"Leveraging Real-World Data through Pragmatic Clinical Trials" and "Insurance Coverage Mandates and the Adoption of Digital Breast Tomosynthesis"

March 16, 2022

Information

Yale Cancer Center Grand Rounds | March 15, 2022

Presentations by: Dr. Joseph Ross and Dr. Susan Busch

ID7544

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00:00I'm delighted to introduce our first
00:02speaker today, Doctor Joel Ross.
00:04He's a professor of medicine in general
00:06medicine and professor of public health
00:09and health policy and management.
00:11After receiving his medical degree at
00:13the Albert Einstein College of Medicine,
00:15Doctor Ross came to Yale.
00:17Follow in the Robert Wood Johnson
00:20Clinical Scholars Program in 2004.
00:22He has had a very distinguished career since,
00:26with a focus on examining factors that
00:28affect use or delivery of recommended
00:31hospital and ambulatory care,
00:33as well as clinical outcomes of such care.
00:37Today his topic is leveraging real-world
00:40data through pragmatic clinical trials.
00:43Doctor Ross the floor is yours.
00:46Thank you chairman.
00:47Thank you for inviting me to
00:49speak before the Cancer Center and
00:50part of the grand rounds today.
00:51I'm delighted to share some of the
00:54work that I've been working on
00:56over the past several years and to
00:58identify potential opportunities for
00:59collaboration with investigators
01:01throughout the Cancer Center.
01:02I also could not be happier to
01:04be sharing the stage with Susan
01:06Bush today because, you know,
01:07when I was a clinical scholar,
01:09kind of lost looking for a mentor.
01:11Way back almost 20 years ago now,
01:13Susan was the only person to
01:15open her door to me.
01:16When she got she helped me get my
01:18career started so I couldn't be more
01:20grateful for everything she's done
01:22to help me get started in my career.
01:23So I'm just going to get started
01:25and talk about this work.
01:26Please you know,
01:27jump in with questions through the chat.
01:29I'll try to keep an eye on it just to note,
01:32some of the potential competing
01:33interests that inform the work that
01:35I'm going to be presenting today.
01:36I do get research grant funding
01:38through Yale from the FDA as part
01:41of the Yale Mayo Clinic Center for
01:43Excellence in Regulatory Science
01:44and Innovation.
01:45I'll talk a little bit about that work.
01:47As well as from the medical Devices
01:49Innovation Consortium to run something
01:51called Nest along with some funds
01:52from Johnson and Johnson for clinical
01:54trial data sharing initiatives
01:56at federal government awards,
01:57as well as the Laura and John
02:00Arnold Foundation.
02:01So this is just,
02:02you know,
02:03to get us started you know here
02:04you know we see some pictures of
02:06individuals you know searching
02:08for evidence to,
02:09you know as they have a clinical question,
02:10they're trying to make a decision
02:11about what to do for their patients.
02:12Or to then, you know,
02:13sit down with their patient and
02:15make a suggestion or recommendation
02:17around a drug to use and you know,
02:19typically you know when we think
02:20about this sort of the hierarchy
02:22of evidence and you know what
02:24we want to guide our decisions.
02:25You know,
02:26we look for evidence that you
02:27know is at this level or higher.
02:29You know randomized control trials.
02:31You know to guide our decisions or
02:34perhaps systematic reviews that
02:35are aggregating RCT evidence,
02:37and ideally when it's been meta
02:39analyzed to put it all together.
02:41But there's been a lot of changes
02:43in the way we understand evidence,
02:45in part because of the advancement
02:48and methods to use a large data sources,
02:51but also because of other challenges
02:53that have faced both the FDA and others.
02:56But what you'll have noticed over
02:58the past decade is, you know,
03:00increasingly thinking about.
03:02A new and novel ways to evaluate
03:05medical products and the the.
03:08With failings of the past to
03:10identify a safety issues earlier,
03:12you began to see ways to think
03:14about what's being called a
03:16lifecycle approach to evaluation.
03:18So it's not just about that
03:20first RCT evidence,
03:21it's going to inform use and in part
03:24that was because premarket studies that
03:26inform FDA approval are often limited,
03:28limited in size, limited in scope,
03:30limited in the end points
03:31on which they're focused on.
03:32They're not looking at the kind of
03:33they're not big enough studies to
03:35identify important safety concerns,
03:36and sometimes they're not
03:37even studies that are.
03:38Guaranteed to confirm the
03:40efficacy of a product.
03:42They're they're focusing on surrogate
03:43markers as endpoints in order to project,
03:45benefit, predict,
03:47benefit through these markers,
03:49and then those are supposed to be done
03:52in tandem with postmarket studies.
03:54You know,
03:54trials that are going to happen
03:56after the the approval and but the
03:58problem has been that those trials
04:01frequently are delayed and they're
04:03just not even consistently completed.
04:05This in combination with the fact
04:07that we were never ever going to
04:09be able to address each remaining
04:11uncertainty through clinical trials,
04:12has led to, you know,
04:14opportunities for you know
04:15what you're hearing now.
04:16Kind of real world data.
04:17Real-world data as a the way forward,
04:21and regulatory science and evaluation.
04:23And you know,
04:24this is this quote from a high level
04:27official at the FDA is illustrative.
04:28You know,
04:29using RWE to begin to address
04:32these questions as preferable to
04:34having no evidence whatsoever.
04:36And you know, with the advent of,
04:37you know industry and FDA talking
04:39more about real-world data.
04:40You're starting to see you know
04:42more and more companies popping
04:43up that you know promising.
04:44We're world analytics to
04:46deliver real-world evidence.
04:47And you know, I'll just sort of say,
04:49you know, this is, you know,
04:50buzzword alert, right?
04:51This is a big problem that where the
04:54the sort of the promise is getting
04:56way ahead of what is actually,
04:58you know what we're capable of,
04:59and what we're capable of,
05:01sort of understanding reliably.
05:03Really,
05:03what we're talking about now are the use of.
05:06You know cohort studies case control studies.
05:08You know,
05:09leveraging observational data resources
05:10and and in part this is not only
05:12a recognition of the limitations
05:14of premarket regulatory approval,
05:15but also you know a major advocacy
05:17push that's happening towards
05:18real-world data that has led to,
05:20you know,
05:21new legislation the 21st Century
05:23Cures Act that passed at the tail
05:25end of the Obama administration,
05:27you know,
05:28had very clear goals that to push
05:31towards a real world data use,
05:32including requiring the FDA
05:34to establish a program.
05:36To evaluate real-world evidence which
05:38that was defined in the legislation
05:40as data regarding the usage or the
05:43potential benefits or risks of a
05:45drug or device derived from sources
05:46other than randomized control trials.
05:49Now, this isn't to say that you
05:51know all real world data are bad.
05:53The typical or traditional PWB of today
05:56is work that you know many investigators,
05:59including my group at Yale,
06:01do right.
