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HIM 371 > Exam 1 Part 5 > Flashcards

Exam 1 Part 5 Flashcards

1
Q

Adding together the values of one data element over a set period of time (i.e. – monthly, quarterly, etc.)

A

Aggregating

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2
Q

To extract useful information from data and making decisions based upon the data analysis

A

Analyzing

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3
Q

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

A

Sampling

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4
Q

Maintain or improve when external is not available

A

Internal benchmarking

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5
Q

Comparing an organizations performance with the performance of other organizations that provide the same types of services.

A

External benchmarking

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6
Q
  • Also called categorical data
  • I nclude values assigned to name-specific categories
  • Male or female
  • Usually displayed on bar graphs or pie charts
A

Nominal Data

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7
Q
  • 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
A

Ordinal Data

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8
Q
  • 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
A

Discrete Data

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9
Q
  • Assume an infinite number of possible values
  • Weight, blood pressure, temperature, and so forth
  • Best displayed in histograms or line charts
A

Continuous Data

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10
Q

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.

A

Histogram

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11
Q

A measure that records level of agreement or disagreement along a progression of categories

A

Likert Scale

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12
Q

The number of times that a score or value occurs in the data set

A

Absolute frequency

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13
Q

The percentage of the time that the characteristic or score appears in the data set

A

Relative frequency

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14
Q

What are the 4 Data Quality Functions?

A

Application
Collection
Warehousing
Analysis

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15
Q

The purpose of that data collection

A

Application

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16
Q

The processes by which data elements are accumulated

A

Collection

17
Q

Process and systems used to archive data

A

Warehousing

18
Q

The process of translating data into meaningful information

A

Analysis

19
Q 
Accessability
Consistency
Currency 
Granularity
Precision
Accuracy 
Comprehensiveness
Definition 
Relevancy 
Timeliiness
A

Characteristics of Data Quality

20
Q

The extent to which the data are free of identifiable errors

A

Data Accuracy

21
Q

The level of ease and efficiency at which data are legally obtainable, within a well-protected and controlled environment

A

Data Accessability

22
Q

The extent to which all required data within the entire scope are collected, documenting intended exclusions

A

Data Comprehensiveness

23
Q

The extent to which the healthcare data are reliable, identical, and reproducible, by different users across applications

A

Data Consistency

24
Q

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

A

Data Currency

25
Q

The specific meaning of a healthcare-related data element

A

Data Definition

26
Q

The level of detail at which the attributes and characteristics of data quality in healthcare data are defined

A

Data Granularity

27
Q

The degree to which measures support their purpose, and the closeness of two or more measures to each other

A

Data Precision

28
Q

The extent to which healthcare-related data are useful for the purpose for which they were collected

A

Data Relevancy

29
Q

The availability of up-to-date data within the useful, operative, or indicated time

A

Data Timeliiness

30
Q

The arithmetic average

Adding all the numbers together and dividing by the range

A

Mean

31
Q

The middle number in a number set

A

Median

32
Q

Very high or low values in a observation that distort the calculated mean and may shift the distribution on direction or the other

A

Skewing

33
Q

Shows the spread of the values in spreadsheet applications or charts

A

Standard deviation

34
Q

Takes the products of statistical analysis and utilizes them in a way that provides organizations the ability to predict future events

A

Predictive Analytics

35
Q
  • 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
A

Common cause variation

36
Q
  • When a special circumstance or unexpected event occurs in the process
  • It is this special cause variation that the PI process needs to investigate
A

Special cause variation

HIM 371 (8 decks)

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