1. Chapter 8 Health Statistics

2. Introduction
A special language that expresses outcomes of mathematical calculations is known as statistics
To understand statistics, one must first build a statistical vocabulary
Because of the variety and complexity of statistics, the need for a common understanding takes on a great importance

3. Statistics Overview STATISTICAL TYPES Descriptive statistics
Can have a narrow or very wide focus
Used to characterize or summarize a given population
Inferential statistics
Reaching conclusions based on data from a sample
Patterns are modeled to address randomness and uncertainty

4. Statistics Overview STATISTICAL TYPES Applied statistics
Use of statistics in real-life situations
Both descriptive and inferential statistics are forms of applied statistics
Mathematical statistics
Theoretical basis of statistics
Vital statistics
Data on human events (births, deaths, marriages, etc.)

5. Statistics Overview STATISTICAL TYPES
Public health surveillance statistics
Similar in concept to vital statistics
Focus on illnesses, conditions, and diseases
Demographic statistics
Study of human populations
Look at the size of populations
How populations change over time

6. Statistics Overview STATISTICAL LITERACY
Statistical measurements used to draw conclusions
Miles per gallon
Miles per hour
Mortality rates
Morbidity rates
Test scores
Statistical literacy
Determining if a study or conclusion is credible

7. Statistics Overview STATISTICAL BASICS Math concepts Percentage
Rounding