06:01So it's advanced observation.
06:03ULL research,
06:04including clinical epidemiology,
06:05to inform product development.
06:07You know issues around disease prevalence,
06:09prognosis and treatment adherence.
06:12This type of evidence is generally used
06:15for secondary indication approvals
06:16for rare diseases or for you know,
06:19diseases that are with well understood
06:21pathophysiology and progression,
06:23and it's very limited and it's used for
06:26initial regulatory approval decisions,
06:28mostly because those products are
06:30not used in such widespread way
06:32that you can actually leverage
06:34existing data sources to study there,
06:36that the effectiveness and safety
06:38of the product.
06:39And of course,
06:40most commonly of these types of studies
06:43are used for safety surveillance
06:45or registry registry based medical
06:47device studies and just to bring
06:49your attention to some of the work
06:51that we've done as part of our group.
06:53And I did want to just sort of flag
06:55this because there's individuals
06:56here attending the grand rounds who
06:58may be interested in collaborating.
07:00I lead a couple of efforts that
07:02essentially work closely with FDA to
07:05generate evidence to address kind of
07:07unmet needs at the at the Agency this often.
07:09This is through our Searcy.
07:11We are one of four that are funded
07:13by the FDA to do collaborative
07:15regulatory science research,
07:16but it's also through nest,
07:17which is a a network of health systems
07:20that are working with real world data.
07:22Or, you know,
07:23essentially,
07:23we're working with our health system
07:25data to try to evaluate medical
07:27devices in practice and these types
07:29of studies you know tend to look
07:32like this project where we look,
07:34try to better understand the
07:36safety and efficacy of individuals
07:38who are switching from branded.
07:39We both are rocks into generic
07:42looking at its impact and effect.
07:45Thyroid stimulation hormone levels
07:47and other markers of efficacy.
07:49This project,
07:50where we're where we're aggregating
07:52data across the state of Connecticut,
07:54including hospital data and mortality
07:57data and other vital statistic data,
07:59then even EMS data to try to better
08:02understand opioid use disorder and
08:04overdose including, uh, you know,
08:07throughout the state.
08:09Work like this where we're trying to
08:11understand the comparative effectiveness
08:12of safety of oral anticoagulants in
08:14patients with atrial fibrillation
08:16who have poor kidney function.
08:18These types of patients are often
08:20excluded from clinical trials,
08:21but FDA is often tasked with trying
08:23to understand and give direction
08:24on their safety and benefits for
08:26use of this kind of research,
08:28as well as this.
08:29This registry based study where you
08:32know we looked at different types of cardio.
08:35Cardiac pump devices and looking
08:37at their safety,
08:38especially for patients who are
08:39having acute heart attack and
08:41are in cardiogenic shock,
08:43so lots of individuals are doing
08:44work like this that are leveraging
08:46existing data sources to try to bring
08:49greater insights into the safety
08:51and benefit of various products.
08:54But I think you know,
08:54as the sort of the call for real
08:56world evidence gets louder.
08:57You know one caution to keep in mind
08:59is that observation ULL data sources
09:01should not be expected to answer the
09:03same clinical questions that are
09:04being answered by traditional clinical.
09:06Clinical trials and we have
09:07to think about ways
09:08to make sure that the evidence is being used.
09:10Compliment you know,
09:12to complement the existing RCT evidence.
09:15This is an example of a project that
09:16a student working with me did a couple
09:18of years ago trying to understand
09:20the feasibility of using real-world
09:22data to replicate clinical trial
09:23evidence and what she did is she
09:26identified among all the clinical
09:28trials that had been published in
09:30high impact medical journals in 2017.
09:33She determined what proportion
09:35had and in clinical intervention.
09:37The clinical indication of the of the
09:40patients who were studied enrollment
09:41criteria as well as a primary
09:43endpoint that could be successfully
09:45in routinely ascertained from
09:47either electronic health records.
09:49Structured electronic health records,
09:50data or claims data,
09:52and what we found is that only 15%
09:54of these trials could feasibly have
09:57been replicated using this kind
10:00of real world data resource.
10:02When the 21st Century Cures Act passed,
10:05the FDA was actually pretty quick to say,
10:07listen,
10:07real-world data should be defined by
10:10the context in which the evidence
10:12is gathered in clinical care or
10:14home and community settings,
10:16as opposed to necessarily in
10:18research or academic environments,
10:20and the distinction is not based
10:22necessarily on the presence or
10:24absence of a planned intervention or
10:26use of randomization randomization.
10:28Essentially, they're saying,
10:29you know,
10:29continue to seek out opportunities
10:31to conduct.
10:32Randomized evaluations using
10:33pragmatic trials that better leverage
10:36kind of the existing data resource
10:38infrastructure to make them perhaps
10:40cheaper or easier to conduct.
10:42But it's not just about substituting
10:45observation,
10:45ULL data analysis for randomized
10:48control trials,
10:49and I'm always reminded of this quote.
10:51You know,
10:52if you want more evidence based practice,
10:53you need more practice based evidence.
10:56So in in the next 10 minutes I'm
10:58going to talk a little bit about
11:00some of the work that we've been
11:01doing to try to better leverage.
11:03Kind of pragmatic clinical trials in
11:06the hopes of showing you what I think is,
11:09I think,
11:09the future of real world data investigations.
11:13It's not just about leveraging
11:15observational data resources.
11:16This this is a slide from Cuba.
11:20Take a data warehouse company that
11:22aggregates information across of you,
11:24know multiple multiple sources and
11:25you know they talk about kind of
11:28all the real world data that are out
11:30there for for an individual from pharmacy,
11:32data,
11:33lab and biomarker data to mortality data,
11:37hospital data claims data survey
11:39data disease registry data.
11:41All these things could ideally
11:42be linked together,
11:43including even potentially social
11:45media data or wearables data or
11:47or even you know something like
11:49credit card data.
11:50And this kind of is like the optimal
11:52environment when you talk to people
11:54like the future of clinical trials,
11:56it's going to pull all this
11:57information together.
11:57Putting the patient at the center
11:59and mostly people talk about that as
12:02being idealistic and not really achievable.
12:04But we've been working with a group
12:07called Hugo that actually does just this.
12:10It aggregates multiple data platforms
12:12into a patient centered medical
12:14record that the patient can then share
12:16out with the research team as part of a,
12:19you know, our research project.
12:20And so we this is the first study
12:23we did at leveraging this platform.
12:26It was done as part of our city.
12:28Our FT had funded center where
12:30we aggregated data for just 60
12:33patients who were getting care
12:35at Yale and at the Mayo Clinic.
12:37We recruited 15 patients at each
12:40site who are undergoing bariatric
12:42surgery or A-fib ablation procedures.
12:44A 59 patients under actually
12:46underwent the procedure and completed
12:48our eight week follow up.
12:50And what's?
