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Data: Instrumentation Building a Smart Laboratory 2017


l Avoiding duplicate data: no need to look in multiple places;


l Avoiding transcription errors: integration will result in electronic transfers that should be accurate; this avoids the need to enter and verify data transfers manually;


l Improving workflow and the movement of lab data: reducing the need for people to make connections between systems – integration facilitates workflow; and


l More cost-effective, efficient lab operations. Te problem of integration, streamlining operations, and better productivity has been addressed via automation before: in manufacturing applications and clinical labs. In the 1980s, clinical lab managers recognised


the only way they were going to meet their financial objectives was to use automation to its fullest capability and drive integration within their systems. Te programme came under the title ‘Total


laboratory automation’ and resulted in a series of standards that allowed instrument data systems to connect with Laboratory Information Systems (equivalent to LIMS) and hospital administrative systems. Tose standards were aggregated under HL7


(www.hl7.org), which provides both message and data formatting. While hospital and clinical systems have the advantages of a more limited range of testing and sample types, making standardisation easier, there is nothing in their structure to prevent them being applied to a wider range of instruments, such as mass spectrometry. An examination of the HL7 structure suggests


that it would be a good foundation for solving integration problems in most laboratories. From the standpoint of data transfer and


communications, the needs of clinical labs match those in other areas. Te major changes would be in elements, such as the data dictionaries, and field descriptions, which are specific to hospital and patient requirements. A cross-industry solution would benefit


vendors as it would simplify their engineering and support, provide a product with wider market appeal, and encourage them to implement it as a solution. Most of the early standards work carried out


outside the clinical industry has focused on data encapsulation, while more recent efforts have


included communications protocols: l In the 1990s, efforts by instrument vendors led to the development of the andi standards (Analytical Data Interchange) which resulted in ASTM E1947 – 98(2009) Standard Specification for Analytical Data Interchange Protocol for Chromatographic Data, which uses the public domain netCDF data base


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structure, providing platform independence. Tis standard is supported in several vendor products but doesn’t see widespread use;


l SiLA Rapid Integration (www.sila-standard. org). Te website states: ‘Te SiLA consortium for Standardisation in Lab Automation develops and introduces new interface and data management standards, allowing rapid integration of lab automation systems. SiLA is a not-for-profit membership corporation with a global footprint and is open to institutions, corporations and individuals active in the life science lab automation industry. Leading system manufacturers, soſtware suppliers, system integrators and pharma/biotech


“In the 1980s, clinical lab managers recognised that the only way they were going to meet their financial objectives was to use automation to its fullest capability and drive integration within their systems”


corporations have joined the SiLA consortium and contribute in different technical work groups with their highly skilled experts’;


l Te Pistoia Alliance (www.pistoiaalliance. org) states: ‘Te Pistoia Alliance is a global, not-for-profit precompetitive alliance of life science companies, vendors, publishers and academic groups that aims to lower barriers to innovation by improving the interoperability of R&D business processes. We differ from standards groups because we bring together the key constituents to identify the root causes that lead to R&D inefficiencies and develop best practices and technology pilots to overcome common obstacles’;


l Te Allotrope Foundation (www.allotrope. org): ‘Te Allotrope Foundation is an international association of biotech and pharmaceutical companies building a common laboratory information framework (‘Framework’) for an interoperable means of generating, storing, retrieving, transmitting, analysing and archiving laboratory data and higher-level business objects such as study reports and regulatory submission files’; and


l Te AnIML markup language for analytical data (animl.sourceforge.net) is developing a standard specification under the ASTM (www. astm.org/DATABASE.CART/WORKITEMS/ WK23265.htm) that is designed to be widely


applicable to instrument data. Initial efforts are planned to result in implementations for chromatography and spectroscopy.


A concern with the second, third and fourth points above is that they are primarily aimed at the pharmaceutical and biotech industries. While vendors will want to court that market, the narrow focus may slow adoption since it could lead to the development of standards for different industries, increasing the implementation and support costs. Common issues across industries and applications lead to common solutions. Te issue of integrating instruments with


informatics soſtware is not lost on the vendors. Teir product suites offer connection capabilities for a number of instrument types to ease the work. Planning a laboratory’s information handling requirements should start from the most critical point, a LIMS for example, and then on to support additional technologies.


Chapter summary


Te transition from processing samples and experiment manually to the use of electronic systems to record data is a critical boundary. It moves from working with real things to their digital representation in binary formats. Everything else in the smart laboratory depends on the integrity and reliability of that transformation. Te planners of laboratory systems may never


have to program a data acquisition system, but they do have to understand how such systems function, and what the educated lab professional’s role is in their use. Such preparatory work will enable the planners to take full advantage of commercial products. One key to improving laboratory productivity


is to develop an automated process for sample preparation, introducing the sample into the instrument, making measurements, and then forwarding that data into systems for storage, management, and use. Understanding the elements and options for these systems is the basis for engineering systems that meet the needs of current and future laboratory work. Te development of the smart laboratory


is at a tipping point. As users become more aware of what is possible, their satisfaction with the status quo will diminish as they recognise the potential of better-designed and integrated systems. Realising that potential depends on the same elements that have been successful in manufacturing, computer graphics, electronics, and other fields: an underlying architecture for integration, based on communications and data encapsulation/interchange standards. n


www.scientific-computing.com/BASL2017


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