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[[File:|right|350px]] Title: What are the most important features of a laboratory information management system (LIMS)?

Author for citation: Shawn E. Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: March 2024

Important LIMS features: The fundamentals

A laboratory information management system (LIMS) can be purpose-built to a specific industry or designed to broadly meet the base needs of most any lab. As such, given different vendor approaches and different industry needs, functionality may vary among LIMS, sometimes significantly. But what are the most important features of these systems?

To be fair, asking a question about "most important features" makes it tough to come up with something other than a subjective answer. On one hand, important to who? The laboratory seeking a LIMS is of course going to have its own workflows and other unique requirements, and that lab will want a LIMS that can address those needs and requirements. On the other, some iteration of the LIMS has been around for decades, and while the way laboratory work has been conducted has changed over time, some LIMS requirements have remained the same. Dating back to the 1970s, LIMS has had the automation of laboratory workflows, reporting, and data management at its core, and that is no different today. The birth of LIMS in the 1970s into the early 1980s saw focus placed upon seven areas of need:

• sample management,
• interfacing and networking (i.e., integration),
• data processing and management,
• inventory management,
• document management,
• quality control, and
• reporting.

If one looks carefully at that list, they will notice that those needs essentially follow the sample's path throughout the lab, from initial acquisition to results reporting and disposal. The sample is registered in the system, and connected instruments can analyze the sample and send results back to the LIMS. The resulting data can be processed, managed, and tracked, as can any aliquots and reagents created and used throughout the workflow. Documents detailing procedures, standardized methods, quality processes, and more can be accessed as needed, and quality checks can be made automatically or by the analyst at the appropriate stages of the workflow, before results are finalized and reported upon. These same needs, and then some, drive LIMS adoption today. As such, most any well-designed LIMS today will have functionality to aid labs with those specific seven needs.

Important LIMS features: The rest of the story

Aside from (and inclusive of) the seven areas of need consistently associated with LIMS over the years, there are other areas along a laboratory's workflow where LIMS has critically stepped in. Again, these areas may differ from industry to industry, and it's beyond the scope of the Q&A to address the specialty functionality specific to a given industry. That said, commonalities among industries and lab type remain. This commonality is best reflected in efforts that examine the regulations, standards, guidelines, and best practices affecting laboratories today. When examined holistically, we find certain LIMS functionality being driven, at least in part, by those regulations, standards, guidelines, and best practices. This includes:

• audit trails and chain of custody;
• version control;
• electronic signature support;
• barcode, RFID, and label support;
• results review and verification;
• user qualification, performance, and training management;
• instrument and equipment calibration and maintenance management;
• sampling and test method validation;
• statistical trending and control charting;
• nonconformance management;
• corrective and preventive action management;
• backup, retrieval, and retention of data and information;
• robust (i.e., granular) access control and other security management; and
• system validation.

Not necessarily driven by regulations and standards are a few other aspects of a LIMS that can arguably be considered important, including:

• workflow management and automation;
• entity management (e.g., supplier, customer, patient);
• data import and export;
• data analysis, visualization, and dashboarding;
• project and experiment management; and
• robust query tools.