From the UC Santa Cruz website: http://press.ucsc.edu
SANTA CRUZ, CA–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
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.
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.
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.
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.
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."
members of the research team are coprincipal investigator Dr. Thomas B. Newman
of UCSF, and Dr. Ellice Lieberman, Dr. John Zupancic, and Dr. Karen Puopolo of Harvard University. The project is funded by a three-year
grant from the National Institute of General Medical Sciences.
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."
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.
to Escobar, the statistical methodology proposed by Draper was a key factor in
enabling them to win funding for this project.
with him has had a tremendous impact not only on this project, but on the
entire way I approach my research," Escobar said.
methods, like the Apgar score developed in 1952, have revolutionized newborn
care. But there are still major shortcomings, Draper said. Existing approaches
to quantitative outcome prediction are limited in that they use only a fraction
of the available information and only for fixed time durations (usually either
12 or 24 hours). Generally, current algorithms examine a few variables in the
time frame, select the worst result for each variable, and generate a single probability
estimate. This severely limits their use in actual clinical situations, which
constantly evolve, he said.
sick babies die or become critically ill during the first 12 to 24 hours after
birth," Draper said. "It would be far better to use information as it
of Bayesian statistical tools lies in their ability to readily take into
account changing conditions. For example, current approaches only incorporate
one value of heart rate and ignore whether it is rising, falling, or staying
the same. The new methods will be dynamic, accounting for changes over time in
variables such as heart rate or respiratory rate as they produce a statistical
gauge of a baby’s health.
in an electronic medical record, these methods could help a doctor decide
whether to take certain risks when treating a severely ill baby. According to
Escobar, only half of the hospitals in the United States are equipped to care
for a severely ill newborn. Transporting the baby to another hospital is not
only expensive, but it also poses inherent risks. On the other hand, failure to
move a baby that needs specialized care could lead to brain damage or death.
the doctor drop everything and get the baby out of there, or can he or she
assume things are going well? That’s what we need to know," Escobar said.
statistical approaches will give physicians important information in making
such critical decisions, and, as a result, could save lives, he said.
methods can also prevent unnecessary and painful tests by giving a doctor a
more accurate description of how sick the baby is and whether tests are really
needed, Escobar said. Another goal of the project is to help scientists see how
well common screening tests–such as counting white blood cells–work, he said.
While the project is aimed at treating newborns, these methods can be applied
toward all patients.
beauty of this project is that the approach we’re going to be using could be
generalized to other conditions," Escobar said.
will spend the first several years of the study period collecting and analyzing
paper and electronic data from 340,000 newborns in 14 hospitals in northern California and Boston.
They expect their statistical methods will be up and running in three to five
years and could be embedded in automated hospital records in five to 10 years. Such
systems will improve over time as the size of the database increases,
generating more accurate estimates based on growing amounts of properly
analyzed data, Draper said.
these methods is challenging, but it is likely that more and more hospitals
will employ them in the future," Escobar said. "It used to seem like
science fiction, but I don’t think it is anymore."
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