ACDIS CCDS-O Certified Clinical Documentation Specialist-Outpatient (CCDS-O) Exam Practice Test

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

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

In 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.

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

In the outpatient setting, diagnoses documented as uncertain (e.g., “possible,” “probable,” “suspected,” “rule out”) are generally not coded as confirmed conditions; instead, coding is based on confirmed diagnoses or, when not confirmed, the presenting signs/symptoms. Here, the provider documented only “possible Zika,” which is not a confirmed diagnosis for outpatient reporting. Even though the CDI specialist sees a positive serology result, lab data alone does not replace provider diagnostic confirmation in the assessment/plan. The appropriate next step is to query the provider to confirm whether Zika is the established diagnosis based on the positive test (and whether it is clinically addressed during the encounter). If confirmed, Zika can be coded appropriately and sequenced based on the reason for the visit; if not confirmed or still under evaluation, the symptom (fever) remains first-listed. Option B is incorrect because coders do not “code the result” of a serology test as a diagnosis; they code the condition the test supports once clinically confirmed.

The record contains conflicting documentation: the problem list includes both “type 2 diabetes without complications” and diabetes with complications (erectile dysfunction association), while the most recent clinic note indicates the provider addressed “DM2 with autonomic neuropathy,” which is clearly a diabetic complication. In outpatient CDI, the first priority is to resolve internal inconsistency so coding accurately reflects the patient’s current clinical status and what was evaluated/managed at the encounter. A query should therefore focus on whether the patient’s diabetes iswith complications(and which complications are active/being addressed) versus trulywithout complications, because “without complications” is generally not appropriate when neuropathy/other manifestations are present and being managed. CDI staff also should not unilaterally remove items from the provider-maintained problem list, and asking the patient is not a reliable documentation/coding source for establishing diagnoses. Once the provider clarifies diabetes complication status, a follow-up clarification can address specific linkages (e.g., erectile dysfunction due to diabetes) if needed for correct code assignment

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.

For outpatient E/M reporting, “new vs. established patient” status is determined using a lookback period based on prior professional services. Anestablished patientis one who has received face-to-face (or other qualifying professional) services from the same physician or another physician/qualified healthcare professional of thesame specialty and subspecialtyin thesame group practicewithin theprevious three years. This definition is critical for compliant coding because it drives which E/M code family is available (new patient codes vs established patient codes), and it affects relative valuation, documentation expectations, and audit risk. Outpatient CDI education emphasizes helping providers document the medical necessity and complexity of the visit regardless of patient status, but also ensuring correct administrative classification so coders select the correct code set. The three-year window prevents inappropriate use of new patient codes when the patient has an ongoing clinical relationship with the practice/specialty, supporting accurate reimbursement and consistent reporting.

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

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.

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.”

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.

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.

Under the CMS-HCC risk adjustment methodology, the RAF is calculated primarily from two categories of inputs: (1) demographic/enrollment eligibility factors and (2) diagnosis codes that map to HCCs based on documented, reportable conditions. Eligibility status matters because Medicare models differentiate beneficiaries by factors such as aged versus disabled status and other enrollment characteristics that affect expected cost. The second major driver is the set of valid, supported ICD-10-CM codes reported for the beneficiary during the data collection period; only certain chronic, clinically significant conditions map to HCCs, and they must be documented as active and applicable to the encounter and coded correctly. In ambulatory CDI, this is why accurate condition capture, specificity, and linkage (e.g., cause/manifestation relationships) are emphasized—because reported conditions directly affect the patient’s risk profile and the expected cost benchmark. By contrast, income status is not a standard CMS-HCC input, “previous risk score” is not itself an input variable, and utilization outcomes like cost of care or readmissions are not used to compute RAF (they may be evaluated separately in quality/cost programs).

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.

Medicaid risk adjustment models generally calculate risk using two major categories of inputs:demographicsanddiagnosis data. Demographic factors commonly includegenderand indicators tied toMedicaid status/eligibility(for example, eligibility category, dual status, disability-related eligibility, or other program qualifiers depending on the state/model). These demographic elements adjust expected cost and are foundational to the risk score even before considering diagnoses. By contrast,race is not a standard inputfor calculating Medicaid risk scores in typical risk adjustment methodologies, so options that include race are not supported. Likewise, “patient noncompliance” is primarily a clinical and quality-of-care issue and may affect treatment outcomes, but it isnot itself a standard risk-score driverunless it is documented as a reportable, supported diagnosis that the specific model recognizes (and most models don’t directly risk-adjust for nonadherence codes). Therefore, among the options given,Medicaid status and genderare the most clearly valid factors that impact the beneficiary’s risk score.

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 CDI, a prime query opportunity is any diagnosis with unclear “status” that materially affects coding, risk adjustment, surveillance, and care planning. “History of colon cancer” paired with “18 months s/p bowel resection” creates ambiguity: the provider must clarify whether the malignancy is still active (current disease, recurrence, metastasis, ongoing treatment) versus no longer present (history of malignancy, in remission, disease-free status, post-treatment surveillance only). This distinction changes code selection substantially and prevents inappropriate reporting of an active cancer when the encounter is actually follow-up after curative treatment. By contrast, ostomy status is not documented as present, and OSA/COPD “status” may be clinically useful but is not inherently ambiguous from the prompt in the same way (they are listed as chronic problems without a conflicting timeline event). ACDIS outpatient CDI practice prioritizes clarifying cancer status because it impacts longitudinal documentation integrity, accurate problem list management, and compliant diagnosis reporting at each visit.

