Israeli and American hospitals are using an algorithm to flag individuals at high risk for Covid-19 complications.

What’s new: Israel’s Maccabi Healthcare Services and U.S.-based Kaiser Permanente are using a model dubbed Covid Complications AlgoMarker to identify patients likely to be hospitalized, develop complications, or die from Covid-19. The developer, Medial EarlySign, is offering it for free to other health systems.

How it works: The model analyzes the electronic medical records of patients in a given health system. It assigns each one a score that indicates their level of risk based on demographics, hospital admission history, prescribed medications, whether they have respiratory and cardiac diseases, and other factors. If a high-scoring patient tests positive for Covid-19, physicians have early warning that they need to take extra care to prevent or manage complications.

Covid AlgoMarker is based on an earlier Medial EarlySign product that measures a person’s risk of developing flu complications.
The flu model was trained using 10 years of electronic medical records from 600,000 patients of Kaiser Permanente, Maccabi Healthcare Services, and the Texas Health Information Network. It was validated using 2 million records covering six years.
The developer tweaked the flu model’s parameters to align with research on risk factors for Covid-19 complications. The most important, an EarlySign spokesperson told The Batch, are a person’s age and sex: Covid-19 hits males and elders hardest.
The adapted model was verified on a dataset of 5,000 Covid-19 patients. It flagged those with the highest risk of developing Covid-19 complications with 87 percent accuracy.

Fast Track: The model identified about 40,000 members as high risk and put them on the fast track for testing. If they test positive, doctors will use their risk scores to help determine whether they should be hospitalized, quarantined, or sent home. EarlySign will continue to retrain the model as more data comes in.

Yes, but: Privacy laws like the EU’s General Data Protection Regulation make it difficult to roll out a system like this, which would work best if allowed to automatically scan a massive number of patients’ health records. Another obstacle: Many healthcare systems in the U.S. and elsewhere use older computer systems that don’t integrate well with newer systems.

Why it matters: With no end to the pandemic in sight, AI that helps hospitals triage patients efficiently can help save lives.

We’re thinking: Although the privacy, data aggregation, and data cleaning issues are formidable, systems like this might help us figure out who to allow back to work, who to keep at home, and who needs special care.

Medial EarlySign

Triage for Pandemic Patients

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