LABORATORY INFORMATICS

"... continually uses foresight to stave off future problems."

Pharma Development and Manufacturer

Laboratory Informatics Systems Architecture Strategy

Your system architecture should reduce support costs, promote collaboration and facilitate data analysis. LabAnswer has architected complex enterprise and multi-enterprise systems for some of the world’s leading life science organizations. These systems manage high volumes of data and include multiple business functions, numerous applications and very large databases. 

Increase Efficiencies and Compliance

When properly architected, technology should accelerate the objectives of your laboratory business. Our focus on the architecture of the system – the vision behind your electronic laboratory roadmap - helps mitigate risks of the “unknown”, drastically increases efficiencies, and maintains regulatory compliance within your labs.

Our customers are continuously challenged by the myriad of laboratory applications and hardware products whose features overlap considerably. Vendors continue to extend functionality while claiming their product should reside at the center of your architecture strategy.

And for too many organizations with multiple lab sites, products are selected independently of each other, thereby eliminating the ability to share knowledge, skill-sets and best practices for implementation and support.

A Pragmatic Approach

LabAnswer provides strategic services based in the reality of having built, integrated, deployed and supported more laboratory informatics systems than any other consultancy. Our experts can help determine what type of systems are actually needed, where specific data should reside, how the systems should be networked and what systems should be consolidated.

LabAnswer practices its own pragmatic approach to alleviating confusion in the marketplace by helping you determine:

  • What technologies will help you reach your business objectives?
  • What systems are used and integrated in laboratories in your industry?
  • How to incorporate best practices into your existing environment?
  • What data model will support your future business needs?