CPHQ NAHQ Certified Professional in Healthcare Quality Examination Free Practice Exam Questions (2025 Updated)

Reviewing the Control Chart DataThe control chart shows"Events/Bed Day" over time, with the Upper Control Limit (UCL), Lower Control Limit (LCL), and a center line (CL) marking the baseline average of the process before the intervention.

Identifying the Impact of the New Process

The intervention to reduce events per bed day was implemented on January 7.

Following this date, there is a noticeable and consistent decrease in the number of events per bed day, with data points gradually moving downward.

Eventually, the values settle well below the original center line, indicating a decreasing shift in the process.

Differentiating Between Trends and Shifts

A shift is characterized by a sustained change in process level, often due to a successful intervention, as seen here with lower event rates maintained over time.

In this case, the shift is in a favorable direction, as the targeted events per bed day have reduced significantly and consistently.

A trend would indicate a continuous movement in a direction, but this chart shows that after an initial decline, the process stabilizes at a lower rate.

ConclusionSince the process has demonstrated a decreasing shift, indicating improvement and reduced events per bed day, the correct recommendation is to continue the process, as it appears successful in achieving the goal.

 The annual evaluation of a quality improvement process should be based on organizational objectives. This is because the quality improvement process is designed to enhance the effectiveness and efficiency of an organization’s operations and align them with the organization’s strategic goals12. The AAAHC (Accreditation Association for Ambulatory Health Care) requires that documentation demonstrates at least an annual governing body review of the Quality Improvement (QI) program to evaluate effectiveness anddetermine if the purposes and objectives continue to be met3. Therefore, the annual evaluation of a quality improvement process must be based on organizational objectives to ensure that the process is effectively contributing to the achievement of these objectives.

Comprehensive and Detailed Explanation From Exact Extract:

Within the Population Health and Care Transitions domain, the principle of care coordination is defined as organizing patient care activities and sharing information among all participants involved in a patient’s care to achieve safer and more effective outcomes.

The use of longitudinal care plans — ongoing, comprehensive plans that span multiple settings and providers — directly reflects this guiding principle. These plans ensure that patients receive coordinated, consistent care over time, minimizing duplication and improving outcomes.

Team-based care and patient engagement are important elements that support coordination, but the core guiding principle underlying longitudinal care management is care coordination.

Effective data analysis in quality improvement requires presenting data in a way that is clear, actionable, and easily understood by the team.

Option A (Use visual, graphical methods to present the data): This is the correct answer. The NAHQ CPHQ study guide states, “Visual, graphical methods (e.g., charts, graphs) enhance data analysis by making trends and patterns clear to quality improvement teams” (Domain 2). Tools like run charts or Pareto charts aid decision-making.

Option B (Collect and present all the completed data collection tools): Presenting raw tools (e.g., surveys) is cumbersome and less effective than summarizing data visually.

Option C (Publish and disseminate raw data in tables): Raw data tables are difficult to interpret and less actionable than graphical displays.

Option D (Direct the team to collect as much data as possible): Collecting excessive data without analysis is inefficient and may overwhelm the team.

CPHQ Objective Reference: Domain 2: Health Data Analytics, Objective 2.3, “Select appropriate data display tools,” emphasizes visual methods for effective analysis. The NAHQ study guide notes, “Graphical displays improve team understanding and engagement with data” (Domain 2).

Rationale: Visual methods make data accessible and actionable, aligning with CPHQ’s focus on data-driven quality improvement.

The Centers forMedicare and Medicaid Services (CMS) Conditions of Participation (CoPs) are health and safety standards that healthcare organizations must meet in order to begin and continue participating in the Medicare and Medicaid programs1. These standards are the foundation for improving quality and protecting the health and safety of beneficiaries1.

The CMS CoPs cover a wide range of areas, including emergency preparedness, physical environment, patients’ rights, nurse staffing, medical records, lab and radiological services, and utilization review2. They also include requirements for policies and procedures that identify when a patient is in distress, how to initiate an emergency response, how to initiate treatment, and recognizing when the patient must be transferred to another facility to receive appropriate treatment3.

Given this broad scope, it is clear that compliance with the CMS CoPs requires integration into each department and service of the facility. This is because all these areas need to work together to ensure the health and safety of patients and to improve the quality of care. Therefore, the most important factor in program compliance with the CMS CoPs is likely to be B. Integration into each department and service of the facility.

