Data Mining at the San Francisco Heart Institute
The San Francisco Heart Institute, a non-profit division of Seton Medical Center and the larger
Catholic Healthcare West, provides and monitors specialized cardiac treatment for hundreds of
patients from around the world each year.
Critical Issue
Like most healthcare organizations today, the institute and hospital must meet a multi-pronged
challenge: strengthen financial health, improve quality of care and achieve accreditation from the
Joint Commission Accreditation of Healthcare Organizations (JCAHO). To accomplish these
things, the institute must:
 Improve patient outcomes from procedures and physician performance
 Decrease patient length of stay (LOS)
 Increase reimbursement from HMOs, insurance providers and Medicare
Solution
The institute has collected and, using data mining software, analyzed hundreds of data elements,
tracking physician performance and patient outcome of more than 20,000 patients and their physicians
since 1985. As a result, the institute helps the hospital save money, improve the quality of care,
and operate more efficiently.
More than ever before, the San Francisco Heart Institute must evaluate performance and target
areas for improvement. One reason is financial health - much of this non-profit organization’s
insurance reimbursement as well as JCAHO accreditation are tied directly to better performance. And the
second reason? Increased quality of care and efficiency depend upon it.
As a result, this division of Seton Medical Center and the larger Catholic Healthcare West is
vigilant about collecting data to evaluate patient risk, the effectiveness of procedures and medications, as
well as physician performance.
In fact, the institute has followed patients and collected data to help evaluate quality of care for
more than 20 years. This wealth of historical data helps analyze current performance against past, tracking
the outcome of changes in patient risk, procedure outcomes, and the effect of various medications. Today,
the institute collects between 500 and 1,000 pieces of medically related data, including patient history and
risk factors, laboratory values, cholesterol levels, outcomes, and cost of procedures, on each of the
hundreds of patients admitted each year to Seton Medical Center. A team led by Dr. Richard Shaw,
director of research and operations, tracks each patient for years following discharge from the hospital, and
uses data mining to analyze medical and behavioral data through traditional paper and phone surveys, as
well as more recently via the institute’s Web site and online surveys.
In addition to conducting about 15 ongoing patient follow-up studies, Shaw’s team examines
data on about 20 procedures each year. To begin, Shaw defines an area to research, such as patient
outcome, mortality, length of patient stay, or cost per outcome. The team - a bio-statistician, a physician, a
nurse, and a researcher with a medical records background - then uses data mining to analyze data from
various diagnostic groups, which include procedures like coronary stenting, cardiac bypass surgery, and
angioplasty. The institute also does physician profiling to review variations in performance, assessing how
length of stay, outcomes, and cost vary from physician to physician.
How does the analysis process work? Shaw, a bio-statistician trained at the University of
California San Francisco Medical School, begins by pulling sample data from the database. Using various
analytical procedures, the team generates results on topics such as patients’ length of stay, procedural
outcomes and mortality risk.
Shaw presents initial results to committees and internal cardiovascular task forces at Seton
Medical Center. After receiving feedback and ideas on new areas to examine, he returns and
mines the data more deeply. With final results in hand, Shaw’s team returns to administrators and
physicians. Based on these results, they decide how and where to implement change to improve
patient outcomes.
Shortened patients’ LOS, saved $500,000 per year
Reimbursement from Medicare and HMO is limited by each diagnosis, which also determines the
length of stay each will cover. Analyzing patient data enabled the hospital to reduce these patients’ length
of stay by one day, and therefore to increase its income by half a million dollars per year. In another
example, Shaw referred to his analysis of the coronary stenting procedure. By analyzing the factors
involved via data mining software, the hospital shortened the average stay from seven to three nights.
Also, doctors now place stents more accurately, and patients no longer require a drug that prevented blood
clots but also lengthened the recovery period.
Developed and shared online models to predict patient mortality risk
Applying data mining to 20 years’ worth of data has enabled the institute to develop risk
models - based on 17 readily available patient factors - that other physicians can use to determine a
patient’s mortality risk. This modeling also enables hospitals to more accurately compare themselves by
comparing risk-adjusted data on their patients. As a public service to increase the level of patient care
nationwide, the institute offers this risk modeling program free to physicians on its Web site.
Facilitated critical JCAHO accreditation
Data mining also helps maintain critical accreditation. Every three years, the hospital must receive
accreditation from JCAHO, an organization that examines factors such as mortality, morbidity, length of
stay, and ways the institutions are improving patient care processes. Using data mining to thoroughly
analyze data, Shaw’s team is able to help the medical center receive its JCAHO accreditation with relative
ease.