A sentinel event is a patient safety event that reaches a patient and results in any of the following:

Death

Severe temporary harm

Permanent harm

An intervention required to sustain life

Option D, "An intervention to sustain life," aligns with the definition of a sentinel event. Such

interventions may include actions like cardiopulmonary resuscitation (CPR), defibrillation, or other

emergency procedures necessary to keep the patient alive following a significant adverse event.

The storming phase is the second stage of team development, where conflicts and differences in

opinions may arise12. During this phase, the team is still figuring out how to work well together1.

The leader’s role is crucial at this stage. They need to provide clear direction for the project and help

individuals on the team get to know and accept each other3. This involves directing the team and

providing role clarification3, which aligns with option A.

The expectation to maintain continuous survey readiness must be supported and driven by the

executive team. Leadership commitment from the highest levels of the organization is crucial for

fostering a culture of readiness and ensuring that all staff are engaged in maintaining compliance

with accreditation standards. The executive team's support ensures that the necessary resources,

policies, and procedures are in place to sustain continuous readiness.

Quality team (B): The quality team plays a key role in survey readiness, but their efforts must be

supported by the executive team.

Risk manager (C): The risk manager contributes to readiness by identifying and mitigating risks, but

they cannot drive the organization-wide effort alone.

Compliance officer (D): The compliance officer ensures adherence to regulations, but continuous

readiness requires broader leadership involvement.

Reference

NAHQ Body of Knowledge: Leadership and Continuous Survey Readiness

NAHQ CPHQ Exam Preparation Materials: Roles in Maintaining Survey Readiness

=========

Errors of omission can lead to delayed or missed diagnosis1. In the context of healthcare quality,

these errors are often preventable and can be mitigated through varioussystems and strategies23.

Option A, a reminder system that is in close proximity to the task and provides sufficient information

about what needs to be done, aligns with the strategies to prevent errors of omission. This system

serves as a proactive measure to ensure that necessary actions are taken and important steps are not

missed. It provides healthcare professionals with timely and relevant information, thereby reducing

the likelihood of errors of omission1.

Option B, a warning system that is contiguous to the task and cues that the individual is about to

initiate the wrong intervention, while useful, is more aligned with preventing errors of commission

(doing something wrong) rather than errors of omission (failing to do something right).

Option C, a proactive risk assessment system that integrates with the task and automatically notifies

the risk manager, is also a valuable tool in healthcare quality. However, it is more focused on

identifying and managing risks rather than preventing errors of omission.

Option D, a detection system that notifies the team when an error has occurred and provides a

checklist for mitigation measures, is a reactive measure. While it is crucial for mitigating the impact

of errors, it does not directly prevent errors of omission.

Therefore, based on the information available, option A would most likely be the most effective

system in assisting an organization with evaluating patient safety actions that will prevent errors of

omission231.

Preparing care team members for interacting with accreditation surveyors is a crucial part of the

accreditation process. The best approach is to brief them onsurvey activities and what questions to

expect (Option C). This involves providing them with an understanding of the survey objectives, the

day’s schedule, and the types of questions that surveyors may ask1. This approach helps to ensure

that team members are well-prepared and confident when interacting with surveyors. Reviewing

patient records proactively (Option A) and summarizing and discussing past survey findings (Option

B) can also be helpful, but these activities are more focused on identifying and addressing potential

issues before the survey, rather than preparing team members for the survey itself. Providing

techniques to defer surveyor questions to leaders (Option D) could potentially create a perception of

lack of transparency or evasion, which could negatively impact the survey results.

Reference:

https://www.jointcommission.org/what-we-offer/accreditation/health-care-settings/laboratoryservices/prepare/prepare-support/8-best-practices-to-prepare-for-the-survey/

The HEDIS measure of the percent of diabetic patients with a HgA1c greater than 9.0% is an indicator

of poor glycemiccontrol and a risk factor for complications12. Reducing this measure is a quality

improvement goal that requires a multidisciplinary approach and data-driven strategies34.

