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

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.

The definition in option B is the official Uniform Hospital Discharge Data Set (UHDDS) definition used for inpatient coding: the principal diagnosis is the condition determined—after evaluation—to be chiefly responsible for the admission. It is not simply the first condition written, nor necessarily the “worst” or most severe condition; it is the reason for admission once the workup clarifies the clinical picture. CDI practice reinforces this because principal diagnosis selection drives DRG assignment, quality metrics, and reporting, and errors often stem from confusing presenting symptoms with the final established diagnosis. Although outpatient settings use different concepts (e.g., first-listed diagnosis for the encounter), ACDIS education frequently contrasts inpatient “principal diagnosis” with outpatient “first-listed” to prevent documentation and coding misalignment. Clinicians should document the definitive condition when known (and link symptoms to that condition), and clearly describe diagnostic uncertainty when not yet established. This clarity supports compliant coding, accurate benchmarking, and defensible medical necessity across settings.

Atrial fibrillation (AF) type is determined largely by episode duration and whether the rhythm self-terminates. In outpatient CDI education,paroxysmal AFis intermittent and typically terminates spontaneously, commonly within7 days(often within 48 hours).Persistent AFis sustained and lastsmore than 7 days, or requires active intervention (e.g., cardioversion) to restore sinus rhythm. This scenario documents AF “for10 days,” which exceeds the 7-day threshold and therefore best fitspersistentAF. The management also aligns with a sustained arrhythmia strategy: rate control (atenolol), rhythm control/antiarrhythmic therapy (sotalol), stroke prevention anticoagulation (rivaroxaban), and discussion of catheter ablation, which is often considered for symptomatic or recurrent/persistent AF. “Chronic” is a nonspecific descriptor and not the preferred current classification term, andpermanent AFimplies a decision has been made not to pursue rhythm control (accepting AF long-term), which is not supported here because rhythm-control options are being considered.

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.

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.

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.

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

Under CMS-HCC risk adjustment (commonly applied to Medicare Advantage), qualifying diagnoses must come from acceptable encounter/claim sources and eligible provider types. Hospital-based outpatient services (including a hospital ambulatory clinic) are among the standard, acceptable settings where diagnoses documented, coded, and submitted on qualifying encounters may be used for risk adjustment—assuming they are supported, assessed/managed, and submitted per program requirements. In contrast, certain facility claim types do not typically contribute to CMS-HCC capture in the same way. Hospice care is generally treated as a carve-out/unique payment environment and is not relied upon as a routine source of risk-adjusting diagnosis capture for the member’s ongoing RAF. Renal dialysis centers (ESRD facilities) likewise operate under specialized payment constructs and are not the typical outpatient setting used to drive CMS-HCC diagnosis capture for risk adjustment in standard CDI workflows. Freestanding ambulatory surgical centers also frequently fall outside the usual risk-adjustment-eligible encounter sources emphasized in outpatient CDI programs. Therefore, the hospital ambulatory clinic is the correct setting among these choices.

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.

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.

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 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.

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.

In the outpatient follow-up setting, when the acute stroke event has occurred in the recent past and the patient is now being evaluated for residual deficits, documentation and coding should focus on thesequelae(late effects) rather than re-coding the acute infarction itself—unless the provider clearly states the stroke is still in the acute phase and being actively treated as such. ACDIS outpatient CDI principles stress selecting the diagnosis that best reflects the reason for today’s encounter and the condition being assessed/managed. Here, the ongoing clinical issue driving follow-up care is the persistent neurologic deficit (right-sided weakness/hemiparesis) after the cerebral infarction. Option C is the most specific and clinically accurate because it captures (1) the relationship to the prior cerebral infarction (“following cerebral infarction”) and (2) laterality and dominance (“right dominant side”), which improves code specificity and reflects functional impact. Option A incorrectly keeps the focus on an unspecified cerebral infarction rather than the residual deficit, and option D is too nonspecific compared with a clearly described hemiparesis.

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.

For outpatient/observation encounters, the first-listed diagnosis is the condition chiefly responsible for the services provided during that encounter. In this scenario, the patient’s ambulatory surgery (herniorrhaphy) has already occurred, and the reason the patient is now in observation is explicitly documented as “post procedural urinary retention.” That makes urinary retention the condition driving the extended monitoring, evaluation, and management in observation status. Benign prostatic hyperplasia and an adverse reaction to anesthesia are documented only as possible etiologies (“likely related to…or…”), and outpatient guidelines do not support coding uncertain diagnoses expressed as “likely” or as alternative possibilities without definitive confirmation. Therefore, those potential causes would not replace the confirmed problem that necessitated observation. The hernia was the reason for the procedure, but it is not the reason for the postoperative observation services described. Outpatient CDI practice reinforces documenting the clinical reason for observation and clearly distinguishing confirmed postoperative complications from suspected causes to support correct first-listed selection.

