Use PEDALs Model to Pedal for Implementation Research

Transcript

• 00:03<v Maur>Okay, great.</v>
• 00:05So good morning and good evening to our friends
• 00:08joining from China.
• 00:08Welcome all, my name is Maur Desai.
• 00:11I'm a faculty member in the Department
• 00:14of Chronic Disease Epidemiology at the Yale School
• 00:15of Public Health and also the school's Associate Dean
• 00:18for Diversity, Equity and Inclusion.
• 00:20It is my pleasure to step in for Donna Spigelman
• 00:23to introduce our speaker, professor Dong Roman Xu.
• 00:27But first I'd like to acknowledge that this seminar
• 00:30is co-sponsored by the Yale Center for Methods
• 00:32and Implementation and Prevention Science,
• 00:35also known as CMIPS, which Professor Spigelman directs.
• 00:37The seminar is co-sponsored by the Department
• 00:40of Chronic Disease Epidemiology and the global health
• 00:42concentration at YSPH, as well as the newly funded
• 00:46NIHT 32 training grant Implementation Science Research
• 00:51and Methods.
• 00:53Based at the Yale School of Public Health, CMIPS develops
• 00:56and disseminates innovative methodological approaches
• 00:59to address implementation gaps and improve public health
• 01:02worldwide strategically selecting the issues
• 01:05that carry the greatest burden and hold the greatest promise
• 01:08for amelioration right now.
• 01:10If you would like to be informed about future CMIP seminars,
• 01:14please let William Tutel know in the chat and he will add
• 01:17you to the CMIPS listserv.
• 01:20Professor Roman Xu is one of the foremost implementation
• 01:25science researchers and methodologists in China.
• 01:26His research focuses on health system innovations
• 01:29and implementation science, particularly those
• 01:32involving chronic diseases quality of primary health care
• 01:35and eHealth in the context of global health.
• 01:38He's leading several large studies including primary
• 01:42healthcare quality cohort in China, the Silk Road Labs
• 01:46for health system strengthening in Nepal and Mozambique,
• 01:50an implementation trial for stroke guidelines in China
• 01:54and the shared medical appointment trial for diabetes
• 01:57or smart trial.
• 01:59Professor Xu received his PhD in global health
• 02:01implementation Science from the University of Washington
• 02:04and his master's in Public Policy from Harvard University.
• 02:09The title of his talk today is Use PEDALs Model to PEDAL
• 02:12for Implementation Research.
• 02:15Roman, over to you.
• 02:18<v Roman>Thank you Maur.</v>
• 02:19Thank you very much for the very nice introduction,
• 02:21let me share my screen first.
• 02:32Okay, I suppose you can now see my screen.
• 02:35Today I'm going to talk about the PEDALs model
• 02:39and in that process I will use one of our ongoing,
• 02:42<v Maur>Oh, sorry, Roman.</v>
• 02:44Roman, do you want to put it in presentation mode?
• 02:47<v Roman>It is not in presentation mode now?</v>
• 02:49On my screen it is in the presentation mode,
• 02:54it's a little strange.
• 02:56<v Maur>Ahh.</v>
• 02:57<v Roman>Let me share again.</v>
• 02:58<v Maur>Okay and while you're doing that,</v>
• 03:00I'll just say very quickly, if you have questions,
• 03:03please hold them to the end, but you're welcome to put them
• 03:05in the chat and then when we get to the Q&amp;A,
• 03:09you can use the raise hand feature, just unmute yourself,
• 03:13jump right in and we'll make sure that we get to
• 03:16as many questions as possible.
• 03:22<v Roman>Can see now.</v>
• 03:27<v Maur>I don't know about others, but I'm still seeing it</v>
• 03:28in sort of the regular mode, not the presentation mode.
• 03:32<v Roman>Okay, just one second, sorry for that,</v>
• 03:35it's a little I tried but just one second.
• 03:53Sorry for that everybody.
• 03:57<v Maur>No problem.</v>
• 03:59<v Roman>Let me share my screen to see whether</v>
• 04:02this will be better.
• 04:13Now, can you see my screen now?
• 04:15<v Maur>Yes and you may want to, we're seeing</v>
• 04:18also the preview slide, so if you swap display
• 04:20maybe that would help, that would help at the top.
• 04:26<v Roman>Does it work now?</v>
• 04:32<v Maur>And we can start, if it's gonna be a,</v>
• 04:34you don't wanna create too much delay,
• 04:36but if at the top, I think it's the second tab,
• 04:38if you say switch display, we should be able
• 04:41to then see it in full mode.
• 04:44So it was fine, but we were just seeing
• 04:49your previous slide as well.
• 05:20There we go, perfect.
• 05:33<v Donna>Oh, Dr Xu, you on mute?</v>
• 05:35<v Maur>Oh, you're on mute.</v>
• 05:43<v Roman>Okay, now it's good.</v>
• 05:44<v Maur>Perfect.</v>
• 05:46<v Roman>Ah, technology, I am supposed to know this well.</v>
• 05:49So today I'm going to talk about the PEDALs model,
• 05:53which is a model we have developed essentially at the
• 05:56beginning for our students so they can understand quickly
• 05:59with a nice acronym about the general procedures
• 06:02of conducting implementation research.
• 06:06And we'll use one of our ongoing trial for shared
• 06:10medical appointment for the management of diabetes
• 06:13as an illustration.
• 06:15And in that process I will talk about some
• 06:18of the common designs for implementation research
• 06:21and the choices and the rationale we choose some
• 06:26of the designs versus others in this presentation.
• 06:31But before that, let me spend a few minutes on some
• 06:34of the advertisement.
• 06:36I'm currently the principal investigator for a lab
• 06:39we call Acacia lab, which is sort of the child of
• 06:45a parent study called Acacia Study.
• 06:49In that study we have set up a consortium
• 06:52of researchers from 10 universities in China.
• 06:56And gradually because of using that study
• 06:59we have formed strong collaborative team in China
• 07:02for primary healthcare and implementation science.
• 07:05So looking to the future, we really want to use
• 07:08this platform to collaborate more with all of you.
• 07:12I'm also leading the Southern Medical University Institute
• 07:16for Global Health site.
• 07:18Southern Medical University is one of the first
• 07:22and largest medical center receiving international patients,
• 07:26especially from the low and middle income countries.
• 07:29And in terms of research, our institute holds largest
• 07:33total competitive grant size in China.
• 07:36And in terms of education, we are one of the four
• 07:38and one of the earliest program for international MPH,
• 07:43every year we gave 25 scholarship to people from low
• 07:47and middle income countries to study MPH in China.
• 07:52I'm also the co-editor in chief along with the professor
• 07:56Ann Sales for a new journal called implementation science
• 08:00communications, which is a facial companion journal
• 08:04to implementation science.
