Early in your career, what prompted your transition from laboratory manager to a manager of systems integration?
From the very beginning of my career, I knew I wanted to work in the healthcare industry. I had a passion for helping patients, and I enjoyed the scientific aspect of work in the clinical laboratory.
However, there was one major challenge I had to face to make that dream a reality. To succeed in the laboratory in Germany, you needed a medical degree, which I did not have. Therefore, it was a natural progression for me to move from the hospital environment into an operational role within the healthcare industry. This career move allowed me to use the skills and education I already had, while still enabling me to make a difference in patients’ lives.
After nearly 15 years in R&D in chemistry, how easy was it to move into IT, informatics and automation?
The world of clinical informatics is very different from the analyzer business. With informatics, the focus is on harnessing the data, discovering insights to support patient care and elevating institutional performance. Making the change and immersing myself in this field has been a very rewarding challenge.
The experience I have had designing analyzers, and my deep understanding of the laboratory’s needs—especially from an analytical point of view—gave me a good, solid base from which to dive into the world of informatics and automation. This solid base of understanding, combined with the awareness of what data is critical from a patient, physician and laboratory manager perspective, really helped ease the transition for me.
How did your experience in R&D prepare you for your role in design automation systems for clinical laboratories?
Knowing the value of in vitro diagnostics for laboratory personnel has been critical for me as I moved into the world of laboratory automation. Because of my R&D experience, I also began to see the links between what happens outside of the laboratory and what happens inside of the laboratory more clearly. That’s because automation can be used to solve so many problems, so it makes sense to think broadly.
For example, the steps that are involved in the pre- and post-analytical preparation of samples are so different from the analytical part of the job. Many factors that occur during the pre-analytical stage, outside the laboratory, can affect the quality of the result within the laboratory. Knowing the details and critical pitfalls of the analytical part helped me to work on the automation that could potentially resolve or mitigate those pre-analytical issues.
Relationships are important, too. In addition to my experience, my personal network within the company—and my connections in the analytical world—have been very helpful when making this move.
What aspects of designing automation and IT systems are you most involved?
As the general manager of the automation and informatics group, I am involved in all areas of the design, ranging from the early ideation stage, all the way through to the launch and post-launch activities. However, my primary focus is always at the beginning of a project to make sure that we understand the needs of our customers and address their main pain points. I also enjoy visiting customers around the world, seeing firsthand how laboratories are evolving and working to find new opportunities to advance patient care, starting in the laboratory. I consider this one of the most important responsibilities of my role.
What role will advanced analytics and AI play in the future to help lab managers improve the timeliness and accuracy of test results?
Advanced analytics will, I believe, be a key driver of laboratory success going forward. Now more than ever, the laboratory can do so much more than just churn out fast test results at scale (although this is important, too.) Laboratories have a tremendous opportunity to deliver value throughout the patient care continuum, with insights that can drive preventative measures, optimal care and improved cost control.
We see this already in several areas, such as sepsis detection and acute myocardial infarction. In both cases, early detection is critical for the success of therapy. I am excited by the promise of novel sepsis biomarkers, such as monocyte distribution width, which can give clinicians early insight into the possibility of sepsis in the emergency department, with a routine complete blood count with differential test. True high-sensitivity troponin assays can now help clinicians rule in or rule out a severe cardiac issue in just 60 minutes.
I also firmly believe that artificial intelligence (AI) will help laboratory managers and clinicians make better decisions for their patients. One key area of artificial intelligence that may play an important role is in the field of oncology. Here, artificial intelligence could be used to optimize the treatment of cancer patients, based on laboratory data combined with visual data from various scans.
