The scenario described in the question is a classic example of sampling bias, also known as selection bias123. This type of bias occurs when the sample chosen for a study or survey is not representative of the entire population the study intends to investigate123.
In this case, the healthcare quality professional wants to assess the satisfaction of the community served by the organization, which includes patients living within a 10-mile radius. However, the professional only sends surveys to households within a 3-mile radius. This means that the sample (households within 3 miles) does not accurately represent the entire population (patients within a 10-mile radius). As a result, the findings from this survey may not accurately reflect the satisfaction levels of the entire community served by the organization123.
To avoid this type of bias, it's important to ensure that the sample chosen for a study or survey is as representative as possible of the population being studied123. This might involve using different sampling techniques or adjusting the scope of the survey to ensure a more diverse and representative sample is obtained123.

Clinical pathways are evidence-based, standardized care plans designed to optimize patient outcomes and reduce variations in care. Their successful development requires collaboration among key stakeholders, with physician involvement being critical due to their role in clinical decision-making and protocol adoption.
Option A (Staff education): While staff education is important for implementing clinical pathways, it is a secondary step that follows pathway development. Education ensures adherence but is not a requirement for creating the pathways themselves.
Option B (Patient education materials): Patient education materials support patient engagement but are not essential for developing clinical pathways, which focus on provider-driven care processes.
Option C (Quality improvement tools): Quality improvement tools (e.g., flowcharts, PDSA cycles) may be used to refine pathways, but they are not a requirement for development. The primary need is clinical expertise and stakeholder input.
Option D (Physician involvement): Physician involvement is essential for developing clinical pathways, as they provide clinical expertise, ensure pathways align with evidence-based practice, and drive adoption among peers. NAHQ CPHQ study materials emphasize that multidisciplinary collaboration, particularly with physicians, is critical for creating effective pathways, as physicians are key decision-makers in patient care protocols.
Reference: NAHQ CPHQ Study Guide, Domain 4: Performance and Process Improvement, highlights the importance of physician engagement in developing clinical pathways to ensure clinical relevance and successful implementation.

Without specific data, the question implies the quality professional has reviewed performance metrics (e.g., readmissions, length of stay) and must determine the next action. In CPHQ practice, reviewing data typically leads to investigating why performance deviates from goals.
Option A (Develop a discharge planning program): Developing a program assumes a specific issue (e.g., poor discharge planning), premature without cause analysis.
Option B (Create dashboard to monitor for trends): A dashboard tracks data but is not the next step after reviewing data, as analysis is needed first.
Option C (Explore underlying causes): This is the correct answer. The NAHQ CPHQ study guide states,
"After reviewing performance data, the next step is to explore underlying causes using tools like root cause analysis to identify why metrics are off-target" (Domain 2). This applies broadly to any data review.
Option D (Perform a literature review): Literature reviews inform solutions but follow cause identification.
CPHQ Objective Reference: Domain 2: Health Data Analytics, Objective 2.5, "Analyze data to identify causes," emphasizes cause exploration post-review. The NAHQ study guide notes, "Cause analysis follows data review to drive improvement" (Domain 2).
Rationale: Exploring causes is the logical next step after data review, aligning with CPHQ's analytics principles.
Reference: NAHQ CPHQ Study Guide, Domain 2: Health Data Analytics, Objective 2.5.

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.
Reference: NAHQ CPHQ Study Guide, Domain 1: Patient Safety, Objective 1.5.