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Laboratory Integration John Trigg

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Presentation given at the 9th Laboratory Informatics Forum, San Diego, November 2011

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Laboratory Integration John Trigg

  1. 1. Overcoming the Challenges Facing Laboratory Integration <ul><li>John Trigg </li></ul><ul><li>phaseFour Informatics Limited </li></ul><ul><li>www.phasefour-informatics.com </li></ul><ul><li>www.theintegratedlab.com </li></ul>
  2. 2. The e-Laboratory vs the i-Laboratory <ul><li>The electronic or paperless laboratory </li></ul><ul><ul><li>Application-centric </li></ul></ul><ul><ul><li>A jigsaw puzzle – a case of trying to make the pieces fit </li></ul></ul><ul><ul><li>Hampered by lack of standards </li></ul></ul><ul><ul><li>Dependent on custom solutions </li></ul></ul><ul><li>The integrated laboratory </li></ul><ul><ul><li>Modular </li></ul></ul><ul><ul><li>Content separated from the application </li></ul></ul><ul><ul><li>Based on data interchange standards </li></ul></ul><ul><ul><li>Based on communication standards </li></ul></ul><ul><ul><li>Products able to work together </li></ul></ul>
  3. 3. Laboratory Integration Objectives <ul><li>Smoother, easier workflows </li></ul><ul><ul><li>Less manual effort </li></ul></ul><ul><ul><li>Avoiding duplication of data entry </li></ul></ul><ul><li>Easier compliance </li></ul><ul><ul><li>Easier to validate and maintain </li></ul></ul><ul><ul><li>Error reduction </li></ul></ul><ul><li>Reduced cost of development and support </li></ul><ul><ul><li>Eliminate custom interfaces </li></ul></ul><ul><ul><li>Improved efficiencies </li></ul></ul><ul><li>Better data management </li></ul><ul><ul><li>Easier access to data/information </li></ul></ul><ul><li>More flexibility </li></ul><ul><ul><li>Easier to upgrade/change </li></ul></ul>Ref: The Integration of Laboratory Systems, Institute for Laboratory Information, 2010
  4. 4. Changing business models, driven by technology <ul><li>Analogue  Digital </li></ul><ul><ul><li>Music </li></ul></ul><ul><ul><li>Film </li></ul></ul><ul><ul><li>Television </li></ul></ul><ul><li>Physical media  Electronic </li></ul><ul><ul><li>Photography </li></ul></ul><ul><ul><li>Newspapers/Magazines </li></ul></ul><ul><ul><li>Books </li></ul></ul><ul><li>Pareto  Long Tail (markets of millions -> millions of markets) </li></ul><ul><ul><li>Amazon </li></ul></ul><ul><ul><li>eBay </li></ul></ul><ul><ul><li>iTunes </li></ul></ul>
  5. 5. Analogue vs. digital photography <ul><li>Upload </li></ul><ul><li>Email </li></ul><ul><li>MMS </li></ul>
  6. 6. Why is digital photography different/better? <ul><li>Easier to take, evaluate and edit photos </li></ul><ul><ul><li>Point & shoot </li></ul></ul><ul><ul><li>Instant results - no need to send films away to be processed </li></ul></ul><ul><ul><li>Doesn ’ t need to involve 3 rd parties to reprint photos </li></ul></ul><ul><ul><li>Metadata automatically assigned </li></ul></ul><ul><li>Easier to store and search images </li></ul><ul><ul><li>No need for shoeboxes, wallets and photo-albums </li></ul></ul><ul><ul><li>Long term storage and archiving needs to be addressed </li></ul></ul><ul><li>Easier to share </li></ul><ul><ul><li>Photo-sharing sites </li></ul></ul><ul><ul><li>Social network sites </li></ul></ul><ul><ul><li>MMS messaging </li></ul></ul><ul><ul><li>Email </li></ul></ul><ul><li>Applies to personal, social and business use. </li></ul><ul><li>And we don’t really need to worry about image formats and connectivity. </li></ul>
  7. 7. A generic laboratory data ‘ architecture ’ <ul><li>The triangle represents the different layers of abstraction that exist in R&D information flows. These are almost always handled by different systems. </li></ul><ul><li>Above the experimental layer is often management context, and is handled by traditional IT tools used elsewhere in the enterprise. </li></ul><ul><li>Cross discipline collaboration tends to happen around experiment (or reports summarising experiments). Anything more detailed than the experiment requires specific expertise and tools to interpret. </li></ul><ul><li>Below the experiment level there is an increasing specialisation of data types and tools, and only a few systems are comfortably deployed across workgroups. </li></ul>INFORMATION DATA KNOWLEDGE
  8. 8. Laboratory ‘ architecture ’ Activity Format Application Programme/ Study Document Enterprise tools (Doc. Mgt.) Project Document/ Files Desktop tools (MS Office) Experiment Notebook Q/A Chemistry Biology Sample Structured data/files LIMS SDMS Chemistry tools Structures Reactions Registration Biology tools (Excel?) Databases Test Raw/ Processed data Lab Automation
  9. 9. Current business drivers in the laboratory <ul><li>Productivity and business efficiency </li></ul><ul><li>Compliance; regulatory, legal, internal, H&S </li></ul><ul><li>IP protection, security </li></ul><ul><li>Knowledge management </li></ul><ul><li>Changing scientific goals </li></ul><ul><ul><li>Increasingly biology-centric </li></ul></ul><ul><ul><li>Increasing complexity </li></ul></ul><ul><ul><li>Multi-disciplinary </li></ul></ul><ul><ul><li>More and more data </li></ul></ul><ul><ul><li>Increasing accountability (health/environment) </li></ul></ul>