This query is non-compliant because it isleading: it asks the provider to “confirm a diagnosis of hemiplegia,” presenting only one targeted outcome rather than requesting clarification in a neutral, clinically appropriate way. ACDIS-aligned outpatient query practice requires queries to be compliant, non-leading, and supported by clinical indicators, typically offering multiple reasonable options (or an open-ended format) and allowing the provider to document the most accurate clinical impression. In this scenario, “history of CVA,” “left-sided weakness,” and “physical therapy ordered” could reflect several possibilities—such as hemiparesis, residual weakness, post-stroke deficits, deconditioning, or other neurologic impairment—so the query should ask the provider to clarify thenature and diagnosisof the deficit (and laterality), not to confirm a single diagnosis. Option A is incorrect because coding cannot assume hemiplegia without provider documentation. Option C is not the best feedback because indicators can justify clarification. Option D is unnecessary; MRI results are not required for a compliant outpatient query.

This scenario describes a query placedbefore the patient is seen, with the intent that the issue be addressedduring the upcoming visit. In outpatient CDI practice, that is the defining feature of aprospectivequery: it is initiated ahead of the encounter so the provider and/or clinic team can capture needed specificity in real time (here, clarifying BMI-related documentation to support an obesity diagnosis when clinically appropriate). By contrast, aconcurrentquery is typically issued while the encounter is actively occurring or immediately as documentation is being created and reviewed in near–real time. Aretrospectivequery occurs after the visit is completed, usually during post-encounter review, when opportunities are identified after documentation is finalized. “Prebill” refers to a workflow timing concept tied to billing hold/review before claim submission, not the clinical timing of when the patient will be seen. Because the query is placed in advance specifically to be addressed during the scheduled visit,prospectiveis the best classification.

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.

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.

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.

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.

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.

Sertraline is a selective serotonin reuptake inhibitor (SSRI) most commonly used to treat depressive disorders and several anxiety-related conditions. In outpatient chart review, recognizing medication–condition relationships supports accurate problem list maintenance and compliant diagnosis reporting, but the diagnosis must still be clearly documented as assessed/managed at the encounter. Depression is the best match because SSRIs like sertraline are first-line pharmacologic therapy for major depressive disorder and are frequently continued long-term with monitoring for symptom control, side effects, and functional status. Schizophrenia is primarily treated with antipsychotic medications; sertraline may be used only as an adjunct if a comorbid depressive or anxiety disorder is present, so it is not the common primary treatment. Asthma management centers on bronchodilators and inhaled corticosteroids, not SSRIs. Heart failure therapy involves guideline-directed cardiac medications (e.g., beta-blockers, ACE inhibitors/ARNI, diuretics), and sertraline is not a standard heart failure treatment. Outpatient CDI education emphasizes documenting the specific mental health diagnosis, current status (stable/worsening), and treatment plan to support coding.

A Stage 4 pressure ulcer (pressure injury) is characterized byfull-thickness tissue losswithextensive destruction, tissue necrosis, or damage to muscle, bone, or supporting structures. The key phrase in the question—“necrosis of soft tissue through the underlying muscle”—signals a depth of injury that extends beyond the subcutaneous tissue and involves muscle, which is consistent with Stage 4. By comparison, Stage 2 involves partial-thickness skin loss with exposed dermis (no necrosis through deeper structures). Stage 3 involves full-thickness skin loss where adipose may be visible, butmuscle, tendon, or bone are not exposed; undermining and tunneling may occur, yet the defining line is that it does not extend to muscle/bone involvement. “Stage 5” is not part of standard pressure ulcer staging used in coding and documentation. Outpatient CDI practice emphasizes documenting the exact stage, anatomic location, laterality when applicable, and whether the ulcer is healing or complicated (infection/osteomyelitis) because stage drives specificity, severity capture, and appropriate care planning documentation.

