LIMSlecture20030825

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Published on January 15, 2009

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Laboratory Information Management Systems : Laboratory Information Management Systems Douglas Perry, Ph.D. IU School of Informatics Laboratory Information : 2 Laboratory Information The sole product of any laboratory, serving any purpose, in any industry, is information Laboratory Informatics Defined : 3 Laboratory Informatics Defined The specialized application of information technology to optimize and extend laboratory operations Data Flow in the Laboratory : 4 Data Flow in the Laboratory 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 Acquisition Information Processing Knowledge Management Data AcquisitionArchitecture : 5 Data AcquisitionArchitecture AB F SP Manual Data Entry LC GC SC GC Information ProcessingArchitecture : 6 Information ProcessingArchitecture Specialized Data System Scientific DataManagement Architecture : 7 Scientific DataManagement Architecture Specialized Data System Enterprise Architecture : 8 Enterprise Architecture Functional Hierarchyin Laboratory Informatics : 9 Functional Hierarchyin Laboratory Informatics knowledge information data rules rules context people SDMS, ELN DAQ, LAB AUTO CDS, LIMS Basic Concept of LIMS : 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 Genesis of LIMS : 11 Genesis of LIMS Sample Labeling Job Assignment Progress Tracking Results Entry Results Verification Reporting LIMS IN OUT Facilitation of Routine Laboratory Operations Sample Labeling Job Assignment Progress Tracking Results Entry Results Verification Reporting Modern Lab Workflow : 12 Modern Lab Workflow Challenge and Opportunity : 13 Challenge and Opportunity 1988 2003 1 experiment 1 gene 10 data 1 experiment 10,000 genes 10,000,000 data CHALLENGE OPPORTUNITY One Real-Life Example : 14 One Real-Life Example Preparation and Analysis : 15 Preparation and Analysis 1988 1 experiment 1 gene 10 data DAYS, WEEKS,OR MONTHS ONEAFTERNOON Universal Need for LIMS : 16 Universal Need for LIMS Regardless of focus, all labs need: Quality assurance and control Error reduction Fast sample turnaround Management of information Increasing Need for LIMS:Information Management : 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 Increasing Need for LIMS:Quality Assurance & Control : 18 Increasing Need for LIMS:Quality Assurance & Control Quality assurance (QA) Quality control (QC) Statistical process control (SPC) ISO 9000 Increasing Need for LIMS:Error Reduction : 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 Increasing Need for LIMS:Sample Turnaround : 20 Increasing Need for LIMS:Sample Turnaround Automated data entry Automatic calculations Rapid data retrieval Automatic reporting Types of DataUsed in LIMS : 21 Types of DataUsed in LIMS Alphanumeric Descriptive Limits Numeric TDU Stamp Types of LaboratoriesUsing LIMS : 22 Types of LaboratoriesUsing LIMS Research & Development labs Analytical labs Manufacturing labs Research & Development Laboratories : 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 LIMS requirements forR&D Labs : 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 Analytical Laboratories : 25 Analytical Laboratories Objective Provide a service (information) Characteristics Large, organization-dependent Routine tests High sample volume Client-driven operations High, narrow data flow LIMS Requirements for Analytical Labs : 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 Manufacturing Laboratories : 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 LIMS Requirements for Manufacturing Labs : 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 Functional Model of LIMS : 29 Functional Model of LIMS data capture systems mgt data analysis lab mgt reporting DBMS Data Capture : 30 Data Capture Sample identification Log-In, reading, labeling Work scheduling Test initiation, test assignment Data acquisition Interfacing, instrument control Data Analysis : 31 Data Analysis Data transfer Buffer tapping, file transfer Data processing Conversion, reduction, specification review, statistical analysis Reporting : 32 Reporting Client-centered reports User-defined reports Automated batch reports Tabular and graphical formats Ad hoc queries Event triggers Exportation to external IS Lab Management : 33 Lab Management Work scheduling Sample tracking Job tracking Standard Operating Protocols (SOP) Pricing and invoicing Cost analysis Systems Management : 34 Systems Management Security External: unauthorized access Internal: data sabotage Data archiving Mirroring Off-loading Data warehousing Long-term storage Far-off retrieval Enterprise-ScaleInformation Management : 35 Enterprise-ScaleInformation Management Research & Development Manufacturing Quality Control Product Support Regulatory Affairs Raw Materials Customer Service Quality Assurance Laboratory LIMS Implementation : 36 LIMS Implementation TIME (months) 4 +6 +8 +9 +3 = 2.5 years LIMS Functionality : LIMS Functionality Examples using Labware™ LIMS Configuring for Each User : 38 Configuring for Each User Configuring LIMS for GxP : 39 Configuring LIMS for GxP Providing SOPs : 40 Providing SOPs Labeling Samples : 41 Labeling Samples Maintaining Instruments : 42 Maintaining Instruments Configuring Test Components : 43 Configuring Test Components Assigning Tests for Samples : 44 Assigning Tests for Samples Scheduling Tests : 45 Scheduling Tests Acquiring Data : 46 Acquiring Data Capturing Data : 47 Capturing Data Setting Result Responses : 48 Setting Result Responses Retesting with Audit Trail : 49 Retesting with Audit Trail Reviewing Sample Status : 50 Reviewing Sample Status Determining Chain of Custody : 51 Determining Chain of Custody Reviewing Results : 52 Reviewing Results Performing Quality Control : 53 Performing Quality Control Using Statistical Process Control : 54 Using Statistical Process Control Analyzing Laboratory Operations : 55 Analyzing Laboratory Operations Submitting Reports : 56 Submitting Reports LIMS Functionality : LIMS Functionality Examples using LabVantage Sapphire™ Web-Based Client Portal : 58 Web-Based Client Portal Source: Terry Smallmon, LabVantage Process-Oriented Navigation : 59 Process-Oriented Navigation Source: Terry Smallmon, LabVantage Automated 2D Gel Loading : 60 Automated 2D Gel Loading Source: Terry Smallmon, LabVantage Slide 61: 61 System Integration Source: Terry Smallmon, LabVantage Access Image Data via LIMS : 62 Access Image Data via LIMS Source: Terry Smallmon, LabVantage Connect DisparateData Sources via LIMS : 63 Connect DisparateData Sources via LIMS Source: Terry Smallmon, LabVantage Link Results toDatabase via LIMS : 64 Link Results toDatabase via LIMS Source: Terry Smallmon, LabVantage Link Multiple Search Enginesto Database via LIMS : 65 Link Multiple Search Enginesto Database via LIMS Source: Terry Smallmon, LabVantage Slide 66: 66 Link Visualization Toolsvia LIMS Source: Terry Smallmon, LabVantage Automate Workflow via LIMS : 67 Automate Workflow via LIMS Source: Terry Smallmon, LabVantage Questions & Comments : Questions & Comments

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