CRC Data Mining and Predictive Analytics 2

Question 1

An MA plan uses predictive modeling to identify patients with suspected undocumented conditions. Which data source combination is most effective?

Accepted Answer

Claims combined with pharmacy data to identify conditions suggested by medication patterns

Answer

Pharmacy data reveals conditions not captured in diagnosis codes, such as a patient on metformin without a diabetes diagnosis.

Question 2

A provider group's mental health HCC capture rate increased 300% year-over-year. What analytical approach should be used?

Accepted Answer

Compare against peer trends, evaluate for staffing changes or new screening programs

Answer

A 300% increase requires investigation through peer benchmarking, temporal analysis, and operational review before drawing conclusions.

Question 3

Which technique is most appropriate for estimating future healthcare costs using historical risk adjustment data?

Accepted Answer

Gradient boosted trees incorporating HCCs, demographics, utilization, and pharmacy data

Answer

Gradient boosted trees capture non-linear relationships and interactions that simple models miss.

Question 4

Data mining reveals 8% of HCC revenue comes from diagnoses captured only during annual wellness visits. What risk does this represent?

Accepted Answer

Moderate RADV audit risk if documentation lacks clinical depth

Answer

While annual wellness visits are valid, conditions captured only once with limited documentation face audit risk.

Question 5

Which feature would be the strongest predictor for a model predicting HCC gaps?

Accepted Answer

HCCs captured in prior year not yet appearing in current year claims

Answer

Prior year HCCs not yet recaptured is the strongest predictor because chronic conditions documented last year are likely still present.

(CRC) Certified Risk Adjustment Coder Practice Test