Managing Laboratories in the Informatics Age

Automation and Informatics are important to lab operations

Management's role in laboratory automation
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Management's role in laboratory automation
Until the middle years of the last century, laboratory work was a matter of human intellect and labor. The rapid development of electronics in those middle years led to the development of instruments and techniques that expanded the scope of lab work and, with the development of automated control systems, allowed scientists and technicians to do less manual labor and spend more time thinking. The trend over the last half-century or more has been toward more sophisticated data-intensive techniques with automated systems conducting data acquisition, analysis and reporting. The age of informatics has arrived in laboratories with a flood of numbers, reports, images and products for handling them. Managing laboratories in this age of informatics encompass not only science and scientists, but also systems that assist people in doing their work — work that has expanded to support the legal and regulatory requirements of modern research, development and testing.

Corporate efforts at integrating labs’ information structure into the overall corporate information architecture, as evidenced by the current trend of connecting laboratory information management systems (LIMS) and enterprise resource planning (ERP) systems, puts more emphasis on technology awareness and connectivity. The demands of meeting laboratory managerial responsibilities today depend on the successful and effective use of laboratory informatics. That dependency brings with it a need for laboratory management at all levels in order to carefully evaluate the decisions made in information technology investments. Given the cost, impact on people and productivity and the legal ramifications of choices in technology, product types and their implementation, managers need to be more deliberate and strategic in their thinking.

As our reliance on data stations, LIMS, electronic lab notebooks, robotics and microprocessor-controlled instruments increases, so does the level of effort needed in technology planning. As with most work, everything depends on preparation. This article will look at management’s role in the use of automation, computing and information technologies in laboratory work.

Why automation and informatics are important to lab operations
Recent surveys by the Association for Laboratory Automation1 show the following:

• 88 percent of those who responded in the 2008 survey saw their labs reliance on automation technologies increasing and the remaining 12 percent were holding steady at current levels
• In the same survey, workload increases, staff and budget reductions, the complexity of the science, and the need to streamline operations and add new capabilities were factors driving the need for automation.
• In the 2006 ALA survey, the reasons cited for an interest in lab automation were:
• increased productivity
• improvements in data quality
• reduction in operating costs
• safety improvements
• improvements in working conditions
These are the same factors that drive any production operation and, if you change “improvements in data quality” to “improvements in product quality,” they would be readily understood in any manufacturing operation. Laboratory automation and manufacturing are rarely thought of as two sides of the same coin. However, they have much in common, enough to consider the concept of scientific manufacturing as an aspect of laboratory automation and informatics. Scientific manufacturing can be defined as the application of process automation practices and techniques to laboratory work, including process modeling and optimization.2

Whether you are concerned with a laboratory or manufacturing, automation and informatics are proven responses to the need for productivity and cost reduction. Implementation in either environment depends on the same foundation: effective planning.

Management’s role in lab informatics and automation planning
The material that follows is appropriate for management of both new and existing laboratories, and those managing a single lab or large complex. Our approach is straightforward: setting goals and then developing a framework or guidelines that helps us to achieve them. This material is covered in depth in the ILA’s course work (

Setting goals
The products of laboratory work consist of three elements: knowledge (research and method development), information, and data (KID). The first of two goals are to preserve the quality, integrity and value of the labs products. Quality and integrity are easily understood — value includes both of those properties as well as your ability to take advantage of them. KID loses its value when it is difficult to locate because the lab’s work is poorly organized, or difficult to use because it is difficult to access (badly designed data bases, storage on inaccessible media). Not only does a lab have to produce good work, but that work has to be accessible to be of value to the organization as a basis for new development and to support the need to meet legal and regulatory requirements.

The second goal is to strive for the development of integrated systems. It’s assumed that integration is a good thing. However, why this is true isn’t often discussed, and knowing why and what benefits to expect may change our thinking about how we achieve it. In manufacturing, integration provides better control over production processes; steps of the process are efficiently linked so that materials move from one step to the next without human effort and cost. The process can be monitored easily and problems identified and addressed. The cost per product element is optimized and the process itself can be optimized as better equipment is developed. This thinking is precisely what is needed to meet the survey objectives noted earlier. It also will result in higher product value, since well-integrated systems should result in better organized and accessible KID.

