Academy guidance on data analytics: Preparing clinical labs for a data-driven future

How does someone interested in making their clinical laboratory more data-friendly take the next step? What’s the best way to get involved in a program that’s already started but may be floundering? 

A new guidance document from the Academy of Diagnostics & Laboratory Medicine was created to answer these questions, and a scientific session at ADLM 2026 on July 28 will highlight the key takeaways.

The document, which is currently in the public-comment phase, seeks to help clinical laboratorians figure out how to use its data in a meaningful way, wherever they may be in their data-analytics journey, said Thomas Durant, MD, associate professor of laboratory medicine and biomedical informatics and data science at the Yale School of Medicine, and corresponding author of the Academy Guidance Document. It offers a high-level overview of the essential components required to establish robust data-analytics programs within institutions.

Durant will also present at the ADLM 2026 session, along with Patrick Mathias, MD, PhD, associate professor and associate medical director of the informatics division in the department of laboratory medicine and pathology at the University of Washington School of Medicine in Seattle, Washington; and Sarah Wheeler, PhD, FADLM, CC(NRCC), chair of ADLM’s Data Analytics Steering Committee and associate professor of pathology at the University of Pittsburg, who is also a coauthor of the document.

“Data analytics is one of the things almost all institutions are dealing with now in some way, shape, or form,” Durant said.

“[The Academy Guidance Document addresses] all the big questions that you’re probably going to want to answer when embarking on a data-analytics journey,” Durant added. It’s designed to help people and institutions at different stages on that path, whether they’re still doing work on paper and Excel, or if their institution already has an analytics infrastructure in place.

In their presentation, Durant and team will tackle three main topics:

1.Why to build a data analytics program.

2. What resources are required, including available infrastructure tools and governance policies.

3. How to operate programs with good practice principles, with ethics and patient safety front of mind.

The presenters will make recommendations that will help laboratorians enhance their analytical capabilities and fully leverage their laboratory’s data assets, whether through independent departmental initiatives or collaborative partnerships with their organization’s leadership.

The session will offer something for everyone, Durant said. Participants don’t need to have a background in coding or computer science to attend. In fact, he hopes those who are new to data analytics will come to the session so they can see how accessible today’s tools are for people at all experience levels. “At my institution, some people who use data most in their everyday lives can’t code a lick, but they know their data,” he said. 

Like it or not, institutional change is coming. “It’s really about having someone who’s an analytics champion in the lab,” Durant said. Knowing how to talk to people who can build that architecture can help you communicate your vision, because successful programs require coordinating the people who know how to make that happen.

Overall, Durant hopes this session will help people realize that data analytics is foundational to the future of healthcare. As much as machine learning and artificial intelligence (AI) are hyped as revolutionary technologies, they cannot be implemented without the right data infrastructure in place. “AI is the Ferrari engine, but you have to design this whole fancy car around it to take advantage of it,” he said. The Academy’s new document and the Annual Meeting session will provide that vital framework.