It’s not about automating, it’s about generating value through automation
As predicted more than a decade ago, clinical microbiology has undergone something of a transformation following the integration of laboratory automation solutions. In spite of the difficulties that a traditionally manual and extremely variable discipline might have presented, the appearance of new automation solutions (AUTOPLAK, MALDI-TOF, Kiestra, VersaTREK, …) has led to unquestionable improvements, the results of which can be seen in the quality and speed of results and in workflow optimisation.
The new utility demanded of laboratory automation
Furthermore, the current pandemic has shown us that there are limitations at present and that the automation of laboratory processes is key to the future management of the kind of situations we are experiencing at the moment.
Consequently, this lesson we are learning offers a new set of opportunities to consolidate the transformation of these types of laboratory, placing demands on companies, research centres and institutions to take a new approach and improve their tools and techniques, so that patients will receive a better service, workflows will be optimised and, in short, greater value will be delivered.
Although the idea of automating a process is always attractive, allowing the technician to spend time on tasks of greater added value, optimising operational costs, increasing the efficiency of the laboratory, and improving the quality and the delivery times of results, there are still many notorious failures.
As is often said:
Therefore, it is necessary to put certain concepts into context and draw a distinction between automation and generation of value through laboratory automation.
“Automation consists in using technology to perform tasks almost without the need for people.”
“The creation of value is the capacity that businesses or companies have to generate wealth or utility.”
“The easiest way to measure this generation of value is, of course, in economic terms.”
On the basis of these definitions, albeit somewhat simplistically, the optimum laboratory automation would be “the process that enables a task to be performed practically without human interaction, generating a service of utility (and in certain cases, bringing an economic return)”.
Would we agree with this description?
An approach focused on patient service as opposed to other indicators
As we observed, the pandemic arrived at a time when microbiology laboratories were partially automated. This has highlighted their deficiencies and shown that some of the existing solutions no longer offer maximum utility in themselves and that economic return must be an additional rather than a core indicator.
Totally automated solutions (TLA) have proved to be optimum, although somewhat independent and not very interconnectable with third-party solutions; semi-automated processes have created bottlenecks that have had a significant effect on diagnosis response time; and some traditionally complementary manual processes have become core components of laboratory workflows…
This new paradigm must lead us to reflect and, as a result, put the concept of “new utility” in the field of clinical microbiology laboratories into context.
Holistic automation, spanning the entire workflow
In response to this new interpretation of “new utility”, the new solutions, those that are destined to complete the transformation of these types of laboratory in terms of their automation, must:
1- Focus on patient service, as opposed to other indicators.
2- Adopt a more holistic approach, intelligently integrating all the parties involved.
3- Establish workflows that will consolidate pre-analytical, analytical and post-analytical processes and view the process as a whole, so that the objective of automation may generate value throughout the working cycle.
4- Give priority to modular solutions, both in instruments and consumables, promoting interconnection between systems, even though the solutions may come from different suppliers.
5- Improve processes not only inside the laboratory, but also with relation to the remaining services in the medical centre, integrating as many patient parameters as possible into the diagnosis automation process.
6- Be sustainable, seeking to minimise the use of resources such as space, energy and waste.
7- Work must be done to integrate the new molecular techniques (T2 Biosystems, Accelerate Diagnostics, …) into traditional systems and workflows. Their contribution to the final objective of speeding up diagnosis is key, complementing traditional flows.
8- Accept the use of artificial intelligence to support diagnosis, freeing specialists from repetitive tasks so they can devote time to tasks of greater value for the patient.
Combining all these objectives is not always easy and frequently highly complex; nevertheless, this is the challenge that we, as specialists in the automation of laboratory processes, must overcome.
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