Defining policies and practices
Improving your return on investment in lab automation depends in part on well-managed projects. Part of the Institute’s work is helping managers understand the policies and practices that, taken together, provide a solid basis for the definition and implementation of successful projects and programs. Using consistent development practices allows you to identify re-usable components and to avoid duplication and incompatible systems. The practices we address are:

• asset management
• process management
• validation
• change management
• security
• working with information technology groups
• project management
• data life-cycle management
• technology management

The implications of a shift from manual methods to automated systems are the same in the lab as they are in any production facility (including automated systems in research); the systems have to be maintained and shown that they are operating properly, and provision has to be made for equipment changes (upgrades, replacement) and process changes. The purpose of the policies and practices listed is to provide guidelines for meeting that need.

A model of lab operations
Not all labs operate the same way. Testing labs, collaborative research and individual research all have different methods of operation. The methodology used by the Institute for Laboratory Automation is designed to help managers develop models for the lab or labs under them and use those models:

• as a basis for product requirements and evaluation
• to review opportunities where automation could be used effectively
• to examine the KID information flow in the lab and with other labs and departments that could benefit from automated interchanges with a given facility

Reviewing project plans against noted goals and policies
Project and program reviews are essential to ensure that the practices we’ve described are being followed. This is particularly important if you are managing multiple labs: are people taking advantage of existing work, or is your budget being spent on different approaches to the same problem without clear justification? This point is at the core of test development, system development and testing that is outsourced. Once projects have been completed, you may not have access to the same developers for improvements and support. If documentation is lacking, or other guidelines are not being met, your ability to effectively use their work could be compromised.

Ensure compliance requirements are being met
Whether you are under ISO 9xxx, EPA, FDA or any other member of the regulatory alphabet soup, establishing compliance is part of management’s catalog of job requirements. The use of automation does make the work more complicated, but the establishment of the practices noted earlier and project reviews should make it easier to demonstrate compliance. They also will increase an inspector’s confidence in the lab’s approach to the development, implementation and validation of automated systems.

Migration planning
Most of the readers of this piece are likely working in labs that have some investment in automation and computing, but do these ideas only apply to new labs? They can apply to any lab at any stage in its development. The lab automation and computing environment you currently have isn’t the situation that will exist in the future. Things are going to change and those changes are going to be opportunities to re-evaluate what you need and where you are going. The first step is to ask yourself and those working with you a simple question: Knowing what we know now, if we had to do it over again, what would we change, how could we do it better? This is the point where you:

• Lay out a plan that describes the desired situation based on your knowledge of the lab and any expected changes.
• Draft a document that gives the benefits of the new automation and computing topography: how will the labs operations improve, any cost reductions, improved   productivity, why would anyone invest in the new structure?
• Create a plan for making the transition from the current situation to a new architecture.

The new architecture and the transition plan are going to change over time as changes in the lab take place; tweaking and adjustments based on new information and new perspectives are part of the game. With these items in hand, you have a basis for questioning vendors (existing and prospective) on their product directions and for telling them what changes you need – you drive the process. You also have a basis for evaluating new products and technologies coming into the lab and for tailoring existing ones: How will they help you get to where you want to be?

This article is not about the next step in the development of lab work. Rather, it is a major shift in the way lab work is conducted. The knowledge base of the lab has to be broadened from science and people management to include the design, implementation, support and the use of automation technologies not just on a work-cell basis but as part of a production system. Workflows will change. Jobs will change. Rather than being focused on the execution of manual procedures, it will be the improvement and trouble-shooting of automated systems that will guarantee the quality of results produced. More time can be spent on data interpretation and analysis, as well as new approaches to performing lab procedures including performance and cost optimization. We have to move from a fix-the-bottleneck methodology to the deliberate design of lab systems. One method will always have us looking to fix past problems, the other will improve the effectiveness of lab operations.

1. 2008 ALA Survey on Industrial Lab Automation – LabAutopedia, and the 2006 ALA Survey reported in the Journal of the Association for Laboratory Automation, August 2007
2. The term “Scientific Manufacturing” was first mentioned to the author by Alberto Correia of Cambridge Biomedical, Cambridge Massachusetts.
3. The purpose of the Institute for Laboratory Automation (a 501(c)3 non-profit organization) is to help people successfully apply the ideas outlined above to their laboratory operations.

Joe Liscouski is Director of the Institute for Laboratory Automation. He may be reached at