Core Habits for Efficient Data Review & Cleaning in Veeva DQS

Veeva DQS is built specifically for data managers and medical reviewers to perform fast, synchronous data review with seamless collaboration. Traditional workflows rely on static spreadsheet files with thousands of rows which are updated asynchronously leading to operational bottlenecks. Veeva DQS offers specialized capabilities for data cleaning and collaboration directly inside the system, as well as automations that eliminate most manual reconciliation tasks, streamlining the review process.

The following 5 core habits for efficient data review and cleaning in DQS can help you and your team extract the full value of the data quality and time-saving features in Veeva DQS. Implement these habits to eliminate redundant reconciliation, reduce error risk, and accelerate review time, saving hundreds of hours of repetitive and tedious work over the course of your study.

Core Habits for Streamlined Data Review and Cleaning

Implementing these recommended practices ensures you unlock the significant benefit of the specialized tools for data review, data reconciliation, and collaboration that Veeva DQS offers. Unlike offline spreadsheet data, DQS data is actionable, allowing data managers to assess, query, and collaborate within a single system, supporting complete visibility into review progress.

Adopting these 5 core habits reduces time spent on redundant review, provides complete visibility into team actions, and maintains clean-state data across batches:

  • Core Habit 1: Automate routine manual reviews with Checks. Any review task following fixed rules can be converted into an automated check. If the review logic is predictable, let Veeva DQS handle your manual work.

  • Core Habit 2: Use targeted, discrepancy-based Review Listings. Focus each review listing on a single objective. Filtering to only the fields and records relevant to the review objective eliminates visual noise and keeps reviewers focused.

  • Core Habit 3: Use the Dashboard and Clean Patient Tracker to identify daily priorities. The DQS Review Dashboard and the Clean Patient Tracker enable you and your team to quickly identify where your focus is most needed.

  • Core Habit 4: Use bulk querying and listing defined quick queries for fast manual querying. Bulk query data directly in DQS then set the record status to In Progress. During review, use Quick Queries to confirm a value in a standardized fashion, or create a listing defined query message that the system dynamically populates with the relevant fields.

  • Core Habit 5: Apply status updates as you clean data. Review status helps you maintain visibility into the cleaning completion of a listing, identify which records to prioritize for review next, and prevent duplication of efforts. DQS automatically saves review status progress and uses change detection to highlight newly updated data.

Core Habit 1: Automate Routine Manual Reviews with Checks

Historically, manual review was unavoidable because automation in EDC could not handle complex and multi-data source reviews. In Veeva DQS, Checks can be used to automate repetitive review tasks that do not require human judgment before sending a query to a site. Many use cases, including external data reconciliation, can be automated using a DQS Check, significantly reducing the burden of manual workload and freeing up reviewers’ time. If a specific discrepancy always requires a query, we recommend you use a DQS Check.

Veeva DQS Checks can automate more than 20% of routine data validations that are traditionally performed manually, including complex advanced checks. When a discrepancy is detected by Veeva DQS, a query is created automatically with rule-specific variables including dynamic text. Queries can be sent to site staff as well as to third-party data providers, eliminating repeated phone calls, emails, and spreadsheet maintenance. DQS Checks are automatically closed by the system when data is corrected, without the need for manual verification.

Core Habit 2: Use Targeted, Discrepancy-Based Review Listings

Traditionally, data review listings listed all the data from the various trial datasets. While simple to set up initially, this listing structure created unwieldy data clutter and obscured data review progress. Using single-objective review listings programmed to only output discrepant data in Veeva DQS substantially reduces the time spent on the data review process over the course of a study.

In Veeva DQS, Review Listings can join data across multiple sources and automatically apply pre-configured calculations, formulas, and filters to display only the records that require review. Review listings are simple to set up and make review significantly more efficient for data management and review teams. Narrowing the target of the listing to a specific review objective also allows for teams to focus on critical reviews as needed for deliverables rather than the thousands of additional records that add noise and distract from the cleaning goals. Learn more about best practices for creating targeted review listings.

