1. HIV Drug Resistance Training
Module 12:
Data Management
Present: In this module, we’ll talk how to manage the different types of data that are part of the genotyping process.

2. A Systems Approach to Laboratory Quality
2005
A Systems Approach to Laboratory Quality
Organization
Personnel
Equipment
Stock Management
Quality Control
Data Management
Remind participants that Data Management is part of the systems approach to laboratory quality.
SOPs, Documents & Records
Occurrence Management
Assessment
Process
Improvement
Specimen Management
Safety & Waste Management
Module 10: Inventory

3. Data Management Information flow Data collection & management
Patient privacy &
confidentiality
Computer
skills
Present: Data management refers to these activities:
Manage incoming and outgoing information
Establish standards for gathering information
Ensure the privacy and confidentiality of patient information
Explain that these activities can often be facilitated by computers. If computers are used, personnel must be trained in relevant computer skills such as word processing, spreadsheet, and database.
Present: This component is closely linked to other components such as personnel, documents and records. 3

4. Topics Importance of Data Management Components of Data Management
Policies and Procedures
Present an overview of the topics in this module:
First, we’ll go over the ways data management can impact our success as genotyping labs.
Then, we’ll look at the various components necessary for quality data management.
Finally, we’ll discuss the policies and procedures that genotyping labs find helpful in data management.

5. 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.
Present objectives for the module.

6. importance of data management
Why is data management important?
Transition to next section. Note that we will try to answer this question.

7. Data Management Means…
2005
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
Discuss: What are some situations in which you might want to go back and find some specific data related to a sample that has been tested?
Emphasize: The big deal is not just having the data, but being able to quickly and efficiently access needed data.
Module 10: Inventory

8. Why Data Management is Critical
2005
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
Discuss: Quality data management contributes to everything you are trying to accomplish as a genotyping lab.
Module 10: Inventory

9. 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?
Transition to next section. Note that we will try to answer these questions.

10. Small Group Brainstorming
2005
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?
Facilitate activity:
Ask participants to work in small groups.
Ask them to think of the entire process from the time they receive a sample, to testing, post-testing, storing, and disposing of that sample.
Ask them to quickly map out that process and then create a chart that shows what data they need at each stage.
Allow fifteen minutes.
Ask one group to present their ideas, then ask other groups to add as needed.
Data We Need
Why?
How?
Module 10: Inventory

11. Data Management Involves…
2005
Data Management Involves…
Before Testing
Accessioning information
Equipment maintenance data (e.g., calibration results)
During Testing
Critical reagent lot numbers, expiration dates
Operator information
In-process results such as PCR product (quantitative or qualitative)
internal QC control results
After Testing
Individual sample and positive control final sequences
Raw chromatogram data
Final reports
Other Uses
Phylogeny
Subtyping
Present: Data management involves…
Emphasize any points that did not come out in the previous exercise.
Module 10: Inventory

12. Prior to Testing Accessioning information Equipment maintenance
2005
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
Review the information on the slide. Emphasize any points that did not come out in the previous exercise.
Module 10: Inventory

13. During Testing Critical reagents Operator information (who did what)
2005
During Testing
Critical reagents
Lot numbers, expiration dates
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
Review the information on the slide. Emphasize any points that did not come out in the previous exercise.
Module 10: Inventory

14. After Testing Final nucleotide sequences Raw chromatogram data
2005
After Testing
Final nucleotide sequences
Individual samples
Positive control(s)
Raw chromatogram data
Final reports
Interpretation system and version
Date/time reported
Review the information on the slide. Emphasize any points that did not come out in the previous exercise.
Module 10: Inventory

15. Other Uses Phylogeny Subtyping 2005
Review the information on the slide. Emphasize any points that did not come out in the previous exercise.
Module 10: Inventory

16. Policies and Procedures
What policies do we need to develop or enhance to ensure the quality control of data gathered for genotyping?
Transition to next section. Note that we will try to answer this question.

17. 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
Present information on recommended policies for labs that use manual processes for data collection.

18. Policies: Automated Processes
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
Training
Present information on recommended policies for labs that use automated processes for data collection.

19. Policies: Gathering Data
Information generated/recorded by
Lab technicians performing the assays
Supervisors
Link all information to its sample via unique accession ID
Discuss the types of policies that should be decided in order to ensure quality in gathering data.

20. 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
Discuss the types of policies that should be decided in order to ensure quality in storing data.

21. 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
Paper records: indexing system
Discuss the types of policies that should be decided in order to ensure quality in retrieving data.

22. Policies: Sharing Data
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
Discuss the types of policies that should be decided in order to ensure quality in sharing data.
Emphasize the importance of sharing the sequence (e.g., with GenBank).

23. 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?
Facilitate a group discussion of the question that was presented at the beginning of the section, as a review and to make sure participants understand the key points.

24. Procedures
Procedures for Data Management may be integrated into testing procedures, or implemented as separate SOPs
See handout for an example
Refer participants to the handout Records Management SOP. Ask them to look through this SOP and identify any concerns they might have about procedures related to data management.

25. 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?
Ask participants to write their own answers to these questions in the blank space on their handouts.

26. 2005
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
Review the key messages.
Module 10: Inventory

27. Summary Importance of Data Management Components of Data Management
Policies and Procedures
Ask select participants to summarize, in one or two sentences, the key points they learned in this module.