 Thanks very much Mark. It's a pleasure to be here and a treat for me to work with Mark on this conference setting it up and Meeting all of you. I don't come from the bioinformatics community. I come from the clinical informatics community And my own work is focused on clinical decision support at scale if you will and the challenges that we find there working with Ken Kawamoto and and others in the room is how do we actually mass-produce knowledge and make it Interpretable computable if you will in decision support systems and EMRs across the country I had the pleasure at the brigham of Before transferring to Vanderbilt of leading the clinical decision support consortium project Which had about 30 industry and academic collaborators looking at this problem of how do we not only codify knowledge into a human Readable and a machine readable form but then distribute that knowledge in an open knowledge repository and make it Exercisable if you will via web services and that led to a variety of very interesting papers, so I can point you all to In addition the at the brigham. I led the advancing clinical decision support Copie with Doug Bell from Rand and UCLA and in that we looked also at the principles of large architecture decision support if you will and I think those lessons if we can merge the clinical decision support community and the lessons derived from it with what's going on in the bioinformatics community with Extraordinary accelerating advances in understanding of genomic medicine and genetic disease We have the chance of actually impacting of course healthcare across the country so with that backdrop the Survey That was distributed was answered by many as mark already said 30 invited attendees 25 responded and an 85 83% response rate The survey was all about the desiderata of Macy's and Welch, and I won't go through these in great detail Dan's in the room and in his keynote will talk about some of these further But I did try to apply a keyword to each and every key element if you will because it's too hard to remember the whole statement I tried to boil it down. I didn't quite get it to a easily Memorizable mnemonic, but with additional work we might get there Obviously the separation of the molecular knowledge molecular observations from the clinical interpretations is key The lossless data compression problem making sure we understand the methods and evolutions evolution of methods So we keep track of how things are being determined in sequencing What are the clinically actionable subsets that can be created for optimal performance? How do we support this notion of human readable knowledge artifact as well as the Computer interpretable knowledge artifact for broad-based decision support at scale anticipate changes in our fundamental understanding of human molecular variation Anticipate the needs of both clinical care and the discovery science of things Keep track of multiple genes and clinical information Keep the CDS knowledge base itself separate from variant classification and allow this these knowledge artifacts to apply to Multiple disparate EMRs and that's kind of where it gets tricky Some of the insights we had in the CDS consortium and in another work is that of course the EHR platforms have widely varying Structure themselves both from the terminology point of view as well as the workflow point of view So finding the right way to intervene in the clinical decision-making process at the appropriate time and place With the appropriate knowledge for the patient for the clinical decision That's under underway Can be tricky But in the CDS consortium for example, we were able to insert web service decision support into epic next-gen GE The partners EMR and the Regan's Reef EMR 11 keep support a large number of the gene variants while trying to simplify the CDS knowledge Related to those variants as much as possible Leverage standards as they emerge both in the CDS terminology representation space as well as the knowledge formalisms and the services architecture that will be used to disseminate the knowledge into disparity in Mars Support a CDS knowledge base that's publicly available This was a key notion of the CDS consortium that this would be an open repository of knowledge that could be Multi-authored or crowdsourced if you will and that was an interesting dimension of that work Access and transmit only the genomic information necessary for CDS try to be parsimonious about what is transmitted from the record So here are the results looking at these 14 elements Summarized with a few keywords on the side we can see that the order of course these are now ranked from Least less important to more important Recalling the the scale one is strongly degree five is strongly disagree So items near one lower down near one in the blue are more Important the red bars reflect the standard deviation for the responses and you can see Among ourselves. I think there's still some degree of variation I'm not sure if that's due to interpretation of the key elements themselves or disagreement about Exactly the meaning or their intent Here is the an eye a measure called the mean difference from ideal capability She remember each element had an importance question and then capability of current EMRs to support that function and that was the capability question mark came up with the interesting measure of the ideal capability minus one to get sort of a an ordered reflection of Of the capability of current EMRs to do all the different things in the different elements you can see these two are fairly divergent with Interestingly big standard deviations and some clustering perhaps occurring But no EMR currently really knocks it out of the park in terms of the capabilities required for genomic CDS We then took mean importance versus that mean difference from ideal measure and did a scatter diagram of the 14 elements And here's how they lay out in this scatter diagram And I thought this might be interesting to think about you know a two by two Where do these elements fall in you know in a high importance near ideal? Quadrant a high importance far from ideal quadrant low importance far from ideal and low importance near ideal and you can see that there is a bit of clustering up here and That may be a target opportunity for us to consider those that are of high importance and near ideal perhaps with some gentle influence Technically regulatory wise policy what have you we might be able to move the ball down the field with those Others are scattered as you can see, but this this might help guide some of the thinking You were then asked we were all asked to rank the top five elements on the survey And here's the sum of priority selections across respondents You can see there is a bit of a separation perhaps around 4.5 with kind of above the line cluster and a below the line cluster and this may help also focus attention in useful ways so from the survey, I think we get Some insights prioritization insights on on the different elements and these are from those two assessments I just described they're ranked here again or displayed here from the import Import versus low diff from ideal and from the top five rankings. There is some consistency with 1 5 12 and 13 So I think the key themes from the GM 7 survey We we have the notion of agreement I think around separation of data and knowledge in all the different ways that might apply to the clinical data Interpretation data variant data CDS data and the like It's important to create machine readable and human readable knowledge artifacts for those of you who don't live in the CDS world Having the conversation with a subject matter expert really can't be done over an XML document It has to be done over a document that they can understand and interpret and discuss Thus the human readable component we need to leverage current in developing CDS and genomic standards and this great idea which I'd love to see come to life of a shared open repository of knowledge that applies to genomic medicine or perhaps to clinical decision support writ large So that's the overview of the survey. Let me ask if there's any Quick questions or discussion if not, it's my pleasure to welcome Dan Macy's to the podium And I know you all eagerly await as do I his keynote address