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Building a Smart Laboratory

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John Trigg sets out the changing demands on laboratories. Only by becoming smart can they respond effectively

Unlike some industries, going digital in the laboratory has proceeded at a leisurely pace. The journey started with data capture, data processing, and laboratory automation. It went on to storing and managing digital data from disparate sources, with the evolution of systems such as LIMS and ELNs, which serve to collate and add context to laboratory data. Throughout this period, the underlying business drivers were process efficiency, laboratory productivity, and error reduction. All purchases had to be justified against these criteria, as industries sought competitive advantage by reducing their costs and time-to-market. Informatics tools therefore focused primarily on eliminating waste (time, effort, errors) while providing management with the added bonus of a perspective on laboratory performance.

Different business issues are now having an impact on laboratory operations, and hence putting new demands on the ‘smart laboratory’. In recent years, the distribution of laboratory processes across geographic boundaries and 3rd parties (externalisation) has become common. Businesses can take advantage of the cheapest source of commodity laboratory functions and, in some cases, tap into external sources of expertise to provide research and development. Laboratory informatics systems can meet this business need by providing capabilities for collaboration and the sharing of laboratory data and information, as long as there are infrastructures and levels of access control to ensure protection of IP.

In addition to externalisation, two other major business issues now challenge the laboratory informatics market. Firstly, there is a growing demand for more and better innovation, and secondly, the systems need to be adaptable and agile so they can cope with relentless market, business, and process changes.

Historically, a considerable amount of scientific innovation came about through serendipity and the investigation of unexpected outcomes of planned experiments – where the primary objective was to advance scientific knowledge and understanding. Nowadays, innovation is a systematic, industrial, and time-pressured process, dependent to a large extent on making sense of existing data, prior knowledge, and evidence-based decision-making. At the same time as they cope with the demand for and changing nature of innovation, organisations have to accommodate the changes driven by externalisation, mergers and acquisitions, as well as other market forces. Frequently, such business changes entail the need to consolidate systems, processes, and workforces.

These are the challenges that the laboratory informatics industry now faces. Increased throughput, cost and error reduction isn’t enough anymore. Long-term, managing laboratory data and information in an integrated way needs to provide not only the ability to acquire and store data, but also to support and advance science by extracting information and knowledge from the repositories of data. The needs now are not just to store but to make sense of the data, to uncover correlations, and to support evidence-based decision making. Moreover, informatics systems need to be able to adapt to changing business and operational needs.

Here lies a paradox: the concept of ROI (i.e. productivity) is used to quantify the benefits and thus justify the purchase and deployment of informatics systems, but the long-term benefits may arise through non-quantifiable factors such as better understanding, better decisions, and better science. In other words, the emphasis needs to change from the elimination of waste (time, effort, errors) to providing greater capability, greater flexibility, and more predictive approaches to supporting science.

The design and infrastructure of informatics tools will influence their ability to adapt to changing business circumstances. Over the years, ELNs, for example, have evolved in a typical software fashion, with extended and more detailed functionality, progressively adding to their complexity.

But the more simplistic ‘paper on glass’ style of ELN has recently been gaining market share with faster deployments and greater user acceptance. If the trend continues, this may influence greater modularity across the market, opening up the options for ‘best of breed’ products to meet the specific demands of different types of laboratory. In some respects this type of approach, modularity and simplicity, reflects what has been happening with consumer technologies. The combination of mobile devices, ‘apps’, and sharing/collaboration through social media has set a precedent for ‘user experience’ and raises some interesting questions for the laboratory informatics industry.

With respect to mobile devices, there is a delicate balancing act that falls somewhere between their desirability and their practical value in the laboratory. Limited screen sizes, gesture/touch navigation, virtual keyboards and their physical vulnerability in a laboratory all conspire to challenge the business case for their use.

But there is potential in that mobility, and ‘apps’ that offer dedicated functionality and are tailored for the small screen sizes. The social media argument is an interesting one.

All of this adds up to some interesting times for laboratory informatics. With evolving business needs and rapid changes in consumer technologies that influence expectations in the laboratory, the gauntlet has been thrown down, and there is good evidence that the market is moving to address these demands.