient had an ER visit showing that their disease was worsening.
00:09:18.159 --> 00:09:21.840
Um, we can see that their discontinuation was linked to a flare.
00:09:22.159 --> 00:09:32.320
And so then we can kind of rule out that that's not due to poor adherence or the patient didn't pick up the drug, they actually had a real life outcome that led to that discontinuation.
00:09:32.559 --> 00:09:41.120
Um, within the clinical notes, we can then look and see the progress note, uh, and the physician noticed that the patient might have had an injection site reaction.
00:09:41.279 --> 00:09:49.600
So we can understand that that switch was clinically warranted and had a reason behind it that we weren't seeing within the structured data.
00:09:49.679 --> 00:09:57.279
Um, and then with the payer access data, we can see maybe the physician notes that they're going to switch the patient to a new therapy.
00:09:57.360 --> 00:10:00.559
Um, and then in the claims, we see that that therapy was denied.
00:10:00.639 --> 00:10:10.720
And when we layer in the access data there, we understand that that was denied because the uh patient didn't have the proper step therapy, allowing them for insurance to cover it.
00:10:10.879 --> 00:10:17.039
So we can see how the access barrier leads to the patient not getting on the switch therapy.
00:10:17.360 --> 00:10:39.679
So, in some, you know, each level layer of evidence as we layer on different pieces of this disparate data source, we see how this patient interaction can be reframed from a lost patient to something that can actually be an access barrier that could be defended against and kind of utilized by teams to then have more uh effective engagement.
00:10:40.000 --> 00:10:44.240
Yeah, and like you said, help to engage and solve for future barriers down the line.
00:10:44.320 --> 00:10:49.360
Um so hopefully we can avoid use that information to sort of educate different policies and procedures.
00:10:49.600 --> 00:10:50.799
Great, amazing Becky.
00:10:50.879 --> 00:10:51.840
Thank you so much for your time.
00:10:51.919 --> 00:10:56.399
I've learned a lot from our conversation, and I'm sure our audience will as well.
00:10:56.960 --> 00:10:57.840
Thank you.
00:10:58.320 --> 00:11:02.639
It's clear that looking at the patient journey through a single lens doesn't cut it anymore.
00:11:02.879 --> 00:11:14.480
When teams can look at a single cohesive narrative, one that respects both clinical realities and pair complexities, it completely changes the game for how therapies are valued, defended, and delivered.
00:11:14.720 --> 00:11:19.120
A huge thank you to Becky Hollenberg from Nostella for sharing her expertise with us today.
00:11:19.279 --> 00:11:22.720
And to our listeners, thank you for tuning in to Theoretically Speaking.
00:11:22.879 --> 00:11:29.840
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