Life is a sexually transmitted disease:

Generally 'Life Science' can be defined as all sciences that have to do with 'organisms', like plants, animals and human beings. With so many challenges facing the life sciences industry – pricing pressures, pipeline pressures, impending patent expirations, and the soaring cost of developing new drugs – the strength of these companies as well as the intellectual property they need to improve the health of the public is being threatened. While the focus seems to be shifting to personalized medicine – the right drug for the right patient – personalized drugs will most likely mandate significant changes to life sciences companies' business practices. How can drug companies remain profitable so they can continue to deliver the best drugs to the public? Our solutions optimize the flow of valuable scientific and operational data within life sciences companies – helping them bring therapies to market faster and more profitably. Our company offers an end-to-end solution to drive efficiencies throughout every stage of a drug's lifecycle: from discovery, through development, commercialization and beyond. We have a deep commitment to the development of data standards in life sciences. Anticipating the movement of the industry and its governing regulatory bodies toward standards.

Features

Data integration:

Investment protection for legacy operational systems and data. Access all data regardless of the source or format. Automated data loads for clinical data on a more frequent schedule

Data mapping:

Flow control, integrated error reporting, job performance monitoring and statistics, and reporting. Full mapping of data source (where data came from), data manipulations (how the data has been manipulated) and the final destination for data. Impact analysis reports on (and helps you plan for) the impact of any change to the process, including: Changes to incoming data formats. Changes in data standards. Additional data requirements for analysis data sets.

Data preparation:

Automated data quality activities let you spend less time validating incoming clinical data. Automatically incorporated data quality ensures consistent, trusted and verifiable clinical information.

Increase operational efficiency while lowering costs.

Automate repeatable tasks to free up resources for more value-added tasks. Increase your capacity to handle additional trials, as well as more complex global trials. Write and validate less code, and potentially reuse code for future trials. Scale clinical studies without adding expensive, hard-to-find headcount. Support adaptive trials through rapid access to clinical data for interim analysis. Reuse the work of others via a common repository that enables the management and reuse of information, thereby reducing both development and maintenance time.

Ensure the proper use of standards.

Validate both the structure and content of data. Visually convert legacy data to standard data.

Deliver consistent, trusted and verifiable clinical information.

Aggregate information from virtually any hardware platform or operating system. Address potential issues before they affect your study by automating data quality and data transformation routines.

Improve productivity.

Build and document work with a user-friendly GUI interface. Reduce the need to write unique code for each study. Get new team members up to speed quickly on work done by others.

Reduce risk.

Standard processes and GUI tools enable other resources to finish tasks for improved continuity of business. Less hand-written code means less code to validate and maintain.

Features

Data integration:

Investment protection for legacy operational systems and data. Access all data regardless of the source or format. Automated data loads for clinical data on a more frequent schedule. .

Data mapping:

Flow control, integrated error reporting, job performance monitoring and statistics, and reporting. Full mapping of data source (where data came from), data manipulations (how the data has been manipulated) and the final destination for data. Impact analysis reports on (and helps you plan for) the impact of any change to the process, including: Changes to incoming data formats. Changes in data standards. Additional data requirements for analysis data sets

Data preparation:

Automated data quality activities let you spend less time validating incoming clinical data. Automatically incorporated data quality ensures consistent, trusted and verifiable clinical information.

Data standardization and validation:

Standards validation and conformance checking. Specialized transformations for mapping clinical data to a standard model.

Standards management to:

Match the application of standards to study requirements. Provide lifecycle management for standards as standards evolve.