Practice Free CCDS-O Certified Clinical Documentation Specialist-Outpatient (CCDS-O) Exam Questions Answers With Explanation

Outpatient diagnosis reporting requires coding conditions that are evaluated, assessed, treated, or that influence care/management at the encounter. The chief reason for the visit is the persistent cough, which is assessed and attributed to an adverse effect of an ACE inhibitor (enalapril). For outpatient coding of medication adverse effects, the documentation supports reporting both the manifestation (cough) and the adverse effect of the drug (ACE inhibitor adverse effect), because the provider identified the causal relationship and changed therapy by discontinuing enalapril. In addition, the provider addresses chronic conditions that are clinically relevant to the visit: COPD is assessed as “stable” and the patient is instructed to continue COPD/asthma medications, demonstrating ongoing management. Hypertension also remains relevant because medication therapy is adjusted due to the adverse effect; the patient still has HTN even though one agent is discontinued. Therefore, all five items—cough, ACE inhibitor adverse effect, COPD, asthma, and hypertension—are supported and should be reported, making option D the most appropriate.

Copy-and-paste of an inpatient problem list into an outpatient note creates significant documentation integrity risks: outdated diagnoses may be carried forward, resolved conditions may appear active, and the note may not clearly show which problems were actually evaluated or managed during the current encounter. Outpatient CDI best practice is not to assume relevance (eliminating D) and not to reflexively query every listed diagnosis (B), which can be burdensome, non-targeted, and may lead to “query fatigue.” Likewise, blanket instruction to “not code” anything pasted (A) is not appropriate because some conditions may still be active and reportable if the provider documents assessment/management (e.g., monitoring, evaluation, addressing, or treatment). The most effective and sustainable action is provider education: explain why indiscriminate copy-forward threatens accuracy, compliance, medical necessity support, quality reporting, and risk adjustment validity; reinforce documenting current status and care provided for each active condition; and encourage updating the problem list and assessment to reflect what is truly addressed at the visit. Targeted queries can still be used when specific contradictions or high-impact ambiguities are identified.

AHA Coding Clinic guidance functions as an authoritative interpretive resource for correct ICD-10-CM/PCS code assignment when official guidelines or code descriptors need clarification. When Coding Clinic publishes an update that revises, clarifies, or supersedes earlier advice, outpatient CDI practice is to operationalize the newest guidance prospectively—meaning it should be applied going forward from the publication/effective timeframe of that update. This supports consistent, defensible coding and reduces compliance risk by aligning current reporting with the most current official interpretation. Applying the original advice for a calendar or fiscal year (choices A and B) is not how Coding Clinic updates are intended to be implemented; the governing principle is “most current advice controls” once released. Similarly, automatically applying updated guidance retroactively to cases from last year (choice D) is not routine CDI practice; retrospective rebilling or recoding is typically limited, policy-driven, and subject to payer rules, auditing constraints, and organizational compliance decisions. Therefore, the best action is to use the updated Coding Clinic guidance from the date it is published/implemented forward.

Across both inpatient and outpatient CDI, the single expectation that must remain consistent isquery compliance. While productivity targets, the types of query opportunities, and the way “impact” is measured can differ significantly by setting (e.g., DRG/CC-MCC focus in inpatient vs. HCC capture, specificity, and MEAT support in outpatient), the compliance framework for querying does not change. A compliant query must be clinically supported, non-leading, clearly written, and must allow the provider to independently determine the most accurate documentation based on the record. It should include relevant clinical indicators, present reasonable options (including “other”/“unable to determine” when appropriate), and avoid language that appears to request diagnoses for payment purposes. These principles protect documentation integrity, support defensible coding, and reduce audit risk regardless of whether the encounter is hospital-based or ambulatory. By contrast, “review productivity” and “revenue impact” vary widely by program design and setting, and “query opportunities” differ because inpatient vs. outpatient have different reportability rules and documentation drivers. Therefore, query compliance is the metric held to the same standard in both environments.

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.