A quality improvement team is a group of individuals with different roles and responsibilities who

work together to achieve a common aim56. The team should include representatives from various

areas of the clinic, such as management, clinical staff, and data analysts78.

The clinic manager is responsible for providing effective and consistent leadership, communicating

the vision and the steps for improvement, engaging the team in planning and monitoring, allocating

resources and training, and fostering a culture of open communication and continuous learning78.

The quality improvement specialist is responsible for analyzing and reviewing the clinical and

business data, suggesting and selecting the key priority areas, implementing and evaluating the

improvement interventions, and reporting the results and outcomes78.

The provider champion is responsible for modeling enthusiasm and support for quality

improvement, leading the clinical discussions and decisions, influencing and educating other

providers and staff, and ensuring adherence to evidence-based guidelines and best practices78.

The HEDIS chart abstractor, the coder, and the primary care provider are also important members of

the quality improvement process, but they are not sufficient to form a comprehensive and effective

team. The HEDIS chart abstractor and the coder are mainly involved in collecting and coding the

data, while the primary care provider is mainly involved in delivering the care. They need the

guidance and coordination of the clinic manager, the quality improvement specialist, and the

provider champion to align their efforts and achieve the desired

outcomes78. Reference: 1: Hemoglobin A1c Control for Patients with Diabetes (HBD) 2: Glycemic

Status Assessment for Patients with Diabetes 3: Quality Improvement Team Roles and

Responsibilities - PracticeAssist 4: The Roles & Responsibilities of A Quality Management

Team 5: QUALITY IMPROVEMENT TEAMS COMPOSITION 6: Comprehensive Diabetes Care -

NCQA 7: HEDIS 2022 Manual - Johns Hopkins Medicine 8: HEDIS® Hemoglobin A1c Control for

Patients with Diabetes (HBD) 9: GSD - Glycemic Status Assessment for Patients With Diabetes

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.

Reference: 1: 8.8 Scatter Plots, Correlation, and Regression Lines 2: Scatterplots: Using, Examples,

and Interpreting

In the context of performance improvement and quality control, variations in a process are typically

categorized as either common cause or special cause12345.

Common cause variation is the kind of variation that is part of a stable process. These are variations

that are natural to a system and are quantifiable and expected1. They are predictable, ongoing, and

consistent1. Major changes would typically have to be made in order to change the common cause

variations1. You can identify common cause variation points on the control chart of a process

measure by its random pattern of variation and its adherence to the control limits1.

Special cause variation, on the other hand, is unexpected variation in the process14. There is a

specific cause that can be assigned to the variation4. These variations are unusual, unquantifiable,

and are variations that have not been observed previously, so they cannot be planned for and

accounted for1. These causes are typically the result of a specific change that has occurred in the

process, with the result being a chaotic problem1. You can identify special cause variation on a

control chart by their non-random patterns and out-of-control points15.

In the given scenario, the performance improvement team has been examining delays in getting

admissions from the emergency room to the nursing units. After six months of collecting data, the

upper control limit was 150 minutes, and the lower control limit was 60 minutes. The next month’s

data shows a time of 155 minutes. This time of 155 minutes is beyond the established upper control

limit of 150 minutes. Therefore, this represents a special cause variation15, as it is an unexpected

variation that significantly deviates from the established control limits.

Reference: 12345

:

The question is asking which patient population is at the highest risk for falling.

By analyzing the provided table, it can be observed that patients who had “falls prior to admission”

have the highest number of falls after admission (30 patients) compared to other categories.

This data indicates that having a history of falls before being admitted increases the risk of falling

again.

Although I don’t have direct access to external websites including NAHQ, it’s generally understood in

healthcare quality and safety that ahistory of falls is a significant risk factor for future falls. This is

likely supported by resources and documents on patient safety and fall prevention available on

professional healthcare quality websites.