The news: Johns Hopkins spinoff Bayesian Health has $15 million in VC backing to offer an AI-based clinical decision support platform designed to make health systems’ electronic health records (EHRs) more predictive.
Here’s how it works: The system analyzes EHR-collected patient data and sends providers real-time clinical signals when a critical moment is detected. It targets high-priority areas such as clinical deterioration, sepsis, pressure injury, and transitions of care.
Results: One of the first studies released on Bayesian's sepsis module screened 500,000 patients over 3 years across 5 hospitals, with upward of 2,000 providers using it.
How we got here: Providers have been hesitant to fully embrace AI for high-stakes clinical decisions due to lack of trust.
AI is typically seen as a “black box” in healthcare and clinicians often won’t trust the risk scores generated by the tech without being informed of the contributing reasons for the risk.
It’s challenging to distinguish sepsis in its early stages—and while developers have integrated early warning systems into providers’ EHR systems, the effectiveness of these predictive tools has been criticized.
Trendspotting: There’s no shortage of AI/machine-learning startups in healthcare, but clinical adoption of the tools they’re producing has been slow. It’s currently easier to apply AI to an administrative process—like medical coding for billing and reimbursement purposes, for example.
For many clinicians, it still comes back to who's teaching the AI—and there's bias in the humans who design the systems, as well as the underlying data sets on which they’re trained.
This is why we could see health systems lean more on their in-house AI models for the time being—or tech spun out from respected health systems like Hopkins.