• 08:07We are a brand new journal two years, but so far
• 08:11we have received last year we have received almost
• 08:16a 400 submission, which is quite good for new journal
• 08:20and the downloads are also increased dramatically from
• 08:25two years ago to last year.
• 08:27So yeah, welcome to submit your work to our journal
• 08:32and thank you so much.
• 08:34So now before I talk about the PEDALs model, I'd like
• 08:37to go over a few key concepts in implementation science.
• 08:41That is very much related to my model as well.
• 08:45So what is implementation science?
• 08:49This is a the question that I normally get in China
• 08:54and sometimes I find it's not so easy as it appears
• 08:58to discuss.
• 09:00According to a review in last HIV journal,
• 09:04they have identified 73 unique definitions
• 09:09for implementation science.
• 09:12So this definition I like it quite a bit
• 09:16but I still feel it is a little long, I prefer a much
• 09:20shorter definition modified on the definition given
• 09:24by the authors of this review.
• 09:26In my view, implementation science is a multidisciplinary
• 09:29specialty to seek generalized ology.
• 09:32So implementation science, one of the questions
• 09:35I got in China is it a field?
• 09:38Is it a discipline?
• 09:39Is it just a method?
• 09:41I would say it's a multidisciplinary specialty
• 09:45to seek generalizable knowledge, because it is a science
• 09:49so it has to generate generalizable knowledge,
• 09:52it is about the scale of reasons for the strategies
• 09:56to close the evidence to practice gap.
• 09:59So what implementation science is about is really to
• 10:03put evidence-based practice into routine practice
• 10:08and our research is trying to understand how large
• 10:12that gap is and what are the determinants barriers
• 10:14and the facilitators for implementing that evidence-based
• 10:19practice and making it to a routine practice
• 10:21and what are the implementation strategies which are also
• 10:24intervention but we distinguish that from
• 10:27the health intervention, so we call it
• 10:29implementation strategies.
• 10:30What are the implementation strategies which can help
• 10:33close this gap?
• 10:34So that is about implementation science,
• 10:37so some of the other key concepts.
• 10:40In order to do implementation research,
• 10:42we have to start with health interventions
• 10:45and those healthy, not all of the health
• 10:48interventions can go into the process
• 10:49of implementation research.
• 10:51We have to first have evidence this health intervention
• 10:57can be regarded as evidence-based practice EBP.
• 11:00Once we have that, we need to understand the determinants
• 11:03of implementation, then based on that we will develop
• 11:06implementation strategies.
• 11:08We which we will tackle the barriers and the facilitators
• 11:12for implementing the EBP, some of them may come
• 11:16in from the health intervention itself,
• 11:18some are contextual factors.
• 11:20Then we need to understand the implementation
• 11:22outcomes which are different from the health outcomes
• 11:25and the clinical outcomes, then after that
• 11:28we make it into routine practice.
• 11:31Now let's talk the PEDALs models we have developed.
• 11:36First of all why we developed this model.
• 11:39Implementation science has already been inundated
• 11:42by the theories, models and the framework.
• 11:45A 2012 review identified more than 100 frameworks
• 11:50and it has ever since been increasing, why we are
• 11:54getting another framework.
• 11:55The motivation is from our students because when we are
• 11:59teaching implementation science to a master
• 12:02and an undergraduate students, very often we are challenged
• 12:05to give them a quick way to understand the essence
• 12:08of conducting implementation research.
• 12:11PEDALs has been developed as a teaching tool
• 12:14to wrap essential steps of conducting implementation
• 12:17research in an easy to remember acronym
• 12:19and also this acronym supposedly need to have an appropriate
• 12:23metaphor which can encompass
• 12:26the implementation science essence.
• 12:30This model has not yet been used, has not yet been published
• 12:34or peer reviewed, but we have been using it
• 12:37for our teaching already, so what is the PEDALs?
• 12:39PEDALs has some of the letters and but first off
• 12:42for the PEDALs it's when your PEDAL, your bike,
• 12:46which has a image of a cycling forward with PEDALs
• 12:49which has a metaphor of moving forward with the
• 12:53short cycles, which indicates implementation science
• 12:56is very often indicating continued improvement.
• 12:58This is not a graphical representation of a model.
• 13:04So for the PEDALs for implementation science,
• 13:07we have to start with your problems in your work,
• 13:11which can be a clinical problem, which can be
• 13:14a public health problem.
• 13:15Once you have identified this problem, you need to search
• 13:18for whether or not there are existing evidence-based
• 13:22practice EBP, which can address the problem you have
• 13:25encountered after you have identified an EBP.
• 13:28To address your problem we have to think about how to,
• 13:32what are the barriers and the determinants to implementing
• 13:35this EBP in your work setting.
• 13:38So after the good understanding of the determinants
• 13:42to the implementation of the EVP, we need to develop
• 13:47implementation strategies which can facilitate
• 13:50the adoption and uptake of this EVP, then finally
• 13:54we hope it can get into sustained use.
• 13:57All across this process there is a S for the PEDALs,
• 14:01a small s some has two meanings here
• 14:05for one way it is plural form so means this might be
• 14:09a cycle, a continuous improvement.
• 14:13Another meaning of small s is it is a scale
• 14:17so it is indicating we need to have monitoring
• 14:22and evaluation designs and the methods for
• 14:25particularly in the last two process of EBP
• 14:30developing of the determined determinants of EBP
• 14:35and also developing implementation strategy
• 14:38and also to test the effectiveness
• 14:41of the implementation strategy.
• 14:42All of that need to have a strong monitoring
• 14:45and an evaluation design.
• 14:47So that's use our shared medical appointment study
• 14:51to illustrate those process first work challenges
• 14:56and problems starting with P.
• 14:59In China, China is facing with a severe diabetic epidemic.
• 15:04In 2000 we only have 22 meaning people with diabetes.
• 15:08But in 2019 it is already 116 meaning the prevalence
• 15:14has increased dramatically from 2.7% to almost 10%.
• 15:21And the way to deal with the diabetic management in China
• 15:25is primary is through primary healthcare.
• 15:27However, we have a challenging here in China
• 15:30with a very much overburdened public health
• 15:32and clinical workforce and there is also reported
• 15:35very poor quality of care and insufficient communication
• 15:39between care providers and the patients,
• 15:41there is also a very much lack of patient centered care.
• 15:45So the service model is insufficient to really
• 15:50integrate public health work and also the curative services
• 15:54in diabetic management here in China.
• 15:58So we performed this gap analysis and we trying to
• 16:01identify whether there is other service model which has
• 16:06evidence which can meet and solve the problem we have
• 16:09encountered in this work setting.
• 16:13So that's come to the second step
• 16:15evidence-based practice EBP.
