Enhancing the clinical data review and cleaning process using available AI technologies, will enable pharma companies to release new drugs to the market following an effective process and ensuring the safety & efficacy of the drugs released. During the past several years, many pharmaceutical companies implemented home-grown or off-the-shelf Clinical Data Review Platforms. They defined […]
Posts Tagged ‘Clinical Data Review’
Using Oracle DMW To Solve Clinical Data Review Challenges
In part one of this two-part blog series, we outlined a number of challenges that organizations face with clinical data reviews. In today’s post, part two of the series, we’ll take a look at how Oracle Health Sciences Data Management Workbench (DMW) can be used to tackle those challenges. DATA MANAGEMENT WORKBENCH (DMW) OVERVIEW When […]
Key Challenges Our Clients Face With Clinical Data Review
When it comes to clinical trials, bad data can result in severe consequences. Research and development can become more complicated and lives can be put at risk. Clean clinical data is critical for accurate analysis and reporting, ultimately enabling safer drugs and devices to be brought to market faster. In this two-part blog series, we’ll […]
Importance Of User Interface Design When Reviewing Clinical Data
In the previous post, I talked about varying levels of scrutiny for data review and what happens when recording the user actions. In this post, I will talk about the importance of the user interface for a review. Successfully navigating requirements, design, development, testing, user acceptance and ongoing support of any application requires user involvement […]
Recording Review Actions During A Clinical Data Review
As was mentioned in a previous post, providing an easy-to-use interface is important for data review, including filtering, audit data, and User Review Action buttons. The user actions buttons initiate recording that all, or a portion, of a review has been performed. In the previous section, we mentioned a Review Timestamp, which is one of […]
Identifying Which Data To Review During A Clinical Data Review
In my last blog post, I presented some thoughts on showing data changed since last review. Today, we will concentrate on the level of scrutiny and what happens when recording the user actions in the review. Not all data may require the same level of scrutiny during a review. Additional focus may need to be […]
Are These Design Elements On Your List For Clinical Data Review?
In a few of my previous blog posts (4 Ways You Can Improve The Clinical Data Review Process and Using On-demand Data With Dynamic Data Writeback In Spotfire), I discussed using Spotfire as an aid for Clinical Review of data along with supporting writeback capabilities to record the user review actions. In the next several […]
Top 5 Life Sciences Blog Posts From January 2017
Now that February is here, I thought it would be neat to look back at what our readers found most interesting last month. Below are the top five blog posts Perficient’s life sciences practice wrote in January – they’re ranked in order of popularity, with number one being the most viewed piece. 4 Ways You Can Improve […]
Leveraging Value Added Data For Clinical Data Review In DMW
In my previous post, we discussed a simple scenario for reviewing clinical data cleanliness in Oracle’s Data Management Workbench. Today, we’ll discuss a similar process, although this scenario leverages value added data. Establishing value added data is another way to help the clinician to more quickly review the data and spot outliers and questionable data. […]
4 Ways You Can Improve The Clinical Data Review Process
In my previous blog post entitled Using On-demand Data With Dynamic Data Writeback In Spotfire, I discussed using Spotfire as an aid for Clinical Review of data along with supporting writeback capabilities to record the user review actions. As a follow-on discussion, I wanted to expand those capabilities to include Clinical Data Cleaning Review in […]