12:51The beauty of this platform for research
12:53purposes is you sit down with a patient.
12:55You enroll them in the platform
12:57you link their electronic health
12:59record data from any health system
13:02from which they're gaining care
13:04or as well as their pharmacy data
13:06and and and other information.
13:08And that takes time.
13:09It took a little over an hour
13:11for all of our patients,
13:13but once you do that,
13:14everything that happens next over
13:16the 88 week follow up for the
13:19patients is all passive patient
13:21their patients data aggregates.
13:23Automatically into the the the system
13:26being shared with the research
13:27team for research purposes and the
13:29patient never has to come back,
13:30and so you know,
13:31this shows you that we were able to do this.
13:34You know,
13:34with 60 patients you know we've had a
13:37nice sort of broad spectrum of age ranges.
13:39You know,
13:39including a number of patients
13:40over the age of 65 who were
13:42able to do this successfully.
13:43And here are the data we aggregated
13:45and I'll start at the bottom left.
13:47The electronic health record data.
13:49So everyone was getting care
13:51at either the Yale at.
13:53Ill or the Mayo Clinic for their
13:55specialty care for this procedure,
13:56but also and so everyone you know
13:58their care is managed through Epic
13:59and they have access to their
14:01my chart and they connect their
14:02my chart to their Hugo account,
14:04but also individuals who have
14:06primary care elsewhere were able
14:08to link their my charts either
14:09through Epic or Cerner based systems
14:11from any health system.
14:13So if we were taking care of a
14:15patient who was getting there,
14:16a FIB ablation here at Yale,
14:17but they're there, their primary care,
14:19perhaps was at Hartford Hospital.
14:21For whatever reason they could
14:22link that system too.
14:24Also,
14:24we linked their pharmacy data,
14:26so that's not the upper right and so
14:28this was individuals were getting care.
14:30Their pharmacies met their
14:31medications through CVS or Walgreens.
14:34They also use a mark.
14:35My chart based system that allows this.
14:37They're essentially their health
14:39record to get linked right into Hugo.
14:42We also then used Hugo to send out surveys.
14:45Patient reported outcome measures.
14:47Both short questions post procedure
14:50along with longer questions at 148
14:52weeks and patients get a link.
14:54Right to their phone they they.
14:56They signify their preference.
14:57If they want a text message
14:58or email, they click the link and they
15:00fill it all out right on their phone
15:02and and and it's all kind of easy peasy.
15:05They don't have to come back to go through,
15:07you know a structured questionnaire with
15:08a nurse or any other study coordinator.
15:11They can just do it on their own,
15:12fill it out and that allows
15:14you to ask more questions.
15:16And then we also gave every patient
15:18some two different digital devices.
15:20Everyone got a Fitbit in order to track
15:23activity and patients who underwent
15:24bariatric surgery got a Withings scale.
15:26Digital scale and people.
15:28Patients who underwent the 8th
15:30ablation procedure got us a two finger,
15:32a single lead EKG that you
15:35measured through Kardia mobile.
15:36And this is just some quick results
15:38to show you kind of what we could do.
15:40Again, this was really just figuring out
15:42the feasibility of doing work like this,
15:45but we were able to link health records for
15:47100% of patients who underwent procedures.
15:49A 55% of patients also had a primary
15:51care that was based at Yale or Mayo,
15:53so all of their electronic health records
15:56get pulled in for purposes of the study.
15:5810 patients, LinkedIn,
16:00additional 13 portals and then we had
16:0340% of patients who are getting their
16:06prescriptions through CVS or Walgreens.
16:08Now, Walmart also has a my chart like
16:11function that allows you to pull in
16:14information like medication names,
16:16dosages,
16:17start and end dates along with refills,
16:20and again,
16:20all these data were passively
16:22aggregated after our initial enrollment,
16:24allowing for Neil near real time,
16:26streaming data aggregation and this
16:28just kind of shows you kind of how it
16:30worked at the time when we did the study,
16:32people had to actually sync their Fitbits.
16:36Now that happens automatically,
16:37but this shows you of course.
16:39Of things tail off over time,
16:40but even over the eight weeks we had,
16:42well more than half of patients syncing
16:45their Fitbits their their cardio mobile
16:48devices and their withing scale which
16:50allows you to kind of project you know.
16:53Scraf,
16:54the sort of trajectories of recovery.
16:56So on the top you can see kind of
16:58average steps per day for patients
17:00who underwent bariatric surgery,
17:01you know,
17:02kind of visually demonstrating the
17:04how patients recovered over time.
17:06The bottom half on the left is the the
17:08steps per day for patient patients who
17:11underwent a fibrillation on the right.
17:13Is that the cumulative weight change for
17:15patients undergoing bariatric surgery
17:17on the lower right is the patient.
17:19The average heart rate and again,
17:21this is more just to determine,
17:22you know, the accuracy.
17:24That the integrity of the data
17:26that was being aggregated here.
17:28Our response rate to the patient reported
17:31outcome measures consistently above 80%
17:33for all the patients for all the surveys,
17:36and it allows you also to to to determine
17:39how patients are doing so you know,
17:41we're over time graphing estimates of pain,
17:44appetite and palpitations
17:46in the two patient groups,
17:49but this is really just more
17:52for illustrative purposes.
17:53And this has led to a lot of future
17:55work that I'm really proud of,
17:56and I'm really excited.
17:57It's all kind of coming soon,
17:59but I did want a sort of flag
18:01for people in case it prompts
18:03potential collaborations,
18:04but this is the biggest of the
18:06studies that we're working on now.
18:08Also funded through the Searcy,
18:09it's a where aggregating sensually
18:13a large cohort study of more than
18:161500 patients who are receiving
18:18a new opioid prescription for
18:20acute pain recruiting from sites
18:21across the United States and Yale
18:23at the University of Alabama.
18:24Birmingham,
18:25including from their network of
18:27dental practices that run up the
18:29Appalachian Mountains from the
18:31Mayo Clinic from Monument Health,
18:32which is basically South Dakota
18:34and Cedar Sinai in Los Angeles.
18:36Patients are being recruited for
18:38in the urgent care settings,
18:40emergency departments,
18:41dental care and patients post
18:43cesarean section.
18:44We started recruitment in about
18:46in September 2020.
18:47We now have more than 1000
18:49patients recruited.
18:49Even with all the challenges from COVID.
18:52Our primary endpoint is the
18:53number of days using.
18:54Opioids and we're following
18:56up patients over six months,
18:57including additional measures
18:58for patient or outcome measures,
19:00from pain and anxiety.
19:02Other measures of health care
19:04utilization activity measured
19:05using Fitbits and opioid disposal,
19:07and just to give you a sense of
19:09the kind of data that this allows
19:11us to aggregate on patients.
19:12This is mean daily pain,
19:14reportings among those reporting
19:16they are in pain,
19:17and you can just see how
19:20pain essentially persists.
