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Milliman Expert Addresses Practical Applications of Predictive Modeling in Stratifying Population for Disease Management

Thursday, June 10, 2010 General News J E 4
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SEATTLE, June 9 Disease management (DM) effectiveness can be significantly improved by targeting the patients who are likely to participate and are likely to produce meaningful savings. Predictive modeling can help predict both types of patients, according to a new white paper from Milliman expert Ksenia Draaghtel.

The biggest obstacle in DM acceptance has been the difficulty in measuring or even identifying the cost savings associated with the implementation of such programs. A 2004 Congressional Budget Office analysis concluded that published studies to date "did not provide a firm basis for concluding that disease management programs generally reduce total costs".(1) Issues such as selection bias, regression to the mean, and presence of confounding factors complicate the analysis and should be carefully considered.

The use of predictive modeling by DM programs to risk-stratify members in order to optimize the utilization of available clinical resources is not a new concept. The notion of identifying opportunities for intervention prior to the occurrence of adverse high cost events has been widely accepted in the industry. The challenge of the programs is to stratify and identify those patients who could derive the greatest benefit from interventions. By focusing limited resources on the right patients, both clinical and economic value can be realized.

The data used in predictive modeling analysis is highly dependent on the model's intended purpose, the company's objective, and data availability. A data source that is gaining momentum in the healthcare industry is publicly available consumer data from data aggregators.

Milliman expert Ksenia Draaghtel, ASA, discusses in her white paper entitled "Predicting Participation and Savings" the practical use of the knowledge gained by measuring and predicting the savings in a DM program. In particular, the ways in which predictive modeling techniques and consumer data enable DM programs to optimize the use of their resources by targeting the right members, leading to an overall improvement in the return on investment (ROI) of the program, are explored.

To request a copy of this informative white paper, please contact Ksenia Draaghtel at (303) 299-9400 or via email at ksenia.draaghtel@milliman.com or another Milliman consultant.

About Milliman

Milliman is among the world's largest independent actuarial and consulting firms. Founded in Seattle in 1947 as Milliman & Robertson, the company currently has 52 offices in key locations worldwide. Milliman employs more than 2,400 people. The firm has consulting practices in healthcare, employee benefits, property & casualty insurance, life insurance and financial services. Milliman serves the full spectrum of business, financial, government, union, education and nonprofit organizations. For further information, visit www.milliman.com.

(1) An Analysis of the Literature On Disease Management Programs, Congressional Budget Office, October 2004. http://www.cbo.gov/ftpdocs/59xx/doc5909/10-13-DiseaseMngmnt.pdf

SOURCE Milliman
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