July 20 - 21, 2016
Hotel Monaco Chicago
Competency (Learn more)
This seminar has now sold out.
To be placed on a waiting list for the July 20-21, 2016, seminar in Chicago, please email Marni Smith in SOA Customer Service at
. Should spaces for the seminar become available (e.g., due to cancellations), potential registrants will be contacted in the order in which their requests were received.
Predictive analytics has become a major force in insurance, and particularly in health insurance. Health actuaries have traditionally been users of aggregate data for pricing and reserving. In the last 10-15 years, a considerable amount of development has taken place in key areas of modeling: "case-finding" (identifying high-risk, high-cost patients for intervention); patient matching (for evaluation of programs, drugs, devices and provider quality) and risk adjustment (financial transfers based on relative risks of different populations). Health actuaries have tended to focus on risk adjustment and application of public-domain models (HCC; CDPS) and commercial models. Other predictive applications have tended to be performed by other professionals such as statisticians, as well as those with public health or clinical training.
Inexpensive computing power and increasingly powerful analytical tools become more widely available each year. One drawback for those interested in modeling applications has been access to relevant datasets. For health actuaries in particular, predictive analytics affords opportunities to ground traditional actuarial work in rigorous statistical methodology and to bring value to their organizations by combining the techniques with traditional actuarial skills, such as understanding and managing risk.
The SOA is offering a seminar in Health Predictive Analytics on July 20-21, 2016, in Chicago. This interactive, hands-on seminar will provide attendees with the opportunity to apply practical knowledge of statistical techniques that are broadly relevant in actuarial work. Core techniques, like regression analysis, generalized linear models, time series analysis and decision tree analysis will be covered, as well as "unsupervised learning" techniques like principal component analysis and clustering. The curriculum will also cover a refresher in basic statistics, as well as hypothesis testing, tests of significance and interpretation of results. Attendees will learn an important component of health analytics work, the ability to understand and interpret claims data and construct appropriate algorithms. Presenters will also cover applications in risk adjustment.
While the seminar will cover statistical theory, it will focus on practical modeling. The medium of the seminar will be the open-source R statistical computing environment. Case studies will apply healthcare datasets and R code (provided to attendees). For attendees not familiar with R, references will be provided for review prior to the course.
To further aid attendee understanding, instructors will be available for discussion of problems during "office hours" via email or skype or in person at University of Illinois at Chicago for a period of one month after the seminar.
- To create an optimum learning environment, this seminar will be limited to 35 registrants
- Attendees will be required to bring their own laptop computers to this seminar
At the conclusion of this seminar, attendees will be able to:
- Apply and interpret standard statistical measures such as tests of significance;
- Understand the construction of typical algorithms for health data;
- Perform basic data manipulations and fit a variety of standard models in the R statistical computing environment;
- Graphically explore data to motivate various modeling choices and graphically criticize models and motivate model improvements;
- Interpret and critically examine standard model output;
- Test the performance of models on holdout data;
- Apply models in typical health insurance applications such as risk adjustment and time series; and
- Translate a business problem into the design of a data analysis strategy.
This seminar is ideal for anyone who is interested in learning about health data and health predictive analytics and would like to have a deeper understanding of the subject by learning with a hands-on approach. It is designed for those with an intermediate knowledge of statistics and modeling (for example, those who have attended the SOA's Advanced Business Analytics Seminar or who have similar knowledge and experience). Health, Long Term Care Insurance and Modeling Section members will also benefit from attending.
Level of Difficulty
The seminar is designed for attendees with moderate level experience in statistics and modeling (for example, current familiarity with ASA-level statistical concepts such as regression, analysis of variance and tests of significance). Knowledge of R is recommended. Attendees will have the opportunity to acquire basic R skills prior to the seminar.
Results-Oriented Solutions; Technical Skills & Analytical Problem Solving; External Forces & Industry Knowledge; Strategic Insight & Integration