HIV Drug Resistance Training
Module 12:
Data Management
1
A Systems Approach to Laboratory Quality
Organization
Personnel
Equipment
Stock
Management
Quality Control
Data
Management
SOPs,
Documents &
Records
Occurrence
Management
Assessment
Process
Improvement
Specimen
Management
Safety &
Waste
Management
2
Data Management
Information
flow
Data collection
& management
Patient privacy &
confidentiality
Computer
skills
3
Topics
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Importance of Data Management
Components of Data Management
Policies and Procedures
4
Objectives
At the end of this module, you will be able to:
 Describe the importance of data management.
 Describe what data needs to be recorded and
organized prior to, during, and after the assay.
 Identify policies and procedures needed to
support efficient collection and retrieval of data.
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importance of data management
Why is data management important?
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Data Management Means…
An organized way to record, store and retrieve
data associated with pre-testing, in process
testing, and post-testing information
 Associated laboratory procedures and policies
instructing operators and supervisors on how to
use available systems and tools
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Why Data Management is Critical
Ensure high quality results
Enable detailed tracking and troubleshooting
Retrieval and reconstruction of assay-associated
information
 Reproduction of results at a later date
 Ongoing laboratory and personnel QAI
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components of data
management
What is involved in data management?
In what parts of the process is it especially important to collect
data?
How can we ensure that the data we collect is organized so we can
find it when we need it?
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Small Group Brainstorming

Discuss! Think of the process from receiving a
sample to discarding it.
– What data would you want to gather specific to that
sample? Why? How?
– What aggregate data would you want to capture?
Why? How?
Data We Need
Why?
How?
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Data Management Involves…
Before
Testing
During
Testing
After
Testing
Other
Uses
• Accessioning information
• Equipment maintenance data (e.g., calibration results)
• Critical reagent lot numbers, expiration dates
• Operator information
• In-process results such as PCR product (quantitative or qualitative)
• internal QC control results
• Individual sample and positive control final sequences
• Raw chromatogram data
• Final reports
• Phylogeny
• Subtyping
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Prior to Testing

Accessioning information
– Specimen ID (unique)
– Patient information (name, DOB, other ID, clinical
parameters)
– Specimen type, date/time of collection,
storage/shipping conditions
– Receipt date/time, condition

Equipment maintenance
– Dates of major service and/or calibration
– Calibration/QC results
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During Testing

Critical reagents
– Lot numbers, expiration dates

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Operator information (who did what)
In-process results
–
–
–
–

PCR product (quantitative or qualitative)
Sequencing signal intensity
Extent of manual sequence editing
Date/time of completion of intermediate steps
Internal QC control results
– Positive and negative plasma controls
– Positive and negative PCR controls
– Positive control for sequencing
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After Testing

Final nucleotide sequences
– Individual samples
– Positive control(s)

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Raw chromatogram data
Final reports
– Interpretation system and version
– Date/time reported
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Other Uses
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Phylogeny
Subtyping
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Policies and Procedures
What policies do we need to develop or enhance to ensure the
quality control of data gathered for genotyping?
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Policies: Manual Processes
Policies for what data to record
Standardized forms for collection of data
Controlled document, like SOPs
Policy for organization, indexing and storage to
facilitate retrieval
 Training
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Policies: Automated Processes
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Policies for what data to record
SOPs for operation of software for:
– Data entry
– QA checks/verification
– Archival of primary results (e.g. agarose gel images,
raw chromatogram data etc.)
– Report generation
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Training
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Policies: Gathering Data

Information generated/recorded by
– Lab technicians performing the assays
– Supervisors

Link all information to its sample via unique
accession ID
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Policies: Storing Data

Safe and reliable location
– Paper records: dedicated room
– Electronic records: regular back-up

Protect Patient/participant information
– Paper records: restricted access
– Electronic records: user-level access directories
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Policies: Organizing Data for Efficient
Retrieval
Unique identifier (accession number) on all
records – allows for searching and retrieval of all
data
 Electronic systems

– Lab information system (LIS)
– Databases and spreadsheets
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Paper records: indexing system
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Policies: Sharing Data
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In general, all in-process data are kept internally
and not shared outside the lab
Results only shared with original client/study lead
Protect patient/participant information (except to
primary physician)
Following publication, or decision not to publish,
make sequences publicly available
WHO database available for managing data
generated during WHO surveys
– Version 2 early 2010
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Discussion
Think of your current lab policies related to data
management.
 What changes, if any, should be made to these
policies to ensure the quality of genotyping
results for policy making at the national level?
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Procedures
Procedures for Data Management may be
integrated into testing procedures, or
implemented as separate SOPs
 See handout for an example
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Reflection
What does data management mean?
What are the different components of data
management in the life cycle of the sample? For
aggregating data?
 What work does your lab need to do in this area?
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Key Messages
Genotyping essentially generates INFORMATION
about a patient specimen
 Managing all the information and data related to
each specimen is a crucial responsibility of every
lab
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Summary
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Importance of Data Management
Components of Data Management
Policies and Procedures
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