• 16:20So in order to do that, first we need to have
• 16:22some understanding to what extent health intervention
• 16:28can be considered EBP, I don't think we have some consensus
• 16:32on that, but most of the people I believe
• 16:35are familiar with this evidence pyramids evidence can
• 16:39change from expert opinions to cross-sectional studies
• 16:43all the way up to individual studies to synthesis of know
• 16:47to systematic reviews on the top.
• 16:50So normally for the journal implementation science
• 16:54and the implementation science communications,
• 16:56we will regard some health intervention as a EBP
• 17:02if they at least have several randomized control
• 17:06studies to support them in a health setting.
• 17:08But I have to say different settings, different studies,
• 17:12like some of the policy study, it's very difficult
• 17:15to have randomized control studies.
• 17:17But normally in the healthcare vicinities in this setting we
• 17:22consider several randomized control studies or even better,
• 17:25more since that systematic reviews is good to prove it
• 17:29is an EBP.
• 17:31So with that standard, fortunately we have identified
• 17:34systematic review for shared medical appointment
• 17:37which has approved SMA is a good way to tackle
• 17:41diabetic management in countries outside of china,
• 17:46so what is the shared medical appointment?
• 17:49Shared medical appointment is a new method
• 17:51of managing your patients.
• 17:53In traditionally in China, patients are managed
• 17:55under one-on-one consultation.
• 17:59So one patient go to see the doctor one-on-one,
• 18:02but for shared medical appointment patients
• 18:05with similar conditions are grouped together
• 18:08and they go and see doctor together and sometimes also
• 18:12it's not only one doctor, it's a group of
• 18:15a multidisciplinary team come together.
• 18:16So it becomes a group and group consultation between
• 18:20and the education and the management
• 18:23between the clinicians and the patient.
• 18:25And there are systematic reviews suggest shared
• 18:28and medical appointment has substantially improved clinical
• 18:31outcomes in terms of glucose control
• 18:36and blood pressure control and also it improves,
• 18:39it has a lot of benefits to improve patient behavior,
• 18:43self-management and also to improve (indistinct) adherence
• 18:47to best practice and adherence to clinical guidelines.
• 18:52And the very good is the study also suggests shared medical
• 18:56appointment for diabetic does not really increase
• 19:01the economics.
• 19:03So you don't really spend extra money,
• 19:06have extra spenditures to manage your patients
• 19:10with a shared medical appointment model.
• 19:12So it looks like it is effective and it does not
• 19:16increase your expenditure.
• 19:18It is no brainer we should use SMA but unfortunately
• 19:21in China, China we don't really use it a lot
• 19:24actually it is not used at all in all across China.
• 19:29So that's why we need to go to the third step.
• 19:32We need to understand what are the determinants various
• 19:36and facilitators which are determine the implementation
• 19:39of SMA shared medical appointment in China in our setting,
• 19:44in our primary care.
• 19:46So there are two types of barriers in the facilitators
• 19:48in my view.
• 19:49One is embedded with the shared medical appointment itself.
• 19:53So the health intervention itself can be a strong barrier
• 19:57then it moves to contextual factors.
• 20:00So first let's talk about shared medical appointment itself
• 20:04and what we need to do is we need to optimize SMA
• 20:07for the context of China.
• 20:11In that we're going to use the method proposed by
• 20:14Professor Linda Conius.
• 20:16Linda Conius is proposing a method she has developed a code
• 20:21optimizing of bio behavior and biomedical interventions,
• 20:26the multi-phase optimization strategy coll it MOST.
• 20:31So according to MOST, we have to first come up with a
• 20:35conceptual model for SMA for the setting
• 20:40of China because almost the all of the health interventions
• 20:44are sort of the complex intervention for shared
• 20:48medical appointment it's the same thing,
• 20:50it has many components.
• 20:52We need to decide what individual components for SMA
• 20:56can be combined together and it can be configured in a way
• 21:00which is best suited for the setting of China.
• 21:03So we have developed this conceptual model,
• 21:06at least it has four components which are important for SMA,
• 21:10one is you put patients with similar conditions together
• 21:13so they go to see the doctor together.
• 21:16So this component can be versus individual
• 21:19one-on-one session.
• 21:20The second component is the patient can go accompanied
• 21:24by their family members.
• 21:26The third component it is a multidisciplinary team
• 21:30from the clinicians come to see the patient.
• 21:34The fourth is the education for diabetes can
• 21:37be either online or offline, so we have at least
• 21:40four components.
• 21:42So in this conceptual model, those campaign,
• 21:44those components works through one of those nine mechanisms,
• 21:48through those two types of mediators finally it can
• 21:52improve self-management of the patients in theory
• 21:56and finally to improve primary outcome of glucose control.
• 22:01So for the conceptual model it is good in a view,
• 22:05you have your components layout clearly and also have
• 22:09your mediators layout clearly.
• 22:11But mostly important, you have all of the mechanisms
• 22:15which are supposed to work to connecting each
• 22:17of the individual component to the mediators
• 22:20and in the end to the outcome health outcome
• 22:24and the clinical outcome you are trying to achieve.
• 22:26So with the development of this SMA conception model,
• 22:30we can develop with the information from literature
• 22:33and also with consultation with clinicians
• 22:36and the stakeholders, we need to really have optimization
• 22:39trial to really understand whether or not
• 22:43those individual components can work and whether
• 22:46you combine those individual components together,
• 22:49they can be working together rather than canceling
• 22:52each other out.
• 22:53So in order to do that we are going to do a factorial design
• 22:58while we are going to do a factorial design,
• 23:00because we have four components.
• 23:02Let's say if we only have three components
• 23:05with the three components you can have eight different
• 23:08configuration of those components and making them into
• 23:12an complex intervention.
• 23:14If we are going to do in the individual two arm trial,
• 23:18we need to do three separate randomized control study
• 23:22that is very time consuming and a very resource consuming.
• 23:27But with the factorial design as proposed by
• 23:30Professor Linda Conius, we can use one trial the same
• 23:34sample size, but we can deal with all three or four
• 23:38components, so with that study we can understand whether
• 23:41or not each individual component in your complex study
• 23:45is effective or not.
• 23:47And even better with the design of a factorial design,
• 23:52they can also test interaction between those
• 23:55individual components.
• 23:56So that is important for implementation science
• 23:59because sometimes individual component can work,
• 24:03but if you put different individual components together,
• 24:07they may cancel out the effects from each other
• 24:08or they can virtually reinforce, so one plus one may be
• 24:13larger than two or less than two, so factorial design
• 24:16can deal with all those issues, so that is very good.
• 24:19And also good to remember is most it's not only
• 24:24concerning factorial design because it is optimization,
• 24:28optimization means not the best if it is not optimization.
