CCDS-O ACDIS Certified Clinical Documentation Specialist-Outpatient (CCDS-O) Free Practice Exam Questions (2026 Updated)

In HCC risk adjustment, chronic kidney disease (CKD) is captured by stage-based HCCs that are hierarchical—only the highest supported CKD stage in the hierarchy is counted for RAF when multiple stages (or a range) are referenced. The documentation includes “CKD – stage 3–4,” which indicates the patient’s baseline CKD severity falls somewhere between stage 3 and stage 4. When selecting from the provided table, stage 4 maps to HCC 327 and is higher than stage 3 categories (HCC 328 for stage 3B and HCC 329 for stage 3 except 3B). AKI due to ATN describes an acute process and does not replace the need to capture baseline CKD stage when it is clinically relevant and documented. Outpatient CDI best practice would be to query the provider to specify the exact CKD stage (since “3–4” is imprecise), but when forced to choose from the hierarchy shown, the correct HCC assignment based on the highest stated stage in the documented range is HCC 327 (CKD stage 4).

ICD-10-CM categoryN18 (Chronic kidney disease)is used to reportCKD by stage, including stage-based descriptors andend stage renal disease (ESRD). Within N18, codes identify CKD stage 1 through stage 5,ESRD (stage 5D), and CKD unspecified. Outpatient CDI review focuses on ensuring providers document thestage(often supported by eGFR trends) because stage drives correct code selection and accurately reflects disease severity for risk, quality, and medical necessity. Options that includedialysisare not part of N18 itself; dialysis status and encounter codes are reported elsewhere (e.g., dialysis dependence/status codes), not as N18 category diagnoses.AKI(acute kidney injury) andATN(acute tubular necrosis) are acute renal conditions and are coded outside N18. Likewise, polycystic kidney disease and “uremia” are separate diagnoses with their own code categories. Therefore, the set that correctly matches what N18 represents is CKD stage-based diagnoses such asCKD stage 3, more advanced/severe CKD stages, andESRD.

A prospective query is initiated early enough (before or during the visit workflow) so the provider can evaluate, assess, and document the condition in real time while the patient is present. This is a major provider benefit because it supports better clinical accuracy and completeness: the clinician can ask targeted questions, perform relevant exam elements, review results, and determine whether the condition is present, active, being monitored, or ruled out—then document the final clinical impression and plan. From an ACDIS outpatient CDI perspective, prospective querying improves efficiency and reduces retrospective “chart-chasing,” late addenda, and documentation gaps that occur when clarification is requested after the encounter is closed. Importantly, prospective queries must remain non-leading and cannot direct the provider to a particular diagnosis (eliminating option A). They also cannot “guarantee” risk-adjusted capture because the diagnosis must be clinically supported and addressed (eliminating option B). Defining the purpose of the encounter is driven by the clinical reason for visit, not by CDI (eliminating option D).

In the CMS-HCC risk adjustment framework, the HCC average score reflects the coded burden of illness for the provider’s attributed panel, driven by documented, reportable conditions that map to HCCs and qualifying demographic factors. If a provider appears “high cost” based on total claims but the panel’s average HCC score is low, the most common CDI interpretation is documentation/coding under-capture: the clinical complexity driving utilization is not being fully documented and coded to HCC-relevant diagnoses. This creates a mismatch—actual resource use is high, but the recorded risk profile is artificially low—leading to unfavorable benchmarking because costs are compared against an expected spend that is too low for the true acuity. Option A would typically raise HCC scores, not lower them. Option C could explain both low HCC and low cost; it conflicts with the high-cost finding. Option D misunderstands HCC mechanics: “unspecified” does not reliably increase HCC capture and often reduces coding specificity/validity rather than improving risk adjustment. Therefore, incomplete capture of relevant diagnoses is the most likely driver.

In outpatient risk adjustment, “disease interactions” refer to model coefficients that are triggered when certain clinically related conditions co-exist, reflecting higher expected resource use than either condition alone. In this case, the record already supports active malignancy care (colon cancer on chemotherapy) with newly documented metastasis, and the lab pattern (anemia, leukopenia, and severe thrombocytopenia) strongly suggests pancytopenia. The highest-yield query opportunity is to clarify whether the cytopenias representchemotherapy-induced pancytopenia(or another specified etiology) because a confirmed, well-specified hematologic complication in the context of active cancer treatment is the type of combination that commonly drives interaction effects in risk models (cancer plus significant systemic complication/manifestation). Options A and B describe clinical context but do not, by themselves, establish an interaction-ready, separately reportable complication. Option C is unrelated to the presented lab-driven severity signal. Querying and documenting chemotherapy-induced pancytopenia supports accurate capture of severity and the interaction impact.

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 codeplus an additional codeto identify the specific manifestation. Diabeticnephropathyand many forms of diabeticretinopathyare 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 diabetesandan 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.

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 lowagree rateindicates 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 reflectsquery 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.

Outpatient ICD-10-CM guidance allows certain code elements to be based on documentation from clinicians other than the patient’s diagnosing provider when those elements are considered objective, routinely assessed, and commonly documented by nursing or ancillary staff. A key example ispressure ulcer staging, which is frequently assessed and documented by wound care nurses and other qualified clinicians as part of routine skin/wound evaluation. Because the stage drives code specificity and is an observable clinical finding, coders may use non-provider documentation to assign the stage when it is clearly documented and not contradicted by the provider record. In contrast, items such as thetype of obesitygenerally require provider diagnosis/clinical assessment rather than ancillary documentation alone. Similarly, while status conditions (like amputations or ostomies) may be observed, the coding guidelines do not broadly permit assigning these diagnoses solely from non-provider documentation without provider confirmation, unless the chart otherwise supports it. Therefore, among the choices, pressure ulcerstageis the appropriate circumstance where non-provider documentation can support ICD-10-CM assignment.

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.

In the outpatient/ambulatory setting, ICD-10-CM reporting rules applied in CDI education distinguish clearly between confirmed diagnoses and “uncertain” or “rule out” conditions. Terms such as “rule out,” “suspected,” or “probable” generally are not coded as established diagnoses in the outpatient record because the encounter is often for evaluation and testing rather than definitive confirmation. Instead, coders report the patient’s presenting signs and symptoms when a definitive condition has not been documented as confirmed by the provider. Here, the clinician ordered diagnostic testing (ECG and chest x-ray) specifically to rule out myocardial infarction (MI), but no final diagnosis of MI or angina is documented in the scenario. Therefore, “rule out MI” is not reportable, and neither is acute MI or angina unless explicitly diagnosed. The reportable conditions are the symptoms that drove the visit and required evaluation: chest pain (captured as “other chest pain” in the options), shortness of breath, and nausea.

Symbicort is an inhaled combination medication containing an inhaled corticosteroid (ICS) and a long-acting beta-agonist (LABA). In outpatient chart review, this medication class is most strongly associated with chronic airway inflammatory diseases requiring controller therapy—especiallypersistent asthma(and also maintenance therapy for COPD, though COPD is not an option here). For CDI purposes, medication-to-diagnosis linkage can act as a clinical indicator supporting clarification when the visit note lists respiratory symptoms but does not clearly document the chronic condition being treated or its acuity/status. Symbicort is not used to treat musculoskeletal degenerative disease (osteoarthritis), peripheral nerve pain from diabetes (diabetic neuropathy), or cardiac pump failure (congestive heart failure). When Symbicort appears on the active med list, outpatient CDI commonly checks that the provider’s documentation appropriately reflects asthma classification (intermittent vs persistent), current control, exacerbation status if applicable, and that the condition is being monitored/assessed/treated during the encounter to support reportability and accurate coding.

In outpatient CDI, diagnoses reported for an encounter must be supported as current and clinically relevant to that visit (evaluated, monitored, assessed, treated, or otherwise affecting care). The note documents ahistoryof sick sinus syndrome with pacemaker placement in 2016 and indicates the current rhythm ispaced. The most clearly reportable, present condition is thepresence of a cardiac pacemaker, which is a status that can affect clinical decision-making (e.g., interpretation of rhythm findings, medication choices, procedures, and future cardiac evaluation) and is appropriate to code when documented. However, the documentation does not show that sick sinus syndrome itself was actively assessed or managed during this visit; it is referenced as a past condition leading to the device. Because outpatient coding does not assume an active diagnosis solely from historical mention, the best action is to capture thepacemaker statuscode only. Retrospective amendment requests or rebilling steps are not the first-line CDI action when the documentation does not support active management of the underlying arrhythmia.

The strongest CDI query opportunity is clarifying whether the UTI iscatheter-associated. The patient has anindwelling Foley catheter, significant leukocytosis (WBC 20), apositive urine culture for E. coli, and is being treated with antibiotics for UTI—these indicators raise a clear question about theetiology of infectionand whether it is related to the urinary catheter. In outpatient CDI practice, linking the infection to a device (when clinically supported) improves documentation accuracy, supports correct code assignment, and has important quality and compliance implications because catheter-associated UTIs are captured differently than uncomplicated UTIs. By comparison, querying the “etiology of BPH” is not supported as an immediate gap (BPH is already stated), and the “etiology of low back pain” is less directly tied to the documented treatment focus (UTI management). “Leukocytosis” is a lab finding that is already objectively supported and often represents a symptom/abnormal result rather than the principal clarification needed. Therefore, confirming whether the UTI is related to the Foley catheter is the best, most clinically anchored query.

In the CMS-HCC model, certain disease groupings are arranged in hierarchies so that when multiple related conditions are reported for the same patient, only the most severe (highest-ranked) HCC in that hierarchy is counted for risk adjustment. This prevents “double counting” of clinically related conditions that represent the same underlying burden of illness. The cancer-related HCCs in the 35–38 range are an example of this hierarchical design: if a patient has diagnoses that map to more than one of these HCCs, the model retains the highest-ranked category and suppresses the lower ones. Therefore, the correct hierarchy order is from the most severe category (HCC 35) down sequentially through HCC 36, HCC 37, and HCC 38. From an outpatient CDI perspective, this reinforces why accuracy and specificity matter: documentation should clearly establish the most clinically severe, active, and treated condition so the correct (highest) HCC is captured, rather than relying on nonspecific or less severe descriptors that could under-represent patient complexity.

Targeted Probe and Educate (TPE) is an education-focused review initiative intended to improve billing accuracy and reduce future denials by combining targeted claim review with direct provider/supplier feedback. In outpatient CDI terms, TPE aligns with a “fix-forward” approach: auditors identify specific error patterns (often documentation, medical necessity, coding, or coverage rule issues), then provide one-on-one education so the provider can correct processes and documentation habits. This is distinct from Recovery Audit Contractors (RACs), which primarily identify and recoup improper payments, often after the fact, and are not structured as an individualized education cycle. The OIG Work Plan identifies oversight priorities and areas of potential fraud/waste/abuse; it does not deliver provider-level coaching to reduce denials. CERT measures Medicare improper payment rates through sampling and can drive policy/education broadly, but it is not designed as individualized, iterative provider education. Because TPE is specifically built around targeted review plus direct education to prevent repeat errors and reduce appeals, it is the best answer.

When documentation states “metastatic uterine cancer,” the most important missing element for complete, accurate outpatient coding iswhere the cancer has metastasized(the secondary site[s]). In ambulatory CDI, identifying secondary sites best clarifies the full scope of disease being monitored and treated because metastatic disease coding relies on documenting both the primary malignancy and the specific metastatic location(s) (e.g., lung, liver, bone, peritoneum, lymph nodes). This supports correct severity representation, risk capture, treatment intent, and medical necessity for ongoing chemotherapy follow-up. While tumor morphology can be clinically relevant, it is usually established earlier in the diagnostic pathway and does not, by itself, define current metastatic burden. Likewise, reviewing labs or MRI results may provide supportive indicators, but they do not replace provider documentation of theconfirmed metastatic sitesbeing managed. A compliant query focused on secondary sites prompts the provider to document the current metastatic disease status (active, responding, progressing) and specific locations, which most directly identifies the conditions under treatment.

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

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.

A compliant query is designed to clarify the record without steering the provider to a predetermined answer. ACDIS-aligned standards stress two essentials: neutrality (non-leading phrasing) and clinical support (relevant indicators). Clinical indicators might include vitals, labs, imaging, medications, documented symptoms, problem list history, or assessment findings that create the need for clarification. The query should present the inconsistency or ambiguity, reference the supporting facts, and offer clinically reasonable answer choices including “other” and “unable to determine” when appropriate. This structure protects documentation integrity and reduces regulatory risk, because leading queries can be interpreted as directing documentation for payment rather than accuracy. Verbal-only queries are not sufficient when they affect coding; CDI programs typically require a documented query trail per policy. Purely open-ended questions without context are also problematic because they do not show why clarification is needed and may not be actionable. Proper non-leading, indicator-supported queries improve accuracy of diagnosis specificity, severity capture, and defensibility in audits while preserving provider autonomy.