Clinical Documentation Specialist CCDS-O Exam Questions and Answers PDF

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.

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.

In CMS-HCC risk adjustment, RAF impact comes from reporting qualifying chronic diseases (HCCs), not from nonspecific symptom-only documentation. “Angina pectoris, unspecified” is frequently a symptom-level statement and, by itself, often does not carry the same risk-adjustment weight as documenting and coding the underlying ischemic heart disease responsible for the angina (for example, coronary artery disease/atherosclerotic heart disease with angina). Ambulatory CDI practice emphasizes that when providers document only “angina,” coders may be limited to a symptom code, which can under-represent the patient’s true disease burden in the HCC model. This makes angina an excellent target for provider education: clarify whether the angina is due to CAD, whether CAD is present and being managed, and whether there are related manifestations (e.g., unstable angina, prior MI history, status post CABG/stent) that support more complete, clinically accurate reporting. By improving documentation linkage from symptom (angina) to the definitive chronic condition (CAD with angina), the provider can more reliably capture an HCC-relevant diagnosis and positively influence RAF accuracy.

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.