Operational Simplicity = Laboratory Efficiency

The modern analytical laboratory is an ever-changing ecosystem with many stakeholders, differing requirements, and a diverse workforce with varied skills and experience. In this fast-paced environment, analysts must use a wide range of instruments, ensuring their smooth function within intricate, multistep workflows. 

The complexity is further compounded by the need to derive greater levels of insight – while reducing the time and costs needed to process each sample. But this increased productivity must not come at a price. Finally, laboratories must be flexible enough to handle varied and shifting demands to provide high quality, right-first-time results under increasingly stringent regulatory requirements.

No wonder it can sometimes feel like an impossible task. But it isn’t. Rather it is possible – and indeed imperative – to have high standards and expectations while also pushing for greater efficiency. The future is operational simplicity. 

At its core, operational simplicity means connecting laboratory software, instruments, workflows, and analysts, automating time-consuming and manual tasks that are prone to error, and mining big data so that meaningful results can be revealed quickly. The good news is that technology already exists to make this a reality.  

By investing in the right software, laboratories can benefit from time and cost efficiencies. But in this world of choice, how do laboratory managers choose the most appropriate software for their needs? Three areas of focus are bringing the biggest returns: single/connected software solutions, automated workflows, and advanced data processing and analysis tools. When combined, these elements can unlock the potential to drive operational efficiency. 

Single software platforms instantly remove one layer of inefficiency (managing multiple instruments through different systems). Analytical techniques, such as chromatography and mass spectrometry, have very distinct protocols and parameters, so they are supplied with proprietary software. Disparate systems bring complexity and make it difficult to manage data and audit trails (error rates can also be increased if manual input is needed). In reality, the underlying workflow requirements for most separation and detection systems are remarkably similar. If their interface parameters can also be standardized and automated, with data stored centrally, we can save time and avoid mistakes.

Harmonizing software platforms can link all instruments (even from different vendors), workflows, procedures, and users in one enterprise system. Data is much easier to manage if it’s centralized and securely stored. In addition, as common parameter requirements are standardized and unique aspects of the workflow are captured, users can be guided through limited choices within predefined and validated workflows that reflect standard operating procedures. Tasks that require little input can be automated and regulatory requirements, such as maintaining data integrity and system compliance, can be conducted with ease. Finally, with easier set-up and automation, analysts benefit from walkaway time (and reduced training), allowing them to focus on value-added tasks that better use their knowledge and experience; for example, developing even more well-defined, repeatable and standardized workflows that use the least number of steps to produce consistent and reproducible results. As the workflow is optimized and then automated, it becomes fine-tuned for efficiency and accuracy – an iterative process that continues to drive efficiency as the laboratory, and the demands placed on it, evolve. 

By connecting big data from multiple instruments, analysis tools can automatically adjust and display relevant real-time information, while allowing analysts to employ smart tools to enhance data reporting. Some systems have algorithms that enable dynamic data processing and linking so that any changes are automatically reflected in the results, making labor-intensive batch processing redundant. And advanced software can automatically detect accurate peak start and end times and assign peak baselines without a long list of detection parameters, automatically recognizing and filtering noise so that peak integration can be correctly defined. 

In short, operational simplicity is driven by purpose-driven software.