Download Medicare Data and Word Mining to Predicitve Modeling

Major innovations
Six major innovations, sometimes inspired by government, sometimes undertaken
independently or in concert with the private sector, are driving health reform: data mining
reform, consumer-driven care, pay-for-performance initiatives, national electronic
infrastructure building, state-by-state reform experimentation, and “disruptive simplification”
innovations at the practice management level. Data mining is the most important and
sweeping innovation, because it gives us the tools to restructure and rebuild the existing
system based on irrefutable and impersonal data. According to Webopedia, the computer
technology dictionary, data mining may be defined as “the class of database applications that
look for hidden patterns in a group of data that can be used to predict future behavior. For
example, data mining software can help retail companies find customers with common
interests. The term is commonly misused to describe software that presents data in new ways.
True data mining software doesn't just change the presentation, but actually discovers
previously unknown relationships among the data.”
Four areas of data mining are transforming healthcare:

Medicare data mining
This form of data mining is not new, but it remains an inexhaustible innovation source
because of its size. John Wennberg and Alan Gettlesohn first explored the Medicare
Mine in 1973 when they published their classic findings on how medical care varied
from one region of the country to the other. Ever since, Medicare data has been
considered the sine qua non for studying and judging health costs and outcomes.
Wennberg considers medical service variation across regions and academic center as
“unwarranted.” The variation data, he concludes, does not correlate with better
outcomes data. He has proven beyond statistical doubt that “more is not better.”
Employers and health plans are aware Medicare data is a treasure trove for data
miners wishing to improve quality and outcomes and to pay hospitals and doctors for
performance, which is why the Business Round Table and others are pressuring the
Bush administration to release all Medicare claims data.

Pharmaceutical data mining
I was present in Minneapolis in the 1970s at the creation of the UnitedHealthcare
Group. Perhaps that is why I maintain that pharmaceutical data mining, outside of the
billion- dollar leadership of William McGuire, M.D., is what made UnitedHealthcare
what it is today. It isn’t generally recognized that 75 percent of United’s profits come
from outside the traditional HMO business. In 2005, I spoke with Brian Gould, M.D., a
former senior executive for United. “In early 1990, I moved to Minneapolis. I was in
charge of United’s Specialty Operations Division--all the non-HMO businesses. These
included a pioneering pharmaceutical benefit company, Diversified Pharmaceutical
Services. In 1993, we sold DPM to Smith Kline Beecham for an astonishing price of
$2.3 billion,” he said. Under the terms of agreement, United HealthCare agreed to
provide Smith Kline Beecham “with access to medical data and outcomes analysis.”
This meant access to United’s pharmaceutical data mining operation data. For
example, if United had pharmaceutical claims data indicating who was taking insulin,
Smith Kline could use that data to study a huge population of diabetics.
United has not abandoned pharmaceutical data mining. Its Ingenix division provides
clinic research services, medical education services, and therapeutic outcomes and
epidemiology research data to pharmaceutical companies, biotechnology companies
and medical device manufacturers.

Printed word data mining
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Google is so powerful, it has become a verb. One no longer looks up information in
medical libraries, one “googles” medical information. Google, I would argue, is turning
the medical world upside-down. Medical journals, for example, are struggling to
survive because of drops in advertising and readership. Moreover, Google has leveled
the information playing field between doctors and patients. The late Tom Ferguson,
M.D., a pioneer and prophet of the consumer-driven movement, put it this way in an
interview I conducted with him in 1999: “Patient knowledge is different from physician
knowledge. Depending on the area of specialization, a specialist might have to stay
current on 30, 200 or 400 medical conditions. A general practitioner might have to
keep up with 600. Patients only have to know about one disease--their own.”

