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CPXP Exam - Domain 2 Study Guide Solutions
1. Data sources and Collection Methods: Surveys - like CAHPS Rounding
Focus Groups Social Media Compliments and Complaints Patient and Family Advisory Councils Discharge Phone Calls Employee Satisfaction / Engagement Surveys
2. Data sources - CAHPS: Strength:
Standard actionable questions Limitations: Timeliness, rater fatigue
3. Data sources - Rounding: Strength:
Real time, engages leaders Limitations: Time intensive
4. Data Sources - Focus Groups: Strength: Deeper
dive into topics Limitations: Small samples
5. Data Sources - Social Media: Strength:
Instantanious Feedback
2 / 15 Limitations: Maybe hard to aggregate data
6. Data Sources - Compliments & Complaints: Strength: Can count, sort
and trend Limitations: Dependant on rater actions
7. Data Sources - PFACs: Strength: Builds
relationships Limitations: Often infrequent
8. Data Sources - Discharge Phone Calls: Strength:
Quick Feedback Limitations: Requires consistent resource
9. Data Sources - Employee satisfaction/engagement survey: Strength: Informs regarding
culture Limitations: It is a 'point in time' survey
10. Regulatory Requirements - CAHPS: - This survey's focus is - What the patients themselves say is important to them
and for which patients are the best and /or the only source of information.
- Focus is on experience not satisfaction
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- Asking patients questions during their stay is ok if they don't resemble HCAHPS questions Questions not permissable:
*Overall how would you rate the care you received from your nurse/doctor?
*On a scale of 0 to 10, how would you rate your hospital stay?
14. Hospital Value Based Purchasing Metrics (VBP): It is a program established by Medicare. It is part of
many quality programs focused on efforts to improve healthcare. This program ties Medicare payment to specific performance measures in quality and cost.
15. The VBP Total Performance Score (TPS) For fiscal 2020: The score is based on a hospital's performance in 4
measurement domains:
1) Clinical Care - 25%
2) Safety - 25%
3) Person and community engagement - 25%
4) Efficiency and Cost reduction - 25%
16. How many hospitals are affected by VBP?: 2700 across the country
17. VBP Payments depend on.. .: - How well the hospital performs compared to their peers.
- How much the hospital improves in the quality of care provided to patients over time.
18. Is it ok to tell patients while they are in the hospital they might receive a survey. (HCHPS) ?: -
Yes, and you can encourage them to fill it out by saying, 'Survey results will help consumers choose hospitals, and help hospitals improve the care they provide.'
- Hospitals are forbidden to influence who receives HCAHPS, or instruct patients on how to respond.
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19. To be an authority in a field, it is important to be.. .: evidence based and data driven.
20. Evidence based:
When reading research articles, they have value.. .: because we trust that the researchers have applied rigorous scientific methodology to their study. This is the essence of 'evidence based'
21. Evidence based is not.. .: best practice. Best practice is a term used often, but has no common definition. It is sometimes
used to describe what has worked for other people, or something that worked within your organization that others feel might be useful to them.
22. Evidence based is.. .: a term that is used when there is hard data to support the effectiveness of a process
and or improvement. When someone doubts your ideas or claims, it is more persuasive to properly collect, analyze and present data to support your position.
23. The value of evidence based information is.. .: it is more believable. Professional clinicians, like doctors, who
have been trained in research based models, place more weight on data that is properly collected, analyzed and presented.
24. Data - Bias is.. .: the action of supporting or opposing a particular person or thing in an unfair way, because
of allowing personal opinions or experience to influence your judgment.
25. Data - Qualitive data: Data that data describes qualities or characteristics. It is collected using question- naires, interviews, or
observation, and frequently appears in narrative form.
26. Data - Quantitative data: Numbers, data that can either be counted or compared on a numeric scale.
27. Data - Population: the big group we want to know more about - It is the collection of a specified group of similar objects,
individuals, or entities that have some common observable characteristics in them.
28. Data - Sample: A data sample is used to identify a subset of a population, that accurately represents all members of a larger
population. It enables data scientists, predictive modelers and other data analysts to work with a small, manageable amount of data about a statistical population to build and run analytical models more quickly, while still producing accurate findings.
29. Data - mean: The "mean" is the "average" you're used to, where you add up all the numbers and then divide
7 / 15 Not all information is in numeric form How obtained: Structured techniques
40. qualitative data: Strengths:
allows for a deeper dive into a problem, high emotional impact Limitations: Nuanced, contextual. Can be time consuming How obtained: Various methods including observation
41. Qualitative vs. Quantitative: Quantitative research is expressed in numbers and is used to test something.
Qualitative research is expressed in words and is used to understand
42. Data - 'N' (Uppercase) means.. .: Population
43. Data - 'n' (lowercase) means.. .: Sample
44. When 'n' is too small.. .: you cannot rely on the results of your research if 'n' is too small. A rule of thumb is
that 30 is the minimum size for results to be reliable and therefor actionable.
45. Central Tendency: a single value used to describe the center point of a data set. The 3 most
common measures of central tendency are the mean, median, and mode
46. More on Validity: The property of a measurement tool that indicates that the tool does what it says it does. Most common types
are:
- Content Validity
- Criterion Validity
- Construct Validity
47. Content Validity: Content validity assesses whether a test is representative of all aspects of the construct. (see construct validity)
8 / 15 To produce valid results, the content of a test, survey or measurement method must cover all relevant parts of the subject it aims to measure. If some aspects are missing from the measurement (or if irrelevant aspects are included), the validity is threatened.