• 24:33We are only looking for health interventions which works
• 24:37best in terms of improving health outcomes,
• 24:39but with optimization we are looking for under the resource
• 24:43constraints which we have agreed upon with the stakeholders
• 24:47what is working best.
• 24:49So we have to set clear, something we call optimization
• 24:54criteria, which can be money, which can be time
• 24:58to implementing SMA.
• 25:00So once you we have those criteria set, we can do the
• 25:04factorial design we and the way we are going to pick up
• 25:06the configuration which best suited the resource
• 25:10concentration constraints.
• 25:12But under that umbrella, whatever configuration works best
• 25:16can be picked up for our final traditional randomized
• 25:21control study.
• 25:22So that is what we are going to do to select the components
• 25:27for our SMA in that way we can reconfigure SMA
• 25:31to the context of China.
• 25:33So suppose after we have optimized SMA itself
• 25:38for the context of China, we still need to understand
• 25:41this reconfigured and optimized SMA and what are the
• 25:47other contextual factors which can determine
• 25:50the implementation of this optimized SMA.
• 25:55In order to do that we will use a lot of the frameworks.
• 26:00You know, implementation science is inundated by frameworks,
• 26:04but use of frameworks is really the essence
• 26:07of implementation research.
• 26:08I very much use this when I'm talking about
• 26:11implementation science theories, models and a framework
• 26:15without a theory.
• 26:17Think about if you have many pieces of clothing, shoes,
• 26:20juries and you do not have really a very nichey
• 26:24and very neat, very nice walking closet,
• 26:27then you are buried in your clothing and if you want to
• 26:31walk up and do a interview, it's very difficult for you
• 26:34to organize your clothing and dress up very nicely.
• 26:37But if you have a very good framework, which is almost like
• 26:41your walkin closet, you can organize the things
• 26:44systematically and you can also standing on the shooters
• 26:47of many giants because other people have done
• 26:49the work of for you what are the items that you need
• 26:52to looking at.
• 26:54So frameworks provide us with more systematic
• 26:57and comprehensive way of looking at it, the things you want
• 27:00to look at.
• 27:03And we have very good taxonomy of series models
• 27:08and the frameworks and for the PEDALs we are going
• 27:11to use a determinants frameworks for the understanding
• 27:15of barriers and the facilitators and we will use
• 27:18process models and implementation series to understand
• 27:22how to develop implementation strategy.
• 27:24Then we are going to use evaluation frameworks
• 27:27for the evaluation of your implementation strategy
• 27:31for health outcomes.
• 27:35So for specifically for SMA, we are going to use
• 27:37a concern related framework for implementation research.
• 27:40Why we use this very commonly used framework,
• 27:43CFIR is probably most widely used implementation framework.
• 27:48Why we choose it, one of the biggest reason
• 27:51is it is very comprehensive and the second is
• 27:54it has a really nice website which has layout all of the
• 27:58tools and options and the literature concerning
• 28:01this framework and also the tutorials available,
• 28:05so it's very easy to find resources.
• 28:07So once we have a implementation team and the research team
• 28:11if we use safer is much easier to teach the entire team
• 28:15how to use CFIR.
• 28:19But CFIR is a framework, it is not a model.
• 28:22So by that it does not really suggest causal linkage
• 28:26between the components and the outcome.
• 28:29So we are also considering use normalization process theory
• 28:33NPT as a complementary framework to CFIR.
• 28:40However, I have to say, even though normalization process
• 28:42theory is very nice in terms of illustrating
• 28:45the causal linkage of implementing process and the outcome,
• 28:51sometimes it is not so easy to use.
• 28:54For instance, in this picture is one of our
• 28:59reconfiguration of the domains and the constructs
• 29:03from normalization process theory.
• 29:06We have spent quite a bit of time in studying
• 29:07and understanding NPT and organize it in a way our students
• 29:12and the research team can understand better.
• 29:14But even with that we still found some of the constructs
• 29:18of this model is a little difficult to distinguish
• 29:21like inter action workability relational integration
• 29:25and skillset workability, those constructs can be very
• 29:30easily like confusing for our researchers.
• 29:34However, it is still one of the rare implementation
• 29:37science theories specifically for implementation,
• 29:40so we are considering using it for SMA as well.
• 29:43So how we are going to use it, this is a picture
• 29:46from the journey to the west, one of the very famous
• 29:49Chinese classic.
• 29:50It has many of the stakeholders for the journey to the West
• 29:54from China to India to fit the classic scripts for Buddhism,
• 29:59so you can get the Buddhism back to China
• 30:02and implementing that.
• 30:04So essentially we want to do stakeholder analysis
• 30:07to determine and engage the community.
• 30:11We want to have community engagement involvement
• 30:15and engage in your stakeholders so we can determine
• 30:17what are possible the facilitators and the barriers
• 30:22for your implementation of SMA, so in this process
• 30:28we will use CFIR and NPT and use them to design
• 30:32or survey form.
• 30:34So those can be used as a quantitative survey form,
• 30:37but we will also use them to design interview guides.
• 30:41So we can use them to do in-depth interview or even use it
• 30:45for focus group and we'll also use those frameworks
• 30:49to analyze data.
• 30:50So implementation science framework, CFIR and NPT.
• 30:54We actually guide us throughout the entire process
• 30:58of our study.
• 31:00This is the barriers and the facilitators we have identified
• 31:04from the literature, not from a actual study as our study
• 31:07is currently ongoing.
• 31:11But now think if we have already determined the barriers
• 31:14and the facilitators to normalize EBP of SMA
• 31:20in our clinical setting in primary healthcare,
• 31:22now we need to develop implementation strategies to deal
• 31:26with each of the barriers.
• 31:29In order to identify implementation techniques,
• 31:32then you can package those individual techniques into the
• 31:36package we call implementation strategy,
• 31:39which can deal with the implementation barriers effectively
• 31:43so we can improve uptake.
• 31:45We already have good studies in developing taxonomy
• 31:50of implementation strategies which are expert recommendation
• 31:55for implementation change.
• 31:56The ERIC, so ERIC is one of the popular framework
• 32:00which have categorized all the available implementation
• 32:04technique they can identify.
• 32:06But the key is really to, I identify those
• 32:09available implementation technique which very often
• 32:13have already some evidence-based and match them
• 32:16to your implementation barrier.
• 32:18So this is a step critical in developing your action,
• 32:22which means developing your implementation strategy.
• 32:25But the key, the big challenge is what are the methods
• 32:29you can use to match entertainer implementation strategies
• 32:36to your barriers in your setting.
• 32:39This is a really a under-researched area in
• 32:41implementation science.
• 32:43However, one of the researcher has suggest four methods
• 32:48we can consider, one is called concept mapping,
• 32:51which is a visual mapping using mixed and methods.
