By Jason Bhan, MD, Co-Founder, Chief Medical Officer at Prognos bio
Laboratories have traditionally directed their information technology (IT) focus to support their main business of testing. The IT infrastructure they have invested in allows them to address key business issues such as quality, turnaround times, and reimbursement.
Laboratories also excel at logistics, clinical operations, and point-to-point connections on the IT front. This focus on the operational aspects makes sense for laboratories, as many operate on single-digit margins and face pressure to maintain profitability.
Despite having significant amounts of data playing a vital role in clinical decision-making, most laboratories (with a few notable exceptions) have neither invested in data strategy nor leveraged their individual data assets. Meanwhile, the rest of the healthcare industry has made great strides in exploring the potential and harnessing the power of big data, advanced analytics, and Artificial Intelligence (AI) to grow business and improve patient outcomes.
Welcoming Healthcare AI in the Lab
Fortunately, the situation has recently begun to change. Several leading laboratories such as Quest Diagnostics and LabCorp now recognize the value of their data and have partnered with AI experts to leverage it and grow their businesses. Other laboratories are taking notice of this aggregated AI data strategy, which can have an immediate impact.
Diagnostic data is critical in the early detection of disease, and yet, most decisions made around population health management or patient care are based on medical claims or prescription information. Both are transactional in nature and both only indicate decisions already made. When combined with advanced analytics and AI, diagnostic information can be used to identify or predict events well before the claim or prescription. This means that laboratories can put their data to work to help various healthcare stakeholders make better treatment decisions, earlier.
Multiple Beneficiaries of Lab Data Insights
Physicians, patients, therapy developers, and health plans all can benefit from diagnostic data insights.
Payers require more clinical specificity to effectively identify and manage patients. The timely and clinically insightful data of laboratory test results can predict patients who need attention. Such early detection enables payers to better design interventions to improve patient outcomes and lower cost of care.
The benefits to patients and healthcare research communities include the ability to better understand disease patterns and positively impact people’s lives. By having large clinical datasets that can longitudinally track patients across different laboratories, payers and providers will be able to better understand health outcomes and identify areas for improvement.
Labs Cannot Go It Alone
When it comes to gaining actionable insights from diagnostic data using healthcare AI, laboratories cannot go it alone. Among the approximately 5,000 community hospital, reference, and academic laboratories in the U.S., no single laboratory has the necessary amount of data to provide meaningful insights using AI. Actionable insights are possible only when large amounts of data are available, aggregated from hundreds of laboratories, then analyzed to identify patterns that can be used to predict risk or outcomes.
Healthcare AI companies use techniques such as machine learning and natural language processing, coupled with massive computational power, on these big data sets to make sense out of reams of non-standard, complex, and heterogeneous data.
Collaborating with other healthcare solution providers eliminates the need for laboratories to invest into building up the informatics infrastructure necessary to capture value from their own data. In order for a laboratory to establish a footprint in the AI space, it would need to invest in hiring expert staff, invest in new technology, and acquire a broader set of data. These are things not within the core capabilities of the typical lab. Laboratories can also benefit financially from sharing their data as well as gain a definitive sense of the markets that they serve and their market share.
While AI is no silver bullet for laboratory industry challenges, it has great potential to highlight the tremendous value of lab testing in the care of patients. Laboratory data is most valuable when it is integrated into larger clinical datasets, which enable AI to work and deliver impactful insights. Healthcare AI will further understanding of clinical patterns, highlight treatment opportunities, and help predict disease earlier.
Jason Bhan, MD, is Co-Founder and Chief Medical Officer at Prognos, www.prognos.ai, an innovative healthcare AI company. He is an expert in the applications of technology to healthcare and medicine. Dr. Bhan obtained his medical degree at the University of Miami School of Medicine.