Powering Medical Research with Data:
The Research Analytics Adoption Model
July 22, 2015
Powering Medical Research With Data: The Research Analytics Adoption Model
July 22, 2015 [00:01]
[Eric Just]
Thank you, Tyler. Before I get started with the presentation, I just wanted to provide a very brief background. I’ve spent the better part of my career connecting researchers to data. I spent about 10 years at an Academic Medical Center first on a genomics project, doing genome analysis and then disseminating that information to researchers through a public portal. And then I transitioned to a data warehouse team where I helped build the data warehouse and then ended up just kind of managing the whole research side of the data warehouse and helping researchers connect to that data warehouse to get enterprise data that powers that research. So, I’ve seen the process of adopting analytics to power research. And more recently, I’ve been in conversations with organizations across the country who are looking to augment their research with enterprise data. And what you’ll see presented today is kind of a compilation of both that experience in the past, as well as the conversations that they have been having nationally. And I hope it’s useful. I’m looking forward to the presentation and absolutely receiving your feedback. I will present my email address at the end. So if you would like to contact me and provide me with feedback, that will be very gratifying to me.
Why are we here? [01:16]
So why are we here today? First and foremost, we want to understand how we can make research better by providing data. And it’s really like everything we do in healthcare, we have to think about how we tie it back to the patients. And there are millions of both sick and healthy patients alike who want better care. These patients are volunteering their time and in some cases, risking, putting themselves at risk to participate in a research. And research really helps determine what we mean by better care. So it’s a critical part of creating a better healthcare system and providing better care in the future. So we owe it to those patients to make that whole process more efficient.
Also, the 27 percent, if not more, of folks on this call who are involved in research understand there’s a lot of waste in research, there’s a lot of duplication, there’s a lot of manual work, and we can greatly reduce the waste with data and analytics. If you’ve been to Catalyst webinars in the past, you are well aware that we’ve done – many, if not, most of our webinars are focused on how we reduce waste in the healthcare delivery system with data and analytics. And the same concepts apply to research.
And where we’re moving as a country in the future, precision medicine aims to deliver the right care to the right patient at the right time.
To get to Precision Medicine [02:51]
So, to get to precision medicine, we really need to work on improving research. So number one, identifying the underlying molecular causes of diseases and refining treatments based on those discoveries and that’s happening now absolutely, but again with data – and we’ll present a vision for how this works later in the presentation – with data, we can make this process of improving research a reality.
We also need to improve care delivery. So how do we deliver to care guidelines and adapt to new guidelines moving forward. And the quicker and more efficiently we can do that, the more value we get out of the research discovery platform. And as I mentioned before, we have been presenting a lot of webinars on really how to improve care delivery and monitor care according to guidelines and providing tools and technology to help healthcare systems adapt to those new guidelines.
Now, currently what we seen in the country is a lot of healthcare systems that are having trouble implementing care guidelines that are 10 years old and we really need to kind of shore up that whole ability to deliver on those existing guidelines before we can really speed up on the adoption of newer care guidelines.
Another thing that needs to happen in addition to improving both research and care delivery is increasing the coordination between the care delivery and the research enterprises. So currently the amount of time that it takes to go from a new clinical or operational best practice to the point where it becomes a habit of all frontline clinicians that’s typically measured in years and our goal is to help reduce that time to weeks or months so that as new discoveries come out, we can efficiently transition those discoveries really from a research project to the way they change in a way that care is delivered. And that’s really kind of setting up why we’re here, what we think the future of medicine is. Obviously there’s a lot more to the research than precision medicine and what you’ll see today applies to all aspects of research but we want to just specifically call that precision medicine because we do think it provides a nice vision for the future of how research and the care delivery systems can work together more coordinated.
Agenda [05:15]
So the agenda for the rest of the presentation is, first, we’ll present a review of the research process. We’ll look at the various steps in research and understanding that all research is pretty unique and we put together kind of a generic research process. Then we’ll be reviewing some roadblocks that prevent an investigator from conducting efficient research. And after that, we set up with the problems in the space. We’ll then present the research analytics adoption model and the vision for how analytics can be adopted to help remove some of these roadblocks. And finally we’ll end with a brief conclusion.
Agenda [05:56]
So let’s get started by looking at the research process.
