Free ACDIS CCDS-O Practice Exam with Questions & Answers

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.

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.

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.

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.”

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.

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

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

In ambulatory CDI risk adjustment, the largest RAF impact typically comes from ensuring accurate capture of high-weight, HCC-relevant chronic conditions—especially active malignancies with metastasis. “Metastatic melanoma (followed by oncology)” suggests an ongoing, clinically significant condition, but the wording could represent active metastatic disease, history of metastatic disease, remission, or no current evidence of disease. Because HCC models distinguish active metastatic cancer from history-only status, clarifying thecurrent status(active/under treatment, recurrent, in remission, history) can materially change whether the condition qualifies for risk adjustment and how the patient’s expected cost is benchmarked. By comparison, adding BMI (when morbid obesity is already documented) generally does not increase HCC capture, and fracture sequelae typically does not drive HCC risk scoring in the same way. Bipolar disorder may map to an HCC, but its relative impact is generally lower than metastatic cancer, making melanoma status the highest-value clarification.

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.

Within the CMS-HCC model, only certain diagnoses map to HCC categories that contribute to the RAF score. Among the listed options,cachexiais the diagnosis most likely to map to an HCC because it represents a serious systemic wasting condition associated with significant morbidity, higher expected resource use, and frequently coexists with advanced chronic disease. In contrast,Barrett’s esophagusgenerally does not map to an HCC in CMS risk adjustment, and symptom-based diagnoses such assignificant weight losstypically do not trigger HCC capture.Anorexiain general clinical usage often represents a symptom (loss of appetite) and, unless it is clearly documented as a qualifying malnutrition-related condition with appropriate specificity, it usually does not map to an HCC. The plan for tube feeding and dietitian involvement strengthens clinical relevance, but for risk adjustment the diagnosis must be one that maps to an HCC category—here, cachexia is the one that meets that criterion and would be the HCC-triggering diagnosis.