ACDIS Certified Clinical Documentation Specialist-Outpatient (CCDS-O) CCDS-O Exam Dumps: Updated Questions & Answers (February 2026)

A key ambulatory CDI distinction between the two major risk models is timing. The HHS-HCC model (used for ACA Marketplace risk adjustment) is commonly described as a concurrent model: it uses the enrollee’s demographics and diagnoses from the same benefit year to reflect morbidity and support that year’s risk transfer/payment balancing. In contrast, the CMS-HCC model (commonly applied in Medicare Advantage) is prospective: conditions documented and coded in the prior data collection year are used to predict expected cost for the following payment year. From an outpatient CDI perspective, this timing difference affects operational priorities. For CMS-HCC, accurate annual capture and recapture of active chronic conditions is essential because last year’s documented conditions drive next year’s risk score and revenue. For HHS-HCC, complete documentation and coding during the current year impacts the current year’s risk measurement. Options referencing CPT codes are not correct for the core HCC risk score calculation, which is driven by demographics and ICD diagnosis reporting mapped to HCC categories.

RVUs are the foundation of Medicare’s physician fee schedule methodology and are built from three core components: physician work (wRVU), practice expense (peRVU), and malpractice (mpRVU). The malpractice expense RVU reflects the relative professional liability insurance cost associated with providing a service and is a defined element of the RVU calculation used to determine payment rates. In outpatient documentation and CDI education, it’s important to distinguish what drivescode selectionversus what is apayment calculation ingredient. Time with the patient and medical decision making influence E/M code selection under current E/M rules, but they are not standalone components of the RVU formula itself—they contribute indirectly by determining which CPT code is billed, and each CPT code has preassigned RVUs. Physician specialty type also is not a direct RVU component, even though specialty patterns can affect typical service mix and overall wRVU productivity. Therefore, among the options, malpractice expense is the explicit RVU component used in the calculation.

In outpatient CDI, the foundational aim is documentation integrity—making sure the medical record clearly and consistently tells the clinical story: why the patient is being seen, what conditions are evaluated/managed, the current severity and associated risks, what was done (assessment and treatment), and how this supports medical necessity and accurate code assignment. While reimbursement can be affected, it is an outcome—not the purpose. ACDIS-aligned CDI education emphasizes completeness and specificity so the record reflects true acuity and complexity (e.g., chronic conditions with current status, complicating comorbidities, medication management, and risk/decision-making). This improves downstream quality reporting, risk adjustment accuracy, continuity of care, and compliance because coders must code what is documented, not what is presumed. Strong CDI reduces denials and audit exposure by ensuring diagnoses are clinically supported (MEAT—monitor, evaluate, assess/address, treat) and linked to the encounter’s work. In short, CDI exists to ensure the record accurately represents the patient’s condition and the care delivered, enabling correct coding, quality measurement, and appropriate payment.

RADV (Risk Adjustment Data Validation) is a CMS audit program used in Medicare Advantage to confirm that diagnoses submitted for risk adjustment are supported by medical record documentation and meet reporting requirements. Its central aim ispayment integrity—ensuring that risk-adjusted capitation payments to Medicare Advantage organizations are accurate based on valid, documented conditions. In outpatient CDI practice, RADV risk underscores why documentation must clearly support each reported diagnosis (e.g., condition evaluated/assessed/treated, clinically relevant, and properly documented by an eligible provider), because unsupported diagnoses can lead to payment recoupment and compliance exposure. RADV is not designed to assess medical necessity of the services provided (that is typically addressed through utilization review and other payer audits), nor is it focused on identifying overpayments to individual physicians (it targets plan-level risk adjustment payments). It also is separate from E/M leveling accuracy, which is governed by CPT/E/M guidelines and distinct audit processes. Therefore, the best definition of RADV’s primary purpose is ensuring the integrity and accuracy of risk-adjusted payments.

