Session 61 L, Applications of Data Analytics in Health Insurance. Moderator/Presenter: Henning Chiv, FSA, MAAA

Transcription

1 Session 61 L, Applications of Data Analytics in Health Insurance Moderator/Presenter: Henning Chiv, FSA, MAAA

2 Session 61: Applications of Data Analytics in Health Insurance Henning Chiv, FSA, MAAA June 15 th, 2015

3 Agenda How is technology changing analytics in both pace and insights? What can actuaries bring to the table? + Overview of the analytics cycle + Analytics examples + Challenges and take-aways + Q&A 1

4 Poll Who has heard of the following terms? Big data Data science Machine Learning Advanced Analytics Structured vs unstructured data Actuarial control cycle General linear model Eigenvalues 2

5 Analytics cycle Measure results Outcomes to improve? Recommendations Analytics process

6 Outcomes to improve Healthcare (insurance) is transforming and faces many challenges: Transparency around pricing and reserving Design and management of provider networks Cost savings and improve health outcomes Retention of members Member satisfaction Provider access Treatment and member out-of-pocket expenses

7 Analytics cycle Measure results Outcomes to improve? Recommendations Analytics process

8 Analytics process External events Reporting (What happened?) Prediction (What will happen?) Analysis (Why did it happen?) Prescription (Make it happen!) Data Cleansing 6

9 Analytics techniques Statistical/Stochastic Loss modeling Regressions Blend of both Markov chains Propensity scoring Algebraic Similarity Matching Support Vector Machines 7

10 Analytics cycle Measure results Outcomes to improve? Recommendations Analytics process

11 Recommendations Communicate results of analysis Put analysis into context of business Implicit/explicit assumptions Timing of assumptions Timing for forecast Confidence intervals Recommendations 9

12 Analytics cycle Measure results Outcomes to improve? Recommendations Analytics process

13 Measure results Need to own analysis and analytics process from end-to-end Cultivate relationship with stakeholders Automation and technology can help track progress and outcomes Have re-usability in mind 11

14 Challenges + Data content is not normalized across partners and systems + Healthcare is not intuitive requires the build out of metrics and topologies + External events and changes in the environment are not captured in the data 12

15 Examples

16 How is my claims experience? $300.0 $250.0 $200.0 $150.0 $100.0 Total Inpatient vs. Mater nity Inpatient PMPMs $50.0 The curse of big data: WHERE are we going to LOOK? $ Total Inpatient Maternity Only ER vs. Urgent Care Utilization Summar y Decision trees Pattern recognition Emergency Room Service Count Urgent Care Service Count 14

17 Is my wellness program effective? + Alternative to conducting a clinical trial + Use observational data to evaluate efficacy of programs of participating and nonparticipating individuals Propensity scoring + Combination of logistic regression analysis and matching algorithms 15

18 What should I expect when I am expecting? + What types of visits and procedures should a member expect during a pregnancy? + How does the treatment path for a typical 20 year-old differ from a 40 year-old? + Which factors are good predictors for complications? Similarity Matching 16

19 Take aways

20 Take aways + Technology enabled acceleration of the analytics cycle + Advancements in technology enable new analytical techniques + Actuaries bring expertise around healthcare finance, but will have to broaden their tool kits to stay up-todate 18

21 Q&A Resources: + Healthcare Risk Adjustment and Predictive Modeling (Duncan, Actex 2011) + Doing Data Science (O Neil & Schutt, O Reilly 2014) + Data Science from Scratch (Grus, O Reilly 2015) + (Anaconda) + Pandas, numpy, seaborn, scikit-learn 19