Free ACDIS CCDS-O Practice Exam with Questions & Answers | Set: 3

The HHS-HCC risk adjustment model (used for ACA Marketplace plans) calculates a member’s risk score using a combination ofdemographic factorsanddiagnosis codesthat map to HHS-HCCs. Among the listed health record elements,genderis a core demographic variable used in the model’s coefficients because expected healthcare utilization and cost patterns differ by age/sex groupings. In outpatient CDI terms, this is why accurate demographic data capture (including sex) matters alongside complete and specific condition reporting. CPT codes do not drive HHS-HCC risk scores; the model relies on diagnosis reporting (ICD-10-CM) rather than procedure codes for risk category assignment. Discharge status is an encounter/billing element relevant to certain facility payment and quality measures, but it is not a standard HHS-HCC risk score input. Ethnicity is not used as a direct risk adjustment variable in the HHS-HCC model for score calculation. Therefore, gender is the correct element that impacts HHS-HCC risk scores.

In the CMS-HCC model, diabetes categories are hierarchical, meaning you capture the highest supported diabetes HCC for the year, not multiple diabetes HCCs simultaneously. Type 2 diabetes with peripheral neuropathy represents achronic diabetic complication, so it maps toHCC 18 (Diabetes with Chronic Complications)rather than HCC 19 (without complications) or HCC 17 (acute complications). COPD is documented as present and clinically relevant (even without an exacerbation) and therefore maps toHCC 111 (Chronic Obstructive Pulmonary Disease)when it is assessed/managed as part of the visit. “History of depression, stable” does not necessarily meet the threshold forHCC 58, which is reserved for specific serious psychiatric diagnoses (e.g., major depressive disorder, bipolar disorder, paranoid disorders). A general “depression” history, especially if not specified as major depressive disorder and not actively addressed, often will not support HCC 58 capture. Therefore, the visit captures HCC 18 and HCC 111 only.

The documentation “decreased muscle mass and significant weight loss in the last six months” raises a strong clinical indicator for a nutrition-related condition (e.g., malnutrition, cachexia, or other clinically significant weight loss) that should be clarified by the provider. In outpatient CDI practice, ACDIS-based guidance emphasizes querying when there are objective or clearly stated indicators suggesting an additional diagnosis that is clinically relevant, affects management, or reflects patient complexity. Malnutrition is particularly important because it can explain functional decline, frailty, and increased risk of falls/fractures, and it often changes the care plan (dietary counseling, nutrition referral, supplementation, labs, monitoring). While “degree of muscle atrophy” and “acuity of the fracture” could matter in other contexts, the note explicitly highlights a systemic decline over six months rather than an acute fracture issue. “Type of osteoporosis” is relevant for specificity, but the new, clinically significant clue here is unintended weight loss with muscle wasting—making malnutrition the most appropriate clarification opportunity.

Effective outpatient CDI depends on provider engagement, efficient workflows, and respectful communication. When a provider is frustrated about time pressures, the most productive approach is to partner with them to reduce friction while preserving compliant documentation improvement. ACDIS outpatient CDI concepts emphasize collaboration and provider education—meeting the provider where they are, understanding their workflow constraints, and jointly designing a query process that is minimally disruptive (e.g., batching queries, aligning with clinic schedules, using prospective queries, leveraging templates, or routing through agreed channels). Option C directly addresses the root issue (workflow burden) and builds trust by seeking the provider’s input and scheduling the discussion at their convenience. Option A is confrontational and frames CDI as a compliance demand rather than a clinical accuracy initiative. Option B is inappropriate because CDI cannot stop querying when clarification is needed for accurate documentation, coding, quality reporting, and risk adjustment. Option D escalates prematurely and damages relationships; escalation is typically reserved for persistent, unresolved non-responsiveness after collaborative efforts and leadership-supported education.

The documented indicators strongly support two clarification needs that affect accurate outpatient reporting. First,morbid obesityis supported by an objectiveBMI of 46, and outpatient CDI practice emphasizes ensuring obesity class is clearly documented as a diagnosis (not only implied by BMI) and that it is clinically relevant to care planning and risk (e.g., contributes to thrombotic risk, impacts treatment options). Second, the Doppler “indicates DVT,” but the record also notes ahistory of DVT, creating ambiguity aboutstatus—is this anacute new/recurrent DVT, achronic/residual thrombosis, or a prior condition now re-identified? Clarifying acuity/status is essential because it changes code selection and clinical severity representation and supports medical necessity for management decisions, especially given “contraindications to anticoagulation.” Hypertensive urgency is not supported (BP 142/88 without crisis features), and “hypercoagulability” is not established by the provided indicators. Therefore, querying for morbid obesity and DVT status is most appropriate.

