LABORATORY INFORMATICS GUIDE 2014 | INTEGRATED LABORATORIES


JOINING UP THE LABORATORY


Peter Boogaard reviews efforts to make the laboratory an integrated operation


I


t is easier to get data into scientific databases than to get valuable information out of it. For years, we


have been spending time and money to integrate systems and processes in the laboratory’s knowledge value chain. Many laboratory integration projects are under pressure to deliver on their expectations, as defined at the kick-off of. So why is it that laboratory integration is so difficult? What are the obstacles to creating value for the consumers of the laboratory data? Do we know what these users need and how they would like to consume this information? Imagine that in the music world, each


label has its own proprietary music file format. How would you be able to share music? By default, standards make it easier to create, share, and integrate data. Do we know the requirements of such a data standard? What about managing metadata- controlled vocabularies? Data standards are the rules by which data are described and recorded. In order to share, exchange, and understand data, we must standardise the format (data container) as well as the meaning (metadata/context). As of today, there is no unified scientific data standard in place to support heterogeneous and multi-discipline analytical technologies. There have been several attempts but they are limited in scope, not extensible or incomplete, resulting in recurring, cumbersome and expensive software customisations.


PAY ATTENTION TO THE CONSUMER OF THE DATA Integrating laboratory instruments started when instrument vendors, such as Perkin- Elmer and Beckmann Instruments, created the first laboratory information management system (LIMS) software, in the early 1980s.


4 | www.scientific-computing.com/lig2014


The initial objective was to support the laboratory manager with tools to create simple reporting capabilities to enable the creation of simple certificate of analysis (CoA) reports. These systems were initially designed to support a single consumer, namely the scientists and lab managers. In today’s world, consumers of laboratory data can be found


Stephen Covey phrased it very


nicely: ‘Seek first to understand…. And then to be understood.’ It may


sound obvious, but it still remains a valuable statement before starting any automation project


across the entire product lifecycle, and may include external organisations such as CROs and CMOs (Table 1). A different mind-set is required to adapt to this expanded view of the world. It is critical to first analyse who these new lab-data consumers are, and get an understanding of what their objectives are. Often forgotten, but as important, is to investigate what their perspective is on usability. The newcomers may be a non- technical audience! Stephen Covey phrased it very nicely: ‘Seek first to understand…. And then to be understood.’1


It may sound


obvious, but it still remains a valuable statement before starting any automation project.


Table 1: Selected consumers of laboratory information data


Consumer Patient


Fellow scientist


Legal Finance


Customer care


Regulation Objective


Assure secure instant access to medical data for doctors.


Re-use experimental data and leverage learning. Higher efficiency and quality. Consistent meta and context data


Protect company IP


Understand overall life-cycle cost of operation


Product complaints and product investigations


Faster responses to compliance inquiries


Management Identify areas for continuous improvement in process. Reduce costs


Stability labs CRO/CMO IT


Simpler mechanism to create e-submissions. Ability to submit standardised e-stability data packages


Focus on lowering cost/analysis by decreasing IT complexity and overhead


Reduce bespoke/custom systems. Consolidation of systems. Reduce costs


Impact / benefit Better healthcare at lower cost Higher efficiency and quality


Consistent externalisation processes (CROs)


Holistic overall view Secure branding image of company


Simpler mechanism to audit heterogeneous scientific data


Risk-based information across heterogeneous data systems


Faster responses during studies, Increased efficiency


Acceleration move from paper to ‘paper-on-glass’


Unified systems. Simplify IT pro- cesses


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