For most clinical laboratories, turnaround time (TAT) is a key performance indicator, or KPI. But from practical experience, I'd define it as something closer to a scientific obligation. When I worked in a clinical lab, our initial target was 48 hours, but we managed to compress it to 32 hours, always pushing to improve TAT because delays directly slow down diagnosis and treatment for critical patients.
Clinical labs are investing in digital tools to close that gap, such as automating sample preparation, pathology image review, or quality control. However, this investment isn’t necessarily equating to a faster lab, with 80% of clinical labs citing sample turnaround as a top operational complaint.
Fragmentation is the real bottleneck
The underlying issue is structural. I've worked with clinical labs running up to 10 separate LIMS instances, and each one could hold a vital fragment of the diagnostic picture, but none of them connect. Lab scientists end up bridging the gap the only way they can: exporting data into shared spreadsheets. This only creates another silo rather than solving the underlying architectural problem.
The consequences go far beyond wasted administrative time. A patient sample might be sequenced in an autoimmune disease lab where a genetic variant of potential significance is identified. The exact same variant may have previously appeared in a completely separate cardiac unit or oncology lab. Without connected systems, there is no way to link those findings across departments, creating a major clinical concern.
And the problem can get worse when specialized skills are involved. In a next-generation sequencing workflow, the bench team runs the samples but hands them off to a bioinformatics team for analysis and validation. Results might be needed within hours if a patient's condition is actively deteriorating, but the sample waits in the queue. I’ve often seen that bottleneck stretch to weeks.
What changes with AI
The practical value of AI in this context isn't about automating a raw diagnosis. It's about solving specific workflow challenges that digital tools alone haven't cracked:
- Natural language interface: Scientists can query their LIMS or ELN in plain language, without writing code or waiting on technical teams to run the analysis for them.
- Cross-system integration: With a single prompt, a clinical lab manager can pull sample status, sequencing data, and variant information from across disconnected systems into a single view and feed that back to clinicians quickly.
- End-to-end traceability: Every action, query, and result is automatically logged. Teams can see exactly who handled what and when, drastically reducing the margin for human error under high-pressure bench conditions.
In practice, a scientist could ask the system to build the experiment, set up the plate, and create the template. Once sequencing is complete, the system can orchestrate the bioinformatics analysis and pull results back into the same record, with relevant literature surfaced alongside them. The informatics team shifts to validating the output rather than owning the entire process.
Laying the foundations for clinical AI
Clinical labs operate under a different kind of pressure than early R&D settings. Any technology that touches sensitive patient information requires a slower, more deliberate path to adoption, and AI is no different. With patients directly impacted by decisions, a clinical specialist must make the final call every time.
But the time lost to manual data transfer, lost samples, and duplicated effort is a problem AI can solve. The first step is getting data out of silos and into a single governed system. The second is involving regulators early rather than presenting them with a finished solution after the fact.
With the right foundations in place, labs don't need to choose between caution and progress. They can pursue both.