This documentation presents a key outpatient CDI problem:hemiparesis is present, but two potential causal conditions are referenced—history of CVAandintracranial trauma—without clear linkage. In ICD-10-CM, correct reporting of hemiparesis often depends on identifying whether it is alate effect (sequela)of a prior stroke, a residual from traumatic brain injury, or due to another neurologic condition. Coding hemiparesis automatically as a CVA sequela (option A) would be assumptive and potentially inaccurate, because the clinician has not documented the relationship. Likewise, simply coding hemiparesis alone (option D) may miss important etiologic specificity, and coding both histories without clarifying the cause (option B) still leaves the main clinical ambiguity unresolved. Outpatient CDI best practice is to issue anon-leading queryrequesting provider clarification of theetiology/sourceof the hemiparesis (e.g., due to prior CVA, due to prior intracranial trauma, both, or other/undetermined). This supports accurate diagnosis reporting, appropriate sequencing, and defensible risk/quality representation.

In outpatient clinical documentation and chart review, diabetes can be supported by recognized diagnostic thresholds. An HbA1c value reflects average blood glucose over approximately the prior 2–3 months and is commonly used to diagnose and monitor diabetes. An HbA1c≥ 6.5%(when confirmed per clinical practice standards and interpreted in the appropriate clinical context) supports a diagnosis of diabetes; therefore an HbA1c of7.0%clearly meets the threshold and supports diabetes. By comparison, a2-hour OGTT value of 90 mg/dLis normal and does not support diabetes (diabetes is typically supported when the 2-hour value is≥ 200 mg/dL).Hypoglycemiais low blood glucose and is not diagnostic of diabetes; it may occur in diabetics due to treatment but can also occur in non-diabetics for many reasons. Afasting glucose of 100 mg/dLis at most borderline/prediabetes range and does not meet diagnostic criteria for diabetes (diabetes is supported at≥ 126 mg/dL).

For outpatient Evaluation and Management (E/M) services, “medical necessity” is ultimately judged against Medicare coverage policy, which is established through National Coverage Determinations (NCDs) and Local Coverage Determinations (LCDs). NCDs set nationwide rules for when specific services are considered reasonable and necessary, while LCDs are developed by Medicare Administrative Contractors to define coverage expectations within their jurisdictions, including indications, frequency limits, required documentation elements, and diagnosis-to-service relationships. Outpatient CDI and coding education emphasizes that correct E/M code selection and documentation must support not only the level of service (MDM/time) but also why the service was needed based on the patient’s condition and the payer’s coverage criteria. Specialty society recommendations and AMA/AHA guidance can inform clinical practice and coding conventions, but they do not define Medicare coverage requirements. Likewise, AHIMA provides professional guidance but does not set payer medical necessity policy. Therefore, the most accurate source defining medical necessity criteria for billing compliance is NCDs and LCDs.

The best query is chronic respiratory failure because home oxygen is a strong clinical indicator that often reflects an underlying chronic hypoxemic condition beyond uncomplicated COPD. Outpatient CDI guidance stresses that queries should be driven by present clinical indicators in the note and should seek clarification that impacts accurate diagnosis capture and ongoing care. Here, the provider documents COPD plus home oxygen and is arranging continued services (medication management and home health), which supports asking whether the patient has a reportable condition such as chronic respiratory failure with hypoxia (or COPD with chronic hypoxemia) and whether it is being monitored/managed. The other options lack support: acute blood loss anemia has no bleeding, hemodynamic instability, or acute findings; peripheral neuropathy is not assessed or described despite diabetes; and CKD has no labs, staging, history, or assessment. A compliant query would be non-leading and include the indicator (home O₂) and request the most accurate diagnosis and specificity/status.

In outpatient risk adjustment (commonly Medicare Advantage), the patient’s predicted cost is derived from the Risk Adjustment Factor (RAF), which is the sum of component risk contributions. Here, the RAF is calculated by adding the HCC diagnoses score (0.166), disease interactions (0.112), and demographic score (0.330). That total equals 0.608. The PMPM (per-member-per-month) baseline cost is $800. To estimate the patient’s expected monthly cost, multiply PMPM by RAF: $800 × 0.608 = $486.40 per month. The question asks for the expected yearly cost, so convert PMPM to annual: $486.40 × 12 = $5,836.80. ACDIS outpatient CDI teaching emphasizes that accurate documentation and compliant coding directly affect RAF through captured HCCs and interactions (when supported), which in turn drives expected resource needs and plan payment. Missing or unsupported diagnoses can understate RAF; vague documentation can prevent valid HCC capture.

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.