While the other options (A, C, and D) are also important aspects of a quality program, they are not as comprehensive as option B. For example, having 12 months of data for each project (option A) and monitoring poor improvement outcomes for an additional 12 months (option C) are important for tracking performance and making improvements, but they do not cover all the areas required for compliance with the CMS CoPs. Similarly, coordination by a full-time healthcare quality professional (option D) is important for managing the quality program, but it does not ensure that all departments and services of the facility are integrated and compliant with the CMS CoPs.

Therefore, based on the information available, the most important factor in program compliance with the CMS CoPs is likely to be B. Integration into each department and service of the facility. However, it is important to note that this is a complex issue and the actual decision should be made by the healthcare quality professional considering all relevant factors and resources.

 A SIPOC diagram is a tool used in the Six Sigma methodology. SIPOC stands for Suppliers, Inputs, Process, Outputs, and Customers. During the Define phase of DMAIC, a SIPOC diagram is often used to identify relevant elements of a process improvement project. It helps to understand the process, identify the resources needed, and establish a clear starting and ending point for the process. It’s particularly useful in the planning stage of a project to define the scope12.

Ground rules in a quality improvement team meeting set expectations for behavior and process to ensure productive collaboration.

Option A (Educate the team about pathways/guidelines): Education is a team activity, not the purpose of ground rules.

Option B (Help team members relate to patient needs): Relating to patients is a goal, not a function of ground rules.

Option C (Agree how meetings will be conducted): This is the correct answer. The NAHQ CPHQ study guide states, “Ground rules establish how quality improvement team meetings will be conducted, including communication, decision-making, and respect” (Domain 3). Examples include punctuality and active participation.

Option D (Eliminate the need for meeting minutes): Ground rules do not eliminate minutes, which document decisions.

CPHQ Objective Reference: Domain 3: Organizational Leadership, Objective 3.2, “Establish team ground rules,” emphasizes setting meeting conduct. The NAHQ study guide notes, “Ground rules ensure effective team meetings” (Domain 3).

Rationale: Agreeing on meeting conduct fosters collaboration, as per CPHQ’s leadership principles.

An accountable care organization (ACO) is a network of health care providers that agrees to be accountable for the quality, cost, and overall care of a defined population of patients1.

ACOs aim to improve population health outcomes by coordinating care across different settings and providers, and by implementing quality improvement initiatives1.

One of the quality improvement initiatives that ACOs may adopt is to increase the influenza vaccination rate among their staff, especially those who have direct contact with patients2.

Influenza vaccination canprevent flu-related morbidity and mortality, reduce absenteeism and presenteeism, and protect vulnerable patients from infection3.

To analyze staff flu vaccination trends for an ACO, a source of data that can be used is electronic health records (EHRs)4.

EHRs are digital versions of patients’ medical histories, diagnoses, treatments, medications, immunizations, and other health information that are maintained by health care providers5.

EHRs can provide data on staff flu vaccination trends for an ACO by:

Identifying the staff members who belong to the ACO and their roles, locations, and contact information6.

Tracking the dates and types of flu vaccines that staff members received, as well as any adverse reactions or contraindications7.

Comparing thevaccination rates of staff members across different departments, facilities, and time periods8.

Evaluating the impact of flu vaccination on staff health outcomes, such as flu-like illness, hospitalization, and mortality.

Generating reports and feedback for staff members and managers on their flu vaccination status and performance.

Therefore, the correct answer is A. electronic health records, as this is a source of data that can be used to analyze staff flu vaccination trends for an ACO. References:

1: Accountable Care Organizations (ACOs): General Information | CMS

2: Increasing Health and Social Care Worker Flu Vaccinations: Five Components

3: P141 FluCare: Improving flu vaccination rates in care home staff: A cluster randomised controlled trial | Journal of Epidemiology & Community Health

4: Frontiers | Influenza vaccination rates among healthcare workers: a systematic review and meta-analysis | Public Health

5: What is an electronic health record (EHR)? | HealthIT.gov

6: The National Association for Healthcare Quality

7: Flu vaccination guidance for social care workers and carers

8: CDC: COVID-19, flu vaccination rates for health care workers low last season

Utilization of Improvement Methodologies by Healthcare Quality Professionals During the COVID-19 Pandemic | Journal for Healthcare Quality

Healthcare Quality and Safety Workforce Report: New Imperatives for Quality and Safety Mean New Imperatives for Workforce Development

Comprehensive and Detailed Explanation From Exact Extract:

In the Performance and Process Improvement domain, low enrollment in an eligible population typically reflects a process failure, not lack of awareness.

