Disease Management & Health Outcomes

, Volume 11, Issue 12, pp 779–787 | Cite as

Predictive Modeling in Health Plans

  • Randy C. Axelrod
  • David Vogel


Predictive modeling in healthcare has been gaining more interest and utilization in recent years. The tools for doing this have become more sophisticated with increasingly higher accuracy. We present a case study of how artificial intelligence (AI) can be used for a high quality predictive modeling process, and how this process is used to improve the quality and efficiency of healthcare. In this case study, MEDai, Inc. provides the analytical tools for the predictive modeling, and Sentara Healthcare uses these predictions to determine which members can be helped the most by actively looking for ways to prevent future severe outcomes. Most predictive methodologies implement rule-based systems or regression techniques. There are many pitfalls of these techniques when applied to medical data, where many variables and many interactive variable combinations exist necessitating modeling with AI. When comparing the R2 statistic (the commonly accepted measurement of how accurate a predictive model is) of traditional techniques versus AI techniques, the resulting accuracy more than doubles. The cited publications show a range of raw R2 values from 0.10 to 0.15. In contrast, the R2 value obtained from AI techniques implemented at Sentara is 0.34. Once the predictions are generated, data are displayed and analytical programs utilized for data mining and analysis. With this tool, it is possible to examine sub-groups of the data, or data mine to the member level. Risk factors can be determined and individual members/member groups can be analyzed to help make the decisions of what changes can be made to improve the level of medical care that people receive.


Care Management Artificial Intelligence Technique Healthcare Industry Artificial Intelligence System Artificial Intelligence Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



No funding was received for this paper. Dr Axelrod has received consulting fees in the past from MEDai, served on its advisory panel and assisted in the development of decision support tools they currently sell. This has been outside this project.


  1. 1.
    Iezzoni LI. Risk adjustment for measuring health care outcomes. Chicago (IL): Health Administration Press 1994Google Scholar
  2. 2.
    A comparative analysis of methods of health risk assessment. Schaumburg (IL): Society of Actuaries, 1996Google Scholar
  3. 3.
    Newhouse JP, Manning WG, Keeler EB, et al. Adjusting capitation rates using objective health measures and prior utilization. Health Care Financ Rev 1989; 10(3): 41–54PubMedGoogle Scholar
  4. 4.
    Newhouse JB, Sloss EM, Manning WG, et al. Risk adjustment for a children’s capitation rate. Health Care Financ Rev 1993; 15(1): 39–54PubMedGoogle Scholar
  5. 5.
    Newhouse JB, Buntin MB, Chapman JD. Risk adjustment and Medicare: taking a closer look. Health Aff 1997; 16: 26–43CrossRefGoogle Scholar
  6. 6.
    CIGNA Healthcare. Predictive modeling in pricing and medical management. Request for proposal No. 2002524. 2001 Jan 26Google Scholar
  7. 7.
    Cumming RB, Cameron BA. A comparative analysis of claims-based methods of health risk assessment for commercial populations. Schaumburg (IL): Society of Actuaries, 2002 MayGoogle Scholar
  8. 8.
    Axelrod R, Zimbro KS, Chetney RR, et al. A disease management program utilizing “Life Coaches” for children with asthma. J Clin Outcomes Manage 2001 Jun; 8(6): 38–42Google Scholar

Copyright information

© Adis Data Information BV 2003

Authors and Affiliations

  1. 1.Anthem Blue Cross and Blue ShieldRichmondUSA
  2. 2.MEDaiOrlandoUSA

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