Year 2 shows a higher total volume of MDD diagnoses (185,090 vs. 155,501), but the key CDI signal is the shift in coding specificity: “MDD, specified” increases substantially (118,516 vs. 76,318), while “MDD, unspecified” decreases (66,574 vs. 79,193). In outpatient CDI terms, this pattern is consistent with improved documentation quality and code capture—providers are describing the condition with greater clinical detail (episode type, severity, remission status, recurrence, etc.), allowing assignment of more specific ICD codes. When an HCC value (0.299) is associated with MDD, improved capture of qualifying, specific MDD codes supports more accurate risk adjustment. That increases the accuracy of projected resource need and affects future cost benchmarking (and potentially quality/utilization comparisons) because the population’s documented burden of illness is better represented. Therefore, the appropriate inference is increased patients plus increased “specified” use and decreased “unspecified,” with an impact on future benchmarking.

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.

To establish and query for a patient’s baseline renal function in the outpatient setting, eGFR is the most direct and standardized measure because it estimates kidney filtration capacity using serum creatinine (and patient factors such as age/sex, and sometimes race depending on the equation used). Outpatient CDI concepts emphasize that chronic kidney disease staging is based primarily on eGFR categories (G1–G5) and persistence over time, making eGFR trends particularly useful for determining whether the patient has CKD, whether it is worsening, and what stage should be documented. Serum creatinine alone can suggest decline but does not translate cleanly to CKD stage without calculating eGFR and considering patient characteristics. BUN is influenced by hydration status, protein intake, bleeding, and catabolic states, so it is less reliable for baseline kidney function assessment. ACR is important for detecting albuminuria and refining CKD classification/risk stratification, but it complements eGFR rather than replacing it as the primary indicator of baseline filtration function.

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.

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.

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.

The patient’s RAF is calculated by adding the applicable HCC weights plus the demographic factor, but only after applying the HCC model’s hierarchy rules. In outpatient risk adjustment education (as emphasized in ACDIS-based training), certain condition groups are hierarchical—meaning multiple related diagnoses in the same hierarchy do not “stack.” Instead, only the highest-ranked HCC in that disease group contributes to the RAF. Here, “diabetes with nephropathy” and “diabetes with retinopathy” both carry the same HCC weight (0.166) and fall within the diabetes complication hierarchy, so they do not add together; only one 0.166 value is counted. Atrial fibrillation contributes separately (0.299), and the demographic risk factor contributes (0.332). Therefore, the final RAF is 0.166 + 0.299 + 0.332 = 0.797. This illustrates why outpatient CDI focuses on documenting all clinically supported conditions for care accuracy, while understanding that some related diagnoses won’t increase RAF beyond the highest applicable HCC in that hierarchy.

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.

CDI staff do not alter the legal health record and should not “code it as is” when documentation is unclear, conflicting, or incomplete in a way that impacts accurate reporting. The compliant next step is to issue a provider query for clarification, ensuring the final record accurately reflects the provider’s clinical judgment. ACDIS-guided outpatient CDI emphasizes that queries are a quality and compliance tool: they reconcile discrepancies (e.g., conflicting diagnoses across notes, missing linkage between symptoms and conditions, unclear acuity such as “CHF” without type/status, or ambiguous infection documentation). The query should be supported by clinical indicators from the chart and should ask the provider to document the clarified diagnosis/status in the record (progress note, addendum, or appropriate attestation). Escalation to compliance is reserved for patterns of nonresponse, suspected integrity concerns, or systemic issues, not routine discrepancies. The objective is to achieve a complete, consistent clinical story that supports coding, risk adjustment, quality reporting, and medical necessity—through provider clarification, not CDI edits.

Outpatient CDI programs should share provider-facing metrics that are clinically meaningful, aligned with ambulatory documentation goals, and unlikely to be perceived as payment-driven prompting.RAF scoresare an appropriate metric because they reflect how well the documented and coded condition burden represents the patient panel’s complexity in risk adjustment models. Discussing RAF supports education around accurate diagnosis capture, specificity, and annual recapture of active chronic conditions that are monitored, evaluated, assessed/addressed, or treated. In contrast,APC payment ratesare facility OPPS payment constructs and typically are not actionable for individual ambulatory provider documentation improvement.HCC per member per month paymentsis explicitly financial and can create compliance risk by tying documentation discussions directly to payment, which outpatient CDI guidance warns against in provider messaging.MCC ratesare primarily an inpatient DRG severity concept and are not the most relevant outpatient performance measure. Therefore, RAF scores best balance provider relevance, program goals, and compliant education focus.

