CCDS-O Certified Clinical Documentation Specialist-Outpatient (CCDS-O) Question and Answers

In outpatient CDI, “pre-visit” or prospective preparation focuses on chronic, clinically significant conditions that are likely to remain active and that should be reassessed and documented with clear MEAT support (monitor, evaluate, assess/address, treat) during the upcoming encounter. Epilepsy, chronic heart failure, and Crohn’s disease are all long-term conditions that commonly require ongoing medication management, monitoring, and periodic reassessment, making them strong candidates for reminder/education to ensure the provider documents current status (controlled vs uncontrolled, exacerbation, complications, and treatment plan). This also supports accurate risk adjustment because chronic conditions with ongoing impact are the ones expected to be recaptured when addressed. In contrast, option C includes “family history,” which is not a current active condition for risk adjustment, and options D includes acute/self-limited problems (syncope episode, pharyngitis) that are less appropriate as pre-visit chronic-condition prompts. Option B mixes chronic disease with items that may be historical or encounter-specific (compression fracture timing/status), making it less consistently targetable than option A.

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 Option C is inaccurate because simply adding more diagnoses can introduce noise and increase the risk of outdated or incorrect conditions being propagated (“problem list bloat”), which can harm patient safety and lead to inaccurate coding.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 typicallydo notindependently 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.

In outpatient CDI and coding education, selecting the correct E/M code starts with identifying theencounter category(e.g., office/outpatient vs inpatient/observation vs ED) and whether the patient isnew or established, because these define the applicable CPT code range. Next, thelevel of serviceis selected within that range based on the documentation supporting the required elements for that code family. For most E/M services, “site of service” (place/setting) and “new vs established” are foundational code-selection drivers, while “level” is determined by the record’s support for the applicable leveling methodology (commonly medical decision making and, when allowed/appropriate, time). Time can be a valid leveling method for many office/outpatient E/M visits, but it is not universally the basis for the majority of E/M services across all categories; it is an alternative pathway when documentation supports it. Physician specialty and patient age do not define the majority of E/M code selection. Therefore, the best overall statement is new/established status + site of service + level of service.

In the outpatient setting, uncertain diagnoses described with terms such as “likely,” “probable,” “suspected,” or “rule out” generally arenotreported as established conditions for coding purposes. Instead, the encounter is coded to theconfirmed signs and symptomsdocumented and evaluated at that visit. Here, the provider’s assessment is “likely UTI,” with urine culture results still pending, so a definitive UTI diagnosis is not yet confirmed within the scenario. Likewise, the organism (E. coli) cannot be coded because it is only suspected and not confirmed until culture results are finalized. Outpatient CDI emphasizes aligning reportable diagnoses to what is clearly supported as present and addressed during the visit. The note explicitly identifies pain, urinary frequency, and fever—symptoms that drove evaluation and treatment (antipyretics and antibiotic initiation). Between the answer choices, “abdominal pain, fever, and urinary frequency” best represents the reportable conditions based on documented, evaluated symptoms without coding an uncertain infection diagnosis or an unconfirmed causative organism.

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.

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.

CMS-HCC risk adjustment assigns a baseline coefficient (weight) to each qualifying HCC condition, but certaincombinations of conditionscan increase predicted cost beyond what would be expected by simply adding the two individual weights. These added increments are captured throughdisease interaction factors, which apply when specific conditions coexist (for example, diabetes with certain severe complications, or other paired conditions defined by the model). In outpatient CDI, this is why documentation must clearly support both diagnoses—each must be clinically evaluated/managed and meet reporting rules—because accurately capturing the interacting conditions can legitimately increase the beneficiary’s risk score. By contrast,hierarchiesare designed to prevent double-counting within related condition families (the more severe manifestation typically supersedes a less severe one), which often limits—not adds—separate weights.Resource-based relative valuesandconversion factorsbelong to physician fee schedule payment methodology for services/procedures (RVUs and payment conversion), not HCC risk score calculation. Therefore, disease interactions are the correct concept that adds risk score weight beyond individual HCC coefficients.

