1. Laboratory Information Management Systems

2. Laboratory Information The sole product of any laboratory, serving any purpose, in any industry, is information 2

3. Laboratory Informatics Defined The specialized application of information technology to optimize and extend laboratory operations 3

4. Data Flow in the Laboratory 4 Lab Automation & Robotics Equipment Interfacing Laboratory Instruments Chromatography Data Systems Laboratory Information Management Systems (LIMS) Data Analysis Data Mining Data Warehousing Electronic Laboratory Notebooks Data AcquisitionInformation ProcessingKnowledge Management

5. Functional Hierarchy in Laboratory Informatics 5 rules context people LIMS

6. Basic Concept of LIMS Laboratory Information Management System Definition: A collection of computerized methods to acquire, analyze, store, and report laboratory data No “standard” LIMS – Developed – Customized – Configured LIMS are various because client labs are highly diverse – Analytical – Clinical – Environmental – Production 6

7. Origin of LIMS 7 Sample Labeling Job Assignment Progress Tracking Results Entry Results Verification Reporting IN OUT Facilitation of Routine Laboratory Operations Sample Labeling Job Assignment Progress Tracking Results Entry Results Verification Reporting

8. Modern Lab Workflow 8 IN OUT

9. Universal Need for LIMS Regardless of focus, all labs need: – Quality assurance and control – Error reduction – Fast sample turnaround – Management of information 9

10. Increasing Need for LIMS: Information Management Advances in instrument automation – Robotics for sample processing – Microarray technology http://www.dnalc.org/resources/3d/26-microarray.html http://www.dnalc.org/resources/3d/26-microarray.html Increased government regulations Demands of enterprise resource planning 10

11. Increasing Need for LIMS: Quality Assurance & Control Quality assurance (QA) Quality control (QC) Statistical process control (SPC) ISO 9000 11

12. Increasing Need for LIMS: Error Reduction Data entry restriction – Acceptable parameters – Drop-down lists Range checking – Customer specifications – Internal controls Sample log-in – Bar code reader Automatic calculations 12

13. Increasing Need for LIMS: Sample Turnaround Automated data entry Automatic calculations Rapid data retrieval Automatic reporting http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2 282400/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2 282400/ 13

14. Types of Data Used in LIMS Alphanumeric Descriptive Limits Numeric 14

15. Types of Laboratories Using LIMS Research & Development labs Analytical labs Manufacturing labs 15

16. Research & Development Laboratories Objective – Support pure or applied research Characteristics – Small, autonomous – Diverse, non-routine tests – Low sample volume – Flexible operations – High internal security – Low, circumscribed data flow 16

17. LIMS requirements for Research & Development Labs Flexibility – Sample types, tests, methods, reports Traceability – Audit trails, on-the-fly notation Security – Very limited access, but with lateral authorization Time – Usually not an issue 17

18. Analytical Laboratories Objective – Provide a service (information) Characteristics – Large, organization-dependent – Routine tests – High sample volume – Client-driven operations – High, narrow data flow 18

19. LIMS Requirements for Analytical Labs Tracking – Samples, orders, reports Scheduling – Tests, equipment maintenance Quality assurance – Validation Data access and sharing – Instrument interfacing – Client-centered reporting 19

20. Manufacturing Laboratories Objective – Assure product specifications – Statistical process control Characteristics – Ongoing testing: raw materials, process, final product, stability – Dynamic, demanding environment – High, wide data flow – Fast turnaround 20

21. LIMS Requirements for Manufacturing Labs Rapid sample turnaround – Automation, bar-code entry Connectivity – Manufacturing resource planning (MRP) – Enterprise resource planning (ERP) – Customer relationship management (CRM) Statistical analysis – Statistical process control Flexible reporting – Diverse information demands 21

22. Functional Model of LIMS 22 C B data capture systems mgt A data analysis lab mgt reporting DBMS

23. Data Capture Sample identification – Log-In, reading, labeling Work scheduling – Test initiation, test assignment Data acquisition – Interfacing, instrument control 23

24. Data Analysis Data transfer – Buffer tapping, file transfer Data processing – Conversion, reduction, specification review, statistical analysis 24

25. Reporting Client-centered reports User-defined reports Automated batch reports Tabular and graphical formats Ad hoc queries Event triggers Exportation to external IS 25

26. Lab Management Work scheduling Sample tracking Job tracking Standard Operating Protocols (SOP) Pricing and invoicing Cost analysis 26

27. Systems Management Security – External: unauthorized access – Internal: data sabotage Data archiving – Mirroring – Off-loading Data warehousing – Long-term storage – Far-off retrieval 27

28. Enterprise-Scale Information Management 28 Research & Development Manufacturing Quality Control Product Support Regulatory Affairs Raw Materials Customer Service Quality Assurance Laboratory

29. LIMS Functionality Examples using Labware™ LIMS

30. Configuring for Each User 30

31. Labeling Samples 31

32. Maintaining Instruments 32

33. Configuring Test Components 33

34. Assigning Tests for Samples 34

35. Scheduling Tests 35

36. Acquiring Data 36

37. Capturing Data 37

38. Setting Result Responses 38

39. Reviewing Sample Status 39

40. Reviewing Results 40

41. Performing Quality Control 41

42. Using Statistical Process Control 42

43. Analyzing Laboratory Operations 43

44. Submitting Reports 44