Beyond the laboratory
Tis chapter considers who cares about how smart the laboratory is, and why? It also looks at the broader business requirements and their impact on the laboratory, with an emphasis on productivity and business efficiency, integration with manufacturing and business systems, patent evidence creation, regulatory compliance, and data integrity and authenticity.
Tere is a quote that states: ‘A couple of days in the laboratory can save a couple of hours in the library’. Tis sentiment once typified the attitude of a lot of scientists, but those days are over. It may not have reached the point where a couple of hours on the computer can save a couple of days in the laboratory, but it’s heading that way. Te laboratory is part of a business process and, as such, it is subject to the same productivity and efficiency targets that apply to other parts of the business. Most laboratory informatics projects
focus on the return on investment, typically quantified by streamlining data input through the elimination of bottlenecks, by interfacing systems, and by removing manual processes involving paper. Most projects will also specify long-term gains through establishing a knowledge repository, but this is where quantitation becomes difficult. It’s not unusual in the early days of an informatics deployment,
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for example, for a simple search to uncover prior work that can save reinventing the wheel. But, as time goes on, it becomes the norm to check before starting an experiment. Tere is, however, another side to exploiting the knowledge base – which we’re only just starting to come to terms with. Te need to delve deeper into the knowledge base to visualise and interpret relationships and correlations is growing. ‘Big data’ is the popular term being assigned to the data
“ Productivity and business efficiency are usually measured in financial terms, although this may be translated into time- savings or, in some cases, the numbers of tests, samples, experiments completed”
problem, as it applies to all walks of life. Tis puts the emphasis on ensuring that we have confidence in the integrity, authenticity, and reliability of the data going in, and that the appropriate tools are available to search, analyse, visualise, and interpret the information coming out.
Tese requirements are driven by the
requirements of the laboratory’s customers for robust, reliable, and meaningful scientific information and data that is delivered in a timely and cost-efficient way. Te time- honoured principles of the scientific method provide the basis for the integrity, authenticity, and reliability of scientific data, but those principles need to be reinforced in the context of regulatory compliance and patent evidence creation.
Productivity/business efficiency
Te basic objective in deploying laboratory informatics systems is to improve laboratory productivity and business efficiency. To maximise the benefits, it is important to consider the wider laboratory and business processes that may be affected by the new system. It is easy to fall into the trap of just ‘computerising’ an existing laboratory function, rather than looking at the potential benefits of re-engineering a business process. Te use of tools such as 6-Sigma or Lean can help considerably. Nevertheless, it is prudent to be careful with the use of these tools, depending on the nature of the lab. For example, high-throughput, routine-testing laboratories, which basically follow standard
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