The documentation shows a patient with advanced COPD (GOLD stage 3) who now requires an increase in home oxygen to 3 liters, along with escalation of respiratory therapies (home nebulizers and systemic steroids). In outpatient CDI, an increased or ongoing home oxygen requirement is a strong clinical indicator that the provider may be managingchronic respiratory failure(or chronic hypoxemic respiratory failure), which is more clinically meaningful than simply documenting oxygen use as a status. “Oxygen dependence” is a status code and does not fully describe the underlying physiologic impairment driving the need for oxygen; chronic respiratory failure captures the severity and ongoing nature of the condition and better reflects risk, complexity, and medical necessity for durable oxygen therapy. Querying for pneumonia organism specificity is not as relevant in a follow-up visit unless pneumonia is still being actively treated and the organism is known. Querying COPD acuity (e.g., exacerbation) may be appropriate, but the most significant clarification prompted by increased home O2 is whether chronic respiratory failure is present and being managed.

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

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.

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.

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.

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.

In outpatient CDI,MEATis a commonly taught framework used to determine whether a condition is sufficiently supported asreportablefor a specific encounter. MEAT stands forMonitor, Evaluate, Assess/Address, and Treat. The concept is that diagnoses should not simply be copied forward on a problem list; they should be tied to provider work and clinical relevance during the visit. “Monitor” includes reviewing status, trends, or test results related to the condition. “Evaluate” includes ordering or interpreting studies, considering disease progression, or documenting response to therapy. “Assess/Address” includes documenting stability, exacerbation, or risk and making a plan (education, counseling, referrals). “Treat” includes medications, procedures, or other therapeutic interventions. Using MEAT helps CDI staff educate providers to document thecurrent statusand management of chronic diseases, supports accurate coding and risk adjustment, and reduces denials by showing medical necessity. OPPS, MACRA, and RADV are important regulatory/payment terms, but they are not the standard acronym used to assess encounter-level reportability.

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

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 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.

RAF (Risk Adjustment Factor) is a population risk stratification metric used in risk adjustment models to estimate expected healthcare resource utilization for an individual beneficiary relative to an average patient. In outpatient CDI, RAF is driven by a combination of demographic elements (such as age/sex and eligibility/status factors) and—critically—documented, coded conditions that map to risk categories (e.g., HCCs). The intent is not to “predict the provider’s reimbursement” for the current year in a direct, visit-by-visit sense; rather, RAF contributes to actuarial projections of expected cost and supports payment benchmarking and budget setting in value-based arrangements (e.g., Medicare Advantage and certain shared savings models). RAF is also not based only on demographics (eliminating option B) and it does not determine reimbursement for each individual office visit (eliminating option D). ACDIS outpatient CDI emphasizes that accurate, specific documentation and coding of active, clinically supported conditions improves the accuracy of RAF, which in turn better aligns projected costs and comparisons across attributed populations.

In hospital outpatient settings paid under OPPS, Ambulatory Payment Classifications (APCs) are influenced by the coded services and, in many workflows, the clinical documentation that supports correct CPT/HCPCS selection, units, modifiers, and—when applicable—medical necessity linkages to diagnoses. Accurate documentation ensures that the record supports what was actually performed (e.g., complexity, laterality, supplies, drug administration details, observation criteria, or separately payable procedures) and that coding can correctly apply bundling/packaging rules without losing legitimately reportable services. While APCs are primarily procedure-driven, documentation remains decisive because incomplete or ambiguous notes lead to downcoding, missed charges, incorrect status indicators, or denials during medical review. From an outpatient CDI standpoint, the goal is to ensure the clinical story supports codeable services and their necessity: clear indications, findings, assessment/plan, and any required elements (time, start/stop, dose/route for medications, device details, etc.). This supports appropriate APC grouping and reimbursement integrity, reducing rework, denials, and compliance risk.

In MSSP-style ACO financial reconciliation, performance is evaluated against a benchmark (expected costs). Here, the ACO’s actual spending ($52M) exceeds the expected benchmark ($50M) by $2M, meaning the ACO generatedshared lossesrather than savings. In risk-bearing ACO arrangements, when costs exceed the benchmark and the ACO is in a track that includes downside risk, the organization may owe CMS a portion of those losses. The shared savings/loss rate (50% in this scenario) represents the percentage of the difference from the benchmark that the ACO shares with CMS, assuming applicable thresholds are met. Thus, instead of receiving a shared savings payment, the ACO would be accountable topay backa share of the excess spending (conceptually 50% of the $2M overage, if all model requirements are satisfied). Option D is not correct because reconciliation is typically performed on a performance-year basis rather than only at the end of the full agreement period, and option C is not how MSSP eligibility works.

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.