How to Eliminate Data Integrity Errors in Your Lab

From receiving samples to recording test results, your lab is likely collecting data at every point of the day. 

Around the clock, your staff is typing on their keyboards or jotting down notes with pen and paper – what happens if they make a typo? Or their handwriting is illegible?

Recording this data is not a mere formality. The data you collect serves as the foundation for your lab’s work and directly impacts the quality of products you produce, the test results you send out, or someone’s health.

Yet every day, manual data entry leads to errors. And these errors can be costly. In this guide, we’ll show the top data entry errors labs make and their costs and show how software like a LIMS can eliminate them.

Top Data Errors in Labs

Specific errors will vary by lab and the type of data you manage. However, the most common that you can expect to make if you are managing data manually are:

• Labeling errors
• Mismatched IDs
• Date format inconsistencies
• “Fat finger” errors
• Sample identification errors

Labeling Errors

Labeling errors frequently occur when your staff incorrectly transcribe information onto sample containers or documentation. 

This can include misspelling test names, using non-standardized abbreviations, or omitting crucial information like concentration or preparation date. The consequences of labeling errors can cascade through your test process, potentially leading to sample mix-ups or incorrect processing parameters.

Mismatched IDs

Mismatched IDs are a critical failure point where sample identifiers don't align across different systems or documentation. 

For instance, a blood sample might be labeled as "PTN-2024-001" in the physical lab but entered as "PT24001" in the database. This discrepancy can make it impossible to trace samples through their analytical journey and may require costly and time-consuming retesting or even sample recollection.

Date Format Inconsistencies

Date format inconsistencies can arise, either as an accidental mistake or miscommunication, if your lab has global reach and interacts with teams from other countries. 

For example, in the United States, we abbreviate dates as MM/DD/YYYY, so July 12, 2024 would be labeled 07/12/2024. However, in some countries, that would be written as DD/MM/YYYY, 12/07/2024 – quite different!

“Fat finger” Errors

"Fat finger" errors occur during manual data entry when staff accidentally hit adjacent keys or transpose numbers. Typos like these are probably one of the most common human errors that are made in labs. 

These mistakes can be particularly insidious because they create valid-looking but incorrect data. They’re also notoriously hard to proofread, as it’s easy to skip over these minor typos when scanning data for errors. While seemingly minor, these errors can significantly impact research conclusions or quality control decisions.

Sample Identification Errors

Sample identification errors cover broader issues in correctly associating samples with their corresponding data points. This can happen when handling multiple samples simultaneously, during batch processing, or when transferring samples between departments. A single misidentified sample can invalidate entire experiments or, in clinical settings, lead to incorrect patient diagnoses or treatments.

The Cost of Data Errors to Your Lab

The exact cost can be a bit fuzzy to predict – it often depends on the type of error and the specific circumstances of the error. That said, there is a helpful illustration from authors George Labovitz and Yu Sang Chang.

In the 1990s, Labovitz and Chang developed the 1-10-100 rule, which demonstrates how data entry error costs multiply as they progress. 

According to Labovitz and Chang, if it costs your lab $1 to fix a data entry error as soon as it’s made, it will cost $10 at the next step in the process, and if the error persists and becomes part of an analytical sample report, it will cost $100 to fix. 

This goes to show how the costs can quickly balloon for a lab resulting in numerous downstream consequences that can slow you down. 

Financial

It’s estimated that up to 42% of your staff’s time is spent on manual paperwork and administrative tasks. Supposing a base salary of $52,000, this means that manual data entry likely costs your lab around $22,000 per staff member. As your lab scales, the financial burden of manual data entry (and the added time to fix errors) increases. 

Compliance

It should go without saying that data integrity errors can put you at serious risk of failing to meet compliance regulations. Issues with data integrity can trigger regulatory inspections, which may require extensive corrective and preventative action plans. You may need to hire an external consultant as well to address systemic issues in your lab or conduct training, which can add to costs. 

