NAHQ Updated CPHQ Exam Questions and Answers by arjun

Active surveillance in healthcare is a proactive approach to identifying and monitoring health-related events or diseases123. It involves the continuous, systematic collection, analysis, and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice1.

Option B, “Identifying disease outbreaks through public health contact tracing,” is an example of active surveillance. Contact tracing is a key strategy for preventing the further spread of infectious diseases. It involves identifying people who have an infectious disease (cases) and their contacts (people who may have been exposed) and working with them to interrupt disease transmission12.

Option A, “analyzing laboratory data for disease testing utilization,” could be part of both active and passive surveillance, depending on the context. However, it does not necessarily involve the proactive steps typically associated with active surveillance13.

Option C, “analyzing infectious diseases based on hospital discharge final coding,” is more related to passive surveillance, which involves the collection of data as they become available, or the data that are passively received by health care providers or health information systems13.

Option D, “reporting of infectious diseases data quarterly to local health departments,” is also more related to passive surveillance, as it involves regular reporting of health data from various sources13.

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.

The NAHQ CPHQ exam blueprint stresses that improvement decisions must be data-driven, especially when perceptions conflict.

Option C is correct because reviewing objective data allows the quality manager to validate performance, identify variation, and determine whether an improvement opportunity exists. Data review should always precede discussion or intervention.

Options A and B are premature without evidence. Option D does not address the reported concern.

The CPHQ framework reinforces that data precede dialogue and redesign, making Option C the correct first step.

To determine the bonus earned, evaluate if each provider met or exceeded the performance target for each indicator. If yes, they earn the percentage of the total $20,000 bonus allocated to that indicator.

Provider A:

BCS: 75% ≥ 74% → earns 25% of $20,000 = $5,000

CBP: 71% < 72% → earns $0

CIS: 63% ≥ 63% → earns 50% of $20,000 = $10,000

Total bonus = $5,000 + $0 + $10,000 = $15,000

Provider B:

BCS: 77% ≥ 74% → $5,000

CBP: 69% < 72% → $0

CIS: 65% ≥ 63% → $10,000

Total bonus = $15,000

Provider C:

BCS: 79% ≥ 74% → $5,000

CBP: 73% ≥ 72% → $5,000

CIS: 64% ≥ 63% → $10,000

Total bonus = $5,000 + $5,000 + $10,000 = $20,000

Provider D:

BCS: 73% < 74% → $0

CBP: 74% ≥ 72% → $5,000

CIS: 62% < 63% → $0

Total bonus = $5,000

Since provider D earned $5,000, none of the other answers directly match this calculation, but option C (Provider D earned a $15,000 bonus) is incorrect. Rechecking the options, the closest and accurate conclusion given the choices is:

Provider C earned the highest bonus ($20,000).

Provider A earned $15,000.

Provider D earned $5,000, not $15,000.

Provider B earned $15,000, which is not the lowest bonus.

Given the choices, Option C is incorrect (as Provider D earned only $5,000). Option B is correct — Provider C earned the highest bonus.

Thus, correct answer is B.