19:21This is over 180 days.
19:23The average pain dots are in blue.
19:25Worst pain or in red?
19:27Here's the median days elapsed
19:28to 1st report of no pain among
19:30patients with pain fully resolved
19:32and you can see the difference in
19:35pain experienced by patients in
19:37different settings with patients
19:38who are recruited either from the
19:41inpatient setting or a primary
19:43care having longer median days
19:44until the first report of no pain.
19:46Whereas patients for the dentist
19:49heading having slightly shorter
19:51durations and then this shows
19:52you the mean daily pain ratings
19:54among those taking.
19:55A treatment for pain and
19:56this could be any treatment.
19:57It could be tylanol it could be an opioid,
19:59it could be anything,
20:00but you can see here the blue dots
20:02are patients who are not using
20:03an opioid for treatment and the
20:05yellow dots are patients who are
20:07using an opioid for treatment and
20:08you can see how the on average the
20:11patients who are taking an opioid
20:13are having higher rates of pain.
20:15All of this is being done in
20:18collaboration with the FDA as
20:19part of their efforts to better
20:21address and understand the risks
20:24associated with opioid use.
20:25Couple of other things,
20:27just to mention briefly one is these
20:29are projects that are funded by Nest.
20:31This is what we call the sleep I study.
20:33It's a prospective RCT of 100
20:36patients with depression receiving
20:37outpatient treatment for insomnia,
20:39comparing usual care of a prescription
20:41digital therapeutic device that's essentially
20:43cognitive behavioral therapy for insomnia,
20:46following patient treating
20:47patients over 9 weeks.
20:49With the primary endpoint of insomnia
20:51severity index and we're following them up
20:52over a year and again just to emphasize.
20:55All of this is done using the Hugo platform,
20:57so we enroll patients at baseline.
20:59They're randomized to one treatment or
21:01another they undergo, you know, they they.
21:04They undergo the treatment associated
21:06with that arm, and they get, you know,
21:07serving questions out, you know,
21:09through their phone or via email,
21:11and all of their data that the health care,
21:13utilization data,
21:14and other information
21:15otherwise passively aggregates.
21:17It's you know,
21:18a pragmatic RCT that's leveraging.
21:19Real world data for all of our endpoints,
21:22we're doing another study that
21:23we call the Heart Watch study,
21:24which is essentially an RCT of the
21:26Apple Watch where we're perspective
21:29prospectively enrolling 150 patients
21:31undergoing cardioversion for AFIB.
21:33They either get an Apple Watch or they
21:36get a Withings watch without any activity.
21:38That's just an activity tracker without an
21:41EKG and abnormal rhythm notification feature.
21:45We're enrolling patients at
21:46Yale Duke in the Mayo Clinic.
21:48We have about 40 patients enrolled thus far.
21:50Our primary endpoint is the the
21:52effect Global Score questionnaire is
21:54essentially at A-fib quality of life prom,
21:56and again,
21:57we're following up patients over a year,
21:59including additional prompts for anxiety.
22:01Other measures of health care utilization,
22:02as well as cagey accuracy.
22:05And then last,
22:06I just want to note this one this
22:08project we're doing in collaboration
22:10with numerous investigators
22:11associated with copper,
22:12the cancer outcomes public
22:14policy and effectiveness Research
22:16Center led by Carrie Gross,
22:18Sarah McLachlan and Scott Huntington.
22:20Where we're quantifying a physical
22:22function in cancer patients undergoing
22:24chemotherapy using a clinician,
22:26reported patient reported and
22:28wearable device data sources.
22:29This is done being done through our Searcy.
22:32The FDA funded center.
22:33We're doing it directly with collaborators
22:35at the oncology Center of Excellence,
22:38a prospective study of 200 cancer patients
22:41undergoing frontline cytotoxic therapy.
22:43Rolling patients at Yale and Mayo Clinic,
22:46100 solid tumor patients.
22:47Breast cancer patients stage one
22:49through three, as well as a hunt.
22:50100 high grade B cell lymphoma
22:53patients and our primary endpoint is
22:55physical function over nine months
22:57that's being measured weekly for
22:58two months and then monthly again,
23:01all leveraging the Hugo platform
23:03for measurements with patient
23:05reported outcome measures.
23:07Clinician reported outcome measures
23:09to the E COG performance measurement.
23:11The six minute walk test at baseline
23:13and at the at the end of two
23:15months and then again later on,
23:17as well as activity measured
23:19as every patient,
23:20has a daily Fitbit to measure daily
23:23steps and again part of the purpose
23:25of this is to work with the FDA to to
23:28better understand a physical function
23:30as a surrogate measure of recovery.
23:32Compare these data sources identifying
23:34change thresholds and inform
23:36the way the FDA thinks about.
23:38Of these measures,
23:39as part of clinical trials,
23:40so I will stop there and I hope that if
23:44anyone has questions you can follow up.
23:46But thanks for the time, show me.
23:50I'll stop sharing.
24:02Thank you very much Doctor Ross for
24:05this very informative presentation.
24:07Renee, I was wondering,
24:08do we ask people to raise hands?
24:16I'm not sure how this is really handled.
24:18Oh, post your questions in the chat.
24:25I do have a question as we're
24:28waiting for others to pitch in.
24:30I wonder when you submit
24:33work for publication.
24:34Is it subject to more scrutiny
24:36because it's not the traditional
24:38trial that people are used to?
24:43Yes, there's a lot of explaining going
24:45on when we you know when we're putting
24:48these papers together and and even just
24:50proposing them for funding right now,
24:52as people kind of question like,
24:54well, how is this done?
24:55I don't get it. You know,
24:56how are you pulling in these data sources?
24:58But when you talk to people who are
25:01clinical trialists and explain the
25:03difference in the approach and the
25:05efficiency that comes with it and the
25:08the you know the kind of trade offs that
25:09are always happening in any clinical trial,
25:11but the you know how much more information.
25:13You can aggregate passively and not
25:15requiring patients to come back,
25:17minimizing the burden on patients
25:20in terms of participation.
25:21People see. Ah, I get it now.
25:23There's a there's there's,
25:24there's great promise to this,
25:25and it's not to say that that
25:27we've worked everything out,
25:28but I feel like we're kind of pilot
25:30testing new ways to do trials like this,
25:34which I hope are going to,
25:36you know,
25:36be useful and informative and and
25:37and set the stage for the future
25:39so it doesn't need to be kind
25:40of an all or nothing either.
25:42Do a kind of a traditional clinical trial.
25:44Bringing patient back every couple of
25:45weeks for kind of standardized assessment,
25:47or we're doing observation,
25:48ULL data source and data analysis.
25:51There's there's kind of a middle Rd.