Regulatory bodies like the FDA and EPA, or ISO auditors may increase inspection frequency or require additional documentation and oversight, creating ongoing compliance burdens. In severe cases, your lab can lose its certification or accreditation status, requiring costly re-certification processes.

Legal

If your lab deals with patient data or manufactures products, data integrity issues could put you at risk of liability claims and litigation expenses. 

These legal costs can include attorney fees, settlements, court expenses, and increased insurance premiums. There's also the cost of mandatory reporting to regulatory agencies and potential fines or penalties. In cases involving clinical trials or regulated research, errors can invalidate studies and create liability issues with research sponsors or funding agencies. The reputational damage from public legal proceedings can lead to lost business opportunities and decreased market value.

As you can see, there’s a lot of stake from a handful of manual data entry errors. Fortunately, software like a LIMS can help. Read on to see how automating your data capture, entry, and reporting can aid your lab. 

How a LIMS Solves Reduces Data Errors for Labs

A LIMS is a software platform that manages and tracks samples, data, and workflows and integrates other software and instruments. By streamlining operations and centralizing data, a LIMS reduces errors and frees your lab up to get more done and grow. 

Lab staff and technicians use LIMS in various settings, including agriculture, food, environmental, manufacturing, diagnostic, and materials labs. Anytime regulations, data complexity, and sample volume are concerns, a LIMS can help. But necessary functions vary by lab.

Here are the top ways a LIMS can automate data entry and reduce errors:

• Standardize data collection
• Automate data capture
• Create and scan barcodes
• Audit trails and version controls
• Data validation

A LIMS Standardizes Data Collection

A LIMS standardizes data collection in your lab by enforcing consistent formatting and required fields across all processes. This eliminates variation in how different staff members might record the same information and ensures consistency throughout your lab as it scales. This standardization is particularly crucial for dates, units of measurement, and sample identification schemes.

A LIMS Automates Data Capture

Rather than transmit data from one piece of equipment to another, you can automate data capture from all your instruments and software with a LIMS. Thanks to 50+ integrations and a RESTful API, QBench LIMS can integrate with pretty much anything in your lab to capture and centralize data. 

Not only does this remove human error from the equation, but it also saves your staff hours of time and brings your data under one roof to prevent duplicate or conflicting records.

Barcode Scanning and Creation

Say goodbye to manually writing labels (and pointing fingers when one cannot be read). 

By creating and scanning barcodes, everyone in your lab can identify and track samples through their lifecycle from receipt to disposal. This reduces the risk of sample mixups or misidentification and standardizes your lab’s labeling practice. 

Thanks to barcode scanning features, your LIMS automatically pulls up the correct record, eliminating the possibility of entering data against the wrong sample ID.

Audit Trails and Version Controls

Should anything go wrong in your lab, you will need a detailed audit trail to piece together where the error occurred. 

A LIMS can provide a robust audit trail to give you a complete history of all data changes in your lab. This audit trail will show who made modifications, when they were made, and what was changed. Thanks to automated audit trails, it is easier to investigate discrepancies and ensure data integrity by preventing unauthorized or undocumented changes.

A LIMS Sets Data Validation Rules

As if automated data capture, centralization, and standardized formats were not enough to prevent data entry errors in your lab, you can also set up data validation rules to mitigate errors.

Data validation rules automatically check entered values against predetermined parameters, flagging entries that fall outside expected ranges or fail to meet specific criteria. This real-time validation catches errors at the point of entry before they can propagate through the system.

Looking for a LIMS? Download the Free LIMS Buyer’s Guide

Manual data entry is holding your lab back, but a LIMS can reduce errors and lessen the burden on your staff. 

With a LIMS, you can generate labels, record them, monitor their location, and alert your staff of any storage issues while you focus on more important tasks at hand. 

If you’re ready to move beyond the manual grind of spreadsheets and select a LIMS to streamline inventory management, sample management, and manual processes for your lab, then download our free LIMS Buyer’s Guide to help you choose the right LIMS for your lab.

Click the button below to download the free buyer’s guide and take the first step toward improving your lab’s inventory management processes.