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

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

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

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

9. Functional Hierarchy in 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 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. Modern Lab Workflow IN
OUT

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

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 Data Used in LIMS
Alphanumeric
Descriptive
Limits
Numeric
TDU Stamp

22. Types of Laboratories Using 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 for R&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. Functional Model of LIMS
systems mgt
data capture C B A
DBMS
lab mgt
data analysis
reporting

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

31. Data Analysis Data transfer Data processing
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 Data archiving Data warehousing
External: unauthorized access
Internal: data sabotage
Data archiving
Mirroring
Off-loading
Data warehousing
Long-term storage
Far-off retrieval

35. Enterprise-Scale Information ManagementKM
Research &
Development
Customer
Service
Regulatory
Affairs
CRM
LIMS
Laboratory
Product
Support
Quality
Control
MRP
Quality
Assurance
Raw
Materials
Manufacturing

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

37. Examples using Labware™ LIMS
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

51. Determining Chain of Custody

52. Reviewing Results

53. Performing Quality Control

54. Using Statistical Process Control

55. Analyzing Laboratory Operations

56. Submitting Reports

57. Examples using LabVantage Sapphire™
LIMS Functionality
Examples using LabVantage Sapphire™

58. Web-Based Client Portal
Source: Terry Smallmon, LabVantage

59. Process-Oriented Navigation
Source: Terry Smallmon, LabVantage

60. Automated 2D Gel Loading
Source: Terry Smallmon, LabVantage

61. System Integration
Source: Terry Smallmon, LabVantage

62. Access Image Data via LIMS
Source: Terry Smallmon, LabVantage

63. Connect Disparate Data Sources via LIMS
Source: Terry Smallmon, LabVantage

64. Link Results to Database via LIMS
Source: Terry Smallmon, LabVantage

65. Link Multiple Search Engines to Database via LIMS
Source: Terry Smallmon, LabVantage

66. Link Visualization Tools via LIMS
Source: Terry Smallmon, LabVantage

67. Automate Workflow via LIMS
Source: Terry Smallmon, LabVantage

68. Questions & Comments