Total laboratory automation: fit for its intended purposes?

Introduction

Since the 1980s, automation has profoundly transformed clinical laboratory operations by improving efficiency, standardization, and throughput [1].

This technological evolution has enabled laboratories to manage increasing testing demands, mitigate the impact of persistent workforce shortages, and control operational costs. Following analytical consolidation, further advancements in laboratory automation have been achieved through the integration of pre-analytical and post-analytical phases, thereby enhancing the overall quality of the Total Testing Process (TTP) [2]. Automation is now an integral component of laboratories across various levels of complexity and size. While its impact on operational efficiency has been well documented, limited evidence is available to support its contribution to improved diagnostic accuracy and clinical outcomes. More recently, Robotic Process Automation (RPA) has been introduced in clinical laboratories, leveraging software ‘robots’ to automate repetitive, rule-based tasks that were traditionally performed by humans. This technology, grounded in machine learning and artificial intelligence, is commonly referred to as software robotics. RPA solutions are now capable of performing tasks such as data entry, form completion, and file transfers with high speed and precision, thereby reducing human error, enhancing quality, and increasing overall productivity [3]. While traditional automation has effectively streamlined manual laboratory tasks – including sample processing, loading, and retrieval – advancements in the automation of electronic workflows, particularly at the human–computer interface, have thus far remained comparatively limited. AI-driven RPA is capable of managing tasks that require language comprehension, contextual awareness, and adaptive decision-making. This advancement holds significant potential to enhance the adoption of automation in clinical laboratories – fostering not only greater efficiency but, more importantly, improving effectiveness and delivering greater value in patient care. This paper aims to critically examine the primary drivers of laboratory automation, elucidate the key benefits realized to date – including documented gains in operational efficiency and diagnostic effectiveness – and to present a forward-looking perspective on emerging trends and innovations. These developments hold the potential to significantly enhance the overall quality of diagnostics, support more rigorous clinical decision-making, and enable improved screening, timely diagnosis, accurate prognostication, and individualized therapeutic strategies.

Automation in clinical laboratory

According to the Oxfords English Dictionary, automation means “automatic control of the manufacture of a product through a number of successive stages; the application of automatic control to any branch of industry or science; by extension, the use of electronic or mechanical devices to replace human labor”. Albeit, no single definition exists, laboratory automation is usually classified according to the complexity of instruments integration, ranging between a) no automation (all analyzers existing as stand-alone machines), b) partial laboratory automation (e.g. development of the so-called “automation islets”, where laboratory analyzers are interconnected and partially integrated with preanalytical workstations such as in the serum work area, integrating clinical chemistry and immunochemistry testing), up to c) Total Laboratory Automation (TLA), where most analyzers performing different types of tests (i.e. clinical chemistry, immunochemistry, hematology, hemostasis and so forth) on different sample matrices (e.g. whole blood, serum, heparinized or citrated plasma) are physically integrated as modular systems or connected by assembly lines (e.g. tracks, belts and other types of conveyers)” [4]. In addition, TLA solutions integrate most pre-analytical and post-analytical phases, enabling clinical laboratories to optimize workflow across the entire TTP. According to current literature, the main drivers of laboratory automation are summarized in Table 1. As a matter of fact, the primary goals of laboratory automation have traditionally focused on improving efficiency within laboratory silos, often overlooking its potential contribution to clinical effectiveness, enhancement of diagnostic–therapeutic pathways, and improved patient outcomes. Reported benefits of laboratory automation are summarized in Figure 1.

Figure 1:

Main advantages of total laboratory automation.

Faster turnaround times (TAT): Following the implementation of TLA, the mean TAT – representing the timeliness of results reporting – decreased by 6.1 %. The 99th percentile TAT decreased by 13.3 %, indicating enhanced consistency in processing speed and a significant reduction in outlier cases. The reduction was more pronounced for immunoassays (41.2 min) compared to clinical chemistry tests (26.0 min) [5]. Comparable findings have likewise been observed in relation to STAT tests [6] while other authors have emphasized the importance of clinical governance and professional oversight to ensure that improvements in TAT are effectively achieved [7].

Cost reduction: Automation reduces labor costs by minimizing manual interventions and enhances operational efficiency through the optimized use of reagents and consumables. Kim and colleagues reported substantial cost savings, decreased staffing expenses, and sustained productivity gains following the implementation of total laboratory automation (TLA), with improvements becoming particularly pronounced three years post-implementation. The same authors estimated a payback period of 4.75 years for the initial investment in total laboratory automation (TLA). Similar data have been reported by other authors [8], 9]. Further cost savings resulted from fewer repeated tests and a diminished need for overtime labor.

Working environment and safety enhancement: By automating repetitive and labor-intensive tasks, TLA significantly reduces both the mental and physical workload of laboratory personnel. This shift enables staff to focus on higher-value activities, such as data review and result validation. More importantly, TLA minimizes the exposure risk to potentially infectious agents, thereby enhancing worker safety and promoting a safer laboratory environment overall [9], 10].

