CRC - Certified Risk Adjustment Coder Data Mining and Predictive Analytics Questions and Answers

Question 1

A Medicare Advantage plan analyzes its claims and pharmacy data to identify members with a diagnosis of diabetes who are also prescribed medications for chronic kidney disease (CKD), but have no corresponding diagnosis of CKD in their claims history.
What is this data analysis process an example of?

Accepted Answer

C Predictive modeling for diagnostic suspecting.

Answer

A Fraud, Waste, and Abuse (FWA) investigation.

Answer

B HEDIS measure reporting for quality of care.

Answer

D A CMS-mandated RADV audit.

Question 2

What is the primary purpose of using predictive analytics in a risk adjustment program?

Accepted Answer

C To identify and prioritize members with the highest likelihood of having undocumented, risk-adjusting conditions for review.

Answer

A To replace the need for certified coders with automated systems.

Answer

B To automatically add diagnosis codes to claims based on lab values.

Answer

D To calculate the final risk adjustment payment from CMS for the entire health plan.

Question 3

A health plan uses a data mining technique that groups members into distinct segments based on shared characteristics like prescription drug usage, frequency of specialist visits, and documented lab values. This helps them tailor different care management outreach programs. Which of the following techniques is being described?

Accepted Answer

D Cluster analysis

Answer

A Regression analysis

Answer

B Anomaly detection

Answer

C Time-series forecasting

Question 4

A risk adjustment analyst runs a model using pharmacy data that flags patients prescribed both insulin and gabapentin (often used for diabetic neuropathy) but who have no corresponding diagnosis code for diabetic neuropathy. The output of this model is primarily used to:

Accepted Answer

B Generate a list of members whose charts should be targeted for review to validate if the condition is documented.

Answer

A Immediately report the providers to the Office of Inspector General (OIG) for non-compliance.

Answer

C Automatically add the diagnosis of diabetic neuropathy to the member's next claim submission.

Answer

D Calculate the final risk score for each flagged member.

Question 5

In the context of data mining for risk adjustment, what is a significant limitation of relying SOLELY on claims data?

Accepted Answer

A It lacks the clinical detail and context found in unstructured medical record notes.

Answer

B It is too voluminous to be processed by modern computer systems.

Answer

C It is only available prospectively and cannot be used to analyze past encounters.

Answer

D It is not considered a valid source of data for risk adjustment purposes.

Question 6

Which of the following data sources is LEAST likely to be a primary input for a predictive model designed to identify members with potentially undocumented risk-adjusting conditions?

Accepted Answer

B Patient satisfaction survey results.

Answer

A Pharmacy claims data (e.g., prescriptions for inhalers, insulin).

Answer

C Laboratory results data (e.g., HbA1c, GFR values).

Answer

D Inpatient and outpatient medical claims data.