NAHQ Updated CPHQ Exam Questions and Answers by clover

This question is identical to Question 95, indicating a potential duplication in the document. The analysis remains the same: a significant medication error causing harm requires a reactive investigation to identify causes and prevent recurrence.

Option A (Multiple regression analysis): Inappropriate for analyzing specific incidents, as it is a statistical tool for relationships.

Option B (Variation analysis): Not suitable for investigating a single harmful event, as it focuses on process variability.

Option C (Root cause analysis): Correct answer. The NAHQ CPHQ study guide reiterates, “RCA is the standard approach for analyzing serious safety events like medication errors with harm, using a multidisciplinary team to identify root causes” (Domain 1).

Option D (Failure mode and effects analysis (FMEA)): FMEA is proactive, not for post-incident analysis.

CPHQ Objective Reference: Domain 1: Patient Safety, Objective 1.5, “Conduct root cause analysis for significant safety events.” The NAHQ study guide emphasizes, “RCA ensures thorough investigation of harmful errors to improve safety” (Domain 1).

Rationale: RCA is the correct tool for a harmful medication error, consistent with CPHQ’s safety investigation principles.

Sampling methodology (Answer A) involves selecting a subset of items from a larger population to represent the characteristics of the whole. This is a fundamental process in statistical analysis and quality management, where it is often impractical or impossible to examine an entirepopulation. Proper sampling methods ensure that the chosen sample accurately reflects the population, allowing for reliable conclusions and decisions.

The other options are distinct concepts:

Outlier identification (B) refers to detecting data points that deviate significantly from other observations, which is not directly related to representing characteristics of a whole.

Statistical significance (C) measures whether a result is likely due to chance, rather than sampling or representation.

Benchmarking (D) involves comparing processes and performance metrics to industry standards or best practices, not selecting representative samples.

Quality measures are used to assess performance and drive improvement, requiring specific characteristics to ensure they are practical and effective.

Option A (Confidential): Confidentiality is important for data protection but is not a core characteristic of quality measures, which focus on measurement validity and utility.

Option B (Inexpensive): While cost is a consideration, it is not a defining attribute of quality measures, which prioritize accuracy and impact over expense.

Option C (Feasible): This is the correct answer. The NAHQ CPHQ study guide states, “Quality measures must be relevant (aligned with goals), scientifically sound (valid and reliable), and feasible (practical to collect and analyze)” (Domain 2). Feasibility ensures measures can be implemented with available resources and data systems.

Option D (Flexible): Flexibility is not a standard requirement for quality measures, which should be consistent and standardized to ensure comparability.

CPHQ Objective Reference: Domain 2: Health Data Analytics, Objective 2.1, “Evaluate the characteristics of quality measures,” lists relevance, scientific soundness, and feasibility as essential attributes. The NAHQ study guide notes that feasibility ensures measures are practical for routine use in quality reporting.

Rationale: Feasible measures can be collected and analyzed without excessive burden, making them sustainable for ongoing quality improvement, as required by frameworks like CMS and The Joint Commission.