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.

In the CMS-HCC model, many related conditions are organized intohierarchiesso that only themost severemanifestation within a disease family contributes to the RAF. This prevents double counting when multiple codes describe progressive severity of the same underlying condition. Cancer categories are a common example: a diagnosis reflectingmetastatic diseaserepresents substantially higher expected resource utilization than a diagnosis of localized/primary malignancy. In this scenario, the February prostate cancer maps to a lower-severity HCC (HCC 23), while the October documentation of prostate cancerwith bone metastasesmaps to a higher-severity HCC (HCC 18). When both are captured within the applicable period, the hierarchy logic retains thehigher-weighted metastatic categoryand suppresses the lower category. The timing of which was coded first does not control the hierarchy outcome, and both HCCs are not counted together when they fall within the same hierarchical grouping. Therefore, the patient’s risk score calculation reflects HCC 18 rather than HCC 23.

Outpatient CDI follow-up must balance responsiveness with confidentiality and professional standards. The most compliant approach is to communicate query-related information in a setting that protects protected health information and minimizes the risk of incidental disclosure. Discussing the query in a private room with the door closed supports timely clarification while maintaining privacy, avoiding conversations that could be overheard by patients, visitors, or staff who are not involved in the patient’s care. Catching the provider in the hallway is faster but increases privacy risk because clinical details can be overheard, and it may also distract the provider in a high-traffic environment. Leaving a sticky note on the next patient’s chart is inappropriate because it can be seen by others, may be misplaced, and can create compliance and medico-legal concerns (including mixing patients or leaving PHI unsecured). Waiting for the next scheduled meeting may be compliant but does not support a quick response, potentially delaying coding completion and data integrity. Therefore, a private discussion is both the quickest and most compliant option.

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

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.

In the CMS-HCC risk adjustment methodology, each qualifying condition maps to an HCC with an associated coefficient (weight). However, CMS also recognizes that certaincombinationsof conditions can drive costs and complexity more than the simple sum of individual HCC weights. To account for this, the model includesdisease interaction terms—additional coefficients applied when specific conditions coexist in the same beneficiary (for example, selected interactions involving diabetes and particular complications, or other defined pairings). This is why outpatient CDI focuses not only on capturing each chronic condition accurately, but also ensuring both conditions are clearly documented as assessed/managed and meet reporting requirements, because capturing both can legitimately trigger interaction weights. By contrast,hierarchiesprimarily prevent double-counting within related disease groups by allowing the most severe manifestation to “trump” a less severe one; this generally controls/limits weight rather than adding extra.Resource-based relative valuesandconversion factorsare physician fee schedule payment components (RVUs and payment calculation), not HCC risk scoring elements. Therefore, disease interactions are the correct answer.

The most compliant query is the one that is clinically supported, non-leading, and focused on clarifying documentation for correct reporting and medical necessity—without directing the provider to “add” diagnoses or document conditions for payment purposes. Option A presents relevant clinical context (no GI symptoms; family history) and asks the provider to clarify whether the planned colonoscopy is screening or diagnostic, which is a legitimate documentation clarification affecting correct code selection and coverage rules. It does not imply a desired answer and does not instruct the provider to document additional diagnoses. Option B is problematic because it instructs the provider to “document these conditions” if treated, which can be perceived as prompting and is not tied to encounter-specific indicators. Option C is based primarily on historical information and asks a yes/no about remission, which can be leading and may not reflect current-visit evaluation. Option D effectively asks the provider to add a diagnosis based on nursing documentation, which risks leading language and requires provider confirmation and assessment. Therefore, A is most compliant.

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.

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.

