When a cross-functional team is assigned the task of improving a process, such as laboratory reporting time in the emergency room, the first step after identifying the problem is usually to understand the root causes of the problem. A fishbone diagram, also known as a cause and effect diagram or Ishikawa diagram, is a visual tool used to systematically identify and present all possible causes of a certain outcome1234.
In this case, the significant delay in receiving laboratory results is the problem that needs to be addressed. The team would use a fishbone diagram to identify and categorize potential reasons for this delay, such as equipment issues, process inefficiencies, human errors, etc. This step is crucial before developing action plans (Option D) because it ensures that the team's efforts are directed towards addressing the root causes of the problem, rather than just the symptoms1234.
Options A (Identify the responsible individual) and C (Plot a scatter diagram) are not the immediate next steps in this scenario. Identifying a responsible individual is more about accountability after the root causes have been identified and action plans have been developed. A scatter diagram is a graphical tool used to understand the relationship between two variables and is not typically the next step in process improvement after identifying the problem1234.
References:
https://fellow.app/blog/management/cross-functional-collaboration-common-challenges-and-tips-to-make-it-wor

Establishing a Quality Council involves defining its purpose and focus to guide quality improvement efforts effectively.
Option A (Educate members on regulatory processes): Education is important but follows setting priorities to ensure relevance.
Option B (Identify quality priorities): This is the correct answer. The NAHQ CPHQ study guide states, "After forming a Quality Council, the next step is to identify quality priorities based on data and organizational needs to focus improvement efforts" (Domain 3). For a behavioral health center, priorities might include suicide prevention or medication safety.
Option C (Charter project improvement teams): Chartering teams follows priority identification.
Option D (Develop quality indicators): Indicators are developed after priorities are set to measure progress.
CPHQ Objective Reference: Domain 3: Organizational Leadership, Objective 3.1, "Establish Quality Council priorities," emphasizes setting priorities. The NAHQ study guide notes, "Identifying priorities guides the council's work" (Domain 3).
Rationale: Identifying priorities ensures the council's focus, aligning with CPHQ's leadership principles.
Reference: NAHQ CPHQ Study Guide, Domain 3: Organizational Leadership, Objective 3.1.

Comprehensive Step by Step Explanation:
A run chart is the best tool to display quarterly comparisons of patient satisfaction surveys. Here's why:
* Purpose of the Tools: Each of the tools mentioned in the options serves a different purpose.
* A fishbone diagram (also known as Ishikawa or cause and effect diagram) is used for root cause analysis. It helps identify, explore, and display the possible causes of a specific problem or quality characteristic1.
* A pie chart is a type of graph in which a circle is divided into sectors that each represent a proportion of the whole1. It's useful for showing simple proportional part-to-whole information, but it's not ideal for comparisons over time1.
* A flowchart is a type of diagram that represents a workflow or process, showing the steps as boxes of various kinds, and their order by connecting them with arrows1. This diagrammatic representation can give a step-by-step solution to a given problem.
* A run chart, on the other hand, is a graph that displays observed data in a time sequence1. It is an effective tool to use for displaying and analyzing the trend of data over time1.
* Why Run Chart: In the context of displaying quarterly comparisons of patient satisfaction surveys, a run chart is the most appropriate tool. It allows for the visualization of data trends over time, which is exactly what's needed when comparing patient satisfaction on a quarterly basis1. The x-axis represents time (in this case, quarters), and the y-axis represents the variable being measured (in this case, patient satisfaction). Each point on the chart represents a specific time period's data (a quarter's patient satisfaction survey results). The points are connected in the order of the time periods, creating a line that makes trends easy to spot1.
* Limitations of Other Tools: The other tools (fishbone diagram, pie chart, and flowchart) are not designed to display trends over time, which is crucial when comparing quarterly data1.
Therefore, based on the purpose of each tool and the need to display trends over time, a run chart is the best tool to display quarterly comparisons of patient satisfaction surveys.

* 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. Thedata 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