Research Process [05:59]
So the first step is Hypotheses Generation, and an investigator can have hypotheses from many different sources, from their previous research, from researching and being current on literature, and also exploratory analysis. If researchers have good tools that enable them to look for trends and identify trends and formulate hypothesis, that actually creates a much more efficient hypothesis generation pipeline.
After a hypothesis is generated, a researcher then decides if they are going to study this further. And they engage in this process, called Cohort Exploration. And Cohort Exploration says, okay, if I want to study this hypothesis that I have in more detail, do I have access to patients that match my criteria? Or do I have enough patients to provide the statistical power that I need to generate conclusions for my study. And this actually can lead to a project not happening. If a researcher doesn’t feel they have enough patients that they can study, then they either have to expend where they’re recruiting patients, which greatly adds cost to the project, or maybe they just move on to another hypothesis. So it’s an important step and it also feeds in to the next step.
So once a researcher decides that they have a good hypothesis, they feel like they have enough patients to power the study, then they typically apply for assistance, financial assistance, in the form of a grant to make that research project happen. The data from that cohort exploration is critical in the grant application. Funders love to see applications that are backed up by data.
And furthermore, there’s other ways that grants can be strengthened with analytics and data. So if an organization is particularly adept at recruiting patients, you know, they have a good success rate for recruiting patients, that strengthens the grant application, as does a description of an enterprise data warehouse. If an organization provides access to researchers to that data warehouse, funders really like to see that that level of investment have been made in providing data to power research.
Another step in the research process is putting together an IRB Application. And if you’re not involved in the research, that IRB may be a new term for you. It stands for Institutional Review Board. And this was regulated by HIPAA and said that each organization who conducts research needs to review research to make sure that it fits ethical standards. So the IRB is typically making sure that the research that’s being conducted doesn’t put patients in harm’s way and it is unnecessary but often can be a time-consuming step in this process. And when the researchers put in together the IRB application, they also need to put in information about the cohort they are going to be studying, size of that cohort, the protocol that will be followed, as well as specifying what data needs they are going to need further on through the process.
The next step once the IRB and the grant comes through is Patient Recruitment. So we know there’s types of research where there’s a waiver of recruitment, but for the most part, investigators need to find patients who fit the criteria for their study and approach those patients and ask them if they want to be in the study. And then at that point, they are also presenting patients with information about the protocol and the potential risks of the study and asking the patients, do you want to be in this study? It’s a very important part of the process. As a matter of fact, if you look at clinicaltrials.gov at the reason why many many research projects fail is their inability to accrue enough patients in a study. So for patient recruitment, it’s not enough to just know who the patients are, although it’s a very important part of it. So oftentimes we see further refinement to that cohort. But the researcher needs to know who the patients are and where they are going to be. So information like scheduling data comes in handy. Information about patients who are admitted who fit the criteria comes in handy and that gives the researcher time to go approach that patient. And then how do I collect their consent. Their consent is when they sign the piece of paper agreeing to be on the study. And consents can be delivered through paper or electronically nowadays. But this patient recruitment set is time consuming and resource-intensive but at the same time a very very critical part of this whole process.
Once the patients are accrued in the study, then Data Collection starts. And there can be prospective data collection. So I need to collect data from a study that doesn’t exist anywhere yet. It might be patient outcome data, typically administered through a questionnaire. We call that prospective data collection, if you’re collecting new data for the research study. Alternatively, or also in addition to, data collection can also involve a data pull from an enterprise data warehouse or similar data resource. Where we have clinical data that exists in our systems and we need to put a data specification together and make a request and pull that data that is available through those systems on those patients. This data collection, the data pull needs to be approved by the healthcare organization. So even if there’s an IRB approval, a healthcare organization may wish to further review the data request to make sure that it is consistent with what was approved in the IRB. And so, data collection can actually be another bottleneck, and we’ll talk about that in a little bit.
Data Analysis, once we’ve got our data set involved doing this statistical analysis, using advanced tools for genomics, if it’s a genomics project, and this data analysis step needs to be secure. You know, I’ve heard stories about researchers doing this analysis in spreadsheets on their laptops that may not be encrypted. So there needs to be a good infrastructure to do this analysis in a secure way and there needs to be the right tools and the right skill sets to do the data analysis.
Next comes the publication step where the conclusions are compiled and manuscripts are submitted.
And finally, hopefully translation to clinical practice. Once a research conclusion is made, it doesn’t always lead to immediate translation to clinical practice. It might need to enter later stage trials. But eventually we hope that most research can find its way in the clinical practice. And the questions they could ask, how can this discovery now be used to treat patients? And how do we work more closely with the healthcare delivery systems to implement these latest best practices?
