The Automation Myth: Why Your Lab Workflow Might Still Be Stuck in the Past

If you ask a laboratory director whether their facility is automated, the answer is almost always a confident "yes." However, if you dig deeper into the actual day-to-day operations, a much more complex reality emerges. According to Jenny Bull, the success director at LigoLab, many laboratories are significantly less automated than they believe. While they might have high-end analyzers on tracks or a modern Laboratory Information System (LIS), the space between these technologies is often filled with manual workarounds and inefficient handoffs.

The Reality of the Partial Automation Gap

Many labs equate the presence of advanced hardware with a fully automated workflow. But as Bull points out, when you actually walk through the physical and digital path of a specimen, you find manual touchpoints everywhere. Someone is still printing out paper requisitions. Someone else is manually typing patient demographics into a screen. In many cases, every single result is being manually reviewed by a human before it is released to the clinician.

This gap is becoming a critical liability. As the industry faces a perfect storm of staffing shortages, skyrocketing test volumes, and tightening financial margins, labs can no longer afford to ignore these hidden bottlenecks. Forward-thinking facilities are now turning to data—specifically turnaround time (TAT) analytics and real-time workflow dashboards—to pinpoint exactly where the human "gears" are grinding against the automated machine.

Progress Beyond the Analytical Phase

For years, the analytical phase—where the actual testing happens—has been the star of the automation show. Instrument interfacing, bidirectional communication, and auto-verification have been standard for a while, largely pushed by instrument manufacturers. However, the real modern gains are happening at the bookends of this process: the front-end and the back-end.

On the front end, we are seeing a shift toward automated order entry, specimen accessioning, and barcode-driven tracking. These tools reduce human error and save precious time. On the back end, the focus has shifted to auto-releasing results, automated report generation, and even automated CPT/ICD coding. Alex Cameron, head of Atellica Solutions marketing at Siemens Healthineers, notes that pre-analytical improvements like automated sample sorting, centrifugation, and decapping are making a massive difference in reducing risk and compressing turnaround times.

Digital Pathology: Speeding Up Interpretation

In the world of pathology, automation looks a bit different but follows the same logic of reducing manual handling. Lisa-Jean Clifford, CEO of Gestalt Diagnostics, highlights digital pathology as a game-changer. By integrating an image management system with the LIS, slides can be scanned and instantly linked to a case. This means a pathologist can start interpreting a case in hours rather than days, regardless of where they are physically located. If they need more stains, they can order them in real-time and see the updated images the very same day.

The Persistent Manual Bottlenecks

Despite these leaps forward, manual steps still cling to the edges of the workflow. Specimen receipt remains a messy area; while some labs use barcodes, others are still deciphering handwritten notes. Communication is another weak link. Handling add-on requests or critical value notifications often involves a flurry of phone calls, faxes, and emails. As Bull notes, "the automation often stops at the original order."

Even in labs with top-tier automation, the sheer volume of results requiring human validation remains a time-sink. Delta checks, critical values, and instrument alerts still pull staff away from more complex tasks. In anatomic pathology, the simple act of matching slides to cases or tracking down "missing glass" remains a frustratingly manual process.

The Forgotten Frontier: Financial Automation

One area that is frequently neglected is the financial workflow. Many labs still treat billing as a disconnected back-office function. Staff are often found manually assigning CPT codes or reconciling claims long after the test is done. Bull argues that this area is ripe for disruption. By moving to real-time, integrated coding at the point of order entry, labs can capture more revenue and eliminate hours of daily rework.

The Integration Crisis and "Human Middleware"

The biggest hurdle to true automation is a lack of integration. When systems don’t talk to each other—when the LIS, the billing system, the EHR, and the middleware exist in silos—staff are forced to perform "swivel chair" data management. This is the act of manually copying data from one screen to another.

Alex Cameron describes a reality where staff essentially become "human middleware," serving as the manual bridge between disconnected platforms. This isn't just inefficient; it's dangerous. Poor integration leads to a lack of confidence in the system and can even stunt the adoption of new, beneficial technologies.

The Human Element in an Automated World

It is important to remember that automation is a tool, not a replacement for human expertise. Fields like microbiology, which require culture reading and morphology identification, still rely heavily on the judgment of a trained professional. Similarly, quality management, regulatory compliance, and instrument maintenance remain hands-on tasks.

Automation requires constant care. Rules need to be updated, and workflows must be refined as test menus change. As Clifford mentions regarding AI, these models are aides, not silver bullets. They are built on specific criteria and require human oversight to ensure accuracy.

The Road Ahead: Augmentation, Not Replacement

The impact of getting automation right is undeniable. Bull shares a case where a mid-sized lab moved to an integrated platform and shifted from manual entry to OCR scanning. They freed up two full-time employees and were able to handle a 30% increase in volume without hiring more staff.

Looking forward, we can expect more AI-assisted order validation, robotics for specimen transport, and predictive workload management. But the philosophy remains the same: the goal is to handle the routine so that people can focus on the complex. The labs that thrive will be those that use technology to free their human experts to do what only they can do.