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Laboratory Information Management System (LIMS) is a specialized software designed to streamline and optimize various laboratory processes. It serves as a centralized platform that effectively manages and organizes data and information related to laboratory operations.
Lab management is the backbone of any successful research environment. From coordinating lab operations, managing resources, and guiding personnel, lab managers ensure that research runs smoothly, efficiently, and safely. Effective lab management doesn’t just keep the lab organized it drives innovation, ensures compliance with safety and regulatory standards, and enables the lab to reach its full potential. Dealing with increasing volumes of data, laboratories can no longer feasibly manage experiments by gluing printed results into a paper notebook. With a Laboratory Information Management System, researchers can now link experiments to specific samples or files, as well as easily share information with other lab members and organizations involved.
Dealing with increasing volumes of data, laboratories can no longer feasibly manage experiments by gluing printed results into a paper notebook. With a Laboratory Information Management System, researchers can now link experiments to specific samples or files, as well as easily share information with other lab members and organizations involved.
There are various LIMS solutions depending on the industry: food and beverage testing, water and wastewater, agriculture and farming, etc. But in this article, we’ll be talking primarily about LIMS in healthcare.Though LIMS keeps expanding its functionality, which in turn changes how the system is defined, we can identify its basic functions focusing on the core function - effective sample management.
This process includes six phases:
1.receiving a sample and registering it along with the related customer data.
2. monitoring: scheduling and tracking the sample.
3. sample processing: managing the utilized equipment, inventory, and the corresponding analytical workload.
4. quality control: inspecting and approving the results.
5. compiling the sample data for reporting.
6. storing the sample analysis data.