Core Habit 3: Use the Dashboard and Clean Patient Tracker to Identify Daily Priorities

Begin your day or data review planning with the DQS Review Dashboard and DQS Clean Patient Tracker. These out-of-the-box tools remove the need for time-consuming custom-programmed clean patient trackers and manual listing review progress tracking. The DQS Review Dashboard and the Clean Patient Tracker enable you and your team to quickly identify daily review priorities, enabling you to spend time directly where it is most needed.

Dashboard

The DQS Dashboard automatically refreshes every hour to provide an up-to-date overview of your team’s review listings, including the latest review progress, data sources, and listing output. You can check the Dashboard to quickly view a progress report detailing how many records are in every data review listing and which listings still require review. You can also schedule the progress report for delivery via email for automated oversight of insourced or outsourced studies.

The Dashboard supports the following actions:

  • Filter the review dashboard to the category of interest (e.g. safety review)
  • Sort listings to prioritize those with the most discrepancies needing review
  • Refer to the w/ Change column to identify listings that include changes to previously reviewed data

Clean Patient Tracker

DQS Subjects, the built-in clean subject tracker, enables data managers to monitor the data collection and cleaning progress for their entire study. The tracker includes details about how many and which review listings require review for subjects, and links out to specific subjects and the filtered dashboard for more information. Teams can quickly see data entry status, query status, and cleanliness at the subject level, or roll up by site, country, and subject status, streamlining planning for site visits and cohort cleaning.

The Clean Patient Tracker helps you:

  • Identify sites with the most data ready for cleaning.
  • Isolate subjects with a particular status, for example, those who have withdrawn.
  • Action patient-level priorities by reviewing pending items from the clean patient tracker. You can click the number displayed in the Incomplete Reviews column to open the Review Dashboard filtered by the incomplete review listings for the given subject.

Core Habit 4: Use Bulk Querying and Listing Defined Quick Queries for Fast Manual Querying

Veeva DQS provides a centralized interface to manage queries across all data sources without logging into separate applications or manually maintaining tracking sheets. Using listing defined quick queries and bulk queries maximizes review efficiency and quality, greatly reducing the time spent creating manual queries.

Include listing-specific query text in every custom listing to reduce time spent manually creating queries during data cleaning and review. These queries can use dynamic tokens, such as AE Number, AE Term, and AE Start Date, which automatically populate with record-specific data when sent to a site. Reviewers can send tailored queries with a few clicks, rather than manually typing repetitive messages.

Bulk querying in Veeva DQS lets you select multiple records and then create a query for each selected record at once. Bulk querying speeds up query creation, and standardizes query messages across discrepancies, significantly reducing site burden by standardization.

Core Habit 5: Apply Status Updates as You Clean Data

Review Status within Veeva DQS lets you maintain visibility over the cleaning completion of a listing, helping you identify which records to prioritize for review next and feeding status information directly into the Review Dashboard and Clean Patient Tracker.

During the cleaning process, data may be updated or corrected. Traditionally, identifying changes required re-reviewing data. DQS eliminates this redundant effort by tracking the review status of every data point and automatically flagging updates to previously reviewed data by displaying a delta icon (Δ) on the row and highlighting the modified cell with an orange border. You can hover over the cell to view the prior value without re-reviewing unchanged records.

We recommend you apply statuses using the following approach:

  • Reviewed: The record has been reviewed and no further work is needed on this row. Important for listings that are not discrepancy-based, where data will not drop off the listing once it is “clean”.
  • In Progress: The record has been reviewed, actioned (for example, a query was sent to the site or external data provider) and is waiting for resolution. Important for discrepancy-based listings where the record will drop off the listing once the discrepancy has been resolved and cleaned.
  • Known Discrepancy: The record has been reviewed and confirmed to be correct as is. Important for discrepancy-based listings to prevent teams from re-reviewing the same discrepant data repeatedly once it was confirmed to be correct. It also provides an easy way to output all known discrepancies at the end of your study or during any study milestone.
  • Unreviewed: This status allows users to reset prior statuses. Important to signal to the team that the record requires re-assessment.