Standardizing the referral process ensures consistent identification and referral of eligible patients, reducing variability and improving enrollment.

Marketing and education are secondary actions, but process standardization directly addresses the root cause — inconsistent workflow or referral steps.

 A prioritization matrix is a tool used in decision-making to compare and rank options based on specific criteria. It helps in identifying the most important or highest priority items from a list of options12. In the context of healthcare quality, when a professional needs to select a new project from a list of requests, and the organization has determined that new projects should focus on patient safety and cost-reduction, a prioritization matrix would be the most suitable tool. It would allow the professional to rank the projects based on these two criteria (patient safety and cost-reduction) and select the one that best meets these criteria12.

Type 2 diabetes management focuses on preventing complications and controlling blood glucose levels through a combination of lifestyle modifications, medication, and monitoring. Primary interventions target the root causes of disease progression, with lifestyle changes being the cornerstone of management, as supported by evidence-based guidelines.

Option A (Lifestyle change education): Lifestyle change education, including diet, exercise, and weight management, is the primary intervention for type 2 diabetes. NAHQ CPHQ study materials and clinical guidelines (e.g., American Diabetes Association) emphasize that lifestyle modifications are foundational because they address insulin resistance and improve glycemic control, reducing the need for medications and preventing complications.

Option B (Free medication delivery): While access to medications is important, it is a secondary intervention that supports adherence rather than addressing the underlying causes of type 2 diabetes. Delivery alone does not promote behavior change.

Option C (No-cost annual screening tests): Screening tests (e.g., HbA1c or retinopathy screening) are important for monitoring and early detection of complications but are not primary interventions for managing the disease itself. They are preventive or diagnostic rather than therapeutic.

Option D (Lowered cost of medications): Reducing medication costs improves access but, like free delivery, is a supportive measure rather than a primary intervention. It does not directly address lifestyle factors critical to disease management.

The use of technology in health care can reduce medical errors by improving the reliability and accuracy of information, enhancing communication and coordination, and supporting decision making and care delivery. However, technology alone is not sufficient to ensure patient safety. It must be accompanied by a culture of safety that fosters a blame-free environment, encourages reporting and learning from errors, promotes teamwork and collaboration, and allocates resources and leadership support for safety improvement123

A culture of safety is defined as “the extent to which an organization’s culture supports and promotes patient safety. It refers to the values, beliefs, and norms that are shared by healthcare practitioners and other staff throughout the organization that influence their actions and behaviors.” 4 A culture of safety can be measured by assessing the attitudes, perceptions, and behaviors of staff and leaders regarding patient safety issues5

A culture of safety can enhance the effectiveness of technology by ensuring that it is designed, implemented, and used in ways that align with the needs and preferences of users, the goals and processes of care, and the context and environment of the organization6 A culture of safety can also mitigate the potential risks and unintended consequences of technology, such as usability issues, workflow disruptions, alert fatigue, and new types of errors78

Therefore, while the use of technology may result in fewer medical errors, in order for this strategy to be most effective, it should be supported by a culture of safety that creates the conditions and capacities for safe and quality care9

Failure mode and effects analysis (FMEA) is a tool for conducting asystematic, proactive analysis of a process in which harm may occur12.

In an FMEA, a team representing all areas of the process under review convenes to predict and record where, how, and to what extent the system might fail12.

FMEA is used to identify all possible failures in a design, a manufacturing or assembly process, or a product or service, and to study the consequences of those failures2.

FMEA is a prospective assessment that identifies and improves steps in a process and reasonably ensures a safe and clinically desirable outcome1.

FMEA is a common process analysis tool that can help healthcare quality professionals to prevent errors, reduce variation, and improve patient safety1234.

FMEA is applied when a new or modified process, function, or service with an associated hazard has not yet been implemented, or when improvement goals are planned for an existing process, function, or service2.

FMEA procedure involves the following steps2:

Assemble a cross-functional team of people with diverse knowledge about the process, product, or service, and customer needs.

Identify the scope and boundaries of the FMEA.

Fill in the identifying information at the top of the FMEA form.

Brainstorm potential failure modes and their causes and effects.

Assign a risk priority number (RPN) to each failure mode based on the severity, occurrence, and detectability of the failure.

Prioritize the failure modes for action based on the RPNs.

Identify and implement corrective actions to eliminate or reduce the high-risk failure modes.

