News
How to speed up analytical laboratories workflow (2026)
23/02/2026 – Gabriele Natalini | Digitalization for environmental testing laboratories
The problem every testing laboratory has (and that wastes hours every week)
Focus: speed up data entry into the LIMS and automate quality control (Shewhart) without turning quality into extra work.
Practical 2026 guide: an incremental approach, without disrupting your LIMS.
🎯 Want a concrete answer quickly?
In 10 minutes we can estimate where you’ll save hours (data import or QC) and tell you the first “low-impact” step.
📑 Table of contents
- Introduction: the “duplicate work”
- Why it happens specifically in environmental labs
- Who it impacts (especially the Lab Manager)
- Where most time is lost (and where errors are born)
- Practical example: a “before/after” flow (typical)
- Method: how to speed up data entry into the LIMS
- Method: how to automate Shewhart control charts
- Why it matters (ISO 17025, audits, data integrity)
- Related resources
- Frequently asked questions (SEO)
- Free checklist (ready to use)
- How we can help (without disrupting your LIMS)
Introduction: the “duplicate work” no one sees (until the audit arrives)
In many environmental testing laboratories, the problem isn’t the analysis itself. The problem is what happens after: the same data gets “worked on” twice.
- First it’s produced by the instrument (HPLC, GC, ICP, etc.)
- Then someone has to make it “compatible” with the LIMS (import, copy/paste, checks, corrections)
- And finally you have to prove the process is traceable and defensible
The key point:
if the lab “runs” on Excel, shared folders, and manual imports, wasted time grows with volume — and quality becomes an extra activity, not integrated.
if the lab “runs” on Excel, shared folders, and manual imports, wasted time grows with volume — and quality becomes an extra activity, not integrated.
✅ Useful next click:
Want to figure out in 2 minutes if you’re stuck in “duplicate work”? Go to Where time is lost and then download the free checklist.
Want to figure out in 2 minutes if you’re stuck in “duplicate work”? Go to Where time is lost and then download the free checklist.
In brief
- Time is mainly lost in LIMS imports and manual quality checks.
- The solution isn’t “be more careful”: it’s standardize + validate + trace the instrument → LIMS data flow.
- With Shewhart, without rules and alerts, you risk “control fatigue” and false alarms.
- An incremental approach (1 department/1 instrument/1 QC chart) can free up hours without changing your LIMS.
- If you want to start “right,” use the ready checklist and then do a mini assessment.
Why it happens specifically in environmental laboratories
Environmental labs often have: different instruments, different methods, different formats and traceability requirements (e.g., ISO 17025 accredited labs). Even with well-known LIMS (common examples: LabWare LIMS, STARLIMS, Thermo Fisher SampleManager LIMS, LabVantage, Autoscribe Matrix Gemini, ProLabQ), the critical point remains: making instrument data travel cleanly.
When that journey is manual, three things happen: time is wasted, “silent” errors increase, and reconstructing the data story during audits becomes complicated.
Quick link:
If your main issue is data import go to LIMS Method.
If the issue is QC / Shewhart go to Shewhart Method.
If your main issue is data import go to LIMS Method.
If the issue is QC / Shewhart go to Shewhart Method.
Who it impacts (especially the Lab Manager)
- Lab Manager: timing, priorities, people, SLAs, deadlines
- Quality Manager / ISO 17025: audit trail, data integrity, non-conformities
- Technicians: imports, normalization, repeated checks, rework
- IT / LIMS Vendor: integrations, permissions, security, roles
Where most time is lost (and where errors are born)
These are the “classics” that waste hours:
- Manual import into the LIMS: CSV/TXT/XLS with changing columns, different units, decimals, extra rows.
- Repeated checks: “I’ll re-check because I don’t trust the file” → rework.
- Multiple versions: same sample, three different files, no one knows which is the latest.
- Unmanaged exceptions: when something goes out of standard, it’s solved “by word of mouth” (and then it’s not traceable).
- QC control charts: checking them every day “because that’s how it’s always been,” without rules/alerts.
