Evolving Trends of Automation in the Medical Laboratory
ABSTRACT
Medical laboratory science has undergone a major transformation with the advent of automation, which is reshaping diagnostic workflows and patient care delivery. Initially, laboratories relied on manual testing, but advances such as the AutoAnalyzer and conveyor-based robotic systems laid the foundation for modern automated facilities. Today, automation integrates analytical and extra-analytical processes, reducing human error, optimizing workflow, and enhancing test reproducibility and turnaround time. Emerging technologies—including robotics, microfluidics, mass spectrometry, artificial intelligence, and laboratory information management systems (LIMS) have further expanded laboratory capacity and diagnostic accuracy. Total laboratory automation (TLA) connects pre-analytic, analytic, and post-analytic phases into unified systems, improving efficiency across specialties such as clinical chemistry, microbiology, hematology, and pathology. While automation offers significant advantages, including improved safety, reproducibility, and cost effectiveness in the long term, it also raises challenges related to high implementation costs, workforce reduction, and the need for new technical skill sets. Despite these challenges, automation remains essential in meeting growing healthcare demands and addressing workforce shortages. This paper reviews the historical development, emerging trends, advantages, and limitations of laboratory automation, underscoring its pivotal role in advancing modern healthcare systems.
CHAPTER ONE
Background
In today's healthcare systems, laboratories are crucial because they provide valuable information from tests done outside the body, helping doctors make important decisions. The concept of "clinical laboratory stewardship" highlights how labs are integral to the healthcare team, contributing significantly to patient care decisions. The organization of clinical laboratories has progressed from the performance of basic tests in the physicians’ office, toward development of large facilities, occasionally distant from hospital and patients, performing a huge number of different testsOver the years, labs have evolved from basic testing at the doctor's office to large-scale facilities capable of conducting a wide range of tests, often located separately from hospitals (Plebani et al., 2018). The organization of medical laboratories has evolved from the performance of basic tests in the physicians’ office, toward development of large facilities, occasionally distant from hospital and patients, performing a huge number of different tests (Plebani et al., 2018). Furthermore, the aim of medical laboratory automation is to replace many routine, tedious and repetitive steps, approximating the levels of efficiency and effectiveness (Genzen et al., 2018).
Lab automation aims to streamline processes, making them more efficient and effective. Different types of machines are used based on each lab's specific needs. One such machine is the automated analyzer, which can analyze various chemical and biological characteristics with minimal human intervention. These analyzers can operate continuously or measure characteristics one at a time (Bezerra et al., 2020).
The implementation of automation in labs has led to improved efficiency and reliability in providing doctors with patient-related information (Hawker et al., 2017).
Introduction
Automation, broadly speaking, refers to processes that require little or no human involvement. It's seen as a major breakthrough in laboratory diagnostics. Medical laboratory automation brings several benefits, including less need for human intervention, higher productivity, better tracking of specimens within labs, faster turnaround times, improved specimen handling, enhanced lab safety, and reduced errors (Bakan, 2015). This shift has significantly changed how clinical labs are organized, with many manual tasks being replaced by automated systems (Dolci et al., 2017). Large labs and companies often have engineering departments dedicated to automating lab processes (Burnham et al., 2017). These departments provide technical support and oversee the implementation of new equipment into automated workflows. They also handle financial and personnel aspects to plan and integrate lab automation. However, scientists are still responsible for operating these systems, which is quite different from manual lab processes (Croxatto et al., 2021).
Automation in the clinical lab covers both analytical and extra-analytical phases, with the latter catching up to the former in development (Bakan et al., 2017). Integrated systems now handle specimen processing, testing, and storage with minimal human intervention. With ongoing shortages of lab professionals, automation offers an attractive, albeit costly, solution for labs planning future growth and workflow needs. Analytical and extra-analytical automation has improved various lab processes, including specimen labeling, sorting, transport, processing, analyzer loading, storage, archiving, and overall test performance (Rifai et al., 2018).
The advancement of full integration in managing samples before and during analysis enhances sample processing efficiency, precision, and quality, reducing the likelihood of errors during tasks like labeling, sorting, or aliquoting. This contributes to maintaining specimen quality and enhancing testing reliability. Automation technologies have significantly impacted the capabilities of clinical laboratories. Nowadays, automation is essential in nearly every bioanalytical lab, particularly in larger facilities where the volume of analytical processes is overwhelming. This includes handling numerous medical samples in diagnostic labs and meeting the demand for high-throughput screening in discovering new compounds (Wolf et al., 2021).
The use of automation technology in healthcare has led to improvements in care, diagnostic accuracy, and overall clinical outcomes. Incorporating automated systems into practice has boosted efficiency and reduced turnaround times for diagnostic tests (Alowais et al., 2023). Automation can also cover all the steps involved in manual assays, including specimen collection, reagent delivery, reaction, measurement, and data analysis. Total laboratory automation (TLA) refers to a system where a track connects all these aspects, including preanalytic, analytic, and postanalytic phases. Manufacturers have designed major steps to mimic manual techniques, such as specimen processing, reagent delivery, chemical reactions, measurements, signal processing, and data handling (Turgeon et al., 2016).
TLA can vary in its effectiveness in reducing turnaround time and increasing lab productivity simultaneously (Yu et al., 2019). Automated core facilities have successfully evolved in clinical chemistry, microbiology, hematology, and pathology (Hawker et al., 2017). While TLA has been widely adopted in clinical chemistry, immunochemistry, and hematology, its implementation in diagnostic microbiology and histology has been slower, with many activities still requiring significant manual intervention.Total automation has raised concerns about reducing the need for lab personnel and changing the skill sets required for lab workers (Lippi et al., 2018). However, another significant concern in the laboratory medicine field is the shortage of clinical laboratory workers, expected to worsen in the coming decade. Factors contributing to this shortage include increasing demand for laboratorians, declining enrollment in clinical laboratory sciences programs leading to fewer graduates, and the retirement of aging lab workers (Garcia et al., 2021).