--- title: Mining Health Claims Data for Assessing Patient Risk description: January 2012 | Ian G. Duncan | Springer, Berlin, Heidelberg --- Publication # Mining Health Claims Data for Assessing Patient Risk [ Arbital Health ](https://arbitalhealth.com/blog/author/arbital-health) Jan 1, 2012 [mailto:?subject=Check%20out%20https://arbitalhealth.com/blog/mining-health-claims-data-for-assessing-patient-risk&utm_medium=social&utm_source=email%20&body=Check%20out%20https://arbitalhealth.com/blog/mining-health-claims-data-for-assessing-patient-risk&utm_medium=social&utm_source=email](mailto:?subject=Check%20out%20https://arbitalhealth.com/blog/mining-health-claims-data-for-assessing-patient-risk&utm_medium=social&utm_source=email%20&body=Check%20out%20https://arbitalhealth.com/blog/mining-health-claims-data-for-assessing-patient-risk&utm_medium=social&utm_source=email) [Publication](https://arbitalhealth.com/blog/tag/publication) Published: January 1, 2012 Publisher: Springer, Berlin, Heidelberg By: Ian G. Duncan   Abstract As all countries struggle with rising medical costs and increased demand for services, there is enormous need and opportunity for mining claims and encounter data to predict risk. This chapter discusses the important topic, to health systems and other payers, of the identification and modeling of health risk. We begin with a definition of health risk that focuses on the frequency and severity of the events that cause patients to use healthcare services. The distribution of risk among members of a population is highly skewed, with a few members using disproportionate amounts of resources, and the large majority using more moderate resources. An important modeling challenge to health analysts and actuaries is the prediction of those members of the population whose experience will place them in the tail of the distribution with low frequency but high severity. Actuaries have traditionally modeled risk using age and sex, and other factors (such as geography and employer industry) to predict resource use. We review typical actuarial models and then evaluate the potential for increasing the relevance and accuracy of risk prediction using medical condition-based models. We discuss the types of data frequently available to analysts in health systems which generate medical and drug claims, and their interpretation. We also develop a simple grouper model to illustrate the principle of “grouping” of diagnosis codes for analysis. We examine in more depth the process of developing algorithms to identify the medical condition(s) present in a population as the basis for predicting risk, and conclude with a discussion of some of the commercially-available grouper models used for this purpose in the U.S. and other countries.   [ Read Full Publication ](https://link.springer.com/chapter/10.1007/978-3-642-23151-3_4)   ### View More News & Insights from the Arbital Health Team: Publication ### [Testing Alternative Regression Frameworks for Predictive Modeling of Health Care Costs](https://arbitalhealth.com/blog/testing-alternative-regression-frameworks-for-predictive-modeling-of-health-care-costs) Arbital Health Jan 8, 2016 Publication ### [Predicting High Need End-of-Life in Medicare Patients through Machine Learning](https://arbitalhealth.com/blog/predicting-high-need-end-of-life-in-medicare-patients-through-machine-learning) Arbital Health Jul 21, 2023 Publication ### [A prediction model for targeting low-cost, high-risk members of managed care organizations](https://arbitalhealth.com/blog/a-prediction-model-for-targeting-low-cost-high-risk-members-of-managed-care-organizations) Arbital Health May 9, 2003 ### Sign-Up for Arbital Health Email Updates Unlock a wealth of valuable insights and stay ahead of the curve with Arbital Health's exclusive email updates. 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