Explanation: An affinity diagram (C) organizes brainstorming ideas into thematic categories, aiding analysis of infection rate causes. Decision matrices (A), Pareto charts (B), and force field analysis (D) are less suited for sorting ideas. NAHQ emphasizes affinity diagrams for idea organization.
NAHQ CPHQ Study Guide, Performance and Process Improvement Section, "Affinity Diagrams and Idea Organization"; NAHQ CPHQ Practice Questions, Quality Improvement Tools.

Performance improvement (PI) in healthcare refers to the systematic process of identifying, analyzing, and enhancing the various aspects of healthcare delivery to improve patient outcomes, safety, and satisfaction1.
PI requires a collaborative and data-driven approach that involves multiple stakeholders, such as clinicians, managers, patients, and quality professionals2.
According to the National Association for Healthcare Quality (NAHQ), one of the core competencies for healthcare quality professionals is to facilitate teams and lead change initiatives that align with the organization's strategic goals and priorities3.
NAHQ also recommends using a variety of performance improvement methodologies, such as Lean, Six Sigma, robust process improvement, and A3 problem-solving, to address complex and cross-functional issues in healthcare.
Therefore, the option that most likely benefits the PI goals of the organization is C: cross-functional processes evaluated by multidisciplinary teams with the support of management. This option reflects the best practices of PI in healthcare, as it fosters a culture of quality, engages diverse perspectives, and leverages data and evidence to drive improvement23.
The other options are less likely to benefit the PI goals of the organization, as they are either too narrow, too top-down, or too siloed. These options may limit the scope, effectiveness, and sustainability of PI efforts, as they do not involve the relevant stakeholders, address the root causes, or align with the strategic vision of the organization23.
Reference: 1: A Guide to Performance Improvement in Healthcare 2: 9 Effective Performance Management Strategies for Healthcare 3: Healthcare Quality Solutions: Ready Your Workforce for Quality : Utilization of Improvement Methodologies by Healthcare Quality Professionals During the COVID-19 Pandemic

An important responsibility of each team member working on a team project is to complete assignments between meetings. This ensures that progress is made continuously, and that meetings can be focused on discussing completed work, making decisions, and planning the next steps. Regular completion of assignments is crucial for maintaining momentum and ensuring that the project stays on track.
Investigate the existing data on the project (B): This may be a task for some team members, but not the primary responsibility of all.
Review team progress periodically (C): This is typically the responsibility of the team leader or facilitator, not every team member.
Teach skills to the team during meetings (D): While sharing knowledge is valuable, it is not the primary responsibility of every team member.
Reference
NAHQ Body of Knowledge: Effective Teamwork and Project Management
NAHQ CPHQ Exam Preparation Materials: Team Roles and Responsibilities in Project Work

* A scatter plot is a graphical tool that shows the relationship between two continuous variables by plotting data points at their corresponding values on the x-axis and y-axis1.
* To interpret a scatter plot, we need to look at the direction, strength, and shape of the relationship between the variables2.
* The direction of the relationship indicates whether the variables tend to increase or decrease together (positive correlation) or in opposite directions (negative correlation).
* The strength of the relationship indicates how closely the data points cluster around a line or curve that best fits the data. A common measure of the strength of the linear relationship is the correlation coefficient , which ranges from -1 to 1. The closer the absolute value of R is to 1, the stronger the linear relationship2.
* The shape of the relationship indicates whether the data points follow a straight line (linear relationship) or a curved pattern (nonlinear relationship).
* Based on these criteria, we can analyze the scatter plots for Setting 1 and Setting 2 as follows:
* Setting 1: The scatter plot shows a clear upward trend, indicating a positive correlation between complication rate and time to positive outcome. The data points are tightly clustered around a line, indicating a strong linear relationship. The R^2 value of 0.9533 on the plot is close to 1, which means that the linear model explains 95.33% of the variation in the complication rate.
Therefore, we can conclude that Setting 1 has a strong positive correlation between complication rate and time to positive outcome.
* Setting 2: The scatter plot shows a scattered pattern, indicating a weak or no correlation between complication rate and time to positive outcome. The data points are widely spread around a line, indicating a weak linear relationship. The R^2 value of 0.4923 on the plot is far from 1, which means that the linear model explains only 49.23% of the variation in the complication rate.
Therefore, we cannot conclude that Setting 2 has a significant correlation between complication rate and time to positive outcome, or that complication rates are causing longer time to positive outcome at setting 2.
References: 1: 8.8 Scatter Plots, Correlation, and Regression Lines 2: Scatterplots: Using, Examples, and Interpreting