Stanford EMR pulls real-time data to assess infant risks

Here's yet another meaningful use (to steal a phrase) of EMRs: assessing the health risks of premature babies.

Stanford University researchers have developed an algorithm called PhysiScore that can predict risks of serious health complications in preemies with 98 percent accuracy, according to a study published in Science Translational Medicine this month. Researchers say their assessment tool updates and outperforms the Apgar score, the standard in assessing risk to newborn infants for more than half a century.

For PhysiScore, the Stanford team factored in gestational age and birth weight with a continuous stream of data normally collected in neonatal intensive-care units in the hours after birth to create a risk-assessment scoring system that was more accurate than Apgar and three other methods that require invasive laboratory work.

According to the research team, better neonatal risk assessment could help keep more pre-term babies at the site of their birth and out of NICUs, potentially avoiding complications related to transportation and helping to reduce the more than $26 billion spent annually on providing specialized care to preemies each year.

The paper's senior author, computer science professor Daphne Koller, notes some potential long-term benefits of the new risk assessment. "To achieve truly personalized medicine, we have to integrate an enormous amount of data: clinical symptoms, diagnostic test results, physiological data streams and, soon, genetic and genomic data," Koller says. "Computational methods derived from real patient records can deliver on the promise of personalized, evidence-based medicine."

For more information:

- view the Science Translational Medicine study abstract

- read this Stanford press release

- check out this Business Week article

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Do the math: Stanford docs could earn $44M in EMR incentives, while children's hospitals chase Medicaid dollars