25:54I do see there was one question
25:56from Doctor Boffa on how to handle
25:58contradicted data from different sources,
25:59and that's an interesting challenge,
26:02right in the sense of you know,
26:04how do you if you see,
26:06you know essentially prescription data
26:09in the electronic health record at Yale,
26:12but not in the pharmacy data
26:14and how to understand that.
26:15And some of it is about understanding
26:17the various functions that
26:18are used for the data sources.
26:20Right?
26:20Prescription is ordered by a
26:22physician at Yale and it's filled
26:24at a pharmacy so that it's at.
26:26It actually gives you a sense of you know,
26:28adherence,
26:28like our patients going and filling
26:30their their their their prescriptions.
26:32But other times you know if there's you know,
26:35particularly for the the physical
26:36function we're going to have to
26:38decide exactly what does it mean.
26:40If if different you know,
26:42patient reported outcome measures
26:43or clinician reported outcome
26:45measures do not align.
26:52I see Kerry Gross asked a question
26:55around thinking about ways to
26:57adapt the EHR and its interface
26:59in order to be more proactive
27:01in terms of making information
27:03like this more readily available.
27:05And I, I couldn't agree more.
27:06Some of the challenges and part of the
27:09reason why we're using survey questions
27:12out to patients is because it's not,
27:14you know, you know,
27:15uniformly collected as part of
27:17the HR and then extracted and
27:19available to investigators who are
27:20leveraging health system data for.
27:22For research or for you know,
27:24to inform clinical practice.
27:25The more structured data we think
27:27about embedding within our reach are
27:29the better the data are going to be,
27:31the more it's going to allow us to
27:34use kind of actually more typical
27:36observational data resources for research.
27:39One of the things when and when I
27:41presented that project done by the
27:43medical student who identify that only
27:4515% of clinical trials could actually
27:47be routinely or fees abli done today
27:49using routinely ascertainable information.
27:51Part of it is because.
27:53Like patient reported outcome measures
27:55are not routinely included as part
27:57of structured data elements,
27:58so there's a real opportunity there.
28:10And then I'll the last question
28:12I see is about addressing self
28:14selection bias in our data.
28:17I think what Doctor Hooley is referring
28:19to is the participation bias that
28:22individuals are going to be more
28:24likely to participate in the study.
28:26And that raises an issue of bias.
28:29I don't think that the selection into
28:31our studies is different any different
28:34than the selection of any individual
28:36individual into a clinical trial,
28:38but hopefully ideally by lowering the
28:41barriers to participation and and making
28:44it easier on patients to participate by.
28:47By diminishing that burden of kind
28:49of haven't come in our trials.
28:51Using this this type of approach may be
28:54more representative of clinical practice,
28:57although that's that remains to be seen,
28:59and it's an important issue
29:01for us to address so.
29:02I'll stop there so that Susan
29:04Bush has plenty of time to
29:05go through her presentation.
29:08Thank you Joe.
29:11It is my pleasure to introduce our
29:13next speaker, doctor Susan Bush,
29:15who is Professor Public House in House
29:18Policy and professor in the Institution
29:21for social and Policy Studies.
29:23She received a master degree in House
29:25policy in a PhD in House Economics.
29:27Those from Harvard University.
29:29A number of us have been lobbying
29:32for her to join the Cancer Center
29:34and very happy when she did recently.
29:37Doctor Bush's research examines the effects
29:40of policies and regulations on health care,
29:42cost and quality,
29:44and she's a renowned and highly
29:47respected expert in this field today.
29:50Her topic is insurance coverage,
29:52mandates and the adoption of
29:54digital breast Tomo synthesis.
29:58Doctor Bush for as yours.
30:00OK, thank you. Thank you so
30:02much Johnny for inviting me.
30:04I just wanna make sure.
30:05Can you see my slide show it's working?
30:12Some show me you can see my slideshow.
30:14Yes, OK perfect. So first,
30:17for those of you who don't know me,
30:19I'm a health service researcher and
30:21health economist, and I teach at
30:23the Yale School of Public Health.
30:24I teach advanced health economics here,
30:26and most of my work is really around
30:29mental health and substance use disorder
30:31with a focus on access to care and how we
30:34can optimize benefit design to increase
30:36the value of the healthcare system.
30:38So I sort of took my knowledge about those
30:41issues and and now I'm applying it to cancer.
30:44So generally I'm interested when you think
30:46about is as we change payment mechanisms.
30:48What are the impacts on access to care,
30:50cost of care and value?
30:52And it's always really tough to get at that.
30:53You know idea of value,
30:55but I I really do strive in
30:57my work to do that.
30:59So if anybody has any problem
31:01projects related to that,
31:02I would love to meet with them.
31:04I also have several projects related
31:06to tobacco control that people that
31:08might be of interest to people.
31:10I'm not going to go into
31:12detail here about those,
31:12but if you're interested I would love to
31:15meet with you and talk about that about that.
31:18So over the past several years wanna
31:21say it's really been a delight to get
31:24to know the faculty at the Cancer Center
31:28both at the medical school and also
31:30here at the School of Public Health?
31:32So in particular,
31:33I want to mention Carrie Gross,
31:35who invited me to work with his
31:38team a couple of years ago and has
31:41really taught me a lot about both
31:44breast cancer screening and about
31:46how to use health care claims.
31:48Related to some of the issues that
31:50we're going to talk about today and
31:52also I want to give a big shout out.
31:55I hope she's on the call to Alana
31:57Richmond and this is very specifically
31:59related to the work of presenting today.
32:01Elan is an internal medicine and she
32:03is the first author on this paper,
32:06and I can't emphasize enough how
32:08much I've learned from having
32:09the opportunity to work with her
32:11over the past couple of years.
32:13So the paper that I'm going to
32:15talk about today is the latest
32:17in a series of papers related to
32:18breast cancer screening related to
32:20issues around patient preferences,
32:22diffusion of new technologies and cost.
32:24And you know,
32:25this paper is not really focused on value,
32:27but also a lot of our papers
32:29are focused on that.
32:31So this paper has not yet it's been accepted
32:33for publication at that not out yet,
32:35but we're thinking it's going to be out
32:36even in just the next couple of days,
32:38so.
32:42OK, so these are some our collaborators,
32:45my collaborators, on this paper.
32:46Alana, as I mentioned,
32:47Jessica Long Kelly Kenco,
32:49who is at NYU. She's a primary
32:52care physician at NYU and Xiaoju,
32:54who is also here at Yale,
32:56and of course Kerry.
33:00So you know, over the past decade,
33:03cancer screening has undergone substantial
33:05technological shift in the US in
33:08which digital breast tone was insist.
33:10DBT has supplanted standard 2D2 dimensional
33:13Mogra fi alone as the standard of care.
33:17Advantages of DBT are that
33:19DBT may reduce recall.
33:21That is that fewer women are called back
33:24for additional testing after screening,
33:26and also that it may improve sensitivity
33:29that we may identify more breast cancers
33:32using DBT compared to 2D mammography.