• 32:56The map here is one of the visualization of the barriers
• 33:01in implementing some EBP.
• 33:07Then the second method is group model building,
• 33:10which is sort of the a causal loop diagram of complex
• 33:14problems.
• 33:15The third is a conjoint analysis, conjoint analysis
• 33:20has different forms.
• 33:21One of the most popular form is called a discrete choice
• 33:24experiment, which we are going to talk a little later
• 33:28because we have opted for DCE Discrete Choice Experiment
• 33:32for our study.
• 33:34The last one is intervention mapping,
• 33:36which is a systematic and multi-step development
• 33:39of interventions.
• 33:41All of those four methods have been extensively used in
• 33:45other fields but not as much in implementation research.
• 33:48So I'm really highly encouraging all of us
• 33:51in doing implementation research to use some of those
• 33:54methods in systematically match and retainer implementation
• 33:58strategies to the barriers you have identified
• 34:01in your study.
• 34:02So for us, we are going to use a difficult choice experiment
• 34:05to tailor implementation strategies for SMA,
• 34:09DCE is widely used in health economics,
• 34:12but not as much in implementation science.
• 34:15DCE belongs to the method in conjoin analysis,
• 34:18DCE in our team we have used the DCE before
• 34:23in understanding healthcare professionals preference
• 34:27for working in the primary care setting job preferences.
• 34:31According to review, they have identified
• 34:3422 DCE studies comparing different implementation
• 34:38strategies.
• 34:40So it is not so much used as as much in other field of work.
• 34:47So use of DCE in our SMA study is like this
• 34:51in basically in DC you it's a combination of a quantitative
• 34:56and a quantitative work.
• 34:58You first it's most likely we'll use a quantitative work
• 35:03and also literature review to identify what are the
• 35:07possible implementation techniques to be developed.
• 35:11So we develop those implementation strategies,
• 35:14the techniques through initial review of literature
• 35:19and expert consensus.
• 35:21So for instance, if we have identified through this process
• 35:26audit and feedback is one of the major implementation
• 35:30strategy to deal with this barriers,
• 35:33we have identified the DCE then can do the work of
• 35:37painter audit and feedback to the specific setting
• 35:41to implementing SMA, because why we are going to do this,
• 35:45because even it is called audit and feedback,
• 35:49it actually has many components.
• 35:52This is very much like EBP of SMA can be complex
• 35:56as many components.
• 35:57Our implementation strategy can also have many components.
• 36:01So we can develop these different components of audit
• 36:06and feedback including format of feedback.
• 36:09Is it a verbal or written recipients of feedback?
• 36:13Do we feedback to individual clinician or feedback
• 36:16to the entire group the source of feedback,
• 36:18is the feedback coming from that influential source
• 36:23like their peers or supervisor or is it coming from
• 36:26the researchers?
• 36:27How we are going to deliver the feedback by emails,
• 36:30by letter or in person?
• 36:32How frequent your feedback should be monthly
• 36:36or every four months.
• 36:38Now how the instruction for feedback need to be developed.
• 36:41Will it be explicit, measurable, targeted but no action plan
• 36:47or should it be accompanied with action plan,
• 36:50but no explicit target or in addition to audit and feedback,
• 36:55do we need to copy that with another
• 36:57implementation strategy?
• 36:59Say giving people financial incentive.
• 37:01If we are going to do the SMA, we give them extra money
• 37:05to do that.
• 37:06Okay, so the audit and feedback and our implementation
• 37:10strategy have all those individual attributes
• 37:13and all those attributes have levels.
• 37:17So we can based on those, we can develop different choices
• 37:22have one to many, many, many, many choices for.
• 37:27So we present those choices side by side to our respondents,
• 37:32to our stakeholders to so they can choose between those
• 37:35two choices.
• 37:36Which set would you prefer is our approach to improve SMA
• 37:41in your organization So they can make the choice,
• 37:43so after the respondents the stakeholders have
• 37:47making all those choices from those choices then we can do a
• 37:53statistical analysis.
• 37:55With that we can determine how preference are influenced
• 37:59by each attributes and we can also give the
• 38:02relative importance of those attributes.
• 38:06And in particular, once we give a financial incentive here
• 38:09have a dollar amount, we can actually measure and transform
• 38:14all those attributes into something called
• 38:16the willingness to pay.
• 38:18So we can precisely quantify the value of all those
• 38:22individual attributes.
• 38:24So after we have done this exercise,
• 38:27we can understand the preference of our stakeholders,
• 38:31what kind of audit and feedback they think might work best
• 38:37even though this is pre-implementation,
• 38:41so after doing this we can develop a complete package
• 38:44of implementation strategy.
• 38:47So after we have done this, the important thing
• 38:50before we can move this to sustain the use
• 38:53is to come up with very good monitoring and evaluation plan,
• 38:57which will entail to develop evaluation designs
• 39:01implementation outcomes and measurement tools.
• 39:05So let's focus on this S part of the S model.
• 39:09First we need to understand what kind of design we want to
• 39:14tailor this into.
• 39:16There are something called a hybrid design.
• 39:19Hybrid design is sort of the design you are trying
• 39:23to balance to what extent you want to have this study
• 39:26as a effectiveness study of your EBP, which is SMA
• 39:30or to what extent you want to test the implementation
• 39:33outcome of your implementation strategy,
• 39:36which in our case can be audited the feedback.
• 39:40So depending on the priority of set to those two outcomes,
• 39:44it can be type one, type two or type three hybrid design.
• 39:49For type two hybrid design, you are going to test
• 39:53both the effectiveness of the EBP
• 39:56and also to test the effectiveness of your implementation
• 40:00strategy, because SMA has not yet been done in China before.
• 40:05So it's very important for us to test the factories of SMA,
• 40:11but it is also important for us as an
• 40:13implementation scientist to test the implementation strategy
• 40:17of audit and feedback in implementing SMA.
• 40:21So we opted for type two hybrid design for implementation
• 40:25for implementing SMA in our setting, but what type of design
• 40:32we are going to use for our trial, we are considering to use
• 40:36something called the step wedge design, step wedge design
• 40:40is a unique type of randomized control study,
• 40:43but it allow gradual implementation of SMA across
• 40:48the primary care institutions centers in our sample.
• 40:53So for the use of a step wedge design, eventually everybody
• 40:58in your setting in your S participants will receive SMA.
• 41:04So this give the gradual implementation of SMA
• 41:09has some advantage, because then we can facing our manpower
• 41:14so we can ensure we really implement SMA in our institutions
• 41:19step by step gradually.
• 41:21And also all sites eventually receive SMA means ethically it
• 41:25is better than some people only serving as controls
• 41:29without the benefits of the SMA.
• 41:32Also it says the step wedge design has a great statistical
• 41:37property so with the same sample size,
• 41:40normally it has a much higher statistical power
• 41:44than conventional two (indistinct) control study.
