Exam 1 Part 5 Flashcards
Adding together the values of one data element over a set period of time (i.e. – monthly, quarterly, etc.)
Aggregating
To extract useful information from data and making decisions based upon the data analysis
Analyzing
A smaller subset of observations of the characteristics or parameter, making certain, however that a sufficient number of observations have been made to predict the overall configuration of the data
Sampling
Maintain or improve when external is not available
Internal benchmarking
Comparing an organizations performance with the performance of other organizations that provide the same types of services.
External benchmarking
- Also called categorical data
- I nclude values assigned to name-specific categories
- Male or female
- Usually displayed on bar graphs or pie charts
Nominal Data
- Also called ranked data
- Expresses the comparative evaluation of various characteristics or entities
- Often uses Likert scales - Best displayed on bar graphs or pie charts
Ordinal Data
- Also called count data
- Numerical values that represent whole numbers
- Number of children in a family
- Number of non-billable patient accounts - Best displayed in bar graphs
Discrete Data
- Assume an infinite number of possible values
- Weight, blood pressure, temperature, and so forth
- Best displayed in histograms or line charts
Continuous Data
A graphical display of data using bars of different heights. It is similar to a Bar Chart, but a ___________ groups numbers into ranges . The height of each bar shows how many fall into each range.
Histogram
A measure that records level of agreement or disagreement along a progression of categories
Likert Scale
The number of times that a score or value occurs in the data set
Absolute frequency
The percentage of the time that the characteristic or score appears in the data set
Relative frequency
What are the 4 Data Quality Functions?
Application
Collection
Warehousing
Analysis
The purpose of that data collection
Application
The processes by which data elements are accumulated
Collection
Process and systems used to archive data
Warehousing
The process of translating data into meaningful information
Analysis
Accessability Consistency Currency Granularity Precision Accuracy Comprehensiveness Definition Relevancy Timeliiness
Characteristics of Data Quality
The extent to which the data are free of identifiable errors
Data Accuracy
The level of ease and efficiency at which data are legally obtainable, within a well-protected and controlled environment
Data Accessability
The extent to which all required data within the entire scope are collected, documenting intended exclusions
Data Comprehensiveness
The extent to which the healthcare data are reliable, identical, and reproducible, by different users across applications
Data Consistency
The extent to which data are up-to-date; a datum value is up-to-date if it is current for a specfic point in time, and it s outdated if it was current at a preceding time but incorrect at a later time
Data Currency
The specific meaning of a healthcare-related data element
Data Definition
The level of detail at which the attributes and characteristics of data quality in healthcare data are defined
Data Granularity
The degree to which measures support their purpose, and the closeness of two or more measures to each other
Data Precision
The extent to which healthcare-related data are useful for the purpose for which they were collected
Data Relevancy
The availability of up-to-date data within the useful, operative, or indicated time
Data Timeliiness
The arithmetic average
Adding all the numbers together and dividing by the range
Mean
The middle number in a number set
Median
Very high or low values in a observation that distort the calculated mean and may shift the distribution on direction or the other
Skewing
Shows the spread of the values in spreadsheet applications or charts
Standard deviation
Takes the products of statistical analysis and utilizes them in a way that provides organizations the ability to predict future events
Predictive Analytics
- Also called normal variation
- The expected variance in a process due to the fact that the process will not or cannot be performed in exactly the same manner each and every time
Common cause variation
- When a special circumstance or unexpected event occurs in the process
- It is this special cause variation that the PI process needs to investigate
Special cause variation
