This is a writing sample by “nycghostwriter,” AKA Barbara Finkelstein. It is a news story published on “Selling for IBM,” the IBM Corporation intranet. You can get professional ghostwriting services from a business writer. Email me or fill out the short form on my contact page.
Selling for IBM | 27 March 2002
An agreement between IBM and the Mayo Clinic to develop an exhaustive database will help medical researchers design more effective treatments for genetic diseases — and begin the transformation of medicine into an IT science.
The deal, expected to bring in a relatively modest $30 million in services and hardware sales, stands to become a $2.3 billion opportunity when IBM merchandises the solution across hundreds of other U.S. hospitals and medical research centers.
While competitors such as Sun, Oracle and Accenture are lining up to sell life science organizations their hardware, database and integration services, IBM’s open platform solutions will distinguish it from the others
The deal was largely built on collaboration between IBM’s Server Group, which developed DB2 database prototypes to run on the IBM eServer pSeries and AIX, (IBM’s version of UNIX) and the Mayo Clinic, whose six million patients provide a wealth of data about diseases such as cancer, asthma and colitis. Server Group has filed seven new patents related to abstracting and searching the Mayo database.
With the help of IT architects and software developers, Mayo’s medical researchers will be able to search a vast archive of clinical information to make precise treatment decisions for current patients. For a given individual with a specific medical history, family background, and genetic makeup, it will be possible to rapidly search a vast archive of historic data to identify similar patients, and to use this information to determine the very best course of treatment.
The project, designed to create an information system that supports ongoing studies in more than 100 specialty areas, is planned in three phases
- The first phase, scheduled for completion in July, will provide the basic IT infrastructure for making precise treatment decisions based on historical data.
- Phase two will extend the existing infrastructure with genetic information.
- Then, more diverse data sources, including some from the public infrastructure, will be included to facilitate more complex research projects using information about protein structure and function.
Changing on a dime
While medicine seems to be the obvious and natural beneficiary of an e-infrastructure solution, some physicians resisted the “IT-ization” of their profession.
“We got a lot of pushback from doctors at Mayo who said they had practiced medicine ‘their way’ for a long time,” says Jeff Augen, director of business strategy for IBM Life Sciences. “We had to convince them that if the Mayo Clinic didn’t embrace molecularbased medicine, some other institution would — and the $3 billion-a-year Mayo Clinic could conceivably become a medical backwater.”
Mayo looked at IBM’s recent work with the Emory University School of Medicine/Winship Cancer Institute and NuTec Sciences to develop an integrated information system that lets physicians tailor cancer treatments based on a patient’s specific genetic make-up. Doctors and trustees were soon convinced that business issues, not technology, had shaped Winship’s IT agenda — and would have to shape theirs.
The cost of instituting an e-infrastructure solution may inhibit smaller research centers from taking the Mayo Clinic route. But the development of Grid Computing will permit even small institutions to lasso the computational horsepower of many distributed, networked computers and to utilize data from genomic research.
The Role of IBM Research IBM Research and the Mayo Clinic began to collaborate in March 2001 on gene expression analysis for Chronic Lymphatic Leukemia (CLL), a cancer of the immune system. Medical researchers are studying patients who have the CLL gene and are comparing them to patients who lack the gene.
Almost any algorithm would locate the differences between these two sets, but using the right algorithmic tool — in this case IBM’s Genes@Work– will help find subtle differences within the CLL subtype itself, thus paving the way to the production of highly customized therapies for individual patients.