RADV (Risk Adjustment Data Validation) is an audit process used to validate that risk-adjusting diagnoses submitted for payment are supported by compliant medical record documentation. A foundational requirement is that the record is properly authenticated by an acceptable provider—meaning the documentation must be attributable to the treating clinician through a valid signature. In compliant documentation standards emphasized in outpatient CDI education, acceptable authentication may be a traditional hand-written signature or an electronic signature/attestation that meets organizational and regulatory policy. Option B is not correct because diagnoses do not have to appear only on a facesheet or “final diagnosis” list; they may be supported within the body of the note (assessment/plan, problem-based charting, or other authenticated sections) as long as they are clearly documented and clinically supported. Options A and D are not reliably true statements in a general RADV context because RADV focuses on diagnoses supported by appropriate provider documentation and acceptable encounter record criteria; “technician-assigned” diagnoses are not acceptable, and radiology documentation alone may not meet all validation expectations depending on the program’s rules and encounter context.

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 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 Oâ‚‚) and request the most accurate diagnosis and specificity/status.

The Medicare Shared Savings Program (MSSP) is designed to move reimbursement away from pure volume-based payment and toward value by rewarding organizations that reduce the total cost of care for an assigned Medicare population while meeting defined quality performance requirements. In MSSP, eligible provider groups participate as Accountable Care Organizations (ACOs) and are compared against a financial benchmark. If the ACO’s actual spending comes in below the benchmark and quality standards are achieved, the ACO can earn a portion of the savings—hence “shared savings.” Outpatient CDI supports MSSP success by ensuring documentation accurately reflects patients’ true disease burden (supporting appropriate risk adjustment for benchmarking), and that conditions addressed during visits are clearly documented as evaluated/managed to support reliable coding and quality measurement. While improving transitions of care may be a strategy that helps achieve savings and quality goals, it is not the core purpose of the program itself. Likewise, MSSP is not intended to increase fee schedule payments or simply optimize risk scores; the primary aim is participating in value-based care and sharing in savings when performance supports it.

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.

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.

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 ambulatory CDI, “risk adjustment impact” means the clarification can change whether an HCC-relevant condition is captured accurately (or captured at all) based on ICD-10-CM reporting rules. Here, “DM2 with peripheral neuropathy with diabetic ulcer” already establishes diabeteswith complications, so querying option B adds little—complications are already documented. “Nicotine dependence” is not supported because the patient stopped smoking two years ago; at most, this supports a history of nicotine dependence, which generally does not drive HCC risk scoring. “Morbid obesity” may be clinically relevant (BMI 43 supports it), but obesity typically does not produce meaningful CMS-HCC risk adjustment impact compared with other chronic categories. Thediabetic ulcerdoes matter: correct reporting requires an additional L97.- code that depends onulcer severity/depth(skin breakdown, fat layer exposed, necrosis of muscle/bone). Clarifying depth supports accurate ulcer severity coding and can affect HCC capture/validation for chronic ulcer burden.

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.

The Relative Factors table shown is a demographic/eligibility-driven component of risk scoring for female beneficiaries, separating patients by setting/status (community vs institutional) and age band. “Institutional” beneficiaries carry higher expected cost because they typically require more resources and support than community patients. In the chart, the institutional relative factor for females age 70–74 is higher than the community factors shown for similar ages and higher than the 90–94 institutional factor displayed. Among the answer choices, option C is the only patient who matches an institutional setting (skilled nursing facility) in the 70–74 age band (72 years). Option D is also institutional, but the table’s 90–94 institutional value is lower than the 70–74 institutional value in this specific chart. Options A and B are community patients, whose relative factors are lower than the institutional values shown. While the listed diagnoses are clinically important and may affect HCC-based risk, the question asks “based on the above chart,” so the highest risk is determined by the chart’s demographic/setting factor—making the 72-year-old institutional patient the highest.

Compliant querying principles taught in outpatient CDI allow the CDI/coding professional to present a multiple-choice query that includes reasonable diagnostic optionssupported by the current encounter’s clinical indicators. Including a “new” diagnosis as an option is acceptable when it is clinically supported by documented findings (signs/symptoms, test results, treatments, clinical course) and the query is written in a non-leading manner—typically with balanced options and an “other” and/or “unable to determine” choice. This approach helps the provider clarify the most accurate condition being evaluated or treated without steering toward a particular response. Option A is not compliant because relying solely on prior encounter documentation (without current relevance) risks coding historical conditions that are not addressed today. Option B is generally discouraged because calling out HCC status can be perceived as prompting for payment impact rather than clinical accuracy. Option D is incorrect because including relevant clinical indicators is essential; omitting them weakens the clinical basis and does not make a query less leading—rather, it makes it less defensible.