Evaluate the results and monitor the effectiveness of the actions.

Update the FMEA as needed. References: 1: Failure Modes and Effects Analysis - Ministry of Health 2: What is FMEA? Failure Mode & Effects Analysis | ASQ 3: Failure Mode and Effects Analysis | Digital Healthcare Research 4: Healthcare FMEA | Healthcare Failure Mode & Effects Analysis - Quality-One

Population health management uses data to identify and target high-risk or high-utilization patients for interventions to improve outcomes and reduce costs.

Option A (Cold-spotting): Cold-spotting identifies areas or populations with low healthcare utilization, not relevant to high ER use.

Option B (Hot-spotting): This is the correct answer. The NAHQ CPHQ study guide states, “Hot-spotting is a population health technique that identifies patients with high healthcare utilization, such as frequent ER visits, for targeted interventions” (Domain 5). Developed by Dr. Jeffrey Brenner, it focuses on high-cost, high-need patients.

Option C (Syndromic surveillance): This monitors disease patterns (e.g., flu outbreaks), not individual utilization.

Option D (Public health surveillance): This tracks population-level health trends, not individual high utilizers.

CPHQ Objective Reference: Domain 5: Population Health and Care Transitions, Objective 5.2, “Apply population health management techniques,” includes hot-spotting for high-risk patients. The NAHQ study guide notes, “Hot-spotting targets high-utilization patients to reduce costs and improve care” (Domain 5).

Rationale: Hot-spotting directly addresses frequent ER users, aligning with CPHQ’s population health strategies.

The question implies that the readmission data for the 2nd quarter has been reviewed, and specific units (e.g., 2 South and 3 North) likely show higher readmission rates, prompting the need to identify improvement opportunities. Health data analytics involves using data to identify trends, prioritize areas of concern, and drive targeted interventions. Further analysis of specific units with elevated readmissions is a logical next step to uncover root causes.

Option A (Take no further action because the data is not definitive): This option is inappropriate, as quality professionals are expected to act on data trends, even if preliminary, to drive improvement. Waiting for “definitive” data risks delaying interventions and contradicts CPHQ principles of proactive quality management.

Option B (Use a scattergram to look for an association between readmissions and unit): A scattergram (or scatter plot) is useful for exploring correlations between two variables (e.g., readmissions vs. staffing levels). However, the question suggests the data already highlights specific units (2 South and 3 North), so a scattergram is less relevant than analyzing those units directly.

Option C (Further analyze 2 South and 3 North to determine possible causes): This is the most appropriate action, as it focuses on the units with likely higher readmission rates. According to NAHQ study materials, quality professionals should use data to drill down into specific areas of concern, applying tools like root cause analysis or process mapping to identify underlying causes. This aligns with the CPHQ domain of Health Data Analytics, which emphasizes targeted data analysis to drive improvement.

Option D (Meet with the Quality Council to share the results for 4 North and 4 South): The question does not indicate that 4 North and 4 South are the units with high readmissions. Focusing on these units without evidence is premature, and meeting with the Quality Council is a later step after causes are identified.

 This is because peer review in medical practice is a process by which a committee of physicians examines the work of a peer and determines whether the person under review has met the standards of the profession. The committee member should have a similar level of expertise to the person under review. Therefore, a plastic surgeon with comparable training to the hand surgeon would be the most suitable choice. They would have the necessary knowledge and experience to accurately assess the hand surgeon’s performance. Please note that this is a general answer and the specific answer may vary depending on the context and specific rules of the medical institution. Always consult with a professional or trusted source when making important decisions.

Failure Mode and Effects Analysis (FMEA) is a systematic, proactive method for evaluating a process to identify where and how it might fail and to assess the relative impact of different failures. It is particularly useful before implementing new technologies or processes to anticipate potential points of failure and to develop strategies to mitigate associated risks.

In the context of introducing new technology, FMEA allows healthcare quality professionals to:

Identify potential failure modes within the new system.

Assess the effects and causes of these failures.

Prioritize the risks based on their severity, occurrence, and detectability.

Develop action plans to address the most critical risks.

This proactive approach ensures that potential issues are addressed before they can impact patient safety or system performance.

Comprehensive and Detailed Explanation From Exact Extract:

In the Health Data Analytics domain, control charts are used to distinguish between common cause variation (normal system fluctuation) and special cause variation (abnormal variation requiring investigation).