  10. 10. What are the barriers to an integrated lab ? <ul><li>Lack of: </li></ul><ul><ul><li>Data interchange standards </li></ul></ul><ul><ul><li>Laboratory communication standards </li></ul></ul><ul><ul><li>Common language/terminology </li></ul></ul><ul><li>Short termism </li></ul><ul><ul><li>The current economic climate and short term tactical requirements do not encourage long term investment </li></ul></ul><ul><li>We don’t have a guardian angel </li></ul><ul><ul><li>No industry association, that represents user communities, to drive the development and adoption of integration standards </li></ul></ul><ul><li>Lack of community action </li></ul><ul><ul><li>No self-forming communities to tackle common problems </li></ul></ul><ul><ul><li>No (not enough?) pressure on vendors </li></ul></ul><ul><ul><li>But, there are signs of community interest in discussion and sharing opinion in the LinkedIn groups, but no call to action. </li></ul></ul>
  11. 11. What might save us? <ul><li>Vendor response (single vendor vs. best of breed) </li></ul><ul><li>Industry (vendors + user community) collaboration </li></ul><ul><ul><li>An organised body (The Pistoia Alliance?) </li></ul></ul><ul><li>Community action </li></ul><ul><ul><li>See : </li></ul></ul><ul><ul><ul><li>Tribes, Seth Godin,Piatkus Books, 2008 </li></ul></ul></ul><ul><ul><ul><li>Here Comes Everybody, Clay Shirky, AllenLane/Penguin Books, 2008 </li></ul></ul></ul><ul><li>Technology </li></ul><ul><ul><li>Electronic Records Management (documents) </li></ul></ul><ul><ul><ul><li>Need for reliability, authenticity, integrity, usability </li></ul></ul></ul><ul><ul><ul><li>Document standards (PDF, PDF/A, ODF, OOXML) </li></ul></ul></ul><ul><ul><li>Data Standards </li></ul></ul><ul><ul><ul><li>JCAMP-DX, ANDI, AnIML </li></ul></ul></ul><ul><ul><ul><li>Need for agreed terminology, common laboratory language </li></ul></ul></ul><ul><ul><ul><li>Web services/XML? </li></ul></ul></ul>
  12. 12. What is necessary for data standards to evolve? <ul><li>“… inefficient practices have become deeply ingrained by a highly risk averse and legalistic corporate culture, often at the expense of opportunities to co-develop early-stage technology tools, establish data standards, share disease target information, or pursue other forms of collaboration that could lift the productivity of the entire industry.” </li></ul><ul><ul><ul><ul><li>Macrowikinomics, Don Tapscott & Anthony D.Williams, Atlantic Books, 2010 </li></ul></ul></ul></ul><ul><li>Collaborative Innovation? </li></ul><ul><li>Do we have the energy and inertia to collaboratively evolve a common language and common terminology to facilitate the development of laboratory data standards? </li></ul><ul><li>Do we have the energy and inertia to collaboratively encourage vendors to adopt agreed data standards? </li></ul>
  13. 13. Technology Milestones 1980 1990 2000 Microcomputers Minicomputers Proprietary OS MS-DOS Standalone Apps MS-Windows (GUI) ‘ Industry ’ standards DBMS Client-Server Integrated Apps Central Control Central Control User Control 2010 Open Source Distributed Apps Smart phones Tablets Distributed Control ‘ Social’ network Internet Networks
  14. 14. The Five Eras of the Social Web: <ul><li>Era of Social Relationships: People connect to others and share </li></ul><ul><li>Era of Social Functionality: Social networks become like operating systems </li></ul><ul><li>Era of Social Colonisation: Every experience can now be social </li></ul><ul><li>Era of Social Context: Personalised and accurate content </li></ul><ul><li>Era of Social Commerce: Communities define future products and services </li></ul><ul><li>Ref: Post by Jeremiah Owyang on Web Strategy </li></ul><ul><li>http://www.web-strategist.com/blog/2009/04/27/future-of-the-social-web/ </li></ul>
  15. 16. Skills and Culture <ul><li>The laboratory is a knowledge ecosystem </li></ul><ul><li>Technology plays an increasing tactical and strategic role in scientific discovery and other laboratory processes </li></ul><ul><li>Innovation is becoming an ‘industrial’ and global process </li></ul><ul><li>How many lab workers receive formal training in laboratory automation and laboratory integration? </li></ul><ul><li>How capable and well equipped are users to understand and use technology? </li></ul>Technology Processes People
  16. 17. Lab Managers Survey:   Lab Staff Qualifications for Modern Laboratories <ul><li>Q: Applicants for positions have well qualified backgrounds in lab automation tools and technologies. </li></ul>Joe Liscouski, Institute for Laboratory Automation, April 2011 http://www.institutelabauto.org/research/LMSurvey.html
  17. 18. Conclusions <ul><li>In order to meet laboratory integration goals, </li></ul><ul><li>We need a non-competitive process in which we, as a community can collaborate with solution providers to achieve common goals, free of politics and commercial interests, and which places the advancement of science at the forefront. </li></ul><ul><li>We need to work together to evolve a common basis (language and terminology) for data interchange standards to be developed, and to encourage their adoption. </li></ul><ul><li>We need to collaborate with higher education establishments to encourage programmes designed to address the technology and process understanding needs of modern industrial science. </li></ul>

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