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Predictive Modeling: Improving Margins by Identifying and Targeting High-Risk Populations
Healthcare Intelligence Network, March 2005, Pages: 61
As technology makes possible the rapid access of patient data, past patterns of behavior, health claims history and pharmaceutical information could hold the key to improving managed care and reining in healthcare costs.
In this special report, 'Predictive Modeling: Improving Margins by Identifying and Targeting High-Risk Populations,' a panel of experts detail ways health plans use predictive modeling to identify plan members who may need proactive care management. By identifying this at-risk population, health plans can accurately gauge future patient expenses based on prior treatments. Using a combination of technology and web-based tools, health plans can use predictive modeling to project future member and group healthcare costs and price more appropriately for risk.
You'll hear from Howard Brill, Manager of Medical Informatics at Monroe Plan for Medical Care Inc.; Danielle Butin, Manager, Health Promotion and Wellness, Oxford Health Plans; Michael Cousins, Ph.D, Director of Informatics, Health Management Corporation; James M. Dolstad, ASA, MAAA, Vice President of Actuarial Services, SHPS Inc.; Dr. Stanley Hochberg, Medical Director, Provider Service Network; Marilyn Schlein Kramer, CEO and President, DxCG Inc.; and Jerry Osband, MD, Chief Medical Officer, SHPS Inc., on theories, application and results of predictive modeling programs. This report is based on the June 16, 2004 audio conference 'Predictive Modeling: Strategies, Trends & Forecasts' and the November 30, 2004 audio conference 'Improving the Quality of Data Collection for Effective Predictive Modeling' during which Brill, Butin, Cousins, Dolstad, Hochberg, Kramer and Osband described the types of predictive models, the impact of predictive modeling programs and how predictive modeling results can be improved.
You'll get details on:
Trends in predictive modeling;
Evidence-based medicine and predictive modeling; Diseases best suited to predictive modeling;
The role of health risk assessments in predictive modeling;
Validating the integrity of the data;
and The bottom line impact of predictive modeling programs.
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