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NEW QUESTION # 132
Which entity is tasked by CMS to process both Part A and Part B beneficiary claims?
Answer: A
Explanation:
CMS assigns Medicare Administrative Contractors (MACs) to administer Medicare fee-for-service operations at the jurisdictional level, including processing and paying both Part A and Part B claims. In outpatient CDI terms, MACs are central because they apply Medicare coverage rules, edit logic, and payment policies that determine whether documentation supports medical necessity and correct coding for submitted claims. This includes adjudicating hospital outpatient (Part B) services and facility-based Part A services, handling provider enrollment functions, issuing Local Coverage Determinations (as applicable through their medical review processes), and responding to claim inquiries and appeals routing. By contrast, Recovery Audit Contractors (RACs) focus on identifying and recovering improper payments (post-payment auditing). Risk Adjustment Data Validation (RADV) contractors validate diagnosis data submitted for risk-adjusted programs (primarily Medicare Advantage), not routine FFS claim processing. Zone Program Integrity Contractors (ZPICs) (and their successors in some contexts) focus on program integrity and fraud/waste/abuse investigations rather than standard claim adjudication. Therefore, the entity responsible for processing Part A and Part B beneficiary claims is the MAC.
NEW QUESTION # 133
Upon review of payer data, a decrease in RAF scores for the organization is noted. After reviewing internal metrics, a CDI specialist notes an increase in the volume of HCC queries across the organization, with accurate coding confirmed. Which of the following is the MOST plausible explanation for these findings?
Answer: A
Explanation:
When internal CDI metrics show increased HCC-related querying and coding accuracy is confirmed, you would typically expect payer RAF outputs to stabilize or improve-assuming the payer receives and processes the same diagnosis data. A payer-reported RAF decrease despite accurate internal capture most strongly suggests a break in the data flow between the organization and the payer. In outpatient risk adjustment, RAF depends on documented, supported diagnoses being correctly coded and then successfully transmitted on the encounter/claim to the payer's risk-adjustment ingestion process. If certain diagnoses are dropped (claim edits, interface mapping issues, encounter rejection, late submissions, or incomplete encounter files), the payer's dataset will under-represent HCCs and RAF will fall even though internal coding looks correct. CPT visibility (B) generally affects utilization/fee-for-service payment and analytics, not HCC-based RAF. Compliant queries (C) describe process quality but don't explain a payer-side RAF decline. A local "model not updated" (D) wouldn't reduce payer-calculated RAF if the payer is applying its own current model to received diagnoses.
NEW QUESTION # 134
Which statement is MOST accurate about the problem list?
Answer: D
Explanation:
A well-maintained problem list supports continuity of care by giving the care team an accurate, up-to-date clinical "snapshot" of active and relevant historical conditions that affect ongoing management, decision-making, and risk assessment. Outpatient CDI education emphasizes that the problem list should be curated-conditions should be current, clinically meaningful, and appropriately resolved or clarified (e.g., active vs history, controlled vs uncontrolled). Option A is incorrect because diagnoses are not removed based on an arbitrary time threshold; they are updated based on clinical status (resolved, inactive, erroneous, or no longer relevant). Option C is inaccurate because simply adding more diagnoses can introduce noise and increase the risk of outdated or incorrect conditions being propagated ("problem list bloat"), which can harm patient safety and lead to inaccurate coding. Option D is inaccurate because CDI professionals typically do not independently update the problem list; rather, they support providers through compliant queries, education, and process improvements so the treating provider validates and maintains the record. Therefore, B best reflects outpatient documentation best practice.
NEW QUESTION # 135
A CDI specialist manager is reviewing the productivity metrics of the outpatient team and notes that one of the CDI specialists has a high query rate and a good physician response, but a low physician agree rate compared to the rest of the team. This likely indicates which of the following?
Answer: D
Explanation:
A high query rate with a strong physician response rate shows the CDI specialist is generating many queries and providers are opening/responding to them. However, a consistently low agree rate indicates providers frequently select "disagree," "clinically undetermined," or otherwise do not validate the query's suggested clarification. In outpatient CDI program management, that pattern most often reflects query quality problems-for example, queries that are not well-supported by encounter-specific clinical indicators, queries that are vague or overly speculative, or queries that do not align with outpatient reportability standards (e.g., prompting for diagnoses not clearly monitored/evaluated/assessed/treated). While leading queries are a compliance concern, the more direct operational inference from "high volume + answered + not agreed with" is that the queries are not clinically compelling or are poorly constructed, resulting in frequent provider non-concurrence. Case complexity alone would not reliably drive low agree rates if the queries were appropriately targeted and evidence-based. Therefore, the most likely interpretation is poor-quality queries requiring coaching on clinical support, clarity, and compliant construction.
NEW QUESTION # 136
An African American male enrolled in Medicaid has not been taking his blood pressure medication. Which of the following factors impacts this beneficiary's risk score?
Answer: A
Explanation:
Medicaid risk adjustment models generally calculate risk using two major categories of inputs: demographics and diagnosis data. Demographic factors commonly include gender and indicators tied to Medicaid status/eligibility (for example, eligibility category, dual status, disability-related eligibility, or other program qualifiers depending on the state/model). These demographic elements adjust expected cost and are foundational to the risk score even before considering diagnoses. By contrast, race is not a standard input for calculating Medicaid risk scores in typical risk adjustment methodologies, so options that include race are not supported. Likewise, "patient noncompliance" is primarily a clinical and quality-of-care issue and may affect treatment outcomes, but it is not itself a standard risk-score driver unless it is documented as a reportable, supported diagnosis that the specific model recognizes (and most models don't directly risk-adjust for nonadherence codes). Therefore, among the options given, Medicaid status and gender are the most clearly valid factors that impact the beneficiary's risk score.
NEW QUESTION # 137
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