This question applies the outpatient risk adjustment “reserve” concept: predicted cost is estimated by multiplying the member’s risk factor contribution by a baseline per-member-per-month (PMPM) amount, then annualizing. The morbid obesity HCC factor is 0.186, and PMPM is $800. First compute the monthly cost impact: $800 × 0.186 = $148.80 per month per patient. Convert to yearly: $148.80 × 12 = $1,785.60 per patient per year. The year-over-year increase in patients with morbid obesity documentation is 11,000 ? 10,000 = 1,000 additional patients. Multiply the annual per-patient impact by the additional patient count: $1,785.60 × 1,000 = $1,785,600. Outpatient CDI programs emphasize that improving documentation specificity (when clinically supported) can change whether an HCC is captured, which can affect RAF-based projections and resource planning. However, documentation must still be accurate, supported, and reflect conditions assessed/managed during the encounter.

The CMS-HCC risk adjustment methodology is designed to estimate an individual beneficiary’s expected healthcare resource use and cost relative to an average Medicare beneficiary. It does this by converting demographic factors (such as age/sex and certain eligibility variables) plus documented, coded chronic conditions into a Risk Adjustment Factor (RAF). That RAF is then used to forecast the likely cost of caring for that specific patient in the payment year and to adjust benchmarks/payments so plans and providers managing sicker patients are compared more fairly to those managing healthier patients. This is why outpatient CDI emphasizes accurate, specific documentation and annual recapture of active conditions that are monitored, evaluated, assessed/addressed, or treated—because those coded conditions drive the predicted cost profile. CMS-HCC is not a mortality prediction tool (eliminating B), nor is it intended to measure “group costs” as the primary target (C), even though aggregated risk scores can be used for population analytics. It also does not measure an individual physician’s cost of care provision (D); it measures patient-level expected cost 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.

In outpatient CDI practice, a common reason to query is a mismatch between what is being evaluated/treated and what is explicitly documented as an active condition for the encounter. A diagnostic chest x-ray aligns with the already-documented symptom (chronic cough), and a nutrition specialist referral aligns with an established diagnosis (obesity); neither inherently suggests an undocumented condition. Prescribing captopril aligns with documented HTN management, so it generally would not create documentation ambiguity requiring clarification (even though ACE inhibitors can be associated with cough, the plan alone does not establish a new reportable diagnosis). In contrast, ordering an HbA1c often signals assessment for diabetes, impaired glucose regulation, or monitoring of known diabetes. Because diabetes is not listed in the history provided, the HbA1c order may prompt the CDI specialist to query whether the provider is evaluating a suspected or existing glycemic disorder, whether there is a diagnosis such as prediabetes/diabetes being addressed, and to ensure the record clearly supports the medical necessity and any reportable condition.

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.

The CDI specialist shouldreview CKD staging criteria with the providerbecause assigning CKD based on asingle eGFR valuecan be clinically unreliable and may lead to inaccurate documentation and coding. Outpatient CDI guidance emphasizes that documentation must reflect a condition that isclinically valid, supported by the record, and accurately described, especially for chronic diseases. CKD is generally established by evidence of decreased kidney function or kidney damage that ispersistent, not a one-time lab that could be affected by hydration status, acute illness, medications, or transient physiologic changes. While an eGFR of52falls within the numeric range commonly associated withstage 3a, the key CDI issue is thefoundation for diagnosing chronic disease, not simply whether the number is “reportable.” Option A inappropriately directs CDI to add diagnoses to claims; CDI supports providers and coding, but does not independently “add” conditions. Option C is incorrect because chronic conditions may be coded when addressed/impact care, not only when actively treated. Option D is unsupported because eGFR 52 does not suggest stage 4.

To establish and query for a patient’s baseline renal function in the outpatient setting, eGFR is the most direct and standardized measure because it estimates kidney filtration capacity using serum creatinine (and patient factors such as age/sex, and sometimes race depending on the equation used). Outpatient CDI concepts emphasize that chronic kidney disease staging is based primarily on eGFR categories (G1–G5) and persistence over time, making eGFR trends particularly useful for determining whether the patient has CKD, whether it is worsening, and what stage should be documented. Serum creatinine alone can suggest decline but does not translate cleanly to CKD stage without calculating eGFR and considering patient characteristics. BUN is influenced by hydration status, protein intake, bleeding, and catabolic states, so it is less reliable for baseline kidney function assessment. ACR is important for detecting albuminuria and refining CKD classification/risk stratification, but it complements eGFR rather than replacing it as the primary indicator of baseline filtration function.

