Get ACDIS CCDS-O Exam Practice Questions - Real and Updated

Answer : D

The only conclusion that is directly supported by the table is that Clinic A's percent RAF captured is higher in every month of 2023 compared with the corresponding month in 2022. The monthly values rise year-over-year (e.g., January 21% vs 17%, February 33% vs 25%, and continuing through December 84% vs 76%), showing a consistent improvement pattern across the entire calendar year. In outpatient CDI and risk adjustment work, ''RAF capture'' is commonly used as a performance indicator reflecting how completely documented and coded risk-adjusting conditions (e.g., HCC-supported diagnoses) are being captured within the measurement period. However, the table does not prove why the improvement occurred. It cannot confirm provider engagement (A) without workflow/participation data, cannot compare to other clinics (B) because no other clinic data are shown, and cannot establish that the population was sicker (C) because RAF capture measures documentation/coding completeness relative to opportunity, not inherent patient acuity. Therefore, D is the verified conclusion.

Answer : C

In ambulatory CDI chart review, clinical indicators should align with assessment and management captured in the note (problem relevance and MEAT-style support: monitor, evaluate, assess/address, treat). This visit has two strong indicators that warrant medication evaluation. First, dyspnea plus a rapid 10-lb weight increase over two weeks is a classic signal of possible fluid overload in a patient with heart failure, making a loop diuretic such as Lasix clinically relevant to assess (effectiveness, adherence, dose changes, exacerbation risk, and whether HF is stable vs decompensated). Second, the A1c has worsened significantly (9.2 from 7.2), indicating inadequate glycemic control that should prompt review and adjustment of diabetes therapy; insulin such as NovoLog is directly tied to diabetes treatment escalation or optimization. The other medication pairs do not logically match the documented problems and indicators (e.g., appetite stimulant/anemia therapy, immunosuppressant, antidepressant/antihistamine). Therefore, NovoLog and Lasix best reflect what should be evaluated and addressed

Answer : D

In ICD-10-CM diabetes coding (as reinforced in outpatient CDI education), some diabetes manifestations are fully captured by a single diabetes ''combination'' code, while others require a diabetes complication code plus an additional code to identify the specific manifestation. Diabetic nephropathy and many forms of diabetic retinopathy are commonly represented by diabetes combination codes that already describe the manifestation with built-in specificity options (e.g., diabetes with nephropathy; diabetes with retinopathy with/without macular edema and severity). Osteomyelitis, however, is typically captured using a diabetes code such as ''diabetes with other specified complication'' (e.g., E11.69) to establish the linkage to diabetes and an additional code from the osteomyelitis category (e.g., M86.-) to specify the site, acuity, and type of osteomyelitis. From a chart review standpoint, CDI often queries to confirm the causal relationship (''due to diabetes'') and to ensure the osteomyelitis details (site, acute vs chronic) are documented so both codes can be assigned accurately and compliantly.

Answer : D

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.

Answer : C

In the outpatient follow-up setting, when the acute stroke event has occurred in the recent past and the patient is now being evaluated for residual deficits, documentation and coding should focus on the sequelae (late effects) rather than re-coding the acute infarction itself---unless the provider clearly states the stroke is still in the acute phase and being actively treated as such. ACDIS outpatient CDI principles stress selecting the diagnosis that best reflects the reason for today's encounter and the condition being assessed/managed. Here, the ongoing clinical issue driving follow-up care is the persistent neurologic deficit (right-sided weakness/hemiparesis) after the cerebral infarction. Option C is the most specific and clinically accurate because it captures (1) the relationship to the prior cerebral infarction (''following cerebral infarction'') and (2) laterality and dominance (''right dominant side''), which improves code specificity and reflects functional impact. Option A incorrectly keeps the focus on an unspecified cerebral infarction rather than the residual deficit, and option D is too nonspecific compared with a clearly described hemiparesis.