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Laboratory Information Management Systems

Laboratory Information Management Systems

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Laboratory Information Management Systems

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  1. Laboratory Information Management Systems Douglas Perry, Ph.D. IU School of Informatics

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

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

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

  5. Local Laboratory Network Instrument Data Manager Specialized Data System PC Instrument Network Data AcquisitionArchitecture Manual Data Entry SC GC LC GC SP F AB

  6. LIMS DBMS Local Laboratory Network Instrument Data Manager Specialized Data System PC Instrument Network Manual Data Entry SC GC LC GC SP F AB Information ProcessingArchitecture Specialized Data System

  7. Data Warehousing Data Mining/ Data Analysis Electronic Laboratory Notebook LIMS DBMS Local Laboratory Network Instrument Data Manager PC Chromatography Data System Instrument Network Manual Data Entry SC GC LC GC SP F AB Scientific DataManagement Architecture Specialized Data System

  8. Data Warehousing Data Mining/ Data Analysis Electronic Laboratory Notebook LIMS DBMS Wide Area Network Enterprise Architecture

  9. Functional Hierarchyin Laboratory Informatics SDMS, ELN knowledge rules people CDS, LIMS information rules context DAQ, LAB AUTO data

  10. 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 disparate because client labs are highly diverse • Analytical • Clinical • Environmental • Forensic • Production

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

  12. IN OUT Modern Lab Workflow

  13. Challenge and Opportunity 1988 2003 1 experiment 1 experiment 1 gene 10,000 genes OPPORTUNITY 10 data 10,000,000 data CHALLENGE

  14. One Real-Life Example

  15. 2003 1 experiment 10,000 genes 10,000,000 data Preparation and Analysis 1988 1 experiment DAYS, WEEKS,OR MONTHS 1 gene 10 data ONEAFTERNOON

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

  17. Increasing Need for LIMS:Information Management • Advances in instrument automation • Robotics for sample processing • Microarray technology • Increased government regulations • GxP: GLP, GMP, GCP • Demands of enterprise resource planning • CRM, MRP, MES

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

  19. 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

  20. Increasing Need for LIMS:Sample Turnaround • Automated data entry • Automatic calculations • Rapid data retrieval • Automatic reporting

  21. Types of DataUsed in LIMS • Alphanumeric • Descriptive • Limits • Numeric • TDU Stamp

  22. Types of LaboratoriesUsing LIMS • Research & Development labs • Analytical labs • Manufacturing labs

  23. 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

  24. LIMS requirements forR&D 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

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

  26. LIMS Requirements for Analytical Labs • Tracking • Samples, orders, reports • Scheduling • Tests, equipment maintenance • Quality assurance • Validation, QA/QC • Data access and sharing • Instrument interfacing • Client-centered reporting, CoA

  27. 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

  28. LIMS Requirements for Manufacturing Labs • Rapid sample turnaround • Automation, bar-code entry • Connectivity • MRP, ERP, CRM • Statistical analysis • Statistical process control • Flexible reporting • Diverse information demands

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

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

  31. Data Analysis • Data transfer • Buffer tapping, file transfer • Data processing • Conversion, reduction, specification review, statistical analysis

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

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

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

  35. KM CRM MRP LIMS Enterprise-ScaleInformation Management Research & Development Customer Service Regulatory Affairs Laboratory Product Support Quality Control Quality Assurance Raw Materials Manufacturing

  36. laboratory objectives laboratory operations functional requirement specifications lab personnel administration product selection vendors customers IT department installation validation LIMS Implementation TIME (months) 4 +6 +8 +9 +3 = 2.5 years

  37. LIMS Functionality Examples using Labware™ LIMS

  38. Configuring for Each User

  39. Configuring LIMS for GxP

  40. Providing SOPs

  41. Labeling Samples

  42. Maintaining Instruments

  43. Configuring Test Components

  44. Assigning Tests for Samples

  45. Scheduling Tests

  46. Acquiring Data

  47. Capturing Data

  48. Setting Result Responses

  49. Retesting with Audit Trail

  50. Reviewing Sample Status