It’s well known that medical and clinical laboratories collect vast amounts of patient data which holds the potential to unlock healthcare insights and improve outcomes. Given this reality, it’s long been the goal of lab stakeholders to better utilize the data they produce to play a more prominent and forward-facing role in the patient care process. However, a direct lack of consistent patient interaction can often reduce the visibility of the vital role of laboratories and the decisions they inform. 

Recent advances in Artificial Intelligence (AI) tools may present an opportunity for labs to extract more insights from their data, but the journey toward monetizing and realizing this potential is complex, to say the least. During a recent panel discussion, Lighthouse President Jon Harol and Dendi CEO Jihoon Baek discussed the nuances of why labs have historically struggled to extract direct value from their data and whether AI’s appetite for information presents new opportunities for overcoming this challenge. 

“Something like 2.6 percent of (overall) healthcare spend goes to labs, and we’re producing 75 percent of the data driving medical decisions,” noted Harol. “Is the data we produce just bound to get digested by others, or is AI going to be the thing that helps put (lab data) in a showcase?” 

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The Demand for Diagnostic Data and the Role of Laboratories 

 

Baek noted BioPharma companies and healthcare entities have a significant demand for diagnostic data, often purchasing it from external sources. This data is crucial for research, drug development, and treatment strategies, which could create an opening for labs to play a more direct role in providing these insights directly. 

“We know there’s a huge demand and that labs are the source of this data,” Baek said. “So theoretically, labs should be able to get some kind of value from their data, even though that traditionally hasn’t been the case.” 

  

Challenges and Opportunities for Laboratories in AI Utilization 

Baek estimated lab diagnostic data plays an even larger role in informing healthcare decisions than the figure cited by Harol, noting he believes the number is closer to 90 percent. Despite this, he listed two primary reasons why labs struggle to directly capitalize on the valuable insights they are providing. 

• Data Resellers and Aggregators: Data resellers often obtain data from electronic medical records (EMRs) rather than directly from laboratories. This aggregated data, including lab results, encounter data, and more, is then sold to entities in need, bypassing the laboratories themselves. 

• Value of Non-Structured Data: Baek said it’s important to remember there’s a difference between “structured” and “unstructured” data and that merely possessing numerical lab values may not be sufficient for monetization. The real value lies in non-structured data, such as imagery or qualitative information, which can be bundled with lab results to provide richer insights. 

This means numerical values likely won’t be the type of data that is monetized since AI isn’t necessary for simple formulas that have been run on spreadsheets for decades. 

“I talk to lab owners regularly that have a play where they are going to try to sell a bunch of lab values with decimal points and hoping somebody pays them for it,” Harol continued. “I don’t know if that paycheck is coming, so maybe start looking for other ways to package your data in a more valuable format.” 

Labs possess unique diagnostic information that EMRs may lack, such as assay-related or quality control (QC) data. Baek reiterated there’s an opportunity for laboratories to capitalize on this untapped potential, especially with advancements like genomic sequencing. 

  

Looking ahead 

The full panel discussion further underscored the possibilities for medical labs to strategically integrate their data with AI solutions to unlock further insights and value. While immense challenges exist to realizing this outcome and not being circumvented by 3rd-part data resellers, there are significant opportunities for labs to leverage their unique non-structured datasets for potential monetization and improved patient outcomes.  

By focusing on non-structured data, exploring emerging technologies like genomic sequencing, and considering innovative partnerships, laboratories can seek to leverage emerging AI tools to help position themselves as key players in the evolving healthcare landscape. 

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