33:35Yet DBT is still not rated A
33:37or B by the US United Service.
33:40United States Preventive Services Task Force.
33:46This map is from an earlier paper,
33:50so just to get a sense of the
33:52variation in DBT adoption, this paper
33:55looks at hospital referral regions,
33:58so the different geographic regions you can
34:00see here are hospital referral regions,
34:02and we look at three years of data
34:04from 2015 to 2017 and over this time
34:08period over the US in the US over the
34:11whole USDBT increase from 13 to 43%.
34:15Of screenings, so this looks very
34:19specifically at trajectories,
34:21and we know by the end of 2017
34:23the lowest use HRR's hospital for
34:26regions are about only about 4%
34:29of screenings where DBT, well,
34:31the highest where it was at 68% of screening.
34:34So there really is significant variation.
34:46So related to insurance coverage
34:48really today we're talking about
34:50private insurance coverage.
34:52And private insurers are
34:54not required to cover DBT,
34:56and that's because it doesn't have the
34:58A or B recommendation by the USPSTF.
35:01So absent a federal mandate,
35:02many private insurers didn't immediately
35:04cover DBT characterizing it as elective,
35:07or citing that there might not be long
35:10term data and states got involved.
35:12To date, 17 states.
35:14I think it's actually maybe 19 now.
35:17It's 17 states where the paper was written,
35:18have enacted laws that require
35:20private health insurance cover DBT.
35:22Without any cost sharing,
35:24so of course self insured plans are not
35:27covered due to the ERISA exemption,
35:29but generally privately insured individuals
35:31and women in these states do not have to.
35:35Pay any out of pocket payments
35:38when they receive DVT screening.
35:40So this figure just gives you a sense of
35:44the variation in timing of these laws,
35:48so we're going to study laws that
35:50occurred from 2016 to 2019, and you can.
35:53You can see it really is like a
35:55staggered implementation that's really
35:57important for identification strategy.
35:59Connecticut was the 3rd state
36:01to adopt in 2017.
36:12So insurance benefit mandates such as
36:14these have been widely used in other
36:17contexts as a policy tool to protect
36:19consumers against high out of pocket cost,
36:22so it reduces their financial
36:24burden and also to facilitate
36:26access to important health services.
36:28However, you know these types of
36:31benefit mandates have have been
36:33criticized by some because they
36:36may have some complex effects.
36:39Potentially, if you mandate may
36:41contribute to higher insurance
36:44premiums and thereby this may increase
36:46uninsurance rates as if insurance
36:48premiums get prohibitively expensive.
36:50There's been some criticism that
36:52they may reduce plan design,
36:54plan benefit, design, flexibility.
36:56And also that increasing the price of
36:59the specific of a specific mandated
37:01service may reduce negotiating power
37:03and this is going to be particularly
37:06problematic for a service or a drug,
37:08potentially where they have the
37:10supplier has some monopoly power.
37:15So our goal in this paper was
37:17to evaluate the relationship
37:19between DBT coverage laws.
37:22The 17 laws that I noted in
37:24the last slide and DBT use
37:27DBT out of pocket payments,
37:28and also DBT price.
37:34So to study this, we use data from
37:37Blue Cross Blue Shield access data set,
37:41which is a a deidentified database
37:43of health insurance claims.
37:45There are claims from all 50 states,
37:47so the geographic diversity of this sample,
37:50along with the fact that has a
37:51longitudinal structure so you
37:52can follow patients over time.
37:53It makes it really well suited to
37:56evaluate policies that vary by state.
37:59Within this data set we identified screening.
38:01Mammography is performed among women
38:03ages 40 to 64 between January 2015 and
38:08July 1st up through June 30th, 2019,
38:10and we have a a standard validated
38:13algorithm that we've been using
38:14to identify a screen mammography.
38:16I won't get into details on that
38:18in this talk,
38:19so we did exclude women 65 and over,
38:21and the reason we did that is because
38:23Medicare is not really represented in
38:25these data or Medicare Advantage as well,
38:28and we felt that.
38:29Older women that were then included in
38:31the BCBS data might be highly selected.
38:35So we use the patient level data
38:36to describe the characteristics
38:37of the women and mammograms,
38:39including the study.
38:40But when we do our additional analysis,
38:43our event study design,
38:44we perform it at the state level.
38:46That is, we collapse cells to the state.
38:49We aggregated data to the state
38:51and six month period and use use
38:53the data in that way.
39:02So the exposure that we're interested
39:04in this study was a legislative
39:06mandate requiring a whether the
39:07patient lived in a state that had a
39:10legislative mandate requiring coverage
39:11of DVT during the study period.
39:14All states included as mandate
39:17states in this analysis.
39:19Also, a limited cost sharing with
39:21the exception of Connecticut,
39:22which eliminated cost sharing
39:24one year after passage of
39:26the general coverage mandate.
39:28So in these laws when we say cost sharing,
39:31these are including out of pocket
39:33payments towards deductibles,
39:34coinsurance or coherence similar
39:36to what the ACA law would have is
39:39for services that are rated A or B.
39:42The control states were states that did
39:44not pass a mandate during the study period.
39:46And we assigned mammograms were
39:47assigned to a state based on
39:49location of the billing provider.
39:53So, as I noted,
39:54the outcomes we looked at were DBT.
39:56Use the proportion of screening
39:59mammograms performed with DVT
40:00among all screening mammograms
40:02for estate in a six month period.
40:05So DBT is many people,
40:06probably on the call,
40:08probably know is typically read and built
40:10in conjunction with standard 2D imaging,
40:12so we consider DBT to have been
40:14performed when there was a claim
40:16for DBT in conjunction with a
40:17claim for screening mammography.
40:19We looked at the proportion of women
40:21with any out of pocket payment.
40:23We did also look at the mean
40:24out of pocket payment,
40:25but it became not that relevant.
40:26So today I'm just going to present
40:28results on the proportion that
40:30had any out of pocket payment.
40:33This is people,
40:34women that had out of pocket payment.
40:36We only looked at those with
40:38DVT because women screened with
40:392D mammography already had no
40:41cost sharing which is mandated
40:42by the Affordable Care Act.
40:48So we used an event study design
40:51which estimates changes in an
40:53outcome among states that pass a
40:55law relative to states that did not.
40:57At each six month interval
41:00after law implementation.
41:01So this specification allows for
41:03the effective laws to vary by
41:05the time since implementation.
41:07So basically what you do in event
41:08study design is you line up the
41:10implementation dates and look at
41:11whether there are changes in our
41:13outcomes in the first six months
41:14post implementation that in the next
41:16six months post implementation.
41:18And this also has the advantage of.
41:21It allows you to see if there were
41:23changes in the six months prior
41:36Which you would not necessarily expect us,
41:39as is typical in any
41:40different difficulty level,
41:41and models were weighted by the
41:43screened population in each state.
