Data as the language of medicine
Clinical surveillance is powered by data. When data is coupled with evidence-based clinical decision support, a single source of truth can connect the associated symptoms that come with the disease, helping to surface how fast a disease is spreading and what the lab tests show.
Keeping pace with the latest changes to medical terminology and the associated diagnosis and procedure codes not only helps secure funding and reimbursement but also is essential to identifying clinical patterns.
COVID-19 fast tracked AI advancements
While normally slow to adopt technology, many health systems have transitioned to figuring out the patterns in COVID-19 and better predicting respiratory and organ failures associated with the virus. And since COVID-19 puts people at risk of developing sepsis, they have also needed to flag those most at risk. It was trial-by-fire with many fast-tracking tools powered by artificial intelligence (AI). This health crisis provides a sense of what may be possible to predict and prevent a range of chronic health concerns. This technology can then save lives and dollars for conditions that have proved resistant to prevention.
Achieving those savings depends on: 1) refining the use of AI for clinical surveillance; 2) expanding access to everything from electronic health records (EHR) to information that lives outside of direct clinical settings, from the omics through the social determinants of health; and 3) distinguishing AI hype from solutions that deliver proven, actionable insights for specific clinical concerns.
Looking back with a vision for the future
While COVID-19 seemed to be a testing ground for machine learning and AI, the industry had been working for some time to harness technology’s power for healthcare-associated infections (HAI). Publicly reported data shows HAIs cost the US healthcare system up to $45 billion annually2 and on any given day, about one in 31 patients will have at least one HAI3. C. difficile infections (C. diff) is one such example. C. diff increases mortality risk and length of stay for inpatients and puts hospitals at risk for financial penalties. However, machine learning can predict patients who are at risk for C. diff infection and, therefore, empower clinicians to respond more quickly to treat patients and prevent spread in the hospitals.
Machine learning analyzes hundreds of thousands of factors that could contribute to C. diff, as well as how those factors might interact. It continuously learns and incorporates new data.
When used in a clinical surveillance system, machine learning can highlight at-risk patients before their infection progresses — introducing variables that far too often clinicians find difficult to spot while juggling their multiple patients — as well as conditions that fall outside of what they typically see.
As researchers know well, rules-based systems are less efficient for these “edge” cases because each new data element necessitates a new rule. Complex cases such as COVID-19, C. diff, and even sepsis can benefit from AI at warp speed to help hospitals and communities act before clusters, outbreaks, or critical medical emergencies escalate.
AI-based clinical surveillance can uniquely track when relevant factors occur and understand how timing figures into interactions. Incorporating time is complicated, but knowing when there has been an increase or decrease in the white blood cell count, for example, is critical to making accurate C. diff predictions.
