Big Data Spells Big Problems ...


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This presentation was given by Remedy Informatics' CEO, Gary Kennedy, at the 2012 Molecular Medicine TRI-CON in San Francisco.

Gary discussed the Data problem that the healthcare IT is facing. Before we tackle the Big Data problem it's clear that we must handle the existing Data problem.

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  • The red text is intented to be small. Think of it as a whisper For those who can take advantage of itThe topic du jour seems to be Big Data. You know it has reached mainstream when USA today writes about it. Some of you are thinking we cannot even handle “big Data’s little brother. Believe me I understnand that. As one of the first 10 employees of Oracle I feel like I have seen every kind of data problem. Sum up my experiences with two observations that may be relevant to your situation. Every other industry has faced and solved their data problem. Second is that I have never seen an industry solve an exponentially larger data problem without solving their existing data problem. Take away better solve the existing problem or you will not solve the bigger one20 years of experience seeing other industries solve these problems best of breed never going to cut it and other industries have learned this the hard wayThe simple universe of the past has been replaced with a cachony of data. In addition to new sources of phenotypic data, researchers are now able to analyze molecular data and that is creating entirely new challenges. Most new grants are forcing coolaberation between institutions each of whom is using a different applications. Even 2 EMR applications from the same vendor cannot be accessed through the same interface. Of course you want access to emr data just as you will need data stored in bispecimen repositories, patient genomic and perhaps clinical trials data and of course the most important data for researchers is often kept in spreadsheets or small databases at their desk. The data problem is healthcare is admittedly a hard one. When I show this diagram or one like it to my friends who have built complex enterprise applications in other industries they all have very similar reactions….okay, I got it. I finally understand why the smartest people in the world can’t seem to make any progress. But we have learned some things…Why not?
  • One of the reasons why takes too long, too expensive, failure rate is too high is the data problem.Discuss each brieflyIf the existing situation seems daunting ask yourself these questions “Is it likely that the future will include more or less disparate data sources? Will it include more data or less data? Terminloogy….Every other industry has faced and resolved their enterprise applications problems. Why havent we? We is LS and HC research. Few drinks in a hospital CIO and they will tell you that they have plenty of tales of woe as well.This industry is filled with the smartest people in the world. Why havent we solved it?
  • All know about the counterincentives contained in the fee for service model well lets just acknowledge the obvious personalized medicine in its purest form can have a negative effect on some pharma business models. If it were possible to use data to accurately predict who would or would not bebenefit from a certain treatment regimine would that be well received by all pharma and device makers. Of course this begs the obvious question of is there any choice. true of device makers (pick on them because they are not here) talking about matching patient characteristis to the type of implant and he looked at me like do you know what you are saying?Researchers must collaberate to find new patterns but the IP guys get in the way. Spoke two weeks ago at americanacademo of orthopedic surgery. Great new registry to track joint replacemnts but no one is signing up because they are afriad other groups will see their results. Without lots of data no patterns can be recognized.Architectures something wrong in the era of browser-based cloud hosted ultra friendly apps that the most utilized EHR app is built on MUMPS that is at least 30 years old and the most utilized CTMS was delivered in 1989. I know because I was the President of Oracle USA when we started it. While there have been improvements the basic architecture has not and cannot change. Starting to see and if VCs would get over their infatuation with social media we would see more. Realy smart investors are going to start investing in enterprise apps and dare I say tools again.Always the case that you need to change the norms talk more about that. Not feeling the real competitive pressure because no one else has figured this out either. They will and you willHard things are…..hardFair question if it is so hard and if we have not solved the little problem how can we ever solve the big data problem?
  • Lets start with a discussion of some commonly shared beliefs (Truisms) Probably some company out there who is selling services and is betting that you do not solve this problem for a few years.PR is key to all meaningful research. While they might not describe it this way researchers, scientists,and even clinicians are engaged in a very sophisticated exercise in pattern recognition. Those who are enlightened will not only identify patterns but they wlll do something to capitalize on the pattern even if it is only write an articleWe can all benefit from as much enlightened insight into the future as possible. Time sqyare exampleCompetition may not be dsirable to all but it certainly forces us to be our best selves. Having a variety of organizations who can do perform the same service or offer the same product does ot necessarily me they are competing just look at hospital if you want a good example of thisAnyone who thinks they can go on as they are going with the tsunami of data and the coming off patent cliff needs to get a clue. Not many would here would ascribe to this idea but then if your actions do not suggest a “pants on fire” need to change mentality then perhaps you do accept the status quoThe challenge of course is to find solutions that solve the immediate problem and are sufficiently flexible and scalable to accommodate the onslaught of “big data”
  • So the solution is to widely adopt a registry centric architecture that takes advantage of all the breakthroughs in Infornation technology over the past 20 years.That instantiates a collect once use many times metaphor. That literally has the ability to include all data from all sources retrospectifely and prospectively while being sufficiently flexible that it can accommodate any new data type or structural change to the underlying data model. There has to be some kind of ontology or normalization layer that enables the linking of all these disparate sources of data and of course you would want tools and applications that provide your enterprise with lots of productivity improvements by having all of the common functions pre-built before you ever start and you would need a way to use some of your existing applications that work well already. At this point you are probably saying that this is too good to be true or well that’s what I thought we had already. I can say that this is not and unachievable dream and without even knowing anything about your enterprise I am quite certain that you do not have this today. Recently met with a leader of quite famous IDN. After describing this in some detail he said well I agree with you. This is precisely the right way to address the data problem and it is essentially what we have spent 20 years developing here. I said really. I had no idea you had developed all this. At least this gives me some comfort that we are going in the right direction. I asked how are you handling existing data that passed through the ontology before a standard was in place such as protoemic data. He said well we do not exactly have an ontolgy yet but we are working on terminolgy standards. I said how are you handling cross disease type queries? He said that is not an issue for us because most our researchers/clinicians are only interested in a single disease or condition. I asked two or three more admitedly “corner case” kinds of questions and received essentially the same answer. The truth is that the model I am presenting here is of course the way it ought to be but few if any organizations are really able to do this even with the relatively simple invironment we face today. While this is probably a significant departure from the the way you are trying address your computing challenges today, the good news is that it is a bundled solution in the sense that doing this right has benefits across the entire enterprise. Like the nautical metaphor “a rising tide lifts all ships” this type of architecture is as beneficial to marketing as it is to drug discovery. It is a way to improve your quality systems every bit as much as it benefits your pharmacovigilence efforts. Of course this is not just important to take advantage of the big
  • PR is one of the few things that all would agree uponCannot fix all problems but we can solve some and we will solve some today
  • Cannot be yoked to the mind-forged manacles of the past
  • Ignore the 3rd dimension for this analysis or say I could discuss what you need form the 3rd dimension but it would sound too much like a sales presentation and that is not why I am here. You take a first pass at the circles. I will work on them later if necessary. Just make them editable.
  • The registries themselves are not the competitive advantage the CA is that registires enable your organization to employ its competitive advantage
  • Travis put small on the vertical axis at the other end of large - DONEThe registries themselves are not the competitive advantage the CA is that registires enable your organization to employ its competitive advantage
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  • All the data is stored once or maybe not but in any case it is completely harmonized so a change in one place may trigger changes in several placesNo one does this very well and it is a major problem. The most illusive and agruably valuable patterns often involve multiple body systemsNot going to discuss the 3rd dimension or this would sound too much like a sales presentation
  • 3) New leaders who have been also rans but who get this rightAnd so you must be asking does Big Data equal a big opportunity or a big headache and of course the answer is yes.
  • Big Data Spells Big Problems ...