41:46So this table represents our patient
41:48characteristics and outcomes at baseline,
41:51so we also are not so for outcomes,
41:54it is the outcomes at baseline, so.
41:59Right, OK at the start of the study period,
42:02women and in mandate and non
42:04mandate states had similar age.
42:05Mean age was 53 in both among women in
42:09mandate states 42% lived in the northeast
42:12versus 12% in the non mandate states.
42:14In early 2015, women living in states that
42:18eventually pass a DBT coverage mandate 16%.
42:21Of women who underwent mammography
42:24were screened with DVT.
42:26Versus among women living in states
42:28that never passed a mandate 11% so
42:31the screen was a little bit lower in
42:32states that never passed a mandate.
42:34Note, this is before the mandate,
42:36though important to our study.
42:38Really very few women in 2015 had any
42:43out of pocket payment for DVT only 7% in
42:47both mandate and eventual mandate and
42:50eventual non and and non mandate states.
42:53You can see that the DBT price was
42:55higher than the mean 2D price.
42:57For example, in mandate states,
42:58the man demean DPT price was
43:01$311.00 versus for two D $266.
43:09Pre mandate.
43:17So next we look at DBT, use and here's
43:20our first outcome that we look at.
43:22So let me Orient you 'cause the next
43:23couple of slides all have the same
43:25sort of framework as this slide.
43:27So we lined up implementation dates
43:29with the period labeled here years
43:32from LA negative .5 being the period
43:34in which the law was implemented.
43:37So this shows the percentage point
43:39change in DBT use in the period before,
43:42and the period after the law was implemented
43:44relative to states with no law implemented,
43:46which is our comparison group.
43:48So by construction,
43:49the value for the period in which
43:51the law is enacted is basically 0%,
43:53because you're sort of normalizing
43:55everything to be to for them to be the
43:57same in that in that moment of enactment.
44:00So first thing to look at is if you look
44:02at the three periods that we can measure
44:04here in the period prior to the law,
44:06you see that there was no significant
44:09effects of eventually passing a law.
44:14We find no significant changes in DB use, D.
44:17Use relative to the comparison test.
44:19So this is really equivalent to
44:21the standard parallel trends,
44:22test apparel pre trans test that you
44:23see in a different different analysis
44:26in the periods after the law we do see
44:28you can see a steady increases in.
44:30Mandate states relative to other states.
44:32So by one year post law these differences
44:35are statistically significant.
44:37One year after enactment of
44:38a coverage mandate,
44:39DBT use increased 7.6 percentage points.
44:44Relative to other states.
44:48Compared to states without a mandate,
44:50I'm sorry 7.6% greater than states
44:52without a mandate, and after two years,
44:55D BTU's had risen 9 percentage points
44:57more in mandate states compared to
44:59states that did not pass mandates.
45:06Next we look at DBT price.
45:10And so it's the same format I noted before
45:13from our patient characteristics table,
45:15the average cost of a DVT was $311.00
45:18among maintenance performed in states
45:19that eventually passed a mandate and
45:22347 states that did not pass a mandate.
45:24And here we find that two years post
45:26mandate and this was a surprise to us.
45:28DBT Price had declined in mandate states
45:30compared to the change in price in nine
45:33non mandate states about $38.00 and I
45:35don't have a graph here to show it.
45:38'cause we have limited time,
45:39but we also did not observe.
45:41A significant change in the price
45:43of 2D mammography.
45:46Next we look at weather.
45:50Here it is. At the percent of DBT DBT
45:54exams with any added pocket payment.
45:59Among women's group with CBT and we found
46:01that even at the start of the study,
46:03as I said earlier, only a minority of women
46:06had any out of pocket payments with DVT.
46:08We did not observe a statistically
46:11significant change in the proportion of
46:13women who had any out of pocket payments for
46:15DVT even as we go to two years post mandate.
46:18We did also look among those that
46:20did have an out of pocket payment.
46:22The mean out of pocket payment and we
46:24did not find a statistic statistically
46:26significant change there either.
46:34So. A central policy objective of the
46:38coverage, mandates or any coverage mandate
46:40is to ensure access to a particular
46:43medical technology or service by protecting
46:46patients against financial liability,
46:49and indeed, our results suggest that
46:51women in states with coverage mandates
46:52were more likely to begin to use DBT
46:54for breast cancer screening,
46:56which probably is one of
46:57the intents of the law.
46:58And this finding is consistent with other
47:01studies across other types of services
47:04that found that expanding coverage,
47:07and in particular eliminating cost sharing,
47:09can increase the use of
47:10specific cell health services.
47:11I'll say it's very difficult in many cases
47:14to get patients to change their behavior,
47:16but changing even by very small
47:18amounts the amount they have to
47:19pay is one way you can get them.
47:21Generally,
47:21the literature is found to
47:23change their behavior,
47:24but in our study this really
47:26raises some new questions about the
47:28mechanism by which mandates.
47:30May increase use of an emerging
47:33technology because we didn't find
47:35changes in out of pocket payments
47:37and even before these mandates,
47:39the out of pocket payment was low,
47:42so it it's unlikely that a change
47:44in what the patient had to pay
47:47is what led to these changes.
47:50So one explanation for these findings
47:53is that by ensuring payment coverage,
47:56mandates may have encouraged
47:57radiologists and other health care
47:59institutions to enter the market.
48:01And offer DBT and this may have led to
48:03a relative price in at least two ways.
48:06One when more radiologists offer DBT,
48:09insurers really may have greater
48:11ability to negotiate lower prices
48:13and this could lead to lower prices
48:15or at least slower growth in prices
48:17over all providers.
48:18Second,
48:19it could be the case that early
48:20entrance we would expect the
48:22early entrance in this market.
48:23When DBT first started to be
48:25providers that have higher prices.
48:27So for example, academic medical centers,
48:29if mandates incentivize new entrants who
48:32tend to offer services at lower prices
48:35compared to established providers,
48:37the average market,
48:38the average price in the market
48:39will decrease mechanically,
48:40so you have a high price pipe,
48:42high price providers, low,
48:43lower price providers,
48:44the average is going to go down.
48:47But in that scenario,
48:48no provider has actually changed their price,
48:50right?
48:50But the the price that is paid
48:52in the market will decline,
48:54so other explanations are possible.
48:56For example,
48:57it's possible that coverage mandates
49:00might be perceived by patients or others
49:03as an endorsement of this service.
49:05And this could increase interest
49:07in this new technology,
49:08so we can't say for certain that this is
49:10one of the two things that is happening.
49:12Unfortunately we don't have a provider
49:14identifier in our data that would
49:17allow us to say whether it is.
49:19Different lower price providers
49:21entering the market.
49:24Hey, I think we need to note
49:26some limitations to the paper,
49:28so there definitely could be some
49:30issues with generalizability.
