A Delicate Operation

Lives may not hang in the balance of your company's data mining efforts. But the experiences of those in the business of curing patients could help you come up with a prescription for a healthier business.

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Intermountain Health Care, an integrated health-care system in Salt Lake City, sidesteps the security issue altogether by stripping its databases of any patient identifiers. Only an account number is used to link elements of a patient's record, stored in six Oracle data marts. Leaving the data anonymous ensures that any of Intermountain's 20,000 or so employees can access its riches through the company's intranet, says Data Warehousing Project Leader Ping Wang. Doctors mine Intermountain's Oracle database themselves using Netscape or Microsoft Explorer browsers to answer what-if questions and determine the most appropriate treatments.

Removing patient identifiers from the database makes sense for organizations interested in looking at their data in the aggregate. But it also limits what can be accomplished with the data. If no names are included, providers cannot find and alert patients whose lab results or vital signs deviate from the norm, indicating that they are at risk for certain medical conditions. Because Norwalk, Conn.-based managed-care provider Oxford Health Plans' database does include patient identifiers, Oxford's medical analysis team can refer problems it finds to the disease management program team, which alerts the affected patients' physicians. Omitting patient identifiers also rules out the kind of real-time decision making in practice at HRG. If a problem arises while an HRG patient is undergoing dialysis, nurses can use SSI's Homer software to compare the patient's real-time data with that of other HRG patients who have had similar problems to find the best solution, says the company's president, Cynthia Jansen.

Cultivating User Buy-In

Getting buy-in from the end user can be another thorny area for IS executives implementing a data mining project. Convincing users in marketing, HR or out in the field (be it branch office employees or doctors in a hospital) to surrender peacefully their standard modes of operation for a new technology is never easy. Doctors might be even less tolerant to forced change than most users since they're accustomed to a high degree of professional autonomy. "The doctors were nervous at first about being compared because they saw their reputations at stake," says Brickman about Sentara's data mining efforts. "They believe they graduated from medical school and should be allowed to make their own decisions."

One way to foster user acceptance is to keep the data model as simple and easy to understand as possible. End users—doctors included—will never agree to change their procedures if they don't understand how the system works. "If I make the model very complex and intricate, how do I expect a novice with no IS background to do a query?" asks Reed.

But the surest way to circumvent end-user resistance in this and all other major IS initiatives is to include users from the beginning and listen to their feedback. Reese's strategy of proving the concept with a real-life example also can't hurt. After seeing the value of data mining for pneumonia patients, the Sentara doctors embraced the concept. "This is not about good and bad doctors, it's about good and bad processes," says Reese. "The premise is that physicians want to help patients. If you can convince the doctors that something's good for the patients, then you're OK."

And, as it turns out, so are the patients. In time, data mining could render obsolete numbers like the 12 percent pneumonia mortality rate at Sentara. And if it can help hospitals save more lives, just imagine what it could do for your manufacturing plant or customer service center.


Copyright © 2007 IDG Communications, Inc.

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