So this is, again, a very generic research workflow. We realize that there’s variations to this and all research is different but we want to kind of present a very generic research process.
Research Process – Roadblocks (Waste) [13:26]
Now we’re going to go through that process again and we’re going to talk specifically about where roadblocks and waste exist along the way. And this is where I would love to get your feedback at the end of the webinar over email, if you could. Just sharing some of these stories. We’ve collected lots of stories from investigators on where they run into roadblocks and it’s helpful for us as we seek to gain a deeper understanding of this research market.
So with hypothesis generation, cohort exploration, grant applications and IRB applications, the existence of exploratory tools is really important. And if those tools don’t exist, then this can slow down the process. So if you think about, you know, if I’m going to do a study on COPD patients and I’m in my cohort exploration tool. If I don’t have to go to a data analyst and ask them how many patients I have, if there is a tool that allows me, the researcher, to go and have that question myself, it’s going to eliminate a huge bottleneck. And when the groups that I ran at the Academic Medical Center first got started, I would say that 60 to 70 percent of our questions were simple counts of investigators putting together requests because they were going to put any grant application on IRB applications. So having tools that allow for the de-identified exploration of data is absolutely critical for all four of these steps.
When it comes to the IRB, I’m sure there’s lots of stories about how things often get held up in the IRB. Slow IRBs are unknown organizational issue and it’s not necessarily because the IRB is bad but it’s because they have a lot of things in their pipeline and most of them have other jobs and they get around to reviewing these things. It’s not part of their everyday job. So organizationally and they might (15:27) appropriately, they may not have enough committees to do the review process. On the technical side, in addition to having insufficient exploratory tools being a roadblock, insufficient tools to support the IRB process, technology tools. And organizations that adopt electronic IRB workflows tend to be more efficient than paper-based IRBs. So there’s some technical issues that can be resolved there as well.
For actually recruiting patients, one roadblock can be organizational restriction. So as I mentioned before, in addition to the IRB step, there’s oftentimes an organizational step where the organization may put additional restrictions on how researchers can approach the patients. So one very common restriction is that researchers need to work through the patient’s physician to understand if the physician approves of the patient being on the study or agrees that the patient will be good for that study, and this can slow things down in a couple ways – number one, if the physician doesn’t provide a timely response to the inquiry, that can slow down patient recruitment. Additionally, just identifying who the right physician is can often be a roadblock. Many patients, especially patients who have chronic conditions, see multiple physicians in this whole question of attribution – who is the physician that we should ask for it becomes a bottleneck in this process. On the technical side, I mentioned that you need to know not only who the patient is but where they are going to be and having insufficient data and tools that present that information to the person doing the recruitment can be a big bottleneck as well.
For data collection, organizationally, things work way better when there’s a process for the release of data. And organizations that invest in putting together a process, even a process that has a lot of steps do better than organizations with no process. When there’s no process, we see healthcare systems taking an overprotective stance on the data and not really releasing that data as efficiently as they could, as if they had a process. On the technical side, if there’s no data warehouse, there could be a lack of a single source for data. So investigators don’t even know where to go and they spend a lot of time cobbling together data from different systems. Insufficient self-service tools can slow the process down. So if investigators have access to tools that allow them to generate their own queries, those can make things way more efficient. And inefficient tools to support the data release process. So going back to that organizational issue, we’ve seen the process take place over email where an analyst will send a note to somebody at the healthcare system, asking for approval to release a particular data set and things just tend to get lost over email, and workflow tools are incredibly helpful in this situation where you have a good process and you can track each step. So insufficient tools to support that release process, not to another technical barrier.
For data analysis, having insufficient analysis tools and platform create some inefficiencies and roadblocks. What happens here is investigators invest in their own tools and it creates a much bigger expense. And organizationally looking and hiring people with the right skill sets is an important part of this. The data gets bigger and bigger. We hear all about big data and the ability to do large scale analysis. As that data becomes more and more available to researchers, there really needs to be an investment and the right skill set to do this analysis from an organizational perspective. And for publications, these bottlenecks are a little bit less than the organizational and technical sides but we did put in one bottleneck. Some organizations we’ve seen have support for manuscript preparation. Especially those organizations with a CPSI would be able to provide support for creation of the manuscript.