49:31Since all data was from Blue Cross,
49:33it is really really good data to look
49:36at these this study because it is from
49:38all 50 states in a very large data set.
49:41Also, there are important known
49:43limitations to using claims data.
49:45Claims could be subjected to error
49:48misclassification problems or bias.
49:50Another issue with this very particular
49:52setting is our approach focused.
49:54We chose to look at the price of the initial
49:56test rather than the screening episode.
49:58In some of our papers we have
49:59looked at the screening episode,
50:01but you know that could be
50:02very very different here.
50:03If DBT does reduce recall and that could
50:06lead to additional cost savings from for
50:09DBT relative to to to 2D mammography.
50:13Also this was an an observation ULL study.
50:18Although we believe we used to study
50:20design that intended to limit confounding,
50:22unmeasured confounding is always
50:24a possibility and could explain
50:26some of our findings.
50:28Could be you know other concurrent
50:30legislative policies or other
50:31things going on in the market.
50:35Finally, although our event study plots
50:38didn't show significant differences in DBT
50:41user price prior to the law being enacted,
50:44it's important to acknowledge that pre
50:46period trends in DBT use or cost and mandate
50:48states may may influence our results.
50:50So there could be some pre-existing trends.
50:55Hey, just to conclude,
50:57although DVD mandates were associated
50:59with an increase in DBT use,
51:01they were not associated with any
51:03change in out of pocket payments and
51:06this suggests that mandates and this
51:08has implications for other services,
51:10well, may influence DBT adoption through
51:12mechanisms other than by reducing
51:14financial liability for patients.
51:23Thank you Susan for a great presentation
51:26that clearly damn straight close link
51:29between policy and clinical practice.
51:31I was wondering whether there are
51:35studies being planned by you or others
51:39to potentially look at the impact of
51:42DBT of identifying more patients.
51:45I was thinking that eventually,
51:48if there's evidence that DBT
51:51would identify more patients
51:53because increased sensitivity,
51:55that more B might be more
51:57incentive for more states to
51:59have similar laws mandating it.
52:02When you see identify more patients,
52:04are you saying that some people that
52:06previously didn't get a mammography
52:08would get a mammography because
52:10the the DBT is available? Right,
52:13I just thinking like like on what
52:15basis would this states that occur,
52:18like not mandating it like what?
52:20Why would they be encouraged to do so?
52:22Why would they be mandating so the
52:241st that is really interesting?
52:25Especially because we didn't
52:27find it like where's the problem?
52:28Out of pocket payments were
52:31not particularly high.
52:33Sort of before these are mandated.
52:35Well, you know there might be some insurers,
52:36but there might be some fear from
52:39suppliers that insurers may stop covering
52:40it or may start implement, you know?
52:43Start putting in some out of pocket payments.
52:46Yep. You know, I think,
52:48why would a state not pass a mandate?
52:52You know they may be looking to
52:53the evidence and maybe looking
52:55to the USPS TF if they're thought
52:56of as an independent body,
52:58they still have not gone up
53:00to the ARDA or B rating,
53:02suggesting there probably there
53:03may be still some uncertainty.
53:05Right in in the studies,
53:07so that's why you might not mandate the
53:10reason that you you know or also because.
53:14It's not really clear that there's a
53:16problem since people are not paying large
53:18out of pocket payments for this service.
53:22Sure. Yeah, because the laws are
53:26at the state level and and the
53:30US preventive taskforce hasn't
53:32made a ARB recommendation.
53:34I think that could be where
53:36states are looking for.
53:40Yes.
53:43Am I supposed to look for questions?
53:48Right? Please feel free to.
53:52Type your question through chat.
53:58Oh, here's Regina. Thanks so much, Susan.
54:06OK, so Regina Hooley just has a comment
54:09that Yale they first started using
54:11DBT in 2011 and they didn't charge
54:13patients for insurance for many years.
54:15Probably not until 2018.
54:16So I think Medicare did start
54:19charging to 2:15 till 2015,
54:21so I think few private insurers.
54:24Maybe we're charging before that.
54:26I think a lot of I think there
54:29wasn't even a code until 2015
54:31to 2 allow people to charge.
54:34But that is great that Yale
54:36was able to do that.
54:59So what are the follow up?
55:02Studies that that you are carrying
55:05your team is planning. I know Alana
55:08has a huge interest in this too.
55:12Yeah, so so that's great and I you know,
55:15I think we're talking about that
55:17right now because this is one of the
55:19this is a project I have two minutes.
55:21I'll just describe how this project started.
55:24And interestingly,
55:24Joe Ross was also on this train ride.
55:27I had a personal experience with.
55:31Breast ultrasound,
55:32which is what we really the technology
55:34we were really interested in studying,
55:36and Joe Ross and Carrie Gross and I
55:40were on the same metro North train down
55:42to New York for the same meeting and
55:44we were just chatting on the train and
55:46I said to Kerry, what's up with this?
55:47You know what's going on this is,
55:48you know, many years ago and I
55:50said this is so interesting that
55:51they're doing this mandate.
55:52Let's write a grant and we ended
55:54up writing a an ACS grant that
55:57was funded to do this work.
55:59And then Alana gotten bored and
56:00we sort of extended it.
56:01It's a DBT,
56:02so it really did start out as this
56:05just sort of kind of very random thing
56:08that people just sort of talking.
56:09It's funny that Joe is here about
56:12this and ended up being this project
56:13so that project has ended now
56:15so that ACS project has ended.
56:17So we're really thinking about what
56:18would be the the best next steps and
56:20what are the most interesting questions.
56:22So I think Regina probably has some
56:24good ideas so she's sort of been
56:26involved in this so we we haven't
56:27sort of gotten to the next project.
56:29We're sort of finishing up the
56:30the old project right now.
56:31The older project.
56:35There's a comment from Carrie
56:37if you could address briefly.
56:42I'm not sure that state
56:44legislatures would look at that,
56:45but I do think like that.
56:46The advocates when you,
56:47if you say you publish something
56:49that suggests that they're really
56:50good benefits to passing a law,
56:52I think that the the advocates may
56:54bring that to state legislatures
56:56and that that can be very helpful.
56:58Especially, I do think like some of the
57:00state laws were the earlier studies
57:02showing that state laws around mammography.
57:04This is pre ACA and cost sharing that
57:07those actually you know led to more
57:10movies and potentially had some interest,
57:11some effect.
57:13On breast cancer identification?
57:15Not necessarily.
57:15I don't know if they ever got to mortality,
57:17but I think those did have an impact.
57:19Those studies.
57:21And there's one more from Alana.
57:25Yes, so a lot of notes and I that
57:28these implications have other firm
57:30or other emerging technologies.
57:32So to thinking about how that
57:34will adopt that, how that those
57:36influence adoption and price.
57:39Thank you so much Susan for taking
57:42the time to share with us your
57:44important work. Also thanks to Joe.
57:48Help you both have a nice day. But by.