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

Transcript

• 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.