Fixing the Data Disconnect in Clinical Laboratories

Clinical laboratories process large volumes of samples daily, generating vast amounts of critical diagnostic data. Yet many still rely on manual processes, outdated technology, or disconnected systems—factors that contribute to delays and inaccuracies in reporting results.

Is half your data missing?

Diagnostic labs generate two types of data: raw data from testing samples, like biochemical markers, pathogens, or genetic predispositions, and metadata, including patient details, sample collection methodology, and instrument or tester identifiers. 

Raw test data is essential but can be seriously compromised without the right context. 

Metadata gives the data meaning, allowing labs and physicians to interpret results correctly.

Ensuring both types of data are correctly collected and managed is at the heart of a clinical lab's operations, but it is not always possible with their existing data strategy.

How fractured is your data?

Data fragmentation occurs when different diagnostic departments operate in isolation, each using different legacy systems, siloed data processes, or manual entry techniques. Departments such as microbiology, hematology, or molecular diagnostics may all function independently, each relying on different systems to manage “their” data. Without integration, tracking, analyzing, and sharing information between specialties is challenging.

Cross-department consistency in how data is recorded is equally important. 

For example, a routine blood glucose result could be recorded as 5.6 mmol/L in one system but just "normal" or "elevated" in another. When information is inconsistently recorded, it becomes time-consuming and difficult to consolidate and analyze, compromising patient care. 

Where are your samples?

Lost or mislabeled samples remain a critical challenge, with some labs reportedly losing track of 25 to 30 specimens per month. While some labs process tens of thousands of samples daily, losing even a few can have catastrophic consequences on patient outcomes such as delays in cancer diagnoses or unnecessary surgical procedures. 

These issues boil down to the data attributed to each sample that enters the lab and how it is collected, stored, and shared across the entire diagnostic workflow. Tracking who handled the sample, what tests were performed, and where the sample was at any given time is often fragmented, and this lack of continuity makes it difficult to ensure complete traceability for results reporting and audits. 

By implementing data-centric processes such as barcode tracking, automated accessioning, and end-to-end sample workflow management, labs can improve visibility, traceability, and error rates.

Is your lab data compliant?

Regulatory compliance is critical for clinical labs, with non-compliance leading to penalties, legal and certification issues, loss of business, and reputational damage.

Yet many labs lack the integration between data collection and reporting systems to make this an automated and efficient process, forcing staff to manually compile reports from multiple sources, increasing the risk of missing or incomplete records. 

For these reasons, continuous monitoring, accurate record-keeping, and integrated data systems are a requirement to conform to these standards and ensure comprehensive audit trails. 

How to fix your diagnostic data

At its core, improving diagnostic data management is about delivering better patient care. Today’s clinical labs are under increasing pressure to provide fast, accurate results, yet outdated systems continue to slow progress and impact efficiency, productivity, and ultimately profitability. 

Moving from fragmented, manual processes to a fully integrated digital environment allows labs to operate with greater accuracy, efficiency, and confidence. A unified data management strategy that connects diagnostic specialties, standardizes data formats and taxonomies, and streamlines sample tracking, result reporting, and compliance monitoring is essential in a modern, efficient clinical lab.  

Labs that embrace a modern laboratory information management system (LIMS) designed for clinical diagnostics will reduce manual errors and improve workflow efficiency. These labs will be best positioned to meet evolving diagnostic demands with greater confidence and better patient outcomes.