Outpatient CDI queries are most commonly triggered when there is a disconnect between the documented diagnosis and the documented treatment plan, suggesting that the clinician may be managing an additional condition that is not clearly stated, or that the diagnosis is inaccurately documented. Options A and B reflect typical, clinically aligned management: luteinizing hormone-releasing hormone therapy is a standard treatment pathway for prostate carcinoma, and amiodarone is a recognized antiarrhythmic used in atrial fibrillation management in appropriate circumstances. Option C can also be clinically consistent because parenteral nutrition is often used when malnutrition is present and the patient cannot meet nutritional needs enterally. Option D is the outlier: “immunotherapy” is not a standard treatment for severe major depressive disorder and more commonly aligns with oncology or certain immune-mediated diseases. This mismatch would appropriately prompt a query to clarify the actual condition being treated (e.g., an active malignancy) or to confirm whether “immunotherapy” refers to something else (such as allergy immunotherapy) and whether depression is the correct, visit-relevant diagnosis being addressed.

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.

Under Medicare, OPPS is the payment system used primarily forhospital outpatient department (HOPD)services paid under APCs, and it also applies to a limited set ofnon-hospital entitiesfor specific covered services. Community Mental Health Centers (CMHCs) are included under OPPSfor certain outpatient mental health services, most notably partial hospitalization–type services that are paid using OPPS methodology, which is why CMHCs are considered “covered under OPPS” in many outpatient CDI education materials. In contrast,clinical diagnostic laboratory servicesare generallyexcluded from OPPSand paid under the Clinical Laboratory Fee Schedule (with separate billing and payment rules).Indian Health Servicesfollow different statutory and payment structures and are not paid broadly under OPPS in the same way as HOPDs/CMHC OPPS services.Physical therapyreimbursement is typically governed by therapy-specific rules and fee schedule methodologies rather than being a standard OPPS-covered category in this context. Therefore, among the listed options, CMHCs are the correct OPPS-covered selection.

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.

A leading query is one that steers the provider toward a particular diagnosis or limits clinically appropriate choices in a way that can be perceived as prompting. Option D is leading because it presents a single, high-impact diagnosis (“alcohol dependence”) and forces a binary yes/no response without offering reasonable alternative interpretations (e.g., alcohol use, alcohol abuse/harmful use, dependence in remission, or clinically undetermined) or an “other” option. In addition, it attempts to obtain a potentially new diagnosis based on one data point (quantity consumed) without a balanced set of diagnostic possibilities and supporting clinical indicators (tolerance, withdrawal, impairment, failed attempts to cut down, etc.). By contrast, A is open-ended and requests clarification of the treated condition; B provides two plausible classification choices (active vs history); and C offers multiple reasonable BMI-related diagnostic options plus “other” and “clinically undetermined,” which supports compliant, non-leading clarification. Therefore, D best fits the definition of a leading query.

Under CMS-HCC methodology, risk adjustment is driven by ICD-10-CM diagnoses that map to HCC categories and are supported as active conditions addressed at the encounter.CKD stage 3is a classic HCC-qualifying chronic condition because it represents ongoing kidney disease severity and expected resource use, and in this note it is actively assessed with labs reviewed and a nephrology referral. Achronic non-pressure foot ulceris also typically HCC-qualifying when documented as ongoing and requiring management, which is supported here by home care/wound assessment planning. In contrast,“depression”(without specification such as major depressive disorder severity/status) commonly does not qualify for HCC in the way major depressive/bipolar categories do, making it less reliable as a risk-adjusting diagnosis. Likewise,“renal failure”is nonspecific and potentially conflicting with CKD stage 3; CDI best practice would be to clarify acuity/severity (acute kidney injury vs CKD stage vs ESRD) rather than assume “renal failure” as an HCC driver. Therefore, the validated HCC-qualifying pair is CKD 3 and chronic non-pressure ulcer.

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.

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

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.

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.

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

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.

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.

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

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.

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

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

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.

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 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.

When the goal is todemonstrate documentation and coding patternsto a mixed audience of providers and administrators, the most effective tool is one that clearly displayscomparisons and trendsin an easily interpretable way. Abar graphis ideal because it can quickly show differences in rates or volumes—such as unspecified diagnosis utilization, HCC capture rates, query response/agree rates, denial categories, or condition specificity—across providers, clinics, or time periods. This supports outpatient CDI education by making variation visible and actionable while keeping the discussion focused on documentation behaviors and opportunities for improvement. A spaghetti diagram is used for mapping physical workflow movement and inefficiencies, not coding patterns. The PDSA cycle is a structured improvement method for testing changes, but it is not primarily a visualization tool for presenting pattern data. The Donabedian model (structure–process–outcome) is a quality framework that helps organize improvement thinking, but it doesn’t display coding/documentation pattern performance as directly as a bar graph.

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.