Traceability and data management: TLA systems ensure full sample traceability, particularly when integrated with both pre-analytical and post-analytical phases. Through advanced software and middleware, they enable seamless connectivity with Laboratory Information Systems (LIS) and Electronic Health Records (EHR), thereby supporting operational efficiency and accurate data management. Additionally, TLA contributes to optimized space utilization by minimizing unnecessary staff movement and reducing time waste [9].

Limitations of current Total Laboratory Automation (TLA)

Despite the well-recognized advantages of TLA – notably improved worker safety and faster turnaround times, a list of limitations of current TLA is shown in Table 2. From the current perspective, major limitations of TLA lie in its inability to align with the evolving departmental organization of clinical laboratories. This new model demands the consolidation not only of traditional disciplines – such as clinical chemistry, immunoassays, and hematology – but also of microbiology, virology, mass spectrometry, molecular biology, and genetics. Clinical decision-making increasingly requires the integrated interpretation of all these data domains, and in the near future, will also necessitate the inclusion of pathology and imaging diagnostics [11], [12], [13], [14]. Furthermore, there is an urgent need to transition from bundled closed automation – where automation systems are tied to a specific in vitro diagnostics (IVD) provider through contractual obligations – to open and independent automation. This shift is essential to preserve the autonomy of laboratory directors in selecting analytical methods that offer the best performance. The current requirement to adopt a complete menu of tests from a single vendor, as imposed by bundled agreements, often compels laboratories to use suboptimal methods, despite clear evidence of their limited analytical and diagnostic value. In addition, the limited integration of proprietary pre-analytical systems – despite the availability of more adaptable and customized solutions – hinders the achievement of full sample traceability and effective sample processing. Similarly, the fragmentation of software and informatics platforms prevents clinical laboratories from reliably and securely exchanging data and information. Therefore, a truly end-to-end automation strategy should be implemented, ensuring traceability across all phases of the testing process. This includes the pre-pre-analytical phase, beginning with patient identification, accurate tube labeling, and reliable sample transport to the laboratory [15], 16]. Miniaturization to reduce specimen volumes represents a fundamental development in the future of laboratory automation, as it enables the reduction of blood waste and minimizes the risk of iatrogenic anemia, particularly in vulnerable patient populations [17]. In the post-post-analytical phase, beyond improved data validation and clinical plausibility checks, automation should support enhanced interpretation of laboratory results through data processing systems and the integration of artificial intelligence (AI) tools, including machine learning algorithms [18]. The introduction of Robotic Process Automation (RPA) is expected to bring further advancements in clinical laboratory operations by streamlining workflows and reducing manual interventions. Importantly, RPA can be applied at various levels of complexity. At a foundational level, it includes programming assistance through large language models (LLMs), enhancing coding efficiency and automation of routine script-based tasks. At more advanced levels, RPA can support comprehensive data stack applications encompassing multiple components such as data sourcing, integration, storage, transformation, versioning, governance, and the development of user-friendly interfaces. This layered approach allows laboratories to tailor RPA implementation according to their specific needs, ultimately supporting both operational efficiency and improved data-driven decision-making (3).

Future trends in laboratory automation

In addition to the operational limitations of current TLA, it should be emphasized that although automation enhances productivity, analytical performance, and turnaround time, there is still no conclusive evidence that increased testing volumes and faster results translate into improved diagnostic quality, more informed clinical decision-making, or greater satisfaction among physicians and patients [19], 20]. Only limited evidence is currently available in the literature to demonstrate that the implementation of TLA contributes to a reduced length of stay in emergency departments and hospital wards, enhances diagnostic-therapeutic pathways, or leads to lower morbidity and mortality. TLA appears to have been primarily developed and adopted with the goal of consolidating workloads and optimizing workflows within laboratory silos, with little emphasis on improving clinical outcomes or patient-centered care [21]. Paradoxically, current literature provides more robust evidence on the impact of decentralized testing solutions – particularly point-of-care testing (POCT) – on clinical pathways and patient outcomes than for TLA [22], 23]. Despite significant investments in TLA, data demonstrating its clinical effectiveness remain scarce, whereas POCT, owing to its simplicity and accessibility, has been more extensively evaluated for both operational efficiency and clinical value. This, in turn, underscores the persistent and concerning disconnect between clinical laboratories and patient care that has developed over recent decades. Efforts to improve efficiency within laboratory silos – through economies of scale and reductions in cost per test by increasing testing volumes – have paradoxically diminished the visibility and perceived value of laboratory professionals. Moreover, the cost savings achieved have rarely been reinvested in other critical areas of laboratory medicine, nor have they led to tangible rewards for the discipline as a whole. It is time to open a strategic and forward-looking debate among laboratory professionals on the future trends of laboratory automation – one that shifts the focus from mere economic efficiency to the assurance of value in laboratory medicine. This requires placing clinical outcomes at the core, fostering integrated diagnostics, promoting departmental reorganization, and embracing a truly patient-centered model of care [24].