What other types of data might modelers use? And which might they find statistically significant in predicting which employees will cost a health plan the most? How about a person's annual review in the workplace, performance appraisals, attendance at work, job type and reported job satisfaction?
Some modelers are already in the formative stages of wrapping this kind of human resources data into the calculus they use to help their clients identify subsets of employees who will demand the most from a health plan.
(Using data traditionally found in a human resources department will enhance the need to use coding that ensures no breach of individual privacy by disease managers or employers looking at results.)
Modelers may also use age, race, zip codes, census data, and other demographic information--in theory, anything that improves their precision in pointing to those who have or will enter a disease-management category. The specifics of the information are used for prediction only, not for management; whereas, claims and health risk assessment data are used for both.
"This is the next level of predictive modeling in the employer market--taking a comprehensive look [at what is known about populations], possibly with the intent of designing benefits packages around key chronic diseases that are driving cost," says Newell.
June 1, 2005
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