Posts Tagged ‘Data Management Workbench’

Top 5 Life Sciences Blog Posts From May 2017

Now that June 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 May – they’re ranked in order of popularity, with number one being the most viewed piece. How To Import Data Into Siebel […]

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 […]

Oh My, Where Has The Time Gone?

Next week, we are exhibiting at Oracle Industry Connect (OIC) 2017 in Orlando, Florida. For those not familiar with OIC, it is a conference dedicated to a variety of verticals, including Life Sciences and Healthcare. Prior to the existence of OIC, the Annual Oracle Health Sciences User Group (OHSUG) Meeting was an event devoted to users […]

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 […]

Reviewing Clinical Data With Value Added And External Data

In my last post, I discussed performing a clinical data review in Oracle’s Data Management Workbench, which takes value added data into consideration. Today, we’ll take it a step further and add external data into the mix. This scenario extends the data available for review by including data from external partners who may be managing […]

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 […]

Tomorrow: Live Oracle Data Management Workbench (DMW) Demo

When it comes to clinical trials, the consequences of bad data can be severe. Research and development becomes complicated and lives can be put at risk. The need for clean clinical data is critical for comprehensive reporting and analysis, ultimately enabling safer drugs and devices to be brought to market faster. Join us this Thursday […]

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