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Challenges in the Digital Transformation of Laboratories

Digital transformation is a process that aims to improve an organization by implementing and practicing significant changes to its processes through combinations of information, computing, communication, and connecting technologies. Digital transformation only happens when the management recognizes the strategic importance of making profound organizational changes to the company that is customer-driven rather than technology-driven.

Digital transformation has proved to be a hard nut to crack in recent times. It’s become the downfall for many who have gone about it poorly, as evident in McKinsey’s research, which estimates that 7 in 10 digital transformation projects end up unsuccessful. The odds are seemingly stacked against laboratories. However, the remedy is to properly design a road map for the hurdles that lie ahead and tap into the right technological solutions.

This article summarises some of the biggest digital transformation challenges laboratories encounter, and ways to overcome them


1)  Lack of clearly defined process flows

It is very critical that the laboratory processes are clearly understood and documented by the stakeholders to have a better insight into the tasks, subtasks, and dependencies. With better insights, it is easy to optimize your current practices.

While it is generally believed by stakeholders that they know all that is happening in the laboratory, it often turns out that there are bottlenecks in the processes that they are not  aware of. The main challenge is to identify and better manage these pain points.

As a first step, it is highly recommended that a laboratory process map needs to be drawn. Then it is to be decided which processes are mapped, which activities consist of, what is their order, and how it starts and ends. To draw the most accurate process map, the end-users, supervisors, and managers must be involved. They will provide the most valuable information on actual processes and tasks happening in their work routine.

Once critical bottlenecks are identified we can derive solutions that solve them. The end goal should be increased efficiency and productivity of the laboratory.


2) Complex Data Handling requirements

Data (when reliable, accurate, consistent, and secure) is at the heart of successful digital transformation projects. But then again, therein lies the problem. Most labs today are still reliant on inefficient and poor data management systems. These are typically paper-based and overall compromise the integrity of the data, affecting data visibility and generally ensuring bottlenecks (and even opportunities for process improvement) remain buried.

Laboratories often generate a huge volume of data that needs to be validated and managed correctly in order to provide added value. In order to do so, we need to consider what has already been digitalized.

In case we start from scratch, it is suggested that we need to consider how we can transform data from analog to digital format. There are many software tools that can be considered and will help to document, store, analyze, share and manage the experimental data in a digital form.

Once there is a central data repository, such as ELN or LIMS, we may proceed to consider the integration of laboratory devices into one seamlessly connected network. This can be done by using APIs or middleware solutions that handle the communication between devices and the central data repository. This level of digitalization has three direct effects:


  • Prevents errors during the manual data transfer
  • Reduces time required for manual data transfer and verifications of data transfers
  • Helps maintain data integrity

A reliable electronic lab notebook or laboratory information management system is a great option, to begin with. This can help improve data quality as well as elevate throughput by taking over iterative data management tasks through automation.

Some laboratories may consider connecting their central laboratory software with other third-party software, such as Enterprise Resource Planning systems (ERPs), production systems, or data analytics software. It is highly recommended to anticipate and provision for at least some of the integrations so that the right laboratory information management system can be selected, which can support third-party integrations in the first place.

3) Adopting Regulatory Compliance and Privacy

Many industries have their labs and they adapted to digital form as early as the 70s and 80s. The pharmaceutical industry was one of the early adopters of software in their processes. The software was first mostly used by their QA departments, where human error could have a significant financial consequence. Digitalization brought great results and became a gold standard in the industry. That was followed by a regulatory framework that strives to provide a legal framework that ensures software quality.

There are essential regulations now that apply to digital transformation in many industries. One of the most widely used is for sure Title 21 CFR Part 11. It is the US Government’s FDA regulation on electronic records and electronic signatures that applies to pharmaceuticals, medical device manufacturers, biotech companies, and many others. This regulation sets the criteria for trustworthy and reliable electronic records and electronic signatures.  It also defines the standard practice to be followed by which electronic records are equivalent to paper records.

Pharmaceutical players and laboratories in other industry verticals as well also have to think about regulatory loopholes as they embark on their digital transformation journeys.

Therefore, laboratories may want to pay great attention to technologies that come with ready features to meet this guideline by the FDA among many other widespread guidelines, such as:

  • GDPR
  • EudraLex Annex 11
  • ISO 17025

To avoid losing gains to digital transformation initiatives and having to reverse implementation later on, laboratories of the modern age need to tap into technologies that are built from the ground up (preferably without requiring third-party intervention or tools) to meet all these standards.


4)  Lack of new technology expertise

New technology often raises concerns, particularly about the learning curve, which can make staff feel intimidated. To counteract this, it’s important that laboratories turn to technologies with intuitive solutions, something that has a soft learning curve and can build off experience from a legacy system already in use. If the solution requires some training then the lab personnel need to be trained to use the same for their existing workflow.

Let’s demonstrate this with an example. For instance, Logilab ELN offers an Excel-like UI. This is particularly ideal because workflows typically have experience with spreadsheets hence the transition becomes seamless. Moreover, this technology also comes with training sessions to get every member of the team with the