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.

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

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

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.

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.

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

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

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

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.

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

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

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

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

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

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

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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

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.