 Okay. Thank you. Thank you, Heidi. I'm going to, in the next several minutes, outline five potential novel or disruptive paradigms that we could be working on. And having worked with EHR data for about a decade now as one of the inception institutes in the emergency, we realize the value of EHR data. It's unmasked in its depth, but certainly there are weaknesses. And one of them is the lack of environmental variable. So some of the novel data sources, as has been discussed throughout the day, could come from measurements that patients, gadgets that track activity and food intake. Direct to consumer genetic testing is becoming more and more prevalent in both genotypes, such as 23andMe. And now companies such as Color that are offering panels for familiar hypercholesterolemia and cancer predisposition. And these could be linked to the electronic health record or a data warehouse that sits on top of the electronic health record to facilitate discovery and implementation. Social media, particularly in conditions that are rare, could be a valuable source of data, both for disease information as well as variant interpretation. And then lastly, family history, unfortunately, is not always very well extractable from the electronic health record. And since eMERGE is an NGRI consortium, I think this is one of the central variables that's often needed for discovery and implementation. So patient-reported family history surveys and medication adherence are often not available in the electronic health record. So a juiced up EHR or a data warehouse would have an ability to tap into all of these sources, but potentially through application program interfaces that in turn link to a data warehouse that sits on top of the EHR. And I guess another name for this would be Sync for Science. So it's kind of a similar paradigm. This was alluded to. So this is the next innovative paradigm that I want to talk about. The linkage of big ohmic data to the EHR phenotype. So this is truly big data because you have all the data in the electronic health record with all the ohmic measurements, including microbiome, genome, epigenome, transcriptome, and so forth. Again, this would primarily be for discovery, but you can realize that the possibilities are endless when you match the very rich phenotypic data, not only prevalent, but longitudinal with this kind of big data to enable us to do big data analytics and further our efforts in discovery and potentially eventually into implementation. All right. So the next three paradigms relate to the main stakeholders, the patients, the public, the providers, and payers. So first, the patients. So one of the things that we've noted is that how do patients perceive these genetic results? And in particular, there was a discussion earlier about negative results. What is the dissonance that occurs when you have a positive family history and you get a negative emerge panel? How do patients perceive pathogenic variants versus genetic risk scores? We all realize the limited availability of genetic counselors. And so there's an opportunity for us to investigate novel approaches to returning results, not only through genetic counselors, but other mechanisms, be they through other providers, such as trained nurses, or through videos or other websites. One of the things we learned, particularly in our myocardial infarction gene study, is that when you give patients risk information about, let's say, heart attack predisposition, you saw a very lack of actually translation into changes in behavior, whether it's diet or physical activity. And even for cancer predisposition, it has been a similar story. So if we measure the attitudes of patients before and then their beliefs, how does that translate into the actions they take after they receive genomic information? I think that would be a very interesting aspect to study. I cannot emphasize enough the importance of family history. How do we try to facilitate cascade screening in future iterations of emerge? And this is currently very clunky in the clinical setting. You give patients letters that they hope they will mail to their first-degree relatives. And so we certainly are several of the sites investigating attitudes to how patients want to engage their family members and share this information. But can the electronic health record be actually used to do this? And what are the legal and policy implications? What are the HIPAA implications? I know that at Kaiser they're doing a project to identify some of these individuals because they all share the same EHR. Is this possible to facilitate this family sharing through the electronic health record? And what might be some of the novel methods to facilitate family sharing? Could there be web applications or smartphone applications that might facilitate instead of having to put a letter in the mailbox and not knowing potentially the addresses? And of course, I think we need to continue to improve a patient's genomic literacy, perhaps mitigate their concerns about genomic test results, and continue to try to mitigate health disparities and underserved communities. Now, can we move towards from the health care institute center data governance to patient center data governance? I think this has some very exciting possibilities. And if patients carry their own data with them, then perhaps that's a solution to interoperability issues. And some of this very exciting transition potentially could occur with the help of community or advocacy groups, keeping in mind that we would need to consider portability, storage and security of such data if it's residing with patients. And so apps like my genome in an app, resources like patients like me or myresults.org or disease foundation advocacy groups could potentially inform us or advise us about such transition, which would be quite novel and disruptive. Another disruptive paradigm is the other stakeholder, the public, the community. And I think eMERGE has an incredible opportunity here to partner with the community to improve public health. And we discussed the tier one genomic disorders, hypercholesterolemia, colorectal cancer, breast cancer. How can we partner with state public health programs, the CDC, federally qualified health centers, and use health information exchanges so that we can actually reduce the burden and morbidity and mortality of these very prevalent conditions in the community. I think eMERGE has a fantastic opportunity in that regard. And finally, important stakeholders, the providers, we discussed some of their concerns. I think physicians are generally in favor of implementing genomic medicine. However, their most common encounter with genomic medicine is a 20-page PDF, which is verbose and dense. And that really puts them off. And they want to implement it, but they're concerned about the burden, such a report when they have 20 minutes for an encounter, places on that encounter. So they need information about the complexity, they need education, their views on CDS. We talked about that. And we've actually tried to do both qualitative and quantitative assessment of how physicians feel about such electronic health-based tools. They have questions about versioning. Some of them, those are more familiar with genomic data. They realize that this is dynamic. So how do we deal with versioning? And medical uncertainty of tests. So if you order a test, and it's negative, is it truly negative? Did the test company look at Dell Dupes and structural variation? Or the medical uncertainty of having a clearly pathogenic variant, but a completely negative phenotype? What does that entail? So those are some of the questions that we can attempt to answer. And of course, I think some of these can be alleviated by decision support that we talked about, improving knowledge resources, and enabling tools that allow patients to engage and share decision-making with their patients regarding the results of their genetic tests. And finally, what are the concerns of payers? Of course, the cost of genetic testing, how these tests will be covered and reimbursed. They're concerned about variable test quality. I just alluded to a panel that might look at all the SNPs, but may not look in depth at structural variation. And what does that mean for the payer? And we should engage payers to inform them or at least engage with them in terms of trying to reduce the barriers to cascade screening. I think this is something that we can really make a very big impact through eMERGE. And they'll also be always be looking at data, randomized trials to demonstrate utility and cost effectiveness, and then trying to model some of the cost implications through forecasting or economic modeling. So I'm going to end here and we can move on to then Matt.