    1. 1. “Big Data” Spells Big Problems R&D Informatics Track Gary D. KennedyMolecular Med TRI-CON Founder & CEO February 21, 2012
    2. 2. “Big Data” Spells Big Problems The data problems we have been struggling with for years are only the tip of the iceberg…
    3. 3. Life Science Progress Is Impeded By Data Problems• Too many disparate data sources• Lack or surfeit of data standards• Multi-site collaboration is essential for breakthroughs• Increasingly complex regulatory landscape• Inconsistent terminology standards
    4. 4. Why Haven’t We Solved the Data Problem?• Inherent conflicts in the existing ecosystem• Step forward causes steps backward in other areas• Moribund architectures exacerbate the problem• Disruptive business models have not kicked in• We have always done it that way• This is not any easy problem to solve
    5. 5. Start from Common Ground• Everyone would like to solve the data problem• Pattern recognition is essential for all stakeholders • Enlightened insight into the future is desirable • Competition sharpens all swords • Status quo is untenable
    6. 6. Registry-Centric Architecture: Preparing For Big Data University Partners Reports/DashboardsImaging Query & Clinical Trials ManagementPaper Records ReportingEDC From Patients Quality Improvement in Trials Master OntologyPatient ReportedData Cohort SelectionEMR Common RepositoryClaims Data National Registries Biospecimen Data ‘omics Data Disease Associationssmall moleculeresearch database Regulators Principal Investigators
    7. 7. A Rising Tide Lifts All Ships…• Everyone benefits from a single 360° perspective of all patients, subjects, procedures, or disease• Early pattern recognition enables competitive advantage• Phenotypic data is fully integrated with genotypic data• Single, definitive source of truth• Quality and adverse events see the same data as reimbursement and clinical affairs• When all data is presented together everybody wins
    8. 8. “Big Data” Requires “Big Changes” 1. Flexible yet structured data model 2. All tools and applications under the control of the Master Ontology 3. Modern, browser-based platform that is fully configurable 4. Pattern recognition through a 360°“mind-forged view of patients or subjectsmanacles” 5. Registries are automatically linked- William Blake for cross-disease research
    9. 9. Flexible yet Structured Data Model High Sweet RDMS Spot Structured Ontology EHR CTMS EDC Build From Scratch Spreadsheets Low Level of Flexibility High
    10. 10. Tools Are Open and Controlled by the Ontology Wide Competitive Open Source Advantage Breadth of the Tool Set Tools EDC Tools EDC Tools Build From Scratch Public Ontologies Narrow No Tools Utilize Harmonized Data Yes
    11. 11. Same Thing Only Different Large EHR Holy Grail CTMS Size of the User Base Oncology Registry AML Registry Build From Scratch Small General Personalized To An Organization Specific
    12. 12. Patient Specific Or Population Oriented Yes Patient Best of Both Registry Worlds 360 View of Each Patient Clinical Registry Outcomes Registry Build From Scratch Quality Registry No Population Centric Yes
    13. 13. Stand-Alone or Linked for Cross Disease Research High Pattern Excel Recognition Each Registry Stands Alone National Build From Registry Scratch Diabetes Registry Auto- Immune No All Registries Linked Yes
    14. 14. “Big Data” Spells Big Opportunity• Produces a competitive advantage for those who get it….and act• Implement “Little Data” right with infinite flexibility and scalability and you are there• The first to recognize patterns wins• Paradigm shifts present disruptive opportunities• Predictive informatics better directs your limited research investment• Going to collect this data anyway…might as well use it
    15. 15. Questions? Happy to take questions via email as well.