Whether a lab is focused on pharmaceutical or industrial research, clinical diagnostics or biobanking, its success hinges on its ability to progress research by meeting aggressive operational goals related to velocity, volume, quality, and accountability. What few realize is that these robust and often complex objectives all intersect in one small, yet vitally important component—the individual sample.
Unfortunately, for many labs, manual workflows, fragmented data, and limited visibility leave scientists spending more time searching for samples and reconciling data than actually working with those samples to drive meaningful progress.
To further intensify the problem, sample management only continues to become more complex. More and more labs are handling a broader range of sample types and therapeutic modalities—from biologics to peptides, cell therapies, and others—and most systems simply aren’t made to support them all. Furthermore, research increasingly involves cross-site collaboration, making a clear and consolidated view of sample status and lineage more important than ever. On top of that, there are evolving regulatory requirements to consider, multi-step analyses, and other complexities that make the impact of outdated sample management systems glaringly obvious.
Research tells us that while sample management shortcomings are among the most prevalent and impactful laboratory challenges, they are also among the most preventable. One study estimates that 73% of laboratory errors could be avoided with greater visibility and careful monitoring.
Is your lab’s sample management system advancing its operational objectives, or acting as an impediment to them? Below, we uncover some key symptoms that may signal your current solution isn’t cut out for the complexity, and explore what’s needed in a sample management system to solve the problem.
1. Data is scattered everywhere
For many labs, sample data is spread across a multitude of different systems and applications. When data is fragmented across tools, decision time stretches and error risk climbs.
For example, sample IDs and chains of custody might reside in the sample management LIMS, with experiment data and observations in the ELN, while sample logs and processing timelines are in different spreadsheets, sample requests and updates are in email threads, and the list goes on. Reconstructing history is not only time-consuming, but also introduces additional room for error.
What does this look like in practice? Imagine a protein purification scientist must confirm whether a sample passed initial QC before performing size exclusion chromatography, but she must search through spreadsheets, the ELN, and the LIMS to reconcile all the data she needs. By the time she is through, the sample has degraded and is no longer usable, wasting valuable sample preparation and sequencing queue time.
The symptoms of data fragmentation:
- The answer to a single sample-related question resides across multiple systems.
- Reconstructing chain of custody involves emailing back-and-forth and reconciling spreadsheets.
- Staff members keep personal sample tracking spreadsheets because they don’t have access to one source of truth.
- Project updates are delayed because finding the data needed requires searching across disparate systems.
The solution:
Maintaining all sample data, including history, lineage, storage location, and other critical parameters, within a single, integrated platform makes sample management and inventory tracking simple and seamless.
Equipped with a single source of truth, scientists can see sample status in real time and everyone can work from the same data, mitigating discrepancies and reducing time to insight. But without automating data capture, manual work takes over, and errors and inefficiency become more prevalent.
2. Almost everything is manual
Sample intake, labeling, routing, status updates, and other critical steps along the sample’s journey are done manually in many labs—leaving room for inconsistency and error. Many of these issues surface in the pre-analytical phase, where small lapses cascade into costly do-overs.
On top of that, without automation to flag inconsistencies or missed steps, issues often aren’t recognized until it’s too late. A scientist may not even realize a sample is missing until they go to analyze it, stalling the entire process.
In a real-world setting, imagine this results in a high-throughput sequencing lab discovering a 3% mislabel rate mid-processing, invalidating clinical trial data and putting timelines at risk. Or, as one study found, in clinical biochemistry labs, pre-analytical errors were responsible for 60-70% of total errors.
The symptoms of lagging manual processes:
- Sample labels are handwritten or manually entered in spreadsheets.
- Errors are only discovered when real results don’t match what was anticipated.
- Scientists spend significant portions of their day preparing and labeling samples.
- Mislabeled or duplicative samples are only spotted through manual, visual checks.
- Time shifts from science to sample shepherding, and defects surface late when they’re most expensive to fix.
