There is an on-off debate about the benefits of artificial intelligence to the decision-making capabilities of medical professionals. A new study from Mount Sinai leans further towards the beneficial zone by suggesting real-time alerts for declining health help to speed up treatment and reduce hospital deaths.
By deploying and evaluating a machine learning intervention to improve clinical care and patient outcomes, researchers can develop a key step in moving clinical deterioration models from byte to bedside.
The study found that hospitalized patients were 43 percent more likely to have their care escalated and significantly less likely to die if their care team received AI-generated alerts signalling adverse changes in their health.
Traditionally medical personnel have relied on older manual methods such as the Modified Early Warning Score (MEWS) to predict clinical deterioration. The new study shows that automated machine learning algorithm scores that trigger evaluation by the provider can outperform these earlier methods in accurately predicting this decline.
To demonstrate this, a non-randomized, prospective study looked at 2,740 adult patients who were admitted to four medical-surgical units at The Mount Sinai Hospital in New York. The patients were split into two groups: one that received real-time alerts based on the predicted likelihood of deterioration, sent directly to their nurses and physicians or a “rapid response team” of intensive care physicians, and another group where alerts were created but not sent.
In the units where the alerts were suppressed, patients who met standard deterioration criteria received urgent interventions from the rapid response team.
Data from the intervention group also demonstrated that patients were more likely to get medications to support the heart and circulation, indicating that doctors were taking early action; and were less likely to die within 30 days/
Hence, the researchers concluded that real-time alerts using machine learning can substantially improve patient outcomes. This indicates that ‘augmented intelligence’ tools can speed in-person clinical evaluations by physicians and nurses.
The research appears in the journal Critical Care Medicine. The research is titled “Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.”
In addition to the clinical deterioration algorithm, the researchers have developed and deployed 15 additional AI-based clinical decision support tools throughout the Mount Sinai Health System.