• 41:47But the complications of step wedge design is the analysis
• 41:52plan is much more complicated than the two randomized
• 41:56control study and also the length of your study
• 42:00is much longer than your study.
• 42:03So in our specific for SMA study in the study we are going
• 42:07to do the effectiveness trial for SMA.
• 42:10So five counties in China, each county have two
• 42:15primary healthcare centers.
• 42:17So we will randomize those five counties in those six steps,
• 42:23randomize them into receiving SMA gradually until in the end
• 42:27all of them are receiving SMA.
• 42:30But for each of the county, one of the primary care centers,
• 42:35one of them will receive the audit and the feedback
• 42:39as a implementation strategy.
• 42:41The other will receive another type of usual implementation
• 42:44strategy, so we can compare in this study
• 42:47both the effectiveness of SMA but also can use experimental
• 42:53design to compare the effectiveness
• 42:57for implementation outcomes for audit and feedback
• 43:01versus usually of implementation.
• 43:07<v Maur>I just wanted to let you know we have less</v>
• 43:10than 15 minutes, so I just wanted to make sure.
• 43:12<v Roman>Sure, sure, sure.</v>
• 43:14I only need two more minutes to wrap this up.
• 43:17I have a big timer on my side.
• 43:19<v Maur>Oh perfect.</v>
• 43:20<v Roman>Reminding of that.</v>
• 43:22So we also have implementation outcomes,
• 43:25which I think people are already familiar with.
• 43:27The one thing I want to emphasize is for implementation
• 43:30outcomes and also for patient and the service outcomes,
• 43:33they all have two dimensions, the absolute obtainment
• 43:37and also the equity which is the distribution
• 43:40of your outcomes among your stakeholders.
• 43:44We're going to use the RE-AIM as well, but there are many
• 43:48challenges actually in using a RE-AIM, RE-AIM
• 43:52is not as simple as it appears, because it is sometimes
• 43:55it's difficult to operationalize say
• 43:58the implementation outcome for RE-AIM framework,
• 44:02say how do you measure fidelity of delivering SMA?
• 44:06One of the things we're considering the measurement tool
• 44:10is to use standardized patient.
• 44:11We have not yet decided on this yet, but because
• 44:13we have already been the conducting a very massive study
• 44:18of using standardized patients in assess quality of care
• 44:23in China, which is a fake patients, but they are trained
• 44:26so they control the case mix and there is no
• 44:30Hawthorne effects compared with other direct observation
• 44:34of your clinical practice and using standardized patients
• 44:41can also enable quick audit and feedback work.
• 44:44So we are considering seriously because of our experiences
• 44:48and the expertise in using this method
• 44:50in assessing primary care quantity, we are considering
• 44:55using this as a quantity outcome collecting tool
• 44:58to understand the fidelity and implementation process
• 45:04of SMA for our settings.
• 45:07If we are interested in this method further,
• 45:09you can check out two of the papers that we have published
• 45:12to illustrating how this can be used in other setting.
• 45:16So that is what I am trying to talk about this PEADLs model.
• 45:21So basically this is the model is to give researchers
• 45:25and students to think about your thought implementation
• 45:28research from identifying the problem in your work.
• 45:32Then you need to identify EBP to address that problem,
• 45:36but you really need to understand what might be the barriers
• 45:39and the facilitators and based on that to develop
• 45:42your implementation strategy in order to achieve
• 45:45sustained use.
• 45:46But all across this process you have to have a very sound
• 45:50and a good evaluation design plans and measurement tools.
• 45:57So thank you so much, I hope this presentation
• 46:00can motivating some people to come for our program
• 46:05for postdoctoral fellows in implementation science
• 46:09in our Acacia lab and our center for
• 46:12Institute for Global Health.
• 46:14We give very nice benefits for people coming to China
• 46:18to do two to three year postdoctoral fellowship
• 46:21in implementation science, all of you are welcome to apply.
• 46:24Thank you so much.
• 46:27<v Maur>Great, thank you Roman, that was fantastic,</v>
• 46:31really a terrific presentation.
• 46:33I'll go ahead and open it up to questions.
• 46:36If you have any questions feel free to just unmute yourself,
• 46:40introduce yourself and ask or I see Donna
• 46:43has raised her hand.
• 46:43Donna, why don't please go ahead.
• 46:46<v Donna>Hi everybody, sorry I'm on the train actually,</v>
• 46:50I had to go into New York City today for an appointment,
• 46:53so there's some background noise I apologize for.
• 46:56But Roman, I just wanna say that this was just an
• 46:59absolutely brilliant talk where you walked us through all
• 47:03of the essential aspects of implementation science
• 47:07from the beginning to end and connected.
• 47:09How these various theories and frameworks where they jump in
• 47:13where we need 'em, what might be a recommended approach.
• 47:16I mean just absolutely fantastic and I'm sure
• 47:20the audience learned very much from this talk.
• 47:23I know all of us struggle with the confusions
• 47:26of these theories and models and frameworks and where they
• 47:30fit in what is implementation science and the steps of it.
• 47:33You just laid it out so clearly it's just,
• 47:36I'm just floored at how nice this was.
• 47:39So thank you so much and on behalf of all of us for this,
• 47:44I have two questions actually.
• 47:46One comment you mentioned, you know I'm somebody who's
• 47:50done research on developing statistical methods
• 47:54for step wedge design, you mentioned two drawbacks.
• 47:57One is that it takes longer, which I completely agree with
• 48:01and I think it's worth documenting that better,
• 48:04because I'm not sure there's any kind of papers
• 48:06or publications that actually show that trade off.
• 48:09And then the other is, and people ask me
• 48:11and I know it's longer but I can't really say
• 48:14how much longer exactly.
• 48:16So I think it probably gets the longer,
• 48:19the more step times you have.
• 48:20But anyway, I'm not gonna speculate right now.
• 48:23But the other thing you mentioned was that the analysis
• 48:26was more complicated and it's true that in
• 48:28a parallel cluster randomized design,
• 48:30which is usually the other alternative,
• 48:33you can just basically compare the mean outcome rates,
• 48:36whether they're continuous or binary at the end of the study
• 48:40using a two sample tests, but you do have to account
• 48:43for clustering even there.
• 48:45And then with the step wedge design, it's only one step
• 48:49more complicated in that that comparison has to adjust
• 48:53the time effect.
• 48:54But there's very standard statistical methods
• 48:58that basically every kind of software to do a
• 49:02generalized linear model or a regression model
• 49:05that accounts for clustering and allows for
• 49:07a binary intervention effect and then indicator variables
• 49:11for every time effect and then perform the test
• 49:14of the difference between the two groups based
• 49:17on the regression coefficient using either a robust wall
• 49:21or a robust square tests.