⚠️ Warning sign:
if you need to open Excel before the LIMS to “fix” files, the bottleneck is already there.
if you need to open Excel before the LIMS to “fix” files, the bottleneck is already there.
Most common errors (and how to prevent them)
| Typical error | Consequence | Prevention |
|---|---|---|
| Different decimals / separators / units | Distorted values + rework | Automatic normalization + consistency checks |
| Unmapped parameters or hand-entered test codes | Incomplete or wrong import | Centralized mapping (once) + versioning |
| Duplicate files / “which is the latest?” | Unreliable data + complicated audits | Anti-overwrite rules + log + sample status |
| Exceptions handled “by word of mouth” | Lack of traceability | Exception handling + structured notes + audit trail |
If this sounds like you, the checklist at the end of the article will quickly show you where to start.
Practical example: a “before/after” flow (typical, without changing your LIMS)
Below is a typical example (common scenario, indicative numbers) of how work changes when you move from “manual” to “standardized and traceable.”
Before (manual flow)
- Export from instrument in variable formats → adjustments in Excel
- LIMS import with repeated checks and “after-the-fact” corrections
- Multiple versions of the same sample and doubts about the latest revision
Effect: rework, deadline delays, fragile audit trail.
After (lightweight, incremental pipeline)
- Standardized output + parameter mapping “done once”
- Automatic validations before import (range, completeness, consistency)
- Anti-overwrite rules + import log (who/when/from which file)
Effect: fewer repeated checks, fewer silent errors, much easier audit reconstruction.
💡 Trick:
instead of rebuilding everything, start with 1 instrument / 1 department / 1 template. If it works, replicate it.
instead of rebuilding everything, start with 1 instrument / 1 department / 1 template. If it works, replicate it.
Method: how to speed up data entry into the LIMS (without increasing risk)
If you’re searching for a “method to speed up data entry into the LIMS,” the solid path is to build a mini-pipeline: acquisition → normalization → validation → delivery → audit trail.
Quick flow schema (instrument → LIMS)
[Instrument] → [Export] → [Normalization] → [Validation] → [LIMS Import] → [Audit trail]
This schema is simple, but it’s exactly what’s missing in “semi-manual” flows.
- Standardize outputs
file naming, folders, export templates (even just this reduces basic errors). - Do the mapping once
define how each parameter (units, decimals, test codes) becomes a LIMS field. Mapping shouldn’t depend on a technician’s memory. - Automatic validations
expected ranges, sample completeness, method/instrument consistency, obvious anomalies before import. - Prevent overwrites
rules: what happens if the same sample arrives twice? block, version, approval. - Traceability (audit trail)
who imported what, when, from which file, which transformations were applied, result and logs.
📌 Practical “content upgrade” (use it right away)
- Mapping template: parameter → unit → decimals → LIMS field → rules
- Validation checklist: range / completeness / consistency / duplicates
- Anti-overwrite rules: version / approval / log
The benefit for the Lab Manager is simple: less time spent “fixing” imports, more predictable timelines, and more peace of mind when you need to rebuild the chain of events.
👉 If you want to take one more step:
after import, the second area that frees up hours is QC: go to automating Shewhart with sensible alerts (without “alert fatigue”).
after import, the second area that frees up hours is QC: go to automating Shewhart with sensible alerts (without “alert fatigue”).
How to automate Shewhart control charts (without going crazy with false alarms)?
This is the question we asked ourselves too. In fact, many people search “how to automate Shewhart control charts” because internal quality control, if done manually, becomes a second job.
The point is: a control chart without clear rules produces two extremes—you either check too little (risk) or too much (fatigue, alert fatigue).
Practical approach:
- Define the rules in advance (e.g., 1 point beyond limits, trend, run, zones) and not “by feel.”
- Notify only when needed: alert the technician, escalate to the manager only for meaningful violations.
- History and context: when a rule triggers, it should be immediate to see batch, instrument, operator, maintenance, reagent changes.
- Reduce false alarms: combining rules increases sensitivity, but can also increase useless alerts if not calibrated.
