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Lab technologists and clinicians can spend more time making critical and better-informed decisions when tedious and repetitive tasks are automated.
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The Path to Automation in the Clinical Laboratory

Decision support systems can cultivate trust and clear the way for widespread automation

Photo portrait of Erez Na'aman
Erez Na’aman, MSc

Erez Na’aman, MSc, is the co-founder and chief technology officer at Scopio Labs. Prior to launching the company with Itai Hayut, PhD, in 2015, he served as the vice president of engineering and business development at OrCam for five years, where he led the product and hardware teams and oversaw business development, purchasing, and manufacturing operations. Na’aman is a graduate of Talpiot and served in the Israeli Air Force for 11 years. He lives in Tel Aviv with his Aussiedoodle, Luna.

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Published:Jun 01, 2023
|2 min read
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Erez Na’aman, MSc, is co-founder and chief technology officer at Scopio Labs. Prior to launching the company with Itai Hayut, PhD, in 2015, he served as the vice president of engineering and business development at OrCam for five years. Na’aman is a graduate of Talpiot and served in the Israeli Air Force for 11 years. He lives in Tel Aviv with his Aussiedoodle, Luna.

As clinical labs face a slew of workflow challenges—including personnel shortages and increasing pressure to reduce costs—automation is making a strong case for adoption. This technology’s affinity for completing otherwise tedious, repetitive tasks means that lab technologists and clinicians can spend more time making critical and better-informed decisions, some of which may lead to life-changing and life-saving results for patients. This applies to both semiautomated technology, which speeds up analysis by providing detailed results and overviews, as well as fully automated technology, which eliminates human input altogether from receipt of samples to test results and even clinical decisions.

The need for trustworthy technology

Given clinical labs’ significant impact on patient outcomes, technological adoption requires prudence. Patient well-being is health care’s primary goal, so changing how any processes operate should be done with the utmost discretion. It’s a balancing act between the need to improve turnaround times, efficiency, and the quality of results and the need to be cautious and prevent critical technical errors.

Owing in part to this balancing act, automation in the clinical lab currently lacks the level of market acceptance needed for widespread adoption. This is one reason that laboratories across radiology, pathology, ophthalmology, hematology, and other fields turn to decision support systems (DSS), which preserve human input.

DSS technology assists laboratorians in interpreting test results by presenting clinically relevant information and recommendations to the technologists reviewing samples. 

DSS serves as a first and second pair of eyes

As a first set of eyes, it analyzes samples and presents all relevant information to the user for review, saving considerable time and reducing errors. As a second set of eyes, it reviews information only after a human does, with the goal of pointing out and correcting potential lapses and misinterpretations.

Encouraging the gradual adoption of automation

By offering some of automation’s advantages, such as standardization and consistency, faster sample processing times, and uniform care across diverse geographies, DSS can help provide laboratory staff with peace of mind and boost efficiency. 

DSS also fulfills a key role in advancing the gradual adoption of automation, which can help increase lab staff’s confidence in automation over time. A more moderate pace of change allows developers to gather data on real-world performance, labs to build safeguards where necessary, and regulatory agencies to complete thorough approval processes. 

No matter how long it may take to progress from DSS to automation, what we do know is that the future of clinical lab workflows does not rest on organizational culture shifts alone. When labs experience technology firsthand, even incremental improvement promotes bottom-up assurance. When automation becomes widely adopted, it will be due in part to the foundation laid by DSS technologies.