Shaping the Labs of the Future

Eric Dingfelder is senior vice president, Clinisys Laboratory Solutions, heading the global LIMS/LIS strategy for Clinisys, ensuring the products meet today’s challenges and evolve to advance laboratory technology for the future.

Q: Please tell us about your career journey and how it has influenced your work in the LIS/LIMS industry. 

A: For starters, I’m not born out of a lab, and have a computer science degree. My first job and the first 10+ years of my career was in consulting, primarily focusing on business intelligence and reporting on the data coming out of applications. I spent those years going around the country and working for various companies including Bank of America, Wells Fargo, First Union, Siemens, and Sony, to name a few. About 18+ years ago, I ended up at the LIMS company which used to be called Chemware, which later got rebranded into Horizon, and was eventually acquired by Clinisys two years ago. At Clinisys, we have been working on a laboratory informatics software that supports testing across healthcare, life sciences, public health, and safety. With the first release of Clinisys Laboratory Solution in July 2023, we were able to offer discipline-specific solutions unified by Clinisys Platform™ SaaS architecture and data model, the first four verticals being Environmental, Water, Toxicology, and Public Health. I see environmental and clinical toxicology spaces as commercial whereas water labs tend to be government municipalities. The interesting take in my approach, given my computer science background, is that when I enter a lab, I always look at things from a technical and data-oriented perspective which leans back toward business intelligence. Between playing in these market spaces, we have grown well-acquainted with the public health space and have become one of the few systems that can cater to environmental and clinical labs. 

Q: How are LIS and LIMS distinct in their utilization and the workflows they present? 

A: While there are some regional variations in how LIS and LIMS are defined, for the most part, LIS incorporates healthcare hospital-based systems whereas LIMS generally concerns systems outside of the hospital. Between the two, the workflows are quite similar once you get into the lab. The distinction is at the entry point, where samples are obtained, and the returning point, where samples have been processed and relevant results are acquired. LIS systems, being hospital-based, are very focused on looking at a patient as a whole, and their history, and fully encompass everything about patient care. LIMS is more of a reference lab. Here, samples can arrive from multiple sources. A LIMS lab does not track this information and rather focuses primarily on the processing of samples and the extraction of accurate data, relevant to the case, that could then be returned to its source as quickly as possible. 

Q: What does a convergence of LIS and LIMS, within the Clinisys platform, offer? 

A: It can be described as a transactional lab system where the architecture, at a design level, may be similar to LIS/LIMS but is distinct in its ability to extract relevant sample data and aggregates that can then be visualized through other systems that run the lab as a whole. Labs are getting smarter and operate at a high level, but it is the nomenclature and lower-level details that make them unique. A clinical lab can provide a patient’s history to a doctor but is now expanding said services beyond the primary source and toward other doctor’s offices and hospitals. This provides the patient the unique opportunity to have full access to their data, wherever they may seek medical aid. Clinisys provides a frame above the general LIS-LIMS and serves as an advanced analytics, lab insights, and data platform. This allows users to see more going forward and aggregate data that may not be accessible through LIS/LIMS, and present them not just to the lab but also to the patient and other clients. The Clinisys platform delivers these results in a manner that is natural and familiar to its users, be it they are from a high-volume reference lab, or a low-volume pathology lab, and caters to experts and laboratory specialists in various industries. By presenting the data in the laboratory’s natural language, providing workflows that are configurable and natural to the laboratory, and having the system be data centric, Clinisys allows the laboratory to enter, confirm, evaluate, and manage its data easily. 

Q: What is the architecture and data model behind the Clinisys platform? 

A: The Clinisys platform is a cloud-based platform. Over the years, we have seen many labs grow to adopt our platform starting with toxicology labs, followed by public health labs. In both cases, we found that a major reason for this is that these labs didn’t want to have any overhead in dealing with IT issues or managing software servers. The Clinisys platform was created on Azure and our architecture is very streamlined and secure. Being on the web, the system does not require a VPN tunnel and is accessible from your phone, home, or tablet. Secured with multi-factor authentication, our platform’s architecture is very light and makes it easy for laboratories to unlock data streams that are natural to them, without the worry of having to install a new application or training new users. As an all-in-one system, Clinisys manages all of the lab’s actual samples while also enabling easy transfer between other laboratories and incoming and outgoing users. With a web-based, secure architecture that is on the cloud, Clinisys allows users to enjoy new features as we add them to the environment while optimizing laboratory workflows. Clinisys’ platform architecture also gives labs the flexibility to move into new sectors without having to start again with a new system, simply by adding the relevant “modules” for a successful transition. 

Q: Moving from traditional sample- and patient-centric workflows, what does Clinisys advocate for labs of the future?

A: Clinisys advocates for a data-centric approach to laboratory workflows. Be it a specimen or slide, we recognize them as data elements. By adopting this approach, we can begin making correlations between data elements hosted on a platform that can house all the relevant data, perform different tests, and garner insights that could help communities become better. For example, wastewater laboratories can now test for COVID outbreaks. This can be used to verify clinical data and potentially prevent further outbreaks or expedite shipments of COVID medicines to treat patients. It is undeniable that our ecosystem is a connective web. From the air we breathe to the food we eat, and the water we drink, everything has an influence. Clinisys builds on this idea to help house incoming data on its cloud-based architecture while also helping users draw connections between data elements and identify significant correlations. Emergent and existing technologies including artificial intelligence (AI) and machine learning are very effective for these purposes. AI, in a laboratory setting, not only helps with the processing and testing of large sets of data, and results analysis but also helps optimize instrumentation use and improve laboratory efficiencies. Combined with LIMS/LIS and cloud-based platforms like Clinisys, these technologies can then set the pace for researchers to branch out in what they can do in a lab as well as arm them with the ability to make correlations between data elements as a means toward obtaining greater insights. 

Q: How does the Clinisys platform cater its products and services to specific laboratories? 

A: Labs are very regimented. We are making it such that the LIMS/LIS presents the data and their workflows in their natural language. By observing and working with various industries, we have been able to pick out effective and efficient characteristics of their workflows and put them together on a platform that caters to everyone. Such a setup makes it easier to automate processes in a laboratory, present data to labs, and provide results in a meaningful way to the end user. This, in turn, makes the actual LIS/LIMS system completely data-centric so that almost anywhere a user looks at the system, they can efficiently input, extract, and aggregate data.