In obstetric and postpartum coding, the most important clarification is thetype/timingof hypertension because ICD-10-CM has distinct categories for chronic (pre-existing) hypertension, gestational hypertension, and hypertensive disorders that persist into or present during the postpartum period. At 6 weeks postpartum with elevated readings (including a systolic of 160) and headache, the documentation “hypertension” is not specific enough to determine whether this represents chronic hypertension that predates pregnancy, gestational hypertension that has not resolved, or another pregnancy-related hypertensive disorder requiring different obstetric coding and follow-up. ACDIS outpatient CDI guidance prioritizes queries that resolve coding-impactful ambiguity using clinically supported options without leading the provider. While postpartum preeclampsia could be a clinical consideration, the note does not provide key supporting elements (e.g., proteinuria or other definitive severe-feature criteria), so jumping directly to preeclampsia/eclampsia is less appropriate than clarifying onset and relationship to pregnancy. Linking hypertension to smoking is not a standard required linkage for diagnosis coding, and “hypertensive crisis” is not supported by the documentation provided.

Outpatient CDI performance is best measured by metrics that reflect ambulatory documentation quality, risk-adjustment accuracy, and provider engagement.HCC capture rateis central because outpatient CDI frequently supports risk adjustment (e.g., CMS-HCC/HHS-HCC) and aims to ensure chronic conditions are accurately documented, linked, and reported when they are actively managed.Unspecified code utilization rateis a practical quality metric for provider education because high unspecified use often signals missed clinical specificity (severity, laterality, acuity, manifestations, staging) that can reduce coding accuracy, obscure patient complexity, and weaken data used for benchmarking and quality reporting.Query response rateis also a core operational KPI: it reflects provider participation, workflow effectiveness, and the CDI team’s ability to obtain timely clarifications that support compliant coding and complete clinical representation. In contrast,Medicare CMIandseverity of illnessare predominantly inpatient-focused constructs and are not the primary yardsticks for outpatient CDI program success. While aggregate RAF and quality indicators matter, the best “program performance” set is the one directly tied to outpatient CDI levers: HCC capture, specificity/unspecified reduction, and query responsiveness.

The only conclusion that is directly supported by the table is that Clinic A’spercent 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 tabledoes notprove 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.

Under the CMS-HCC model, a beneficiary’s risk score (RAF) is intended to represent the expected cost of caring for that individual relative to an average beneficiary. The score is calculated using two primary inputs: (1) the beneficiary’s demographic factors (such as age, sex, Medicaid status/dual eligibility, disability status, and original reason for Medicare entitlement, depending on the model segment), and (2) the beneficiary’s documented disease burden captured through ICD-10-CM codes that map to Hierarchical Condition Categories (HCCs). Those HCCs reflect the person’s health status and severity, with hierarchy rules preventing “stacking” of related conditions and with certain interaction terms in some model versions. Social determinants are not generally described as the defining basis of the traditional CMS-HCC RAF in CDI education, and “family demographics” are not used. The model is not a mortality predictor; it is a cost/risk prediction tool for payment adjustment. Therefore, the best definition is the beneficiary’s individual demographic and health status.

In hospital outpatient settings paid under OPPS, Ambulatory Payment Classifications (APCs) are influenced by the coded services and, in many workflows, the clinical documentation that supports correct CPT/HCPCS selection, units, modifiers, and—when applicable—medical necessity linkages to diagnoses. Accurate documentation ensures that the record supports what was actually performed (e.g., complexity, laterality, supplies, drug administration details, observation criteria, or separately payable procedures) and that coding can correctly apply bundling/packaging rules without losing legitimately reportable services. While APCs are primarily procedure-driven, documentation remains decisive because incomplete or ambiguous notes lead to downcoding, missed charges, incorrect status indicators, or denials during medical review. From an outpatient CDI standpoint, the goal is to ensure the clinical story supports codeable services and their necessity: clear indications, findings, assessment/plan, and any required elements (time, start/stop, dose/route for medications, device details, etc.). This supports appropriate APC grouping and reimbursement integrity, reducing rework, denials, and compliance risk.