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.

Outpatient CDI practice supports accurate, provider-validated diagnoses; CDI should not “diagnose,” direct the provider to add a specific condition, or independently add diagnoses to the claim. Here, the documentation shows clinical indicators (fatigue/lethargy and hemoglobin drop from 10.4 to 8.6) and a treatment plan (oral iron and repeat H/H), but the provider has not stated a definitive diagnosis such as postoperative anemia, iron deficiency anemia, acute blood loss anemia, or anemia due to chronic disease. The best CDI action is to issue a compliant query that summarizes the relevant indicators and treatment and asks the provider to document the appropriate diagnosis and etiology, if clinically supported, and to link it to the plan of care. Option A is inappropriate because it leads the provider toward a specific diagnosis. Option D is noncompliant because coding must follow documented provider diagnoses. Option B may be a reasonable internal check, but it does not resolve the documentation gap.

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.

In outpatient risk adjustment, the highest-impact clarification is often the one that determines whether a condition is currently active (and therefore risk-adjustable) versus historical/resolved. “Leukemia” listed on the problem list, plus active review of CBC/WBC and referral to oncology, strongly suggests ongoing disease evaluation/management. ACDIS outpatient CDI principles emphasize querying to confirm whether the leukemia is active, in relapse, or in remission because that distinction can change code selection from an active malignancy to a history code, and history codes typically do not carry the same risk adjustment impact as an active HCC-bearing diagnosis. While heart failure type/acuity and malnutrition severity are also important for specificity and may affect risk capture, they generally represent refinement of already-established chronic conditions rather than a potential “on/off” determination of a major disease category. Likewise, atrial fibrillation subtype differentiation is clinically useful but usually does not materially change risk adjustment compared with confirming an active hematologic malignancy. Therefore, clarifying leukemia status/acuity is the most impactful risk-adjusted query opportunity.

In outpatient and physician-office reporting, the diagnosis that best describes the main reason for the visit is reported as thefirst-listed diagnosis. Outpatient coding guidance emphasizes that the “principal diagnosis” concept is primarily an inpatient construct (the condition established after study to be chiefly responsible for admission). In ambulatory encounters, patients are often seen for evaluation, management, follow-up, or symptom assessment, so the coding framework usesfirst-listedto identify the condition, problem, or symptom chiefly responsible for the services provided during that encounter. Co-existing conditions may also be reported when they are addressed or affect care (e.g., monitored, evaluated, assessed/managed, or treated), but they do not replace the requirement to sequence the primary reason for the visit first. Differential diagnoses are not used as the “reason chiefly responsible” in outpatient coding unless a confirmed diagnosis is established; if uncertainty remains, symptoms may be reported instead. Therefore, “first-listed diagnosis” is the correct term for the outpatient setting.

In stage 3 chronic kidney disease, impaired vitamin D activation and early disturbances in calcium-phosphate regulation commonly drive a compensatory rise in parathyroid hormone (PTH), known as secondary hyperparathyroidism of renal origin. Outpatient CDI chart review looks for clinical indicators that suggest a condition being evaluated or requiring management, and an elevated iPTH in a CKD patient is a classic indicator that supports querying the provider for CKD-related mineral and bone disorder, specifically renal secondary hyperparathyroidism, if it is clinically being assessed/treated (e.g., monitoring trends, prescribing vitamin D analogs, calcimimetics, dietary counseling, nephrology follow-up). Primary hyperparathyroidism is less supported here because it typically requires a different biochemical pattern and clinical context (often hypercalcemia) rather than being driven by CKD physiology. Hypoparathyroidism is the opposite process (low PTH), making option C inconsistent with the lab finding. Option D is not supported because phosphorus is normal, not low, and hypophosphatemia is not documented as a driver. Therefore, querying for renal secondary hyperparathyroidism is most appropriate.

