Health Predictive Analytics Seminar

Date

October 02 - 03, 2017

Location

The Elaine Levitt and Joel Gershman Education and Conference Center at the Franklin Institute
Philadelphia, PA

  • External Forces & Industry Knowledge
  • Results-Oriented Solutions
  • Strategic Insight and Integration
  • Technical Skills & Analytical Problem Solving

Event Home  |  Agenda  |  CPD  |  Presenters  |  Registration  |  Seminar Location  |  Seminar Preparation Resources

 

Description

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 financial risk.

The Health Predictive Analytics Seminar is an interactive, hands-on seminar that provides 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, decision trees, time series analysis and machine learning techniques will also be discussed.  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. All techniques will be illustrated with health insurance applications and case studies.

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 R code to different healthcare datasets (provided to attendees). For attendees not familiar with R, references will be provided for review prior to the course.  

To further aid attendee understanding and for attendees who wish to continue to practice techniques after the seminar, 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.

Note:

  • 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. Ideally, attendees will have (or be able to arrange) software installation privileges for the seminar's purposes.

Educational Objectives

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.

Who Should Participate

This seminar is ideal for experienced actuaries who are unfamiliar with, but interested in learning about, health data and health predictive analytics and would like to gain an 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). The seminar combines a hands-on approach for those participants wishing to apply R models, with the opportunity for actuaries in a leadership role to identify appropriate models for solving business problems and to assess the suitability of models developed by analysts. 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.

 

Back

Legend

Communication

Demonstrating the listening, writing and speaking skills required to effectively address diverse technical and nontechnical audiences in both formal and informal settings.

Professional Values

Adhering to standards of professional conduct and practice where all business interactions are based on a foundation of integrity, honesty and impartiality.

External Forces & Industry Knowledge

Identifying and incorporating the implications of economic, social, regulatory, geo-political and business changes into the design and delivery of actuarial solutions.

Leadership

Initiating, innovating, inspiring, creating or otherwise acting to influence others regardless of level or role toward a common goal.

Relationship Management & Interpersonal Collaboration

Creating mutually beneficial relationships and work processes toward a common goal.

Technical Skills & Analytical Problem Solving

Applying the actuarial knowledge, skills and judgment required to provide value-added services.

Strategic Insight & Integration

Anticipating trends and strategically aligning actuarial practice with broader organizational business goals.

Results-Oriented Solutions

Providing effective problem solving that addresses relevant interests and needs.