48. Criterion validity: 1) Criterion validity evaluates how closely the results of your test correspond to the results
of a ditterent test.
2) The criterion is an external measurement of the same thing. It is usually an established or widely-used test that is already considered valid.
3) To evaluate criterion validity, you calculate the correlation between the results of your measurement and the results of the criterion measurement. If
there is a high correlation, this gives a good indication that your test is measuring what it intends to measure.
49. Construct validity: - Construct validity evaluates whether a measurement tool really represents the thing
we are interested in measuring. It's central to establishing the overall validity of a method.
- Construct validity is about ensuring that the method of measurement matches the construct you want to measure. If you develop a questionnaire to diagnose depression, you need to know: does the questionnaire really measure the construct of depression? Or is it actually measuring the respondent's mood, self-esteem, or some other construct?
50. More on reliability: Data is reliable when it produces similar results under consistent conditions: Types
are:
- Test-Retest Reliability
- Parallel Forms Reliability
- Internal Consistency Reliability
- Interrater Reliability
51. Data - Test-Retest Reliability: The consistency of a measure across time: do you get the same results when you repeat
the measurement?
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2. second, square the deviations done in step 1 so all the values are positive
3. take the sum of all the deviations
4. divide by the number of cases (n-1); why subtract by 1 is called "degree of freedom"; if sample size is close to infinity the subtraction won't matter;
vice versa if sample size is small
5. square root of this average will result in SD (standard deviation)
The formula is s = (x-x')²/(n-1)
62. Frequency distribution: an arrangement of data that indicates how often a particular score or observa- tion occurs. Usually
shown by a histogram.
63. Histogram: A graph of vertical bars representing the frequency distribution of a set of data.
64. Normal distribution: Normal distribution, also known as the Gaussian distribution, is a probability distribution that is
symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.
65. Normal Distribution - points to remember/1: - The mean, median and mode are exactly the same.
- The distribution is symmetric about the mean—half the values fall below the mean and half above the mean.
- The distribution can be described by two values: the mean and the standard deviation.
66. Normal Distribution - points to remember/2: - The middle vertical line at the highest point of the curve is the
mean.
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- The Vertical lines show where the standard deviation lines fall.
- One standard deviation = 68% of the data
- Two standard deviations = 95% of the data
- Three standard deviations = 99.7% of the data
67. Correlation vs. Causation: Correlation means there is a statistical association between variables. Causation means that
a change in one variable causes a change in another variable.
68. correlation: A correlation where as one variable increases, the other also increases, or as one decreases so does the other. Both
variables move in the same direction. Correlation means there is a statistical association between variables.
69. Correlation does not equal causation: Correlation describes an association between variables: when one variable
changes, so does the other. A correlation is a statistical indicator of the relationship between variables. These variables change together: they covary. But this covariation isn't necessarily due to a direct or indirect causal link. Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relation- ship between variables. The two variables are correlated with each other and there is also a causal link between them. A correlation doesn't imply causation, but causation always implies correlation.
70. Correlation does not equal causation: Two conditions may appear together but not cause each
other. There could be a possible presence of a third underlying variable (Confounding variable). Example: Ice Cream sales and Crime have a strong positive correlation. Does that mean that crime causes an increase in ice cream sales? No. Yet they are strongly correlated. Why? Because both are also strongly correlated with a rise in air temperature.
71. correlation coefficient: A correlation coefficient is a number between -1 and 1 that tells you the strength
and direction of a relationship between variables. Positive number = positive correlation Negative number = Negative correlation Zero = no
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75. run chart: A chart that displays the history and pattern of variation of a process over time Graphically
displays cycles, trends, shifts,or non random patterns of behavior overtime. Helps to identify problems and the time the problem occurred. Helps monitor progress when solutions are implemented.
76. Run charts - shifts vs. trends: Signals of change can be shifts or trends
Shifts are bigger than trends A shift is 6 or more consecutive points above or below the median A trend is 5 or more increasing or decreasing points. Note - 2 data points is not a trend
77. Run charts - Important points: For each line in the run chart, the following statistics are calculated:
-mean, the average of all points in the series -Maximum, the maximum value in the series -Minimum, the minimum value in the series -Sample size, the number of values in a series -Range, the max value minus the min value -Standard deviation, which indicates how widely the data is spread around the mean.
78. Affinity Diagram: A technique that allows large numbers of ideas to be classified into groups for review and analysis.
79. Affinity Diagram - when to use: Confronted with many facts and ideas in apparent chaos Issues seem too
large and complex to grasp. Group consensus is necessary Brainstorming exercise
14 / 15 Analyzing verbal data like survey results
80. Affinity Diagram - example: What are the barriers to on-time delivery of medications?
All of the comments submitted can be categorized in the following groups:
- Statt issues
- Process interruptions
- inadequate equipment
- Problems with procedures
81. Dashboards: - Reporting mechanisms that aggregate and display metrics and key performance indicators.
- A good way of reporting data to: Managers, Board of Directors, frontline staff, etc.
- Intended for 'at a glance' review of key performance indicators.
- Enough data to tell the story, but not so much that it becomes confusing.
82. Communicating Data Analysis, Outcomes, Plans - 1: Any strategy for communicating data always begins
with: Who is the audience? Then define overall goals and objective
- Use key drivers and vendor resources
- Ensure the stakeholders understand --> Why the data is important --> Their important role in improvement