Clinical, practice management and practice pattern data mining
In the 1970s and 1980s, in a clinical laboratory setting, Russell Hobbie, Ph.D., a
physics professor at the University of Minnesota, and I used the Internet to develop
two practical clinical applications using data available in physician’s offices--patient
age and gender, physical measurements (height, weight, blood pressure), and
laboratory data. From this universally available data, we developed two products--the
Unified Presentation of Relevant Tests, a differential diagnosis report listing the top ten
diagnostic possibilities, and the Health Quotient, a health status report based on
height, weight, blood pressure, family or personal history of heart attack or stroke,
and laboratory findings. UNIPORT was 80 percent accurate and was commercially
successful; the HQ was acclaimed by its recipients and predicted imminent heart
attacks with unexpected precision.
True potential
The real potential of data mining lies in two areas: practice pattern grouping using existing
data to define costs and consequences, and predictive modeling using broad clinical and
financial databases to define the effect of current patient behavior, diagnoses, and
interventions on future outcomes and costs.
Practice pattern grouping often goes by the name of episode grouping. As government and
private healthcare organizations seek to deliver top-quality care more cost-effectively, episode
grouping has come into vogue. By clustering costs around a clinical episode--everything from
doctors involved, to diagnoses, to medications, to interventions, to hospitalization, to
rehabilitations, to nursing home care, to outcomes-- you can more precisely analyze total
outcomes and costs. You can also more accurately—and fairly—assess physician performance.
Much of the total cost, for example, of hospitalizations resides in the hospital’s costs. Hospital
charges make up about 80 percent of physician costs in the hospital setting. The hospital
charges may be beyond the doctor’s control. On the other hand, drugs doctors prescribe or
interventions they choose are not. It has been found that total episode costs may vary by
factors of as much as 20 to one. In these instances, and even with smaller variations,
systematic or structural reforms are in order. True reform lies in rationalizing, not rationing,
care.
Predictive modeling requires a more sophisticated mathematical approach and artificial
intelligence deployment. One of the pioneers in this field is David Eddy, M.D., Ph.D., who, over
the last 10 years at Kaiser Permanente, has developed a predictive model called the
Archimedes Model. This model provides a mathematically based lever that moves and
manipulates vast amounts of data in a way that simulates reality. It improves and speeds
healthcare decision-making at decision points along the healthcare spectrum. Archimedes,
funded by Kaiser, has been 10 years in the making. It uses mathematical simulation to create
a visual world to help healthcare organizations make critical and administrative decisions. The
model has been repeatedly tested and validated to answer complex real-world decisions. In
the words of a Kaiser publicist, “The Archimedes model has virtual people who get virtual
diseases, go to virtual doctors, get virtual tests, receive virtual treatment, and have virtual
outcomes.” Using Kaiser’s eight million-member database, Archimedes played a role in the
Vioxx recall, and it is currently being used as a tool to conduct virtual clinical trials by major
pharmaceutical companies.
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Another company pursuing goals similar to Archimedes is MedAI (short for Medical Artificial
Intelligence) in Orlando, Fla. MedAI’s outcomes measurement application, Pin Point Quality,
enables users to easily identify specific steps to monitor and improve clinical outcomes while
reducing healthcare costs. Clients can integrate data from clinical and financial legacy
systems. This allows clients to undertake quality initiatives. Medical directors, administrative
directors and other members of the organization can create reports of quality indicators, which
they can then use to drive practice changes in their organization.
In formulating the argument that America innovation in general and innovation in the handling
of data in particular will change the world, I have only touched briefly on such innovative and
powerful movements as consumer-driven care, pay-for-performance, the building of a national
electronic infrastructure, the political innovation in Massachusetts, or “disruptive innovations”
that are simpler, less costly, and more convenient to use. These are all terribly important, and
their full potentials will, no doubt, require data-based innovations.
Richard L. Reece, M.D., is a pathologist, writer, editor, speaker and consultant in Old
Saybrook, Conn. His latest book, Key “Under the Radar” Innovations Transforming U.S. Health
Care, will be published later this year. Reece may be reached at [email protected].
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