• 49:23So I'm not sure why you felt it was like an actual barrier,
• 49:28I just don't feel that that should be so,
• 49:31and then my last comment, because we've been chatting about
• 49:34this is the issue of quality and how that fits into,
• 49:37especially in low and middle income countries,
• 49:41I think it's sort of assumed in the United States
• 49:44all you have to do is get the service out to somebody
• 49:47and the quality is already very high,
• 49:49we don't have to worry about that.
• 49:51It's probably not true, but that's the assumption.
• 49:53But in low and middle income countries may be,
• 49:55and it may not even be true, the quality issue
• 49:57is even bigger and it doesn't seem to be something,
• 50:01it seems to be addressed in the health systems
• 50:03research field but not, I haven't heard any chatter about it
• 50:08implementation science.
• 50:09So anyway, my question was complexity of step wedge design
• 50:13analysis and then this issue of quality.
• 50:16And just thank you so much again for an absolutely
• 50:19like fantastic crystal clear talk.
• 50:23<v Roman>Thank you Donna.</v>
• 50:25I think I'm not a statistician, but I think this is
• 50:29precisely where statisticians like Donna,
• 50:33you can play a really big role in help strengthening
• 50:37the methods in implementation science.
• 50:39Step wedge design has a lot of potential
• 50:42for implementation science, I think that
• 50:44is my understanding, but statistician can correct me.
• 50:47I think it's generally longer than traditional two RCT.
• 50:52The reason is for the steps, like the step here,
• 50:56we allow it three months for one phase, one step
• 50:59because for each step you have to have long enough
• 51:03a duration to allow SMA effect to be fully released
• 51:10if the effect cannot be fully released during one step,
• 51:14you have to use even more complicated statistical
• 51:18method for analysis.
• 51:20So because in theory if it is a two arm RCT
• 51:27in three months you can wrap up this study.
• 51:30But for implementation for step wedge it is much longer,
• 51:34so that is one reason.
• 51:35In terms of analysis, I agree with
• 51:38Donna now compared with the several years ago
• 51:42there now have been many software R package coming out,
• 51:47which can enable analysis much easier.
• 51:50But still sometimes it's difficult for researchers
• 51:53which have no statistical background to understand
• 51:57why they need to do this and why not.
• 52:00Sometimes it's always good to embed some good statistician
• 52:05in your team even though the software can do a lot of
• 52:08work for you.
• 52:09In terms of quality of care,
• 52:11I agree with Donna, it's a severe issue,
• 52:14increasing coverage is not the only thing to do
• 52:18in low income countries.
• 52:20The coverage without a quality service can be harmful
• 52:24and the risk of your resources.
• 52:27So that's why our research team case study focus,
• 52:31a lot of our study we use as standardized patient
• 52:34to assess quality of care across seven provinces in China,
• 52:38which is unprecedented, because of the implementation
• 52:42of standardized patients is really very difficult
• 52:47to that scale, but we have demonstrated it's possible even
• 52:50using in that setting.
• 52:53So we now have a precise understanding of the quality
• 52:56in China, which is, I have to say very, very poor.
• 52:59We have not yet get this paper out,
• 53:01but once it is out we'll share, thank you so much.
• 53:08<v Maur>Great, thank you.</v>
• 53:10I don't know if there, we have a couple minutes left.
• 53:12I don't know if anyone has any last comment or question.
• 53:25Go ahead, I see your hand raised.
• 53:27<v Attendee 1>Yeah, thank you Dr. Xi</v>
• 53:30and it's very great to meet you here.
• 53:33Yeah, I think this is a great talk.
• 53:36I really learned a lot about implementation science
• 53:39and as I can see, I actually have a question about
• 53:44the third stage in PEDALs, the third stage is determinants.
• 53:48So I can see you list health intervention factors
• 53:51and also contextual factors.
• 53:55So I would like to know whether you have insights on
• 53:59how to analyze or disentangle the relationship between
• 54:04health intervention factors and contextual factors,
• 54:10how would you consider their relationship?
• 54:12Like whether contextual factors can be considered
• 54:15as mediators or whether it's hierarchical design
• 54:21that contextual factors need to be on a higher level
• 54:25and whether it involve very complex analysis
• 54:31and yeah, like what we want to get out of this analysis.
• 54:39<v Roman>Thank you Pungfe, actually in most of the</v>
• 54:43implementation science framework, they put the
• 54:47barriers associated with health intervention
• 54:53itself and the contextual factors together
• 54:57in the framework like CFIR and TDF, many other frameworks,
• 55:04which already wrapped both elements in one framework,
• 55:11but I have tear them apart because in our SMA study,
• 55:17we essentially have decided to do this in two steps.
• 55:22One is to optimize SMA so it can work better
• 55:29in the Chinese setting,
• 55:31as I have described in the past SMA, although there are many
• 55:35RCTs to prove its effectiveness,
• 55:38but most of them are in high income countries
• 55:41and almost none of them have compare head to head
• 55:45the individual components in that complex SMA study,
• 55:50so we don't really know what individual components
• 55:53can work best.
• 55:56So after we have done that, then we goes to understand
• 55:59for the optimized package complex intervention,
• 56:03what are the contextual factors which can contribute in
• 56:06to the implementation of that health intervention SMA.
• 56:10So in our study we sort of have clearly divided those
• 56:15into two steps even though the optimized SMA
• 56:20can still be a factor, which can create barriers
• 56:23in our final study I have to say.
• 56:26The other thing is it's very difficult to distinguish
• 56:29sometimes, sometimes people use contextual factors,
• 56:32environmental factors, settings.
• 56:34Sometimes it is very difficult to distinguish
• 56:38the difference be between them.
• 56:39I tend to not to distinguish them, because different people
• 56:43have different ideas whether one off to use
• 56:45other hierarchical analysis for the intervention factor
• 56:51and the contextual factor, I can't answer that
• 56:55because I need more time to think about that.
• 56:57I don't yet have a clear answer to that yet,
• 57:02but I tend to think it may not be a hierarchical
• 57:05analytical question here.
• 57:07Thank you Pungfe.
• 57:09<v Pungfe>Yeah, thank you very much.</v>
• 57:14<v Maur>Great, I see a couple of other hands raised.</v>
• 57:16I think Gloria was next.
• 57:19<v Gloria>Yes.</v> <v Maur>Go ahead.</v>
• 57:20<v Gloria>Thank you very much Maur, thank you very much.</v>
• 57:22Very nice talk, I really enjoyed.
• 57:26You mentioned several techniques in order to choose
• 57:30the implementation strategies.
• 57:33And this is, you know very complex issue
• 57:34because you have a lot of implementation strategies
• 57:38that how to use them in the context or with the problem
• 57:43that you have and how to choose them, right?
