New statistical approach could improve hospital care for sick newborns

The movement to computerize patient records in a growing number of hospitals is paving the way for the use of sophisticated statistical methods to assist doctors' decision making. The National Institutes of Health has provided $1.35 million to a team of researchers working to develop new statistical approaches that could dramatically improve the care for severely ill newborn babies. These new methods could eventually be applied to all patients, radically changing the face of hospital care, the researchers said.

"We are at the forefront of a new wave in medicine," said David Draper, professor of applied mathematics and statistics at the University of California, Santa Cruz.

Draper is providing statistical expertise for the project, which is led by Dr. Gabriel Escobar, a pediatrician at Kaiser Permanente Medical Center in Walnut Creek and director of the Perinatal Research Unit at Kaiser Permanente's Division of Research in Oakland.

The researchers will develop methods that can be incorporated into automated medical records to monitor very ill and critically ill newborns. Their hope is that these methods will eventually take advantage of Kaiser Permanente's decision to deploy an automated medical record system, Escobar said. Electronic records will make it possible to perform instant computer analyses of an individual patient's medical data, while also giving researchers access to an entire database of medical records for similar patients.

In particular, Escobar's team wants to improve treatment strategies for newborn babies potentially at risk for infection. Their approach would permit a bedside computer to estimate a newborn's risk by combining the baby's data with information from previous newborn patients. Based on the baby's current condition and how other babies fared in the past, computer algorithms could provide physicians with accurate estimates of the probabilities of a variety of outcomes, including death or the need for highly invasive treatments. Doctors could provide early intervention if the analysis indicated a high probability of an adverse outcome.

Researchers envision that in the future such algorithms could even suggest treatment options to doctors. Draper, who has been working on statistical approaches to medicine for 20 years, said this is "by far the most exciting development that's come along in quite awhile for making optimal use of medical information to improve clinical care."

Draper and one of his former Ph.D. students, Milovan Krnjajic (now at Lawrence Livermore National Laboratory), are working on the statistical tools and algorithms that drive the entire project. Their methods use Bayesian statistics, an approach Draper predicts will become "the dominant statistical paradigm for the 21st century."

The Bayesian approach provides a rigorous mathematical framework for combining new information with existing knowledge to assess a given situation. These tools will enable the system to calculate a likely prognosis for a newborn based on the past history for that baby and the medical histories of other babies. Armed with the experience of an entire database of case histories, doctors will be able to make better-informed decisions.