Useful note:
even the “1 point beyond limits” rule (±3σ) can generate a false alarm on average about once every ~370 observations; adding more rules increases sensitivity but can also increase the frequency of false alarms if it isn’t managed well.
even the “1 point beyond limits” rule (±3σ) can generate a false alarm on average about once every ~370 observations; adding more rules increases sensitivity but can also increase the frequency of false alarms if it isn’t managed well.
✅ Next operational step:
If you want, bring us one QC chart and one history: we’ll define rules + escalation (technician → manager) and tell you how to automate it without noise.
In practice, automating Shewhart doesn’t mean “delegating quality”: it means making quality continuous and sustainable, with interventions before issues become non-conformities.
That’s why we created intelligent control charts. You can find details on the Esobit Datalink page (or go back to LIMS Method if your bottleneck is import).
Why it matters (ISO 17025, audits, data integrity)
During audits, the question isn’t only “is it correct?”
it’s often: “can you show me how you got there?” (source file, transformations, who validated, when, with which rules).
it’s often: “can you show me how you got there?” (source file, transformations, who validated, when, with which rules).
For an accredited laboratory, the audit question isn’t only “is the result correct?”, but also “can you show me how you got there?” This is where data integrity principles such as ALCOA+ come into play (attributable, legible, contemporaneous, original, accurate… and complete/consistent/enduring/available).
And when data is transcribed manually, errors become physiological: in a study on manually entered point-of-care results, 3.7% of entries were discrepant compared to interfaced data, and clinically significant discrepancies were about 5 out of 1000. Even if the context is clinical, the message is universal: manual entry is a structural risk.
Related resources
Frequently asked questions (SEO)
How can I speed up data entry into the LIMS?
Reduce duplicate work: standardize instrument outputs, apply mapping and automatic validations, and use a traceable flow (with overwrite prevention and an audit trail). The goal is to reduce rework and repeated checks.
How do you integrate instruments (HPLC, GC, ICP) with a LIMS?
The hard part isn’t “reading a CSV”: it’s handling real-world file variants, metadata, units, lab rules, sample status, and transformation logs. Integration works when it’s designed around the real process.
What’s a practical way to automate Shewhart control charts?
Define rules upfront (out-of-limit, trend, runs), calibrate sensitivity to avoid false alarms, and enable targeted notifications with history and context (instrument, batch, maintenance). That’s how QC becomes continuous and sustainable.
What are the most common errors when importing data into the LIMS?
Decimals and separators, units of measure, rounding, unmapped parameters, duplicate files, wrong sample association, and untracked changes. These are typical errors in manual and “semi-manual” workflows.
Which LIMS do environmental labs use? (And does it change anything?)
There are widely used LIMS (e.g., LabWare, STARLIMS, SampleManager, LabVantage, Matrix Gemini), but the bottleneck is often the same: transferring and validating instrument data in a repeatable and traceable way.
Free checklist: quickly see where you’re losing hours (and where to start)
📎 Ready-to-use checklist (import + QC)
If you answer “yes” to 2–3 questions, you likely have huge room to recover time.
- Do we open Excel before the LIMS to “fix” files?
- Do we often wonder which file is the latest version?
- Are exceptions handled “by word of mouth” and then not traceable?
- Are QC charts checked “every day” without rules/alerts?
- During audits, is reconstructing source file → transformations → import painful?
How we can help (without disrupting your LIMS)
If you recognize yourself in these scenarios, it usually makes sense to start with one critical flow: 1 instrument / 1 department / 1 QC chart. The goal is to free up hours quickly and make the process defensible during audits.
How we start (lightweight)
- We map the real flow: instruments → files → checks → LIMS
- We measure where time is lost (import, rework, QC)
- We apply an incremental pipeline with an audit trail
- Analysis of bottlenecks (instruments → files → checks → LIMS)
- Design of mapping, validations, exception handling, and audit trails
- Implementation of automations and integrations (even incremental)
- Ongoing support: new instruments, multi-site extensions, continuous improvement
👉 Want a quick “yes/no” answer?
Tell us your pain point (data import or QC): we’ll map it together and tell you where time can be recovered.
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