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 outpatient coding, selection and sequencing must follow ICD-10-CM Official Guidelines, including obstetric chapter rules. When HIV is documented in a pregnant patient, the pregnancy complication code is sequenced first because the pregnancy status frames the encounter and drives the obstetric complication coding structure. The phrase “HIV positive” (without documentation of HIV-related illness or “HIV disease”) is treated asasymptomatic HIV infection status, which aligns with the status concept rather than active HIV disease. Therefore, the correct approach is to codepregnancy complicated by asymptomatic HIVfirst (obstetric complication category), followed by the HIV status code to fully describe the condition affecting the pregnancy. Options that place “pregnancy” second do not follow obstetric sequencing conventions, and options that assume “HIV disease” overstep the documentation because “HIV positive” alone does not confirm symptomatic HIV disease. Outpatient CDI best practice would be to query if the provider intends HIV disease versus asymptomatic status, but based strictly on the given statement, pregnancy with asymptomatic HIV is most appropriate.

Outpatient/provider coding relies on two major code sets: ICD-10-CM for diagnoses and CPT/HCPCS for professional services, procedures, and supplies. Because of that, outpatient coding authority is anchored not only in the ICD-10-CM Official Guidelines and AHA Coding Clinic guidance for diagnosis reporting, but also in the authoritative guidance that clarifies CPT/HCPCS reporting. ACDIS outpatient CDI education stresses that CDI specialists must understand both sides: the diagnosis coding rules (ICD-10-CM) and the procedural/service reporting rules (CPT/HCPCS) that drive much of outpatient reimbursement. AMA’s CPT Assistant is a key interpretive authority for CPT coding guidance, while AHA’s Coding Clinic for HCPCS provides clarification on HCPCS Level II reporting. The other options focus on ICD-10-PCS guidelines and DRG tools, which are primarily inpatient facility concepts (PCS is inpatient procedure coding; DRGs are inpatient payment groupers). Therefore, the correct supplemental outpatient authority pair is AHA’s Coding Clinic for HCPCS and AMA’s CPT Assistant.

The clinical indicators strongly supportalcohol dependence, not merely alcohol “use” or “abuse.” The patient reports heavy, compulsive intake (early-morning drinking and at least a pint daily), and the provider explicitly documents that she “continues to be dependent on alcohol” and discusses treatment options—this aligns with a dependence-level disorder being addressed.Hypertensionis also supported because the BP is elevated (165/89) and the provider documents management activity (medications and diet counseling), meeting encounter relevance/reportability expectations.Diabetesis listed among chronic conditions, but the scenario provides no indicators of complications (no neuropathy, CKD, ulcers, retinopathy, etc.), so the supported choice is DM type 2without complicationsrather than “with complications.” Although COPD is listed in the “including” statement, no COPD-specific assessment/monitoring/treatment is described in the indicators provided, so the best-supported grouped option focuses on the conditions with clear supporting indicators and management in the note: DM2 without complications, HTN, and alcohol dependence.

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.

ACOs commonly focus on strengthening documentation and coding of clinically relevant chronic conditions because many quality and utilization comparisons—including readmission-related performance assessments—are influenced by patient complexity and risk adjustment. When chronic diseases and their specificity (e.g., stage, severity, manifestations) are under-documented, attributed patients can appear “healthier” on paper than they truly are, which can worsen an organization’s apparent readmission performance when outcomes are compared to an expected benchmark. Provider education that reinforces documenting active conditions that are monitored, evaluated, assessed/addressed, or treated helps ensure an accurate picture of acuity and comorbidity burden. This improves the integrity of risk adjustment inputs and supports fairer benchmarking, while also strengthening medical necessity and continuity-of-care communication across settings. The other choices represent either noncompliant behavior (avoiding condition reporting), poor documentation practice (promoting unspecified codes), or operational maneuvering that may be perceived as gaming rather than a sustainable quality strategy. Therefore, educating providers to capture chronic conditions accurately is the best and most commonly used improvement approach.