A project to discover the differences in medicare cost across United states and detect anomalies.

1. Medicare Cost Analysis
Group 8
Ganesh, Rafael, Zhuang

2. Outline
• Introduction
• Business Objectives
• Exploratory Analysis
• Unsupervised Learning
• Supervised Learning
• Predictive Modeling
• Recommendations

3. Part One
Introduction01

4. What’s Medicare?
• Medicare is the federal health
insurance program for people
who are 65 or older and certain
younger people with disabilities

5. 2013 Medicare Facts
• Accounted for 14% of the federal budget
• Represented 20% of total national health spending
• Covered 54 million beneficiaries
• Benefit payments totaled $583 billion
Source: Kaiser Family Foundation - https://kaiserfamilyfoundation.files.wordpress.com/2014/07/7305-08-the-facts-on-medicare-spending-and-financing.pdf

6. • Submitting false statements
• Billing for services or supplies not furnished
• Billing for services not medically necessary
Sources of Medicare Fraud and Abuse

7. Business Objectives
Finding similarities among medical procedures
(Clustering)
Discovering consistency among charges
(Classification – Decision Tree & Logistic Regression)
m
Predicting cost of a procedure in a given state
(Predictive Modeling)

8. Data Description
Hospital Data
3 million rows
Columns
- State
- Procedure
- Cost
……
Medicare Provider Data
9 million rows
Columns
- Demographics
- Specialty
- Patient Distribution
……

9. Part Two
Exploratory Analysis02

10. Average Medicare Payments
$1,068
$281
$218
Submitted Allowed Actual Payment
MedicarePayment

11. Most Frequent Procedures
Frequency
Avg.MedicarePaymentPerProcedure
- Broken Ankle
- Broken thigh
- Correction of Bunion
- Removal of plaque and insertion of stent
- Transplantation of donor heart

12. Costliest Procedures
Frequency
Avg.MedicarePaymentPerProcedure
- Removal of plaque and insertion of stent
- Transplantation of donor heart
- Transplantation of donor liver

13. State wise Procedure Frequency

14. State wise Procedure Payment

15. Part Three
Data Analysis03

16. Unsupervised Learning – Clustering
• Best Clustering - 3 Clusters
• Important attributes:
- Average cost
- Patient age
- Patient race
- Disease type
- Provider type

17. Unsupervised Learning – Clustering

18. • Business question: Are costs of different procedures in different states consistent
with the national average?
• Input:
- State
- Procedure
- Average Cost
- Number of Services
Supervised Learning – Decision Tree & Logistic Regression

19. Decision Tree - Result
Procedure: Anesthetic
No. of Services: >369,366

20. Decision Tree - Result
Procedure: Surgical Treatments
Drug Administration: N

21. Decision Tree - Result
Procedure: Removal of Leg Plaque, Balloon Dilation…
No. of Providers: < 4
No. of Services: < 400

22. Decision Tree - Result
Procedure: Cardiac Surgeries
No. of Providers: < 4
No. of Services: < 400

23. • Business question: How much will a procedure cost in a given state?
Predictive Modeling – Linear Regression

24. Linear Regression Coefficients

25. • Business question: How much will a procedure cost in a given state?
• Higher the number of services, lower the expected cost
• Higher the number of providers, lower the expected cost
• Procedures without drug have higher expected cost than the ones with drug
administration
Predictive Modeling – Linear Regression

26. Recommendations

27. Recommendations
Addressing procedure cost differential between states:
Increase physician enrollment in states with lower representation to
increase competitiveness of physician availability.

28. Recommendations
Addressing potentially improper billing of commonly “abused” procedures:
Institute more stringent rules for procedures known to be most common and
expensive via expanded review edits.
Costly and Frequent Procedures:
- Plaque removal in leg artery
- Balloon Dilation
- Surgery equipped with Endoscopy