In a clinical laboratory, a number on a screen can shape a life. A test result may help diagnose a rare metabolic disorder, guide treatment, or reassure a family that a feared condition is absent. Before that number reaches a doctor, however, the laboratory has to answer a quieter question: can we trust it? That question is the purpose of quality control, or QC. Laboratories repeatedly test samples with known properties and examine whether their instruments are producing results that remain accurate and precise. When something begins to drift, QC can reveal the problem before unreliable patient results are released. The principle sounds straightforward. In biochemical genetics laboratories, however, the reality is anything but simple. More
These laboratories often perform highly specialized tests involving many analytes, the substances being measured in a sample. One assay may contain numerous analytes with different chemical characteristics, while several instruments may be used to run the same tests. Each produces streams of data that need to be reviewed, compared, interpreted, and documented. Traditional QC systems can struggle with this complexity, leaving skilled laboratory staff to move between platforms, copy information, inspect charts, and keep track of multiple rules and limits.
Dr. Pranoot Tanpaiboon and colleagues at Quest Diagnostics set out to make this work more manageable. Their goal was more than simply digitizing an existing checklist. They developed a customizable, cloud-based software application that brings QC information together, automates parts of the review process, and presents data from multiple instruments in near real time. The idea was to create one place where laboratory professionals could see what was happening across complicated testing systems without sacrificing the detailed oversight that these tests require.
At the heart of the system is a useful change: data can be collected directly from laboratory instruments rather than being manually transferred through USB drives or copied from one computer to another. Once inside the program, results can be analyzed and displayed in familiar quality-control formats. Levey-Jennings charts, widely used to show whether measurements are staying within expected limits, can combine information from several instruments. Staff can also explore trends over customized date ranges, examine statistical measures, and monitor patterns such as positivity rates, abnormal results, and patient median values.
The software is designed to do more than make attractive graphs. It centralizes information that was previously scattered, records a time-stamped audit trail, and can send email notifications when QC materials are approaching expiration, when a review is due, or when a critical result needs attention. It can also generate reminders for regular supervisory reviews. These details are important because quality control is an ongoing chain of observation, judgment, documentation, and response, rather than a single moment of checking.
The challenge was to find out whether bringing those tasks together could make a complex laboratory both more efficient and more reliable. Just as importantly, the program was built to change with the laboratory. New assays can be added by updating configurable settings for analytes, lot numbers, calibration details, allowable error, and other test-specific requirements. When an incoming file has a different structure, a custom parsing process can extract the relevant information. That flexibility addresses a persistent problem in specialist laboratories: a tool that works well for one assay may be poorly suited to another, especially when testing is technically demanding and continually evolving.
The program began with 12 clinical biochemical genetics assays in 2019 and expanded to more than 26 biochemical genetics and clinical chemistry assays by March 2025. That growth shows that the system was not confined to a single experiment but became part of everyday laboratory work, handling different instruments, analytes, and quality-control requirements.
The clearest benefit was time. Before automation, staff had to gather QC information, transfer files, inspect data, prepare charts, and complete routine reviews through a series of manual steps. Dr. Pranoot Tanpaiboon and the team compared the time spent on QC review and range assignments before and after introducing the program. Their findings showed a substantial reduction in hands-on work. That is very useful in a laboratory where highly trained professionals have limited time and where repetitive data handling can compete with tasks that demand human expertise.
Automation also tackles another, less visible problem: transcription error. Moving numbers manually between instruments, spreadsheets, and other systems creates opportunities for mistakes. Direct data uploads reduce that risk while preserving records that can be revisited later. The software stores retrospective data, allowing laboratory staff to recalculate reference intervals and investigate performance over time rather than treating each QC check as an isolated event.
Yet the project is not an argument for removing people from quality control. The software can organize data, calculate statistics, highlight unusual results, and send reminders, but trained professionals still interpret what those signals mean. Westgard rules, for example, can flag patterns suggesting systematic or random error, but laboratory directors and staff remain responsible for reviewing performance and deciding what action is appropriate. Automation is most valuable here as a way of directing human attention toward the places where judgment matters most.
There are limitations. A customized system requires maintenance, technical expertise, secure infrastructure, and continuing updates as assays and laboratory needs change. A tool designed around one laboratory’s workflow may also require substantial adaptation elsewhere. The researchers acknowledge that keeping the program current and customizing it for new, highly complex tests remain important challenges.
Even so, the broader lesson reaches beyond biochemical genetics. Modern laboratories generate enormous quantities of information. This information becomes useful when it is organized so that problems can be spotted early, patterns can be understood, and the right person can respond quickly.
That is what makes this work compelling. Quality control usually operates out of public view, yet it is one of the safeguards behind dependable medical testing. By reducing repetitive work and bringing scattered information into one adaptable system, Dr. Pranoot Tanpaiboon and colleagues show how thoughtfully designed automation can strengthen that safeguard.
For patients, the software itself may remain invisible. What matters is the result: laboratory professionals spending less time moving data around and more time evaluating it, with clearer tools for detecting trouble before it reaches a medical report. In a field where a single number can influence diagnosis and treatment, making quality control faster, more coherent, and easier to oversee is more than an administrative improvement. It is part of making laboratory medicine safer, more responsive, and more trustworthy.
