Pass the ACDIS Clinical Documentation Specialist CCDS-O Questions and answers with Dumpstech

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 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.

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.

For reporting Evaluation and Management (E/M) services, the longstanding “key components” framework recognizeshistory, examination, and medical decision making (MDM)as the core elements used to determine the appropriate E/M level when the service is not reported based on time. In outpatient CDI education aligned with ACDIS concepts, MDM is emphasized because it reflects the clinician’s cognitive work and risk-based thinking: the complexity of problems addressed, the amount/complexity of data reviewed and analyzed (labs, imaging, external notes, independent interpretation), and the risk of complications and/or morbidity from additional testing or treatment. “Review of systems” is a sub-element of the history component, not a separate key component. “Nature of presenting problem” and “coordination of care” can be clinically relevant and may support medical necessity or time-based billing (when documented appropriately), but they are not one of the three key components that define E/M reporting structure. Therefore, the best answer is Medical decision making.

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.”

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

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.

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.

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.

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