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Unleashing laboratory productivity

One common obstacle for laboratory productivity is the prevalence of old analytical methods. While new technologies keep coming up, many organizations still rely on old experiment protocols, while keeping track of results using paper-based records. The sheer volume of data produced means that most of the time technicians are busy in record keeping while also hindering team communication and multitasking possibilities. And with pressure on completing the tasks for today in the lab, it’s often impossible to find the time to go back and fix the methods and systems from the past - even when we know they’re not up-to-date.

What a productive laboratory looks like in 2022

An efficient laboratory in 2022 should be able to gather data from instruments and manual processes into a single Laboratory Information Management System (LIMS) that allows it to be shared with the rest of the departments involved in the analysis. Record-keeping should be done as paperlessly as possible through the use of electronic laboratory notebooks (ELNs) and scientific data management systems (SDMS). This creates a system that is both redundant and less time-consuming than written records.

In addition, the analytical methods employed should rely on updated technologies that reduce the time of each test cycle (as shown in our case study at the end of this article). The keyword for a modern laboratory should be “integration,” as in integration of new technologies into old analytical methods, integration of disconnected data sources to a single cloud-based management system, and integration of independent processes into a single workflow.

Removing the obstacles to lab productivity

Laboratory managers often have to report test results and data that are not streamlined, which reduces their productivity since they have to reformat everything. By updating to a system that harmonizes data collection it is possible to create a scalable and consistent reporting process that can be replicated and even automated. In other words, laboratory managers need to work smarter, not harder.

It is key to promote changes that update outdated data-capturing tools and inefficient workflows. The major reason for this lies in lack of accuracy, as paper record-keeping can be wrongly transcribed or more easily lost, requiring then the repeat of previous experiments or tests.

By implementing sophisticated analytical methods with digital record-keeping, it is possible to reduce work stress, increase job satisfaction, and the overall productivity of each lab researcher. Thus, it is important to create organizations (such as The J. Molner Company) that are agile and respond to industry trends.

An efficient laboratory should also be flexible in terms of productivity escalation, which means that they can use their updated analytical methods to respond to the growing demands of the market with the same (or even less!) human resources.

Implementing new analytical methods

Changing the way a researcher carries their work may seem like a daunting task. However, the lab manager can ease the adoption of new analytical methods by structuring together a system overview of the technological and reporting changes, as well as the points in which time is saved and processes are automated.

Mapping and updating the workflow of a laboratory, entails to integrate all the data entries into a single system that serves a range of individuals that work in different stations or locations. An efficient workflow should allow administrators to carry out a variety of tasks including: identifying when a reagent was used, where a certain experiment was carried out, and managing the personal and team workloads.

Transitioning to a digital system with updated methods can save time and money. Much of the time savings come from streamlining the way data flows across the laboratory without needing to print a single page. Online data updates are a breeze, since many analytical methods can report directly to a cloud-based system without needing a human to input the information. And in the case a human is needed, Manufacturing Chemist points out there are still ways to save time:

“One laboratory estimated that they gained one day per week in lab time per analyst by reducing the time spent writing up experiments in paper notebooks. Much of these time savings come from the ability to attach data via drag and drop, rather than having to scan, print and compile all information manually,” the magazine reports.

But what about regulations?

If innovation in laboratories is slow, its regulations go much slower. Many organizations are not open to change due to fear of using a new analytical method that is not compliant with current laws. That being said, laboratories interested in making the digital leap can create automatic self-audits and request the e-signing of records to keep things as transparent as possible.

In the case of cloud storage of clinical data, it is important to consider systems that are HIPAA compliant and are clear on which laboratory researcher inputted and has accessed the stored data.

Building the future analytical methods

Due to the global changes of recent years, it is necessary to decentralize laboratories so research teams can access the project data in an organized way from different locations, while also keeping up with industry regulations. Having a single data system and automating the input of results ensures high data quality, while using new analytical methods can save time without compromising the results of each test. Richard Hogan, CEO of data analysis firm ISO Budgets, promotes the use of data as a tool to prevent system failures:

“The ability to evaluate past performance and predict future failures is the key to ensuring measurement capability and quality. Therefore, it is important that laboratories track, monitor, and evaluate equipment failure rates.”

Thus, by creating a system that uses new analytical methods to improve efficiency, laboratory managers can generate more accurate data, better inventory management, promote collaboration through data re-use, scale experiments faster, make the workday easier and promote research innovations.

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