The CMS-HCC model is arisk adjustmentmethodology used primarily to setcapitated paymentsfor Medicare Advantage (MA) organizations based on the expected cost of caring for their enrolled beneficiaries. Under this approach, CMS calculates a Risk Adjustment Factor (RAF) for each member using demographic variables (such as age/sex and certain entitlement factors) plus disease burden captured from ICD-10-CM diagnoses that map to Hierarchical Condition Categories (HCCs). The resulting RAF increases or decreases the plan’s payment to better match predicted healthcare needs—higher RAF for sicker, more complex patients and lower RAF for healthier patients. ACDIS outpatient CDI education emphasizes that the purpose is not physician reimbursement based on a “principal diagnosis” (an inpatient concept) and not payment distribution tied directly to quality performance (that aligns more with MIPS/VBP frameworks). It also does not adjust capitation payments specifically “to physicians,” nor does it exclude advanced practice providers in the way described. The correct use is todetermine MA plan capitation paymentsthrough risk-adjusted member-level projections.

A core outpatient guideline difference is how to handle uncertainty in diagnoses. In the inpatient setting, facilities may code diagnoses documented as “probable,” “suspected,” “likely,” or “rule out” at discharge if they meet inpatient reporting rules. In outpatient/office settings, however, uncertain conditions generally are not coded as established diagnoses because the encounter is often focused on evaluation rather than confirmed final diagnoses. Instead, outpatient coding relies on confirmed conditions and/or signs and symptoms when a definitive diagnosis has not been made. This is why outpatient CDI education emphasizes precise provider language: if the clinician is still evaluating, they should document the symptom/abnormal finding and the assessment plan; if the condition is confirmed, they should state it clearly and link it to evaluation/management performed. Options A, B, and D are incorrect because chronic conditions may need to be reported whenever they are assessed/managed, “first-listed” is an outpatient concept distinct from inpatient “principal,” and documentation should support all clinically relevant conditions addressed, not only the chief complaint.

When a CDI query references information from an externally shared note, best practice is to include enough identifying detail so the provider can locate and validate the sourceeven if access is later revokedor if the shared record becomes unavailable. From an outpatient CDI and HIPAA-aligned workflow perspective, the query should clearly cite: where the information came from (thelocationof the shared note within the EHR/external record set),whoauthored it (provider name),whenit was created (date of shared note), and thespecific clinical documentationbeing referenced (the relevant statement/findings). This supports transparency, auditability, and minimizes the risk of misattribution or relying on inaccessible information. Options B–D are missing one or more critical elements—most notably the date and/or location of the shared note—making it harder to verify the source. Including “follow-up procedure” is not the priority for HIPAA-compliant source identification; the key need is traceability of the external documentation used to support the clarification request.

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.

RAF (Risk Adjustment Factor) is best explained to providers as a population-health and resource-planning tool, not a visit-level payment lever. In outpatient risk adjustment models, diagnoses and demographics are used to estimate the patient’s overall disease burden and the expected cost/resources required to meet that patient’s healthcare needs. When documentation accurately reflects active conditions and their specificity, the patient’s risk profile is represented more realistically. That improves care in practical ways: it supports appropriate allocation of care management services (e.g., nurse navigators, chronic care programs), helps organizations anticipate medication, testing, specialist, and follow-up needs, and improves fairness of performance benchmarking by comparing outcomes and costs against similarly complex patients. Option A is overly simplistic because RAF does not directly determine an individual provider’s reimbursement for a given encounter; it influences broader payment and benchmarking methodologies tied to attributed populations. Option C is not what RAF measures, and option D confuses RAF with medical necessity, which is based on clinical documentation and coverage rules, not a risk score.

CMS assignsMedicare Administrative Contractors (MACs)to administer Medicare fee-for-service operations at the jurisdictional level, includingprocessing 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) contractorsvalidate 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.

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.

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.

APCs and DRGs are both prospective payment classification systems designed to group services that consume similar resources, supporting standardized reimbursement. DRGs group inpatient stays largely around the principal diagnosis, key procedures, complications/comorbidities, and discharge status to estimate expected hospital resource use for the admission. APCs, used primarily for hospital outpatient services, group billable procedures and services that are clinically comparable and expected to require similar levels of resources (staff time, supplies, equipment, intensity). While APCs often allow multiple payment classifications within a single outpatient encounter (because multiple procedures may be performed), that feature is not the fundamental similarity to DRGs—it’s a key difference in operational payment mechanics. Likewise, APC assignment is generally driven by CPT/HCPCS and revenue codes rather than being primarily diagnosis-dependent. The shared concept emphasized in outpatient CDI education is that both systems aim to align payment with anticipated resource utilization, which is why complete, accurate documentation is essential to support correct coding of the services and conditions that justify the level of care provided.

