CRC - Certified Risk Adjustment Coder Data Mining and Predictive Analytics Questions and Answers 1 — 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?
- A Fraud, Waste, and Abuse (FWA) investigation.
- B HEDIS measure reporting for quality of care.
- C Predictive modeling for diagnostic suspecting. (Correct answer)
- D A CMS-mandated RADV audit.
Correct answer: C Predictive modeling for diagnostic suspecting.
This scenario describes predictive modeling, or suspecting, where data is mined to find patterns (e.g., specific prescriptions) that suggest the presence of an undocumented or uncoded chronic condition. The goal is to identify potential gaps in clinical documentation to be reviewed for accuracy, not to assume fraud or conduct a formal audit. [10, 6]
Question 2: What is the primary purpose of using predictive analytics in a risk adjustment program?
- A To replace the need for certified coders with automated systems.
- B To automatically add diagnosis codes to claims based on lab values.
- C To identify and prioritize members with the highest likelihood of having undocumented, risk-adjusting conditions for review. (Correct answer)
- D To calculate the final risk adjustment payment from CMS for the entire health plan.
Correct answer: C To identify and prioritize members with the highest likelihood of having undocumented, risk-adjusting conditions for review.
The core function of predictive analytics in risk adjustment is to efficiently sift through vast amounts of data to forecast which members are most likely to have active, but uncaptured, HCCs. This allows the organization to prioritize its resources, such as chart reviews and provider queries, on the cases with the highest probability of yielding accurate and complete coding. [4, 6, 12]
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?
- A Regression analysis
- B Anomaly detection
- C Time-series forecasting
- D Cluster analysis (Correct answer)
Correct answer: D Cluster analysis
Cluster analysis is an unsupervised data mining technique used to partition a dataset into groups (clusters) where members in the same cluster are more similar to each other than to those in other clusters. This is commonly used in population health to identify patient cohorts with similar clinical profiles for targeted interventions. [1, 3, 14]
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:
- A Immediately report the providers to the Office of Inspector General (OIG) for non-compliance.
- B Generate a list of members whose charts should be targeted for review to validate if the condition is documented. (Correct answer)
- C Automatically add the diagnosis of diabetic neuropathy to the member's next claim submission.
- D Calculate the final risk score for each flagged member.
Correct answer: B Generate a list of members whose charts should be targeted for review to validate if the condition is documented.
The output of a predictive model is a lead, not a confirmed diagnosis. The compliant and logical next step is to use this information to investigate further. This involves a targeted review of the patient's medical record to see if the suspected condition is clinically documented. If it is, but was not coded, then it can be submitted. If it isn't, no action is taken on the code itself. [10]
Question 5: In the context of data mining for risk adjustment, what is a significant limitation of relying SOLELY on claims data?
- A It lacks the clinical detail and context found in unstructured medical record notes. (Correct answer)
- B It is too voluminous to be processed by modern computer systems.
- C It is only available prospectively and cannot be used to analyze past encounters.
- D It is not considered a valid source of data for risk adjustment purposes.
Correct answer: A It lacks the clinical detail and context found in unstructured medical record notes.
Claims data contains structured information like diagnosis and procedure codes but often lacks the rich clinical narrative, test results, physician reasoning, and symptomatology found in unstructured progress notes. This clinical nuance is often crucial for validating or identifying complex diagnoses, making claims data alone an incomplete picture for analysis. [2, 5, 7]
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?
- A Pharmacy claims data (e.g., prescriptions for inhalers, insulin).
- B Patient satisfaction survey results. (Correct answer)
- C Laboratory results data (e.g., HbA1c, GFR values).
- D Inpatient and outpatient medical claims data.
Correct answer: B Patient satisfaction survey results.
Predictive models for risk adjustment rely on clinical and utilization data to predict the presence of disease. Pharmacy data, lab results, and historical claims are all direct indicators of a patient's health status. Patient satisfaction surveys measure a patient's experience and perception of care, which is not a direct clinical indicator of specific chronic diseases. [10]
CRC - Certified Risk Adjustment Coder Data Mining and Predictive Analytics Questions and Answers 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?