• 57:45Is a like a real point in implementation process.
• 57:49Can you please elaborate on that?
• 57:52Thank you.
• 57:55<v Roman>I have some challenges of understand the question.</v>
• 58:00Maur, can you paraphrase the question?
• 58:04<v Gloria>Yeah.</v>
• 58:04So basically, you know, you mentioned that you use
• 58:07several techniques to choose the implementation strategies.
• 58:12So can you please elaborate on those techniques
• 58:16or methods that you use to choose
• 58:18the implementation strategies?
• 58:20<v Roman>Oh, okay.</v>
• 58:21Okay, thank you.
• 58:22Thank you, sorry for that.
• 58:23<v Gloria>That's okay, thank you.</v>
• 58:25<v Roman>Yeah, there are many implementation,</v>
• 58:30there are many methods which can be used to map
• 58:34your strategies to barriers.
• 58:38I would say the simplest strategy is not one of those four.
• 58:44The simplest strategy is simply stakeholder consensus.
• 58:50For many time, if people do not have a higher level of
• 58:54methods, you can simply have a group consensus
• 59:00to be achieved through a Delphi process or a nominal group
• 59:06process.
• 59:08There are many simple way of achieving stakeholder
• 59:13consensus on what type of implementation techniques,
• 59:18which you can select from the ERIC framework
• 59:21to match each of the barriers you have identified,
• 59:24but the four methods here listed on this slide
• 59:28are more methods driven and I have never used
• 59:33a concept mapping group model building
• 59:37and intervention mapping, but we have used
• 59:39a conjoint analysis in a way to use difficult
• 59:43choice experiments.
• 59:45So as I've discussed earlier, which is the sort of
• 59:48the questionnaire you have developed (indistinct).
• 59:51So you present those products, each product consists
• 59:55of different attributes of your implementation strategy.
• 59:59So you'll present those products side by side
• 01:00:03to your stakeholders.
• 01:00:04They make a choice out of the two, but you have
• 01:00:08many of them.
• 01:00:09So once they have complete data, all of the choices,
• 01:00:12then you can perform a logistical regression
• 01:00:14and other statistical methods to really evaluate
• 01:00:19and quantify the value of those individual attributes.
• 01:00:24So then you can choose the attributes with the highest
• 01:00:27valuation and package them into the packaging
• 01:00:32of your implementation strategy, I hope this helps a bit.
• 01:00:36<v Gloria>A lot if you, thank you.</v>
• 01:00:38<v Roman>There are lots of literature.</v>
• 01:00:40I'm using DCE in health economics and health service
• 01:00:46literature, it is not really a very difficult method
• 01:00:48to understand, so I can send out some of the literature
• 01:00:55as well.
• 01:00:56<v Maur>Okay, you thank you very much.</v>
• 01:00:58<v Roman>Thank you. (indistinct)</v>
• 01:01:00<v Maur>Roman, thank you.</v>
• 01:01:02I know it's very late where you're now in China,
• 01:01:05but we have one last question if you wouldn't mind.
• 01:01:08Mariana, go ahead.
• 01:01:10<v Mariana>Yes, thank you so much for these great talk.</v>
• 01:01:13Mariano Kaori from the University of Miami Miller
• 01:01:15School of Medicine.
• 01:01:18I just have a quick question.
• 01:01:19Can you implement this very comprehensive study
• 01:01:23in five years?
• 01:01:26<v Roman>Ah, that's a good question.</v>
• 01:01:30That's our hope in five years, but let me share the story.
• 01:01:36I used to do a study using texting as a reminder
• 01:01:42for people with schizophrenia in the rural Chinese
• 01:01:46village to take medication.
• 01:01:49The implementation is for six months and we thought
• 01:01:52we are going to have that done simply within eight months,
• 01:01:56but it takes us three years.
• 01:01:58So implementation of those trials always take longer
• 01:02:02than we thought, but I have to say we are exactly trying
• 01:02:08to do this within four years time.
• 01:02:12But with, I think one of the element is the pandemic.
• 01:02:19A (indistinct) know China is fortunate in a way.
• 01:02:23We are not very much affected by the pandemic
• 01:02:27for the past two years.
• 01:02:28Our life here in China is essentially normal
• 01:02:31for majority of the people, so we are able to done
• 01:02:32a lot of the field work.
• 01:02:34But now with the omicron all the other part of the world
• 01:02:39are opening up, China has some challenges.
• 01:02:42So we don't know whether in the future years
• 01:02:44whether this will be playing a part,
• 01:02:46but even without pandemic, sometimes it's difficult
• 01:02:48to get the implementation done.
• 01:02:50However, what I have to say is we have develop it
• 01:02:55and really excellent consortium of collaborators in China.
• 01:03:00We have 12 research teams in China.
• 01:03:03We have always been working together
• 01:03:05and many of the health service and implementation research,
• 01:03:09even without any grant support, our teams are
• 01:03:12working together, so we know each other extremely well.
• 01:03:15So when we are doing those multi-site trial,
• 01:03:18it's almost much easier now to set up your team,
• 01:03:24because it's communication is a simple and how to divide up
• 01:03:27your work is already established and how to share
• 01:03:29your intellectual property is prior grade upon
• 01:03:34and how to mobilize your resources and what are
• 01:03:36the statistical data management platform,
• 01:03:41all this has already been been constructed
• 01:03:44in our prior studies.
• 01:03:45So very easy for us to conduct multi-site study in China
• 01:03:49because of our existing work of the Acacia Labs
• 01:03:53with those a dozen research teams always
• 01:03:55be working together.
• 01:03:57We also have very strong support from the clinical centers,
• 01:04:01because we work with some of the clinical centers
• 01:04:04in many other ways, so we get to know them much better
• 01:04:08and we can get a support from them as well.
• 01:04:10So hopefully we can get this done within four years.
• 01:04:13But I have to say things happens, it may get longer
• 01:04:16than we thought, thank you.
• 01:04:22<v Maur>Great.</v>
• 01:04:23Great, well thank you again.
• 01:04:24I don't wanna cut off the discussion and the comments,
• 01:04:28but I know it's getting late there.
• 01:04:31If there are no other questions, I'll just end by thanking
• 01:04:35you again, Roman, for being with us today,
• 01:04:37it was a fantastic presentation, really enjoyed it
• 01:04:41and learned a lot as I know everybody else did on the call.
• 01:04:45So thanks so much for being with us
• 01:04:47and see you all again soon, thanks everyone.
• 01:04:52<v Roman>Thank you, bye-Bye</v>
• 01:04:54<v Donna>bye.</v>
• 01:04:55<v Roman>Thank you, bye-Bye, bye-Bye.</v>
• 01:04:57<v Maur>Bye everyone.</v>