The solution:
With an automated, configurable sample management system, labs can easily configure sample intake and handling processes in a way that is best suited to their requirements. This allows scientists to define how samples move, set up triggers for next steps, and customize rules for validation. And once steps are machine-enforced rather than person-remembered, proving what happened gets much easier—which matters at audit time.
3. Compliance is increasingly challenging
Research and diagnostics labs operate under strict regulatory requirements, including FDA 21 CFR Part 11, GLP, CLIA, or ISO. Complying with these frameworks requires comprehensive documentation, robust standard operating procedure enforcement, and complete traceability—tasks that many outdated sample management systems can’t keep up with.
When operating from outdated or disconnected systems, preparing for audits is challenging, time-consuming, and stressful, and the chance of error or non-compliance is much greater.
Take this example: an API manufacturer prepares for GLP inspection, spending three weeks consolidating data and refining audit trails, yet still faces corrective actions due to inconsistent SOP documentation across departments. Those three weeks can push trial milestones out of alignment with FDA submission windows. If you are doing your part to reach compliance, it’s important that your sample management software is doing the same.
The symptoms of compliance complications:
- Preparing for audits means pulling data and records from multiple systems and reconciling them manually.
- SOPs aren’t truly ‘standard’, with multiple versions in different folders.
- Missing documentation has resulted in one or more audit warnings.
- Any change in regulation requires manual updates in multiple places.
- When regulations change, it means scrambling to update forms, templates, and workflows across multiple tools.
The solution:
A solution that automatically embeds compliance into every stage of the sample lifecycle ensures that every action is recorded and time-stamped, enabling audit readiness at all times without added effort.
Furthermore, embedded SOPs with role-based access support continued compliance without added complexity. But even with perfect SOPs, you can’t protect what you can’t see, especially across sites.
4. Sample visibility is limited
Without real-time visibility over every sample and status, labs are at constant risk of sample loss or degradation, which can prove costly.
When a scientist goes to look for a specific sample, they must navigate multiple systems or wait for an update from a colleague, leading to inconsistencies when working on projects across departments or teams.
This could result in significant and costly oversights—consider a freezer failure at a partner site goes undetected for 48 hours, resulting in dozens of unusable samples and significant amendments to clinical trial protocol. Without live status and automated alerts, you only learn about excursions after the damage is done. Each lost sample represents thousands of dollars, potentially months of work, and irreplaceable material.
The symptoms of limited visibility:
- The real-time location or condition of every sample in your inventory is not readily available.
- Finding a specific sample requires emailing or calling colleagues, sometimes at different sites.
- Freezer failures and other temperature impacts are discovered too late.
- Samples are sometimes found in unexpected storage locations, or go missing altogether.
The solution:
To effectively track and manage samples, labs require full visibility from sample intake through every stage of processing and analysis.
Furthermore, built-in AI allows scientists to instantly locate samples that meet certain criteria—for example, using a simple, natural language prompt, they can ask the system to find all blood samples stored below a certain temperature that are missing QC data, and receive a full list in seconds.
Taken together, these four failure modes share the same root cause: fragmented, manual, opaque processes. The fixes are equally interrelated: unify the data, automate the handoffs, and make status visible in real time.
From roadblock to breakthrough: Make sample management a strength
All the ‘big picture’ objectives in research and diagnostics are built on the smallest foundational unit—the individual sample. But when those samples are hard to track or prone to error, progress stalls.
Because of each sample’s critical importance, lab sample management shouldn’t be a blocker. Instead, it should be a catalyst for discovery and insight. Automated workflows, unified data, and AI-powered insight allow labs to manage and track samples with granularity and confidence. When those three are in place, the four failure modes collapse and cycle times, quality, and audit readiness improve together.
Sapio transforms sample management from a bottleneck into a strategic advantage, with a unified platform that empowers scientists and lab ops teams to access the same, accurate, up-to-date information for every sample at any time.
See Sapio’s lab sample management system in action. Book a 20-minute walkthrough with our team to map your workflows and find out how we can address your most pressing sample challenges.