In the chart shown, several points toward the end of the series approach or exceed the upper control limit (UCL) and display an upward trend beyond what would be expected due to random variation.

This pattern indicates the process is out of control, suggesting a special cause of variation such as a change in practice, infection control failure, or procedural deviation.

Therefore, the correct interpretation is that evaluation is needed to identify and correct the cause of this special variation.

Detailed Explanation:

The storming stage involves conflict and differing opinions, which can lead to premature dissolution if not managed effectively.

Option B: Storming

During this stage, teams often experience disagreements, which can lead to dissolution if members cannot resolve conflicts.

Latent conditions are underlying system vulnerabilities that increase the risk of errors but may not immediately cause harm, distinguishing them from active errors.

Option A (Specific unsafe acts that have adverse consequences): This describes active errors (e.g., wrong medication dose), not latent conditions.

Option B (Defects that may go undetected for long periods of time): This is the correct answer. The NAHQ CPHQ study guide states, “Latent conditions are systemic defects, such as poor equipment design or inadequate protocols, that remain undetected and increase error risk over time” (Domain 1). Examples include look-alike medications stored together.

Option C (Unintentional mistakes made by an individual): These are active errors, not latent system issues.

Option D (Errors having a direct and immediate effect on safety): These are active errors with immediate impact, not latent conditions.

CPHQ Objective Reference: Domain 1: Patient Safety, Objective 1.4, “Identify latent conditions contributing to errors,” emphasizes recognizing systemic vulnerabilities. The NAHQ study guide notes, “Latent conditions are hidden system flaws that predispose to safety events” (Domain 1).

Rationale: Latent conditions are undetected system defects, aligning with CPHQ’s focus on systemic safety risks.

The first step in building a new population health program is to analyze the data infrastructure capabilities and sources of information. Effective population health management requires robust data collection, integration, and analysis capabilities to identify and stratify patient populations, track health outcomes, and monitor program effectiveness. Understanding the current data infrastructure will enable the director to assess whether the existing systems can support the new program and what enhancementsmay be needed.

Implement artificial intelligence programs to stratify patients into categories of risk (A): While important, this is a later step that depends on having a solid data infrastructure in place.

Identify strategies to incorporate social determinants of health screenings (B): This is also crucial but should follow an understanding of the data infrastructure and capabilities.

Design a complex care management program focused on chronic health conditions (C): Designing the program is important but must be informed by a thorough analysis of the data capabilities.

References

NAHQ Body of Knowledge: Data Management in Population Health Programs

NAHQ CPHQ Exam Preparation Materials: Steps to Building a Population Health Program

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Evaluating infection control processes across departments is a system tracer, assessing organization-wide coordination and compliance. Program-specific tracers (B) focus on defined programs, individual tracers (C) track patient journeys, and focused tracers (D) target specific issues. NAHQ specifies system tracers for organization-wide processes.

NAHQ CPHQ Study Guide, Performance and Process Improvement Section, “Tracer Methodology for Accreditation”; NAHQ CPHQ Practice Questions, Survey Readiness.

The question pertains to key variables missing in a graph that tracks infection rates for benchmarking purposes. The options provided suggest various combinations of data that could potentially be missing, impacting the interpretation of the graph.

Option A suggests a need for historical data and specific denominators, but it doesn’t address immediate contextual needs like timeframe or source/target lines.

Option C introduces external hospital expected rates and modes of data points, which might not be directly relevant to interpreting a specific organization’s infection rate trends.

Option D focuses on qualitative aspects like patient quality and compliance with handwashing protocols, which are essential but not directly related to interpreting graphical data.

Option B is verified as correct because it highlights two critical elements: “the timeframe for each data point” and “the source (or target line).” These elements are fundamental to understanding any graph as they provide context regarding when the data was collected and what benchmarks or standards are being compared against.

The timeframe is essential to identify trends over time, seasonal variations, or impacts of specific interventions or changes in practice.

The source or target line provides a benchmark indicating expected performance levels or goals that the organization aims to achieve.

Without these two pieces of information, it would be challenging to derive meaningful insights from the graph about infection rate trends and their implications for future benchmarks.

A medically underserved population is a population of individuals with either a large elderly population, high infant mortality rate, high level of poverty or lack of providers1. This definition aligns with option D, which refers to patients living below the income poverty line. These individuals often lack access to primary care health services2, which is a key characteristic of medically underserved populations. Therefore, the answer is D. patients living below the Income poverty line.