A key distinction is that inpatient coding is tightly linked to MS-DRG assignment and inpatient-specific sequencing rules, including selection of the principal diagnosis using the “after study” standard and capture of secondary diagnoses that qualify as complications/comorbidities (CC/MCC) when they meet reporting criteria. This makes diagnosis sequencing and documentation of severity/acuity central to inpatient reimbursement and quality measurement. Outpatient coding does not use MS-DRGs; instead, it typically uses “first-listed” diagnosis concepts for the encounter and assigns ICD-10-CM based on conditions addressed that day, with procedure payment often driven by CPT/HCPCS and, in hospital outpatient departments, packaging/OPPS logic. Therefore, statement A is incorrect (principal diagnosis is not the outpatient focus), C is incorrect (there are meaningful differences), and D is incorrect because outpatient coding absolutely depends on encounter diagnoses being documented and supported. Outpatient CDI education stresses documenting the reason for visit, linking symptoms to confirmed conditions when known, and showing MEAT for chronic conditions so outpatient coding is accurate and defensible.

In the outpatient setting, when the provider does not document a definitive diagnosis for the acute complaint (e.g., influenza, sinusitis, URI), ICD-10-CM guidance directs coders to report thesigns and symptomsthat are documented and addressed. Here, the clinician documentscongestion, fever, and malaiseand provides treatment instructions for those symptoms (OTC meds, rest, follow-up). That makes the symptom codes the most appropriate representation of the reason for the encounter. Outpatient CDI principles further emphasize that chronic conditions likeDM, GERD, and HTNshould only be coded when the documentation shows they wereevaluated, monitored, assessed/managed, or treatedduring the visit (e.g., status, control, medication adjustment, related testing, counseling). In this note, the plan targets only the acute symptoms and does not demonstrate active management of the chronic conditions beyond listing history/medications. Therefore, the guideline most directly applicable to correct coding of the encounter iscodes that describe symptoms and signs.

In outpatient CDI and coding guidance, an “uncertain diagnosis” is identified by wording that indicates the provider has not confirmed the condition (e.g., possible, probable, suspected, rule out, question of, concern for). These terms reflect diagnostic consideration rather than an established diagnosis. Option A uses the phrase “concern for,” which is a classic uncertainty qualifier and signals the provider is considering streptococcal pneumonia but has not definitively diagnosed it. In contrast, options B and D describe active treatment “for streptococcal pneumonia,” which implies the provider is managing the condition as a working diagnosis; however, in outpatient coding, treatment alone does not automatically make a diagnosis confirmed if the documentation still reflects uncertainty—CDI would look for explicit provider confirmation. Option C (“evidence of”) generally suggests supportive findings and is commonly interpreted as stronger than “concern for,” though CDI would still assess whether the provider has clearly stated a confirmed diagnosis in the assessment/plan. Therefore, the clearest uncertain descriptor is “concern for.”

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.

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.

Pulmonary rales (crackles), radiographic pulmonary edema, and peripheral (ankle) edema together strongly suggest a systemic volume overload state, most classically due to heart failure. In ambulatory CDI chart review, these findings function as clinical indicators that drive the provider’s diagnostic reasoning and typically prompt further evaluation of heart failure type and status (e.g., acute vs chronic, systolic vs diastolic, preserved vs reduced EF), along with assessment of severity and potential decompensation. Providers commonly correlate these indicators with additional data such as weight gain trends, BNP, echocardiogram findings, medication adherence (diuretics), and signs of congestion to determine whether the patient is experiencing a heart failure exacerbation requiring treatment adjustments. While pleural effusion may coexist and pneumonia can cause rales, the presence of pulmonary edema on chest x-ray plus bilateral ankle edema points more directly to a cardiac/volume etiology than an isolated infectious process. Pulmonary hypertension may contribute to dyspnea and edema but does not most directly explain pulmonary edema on imaging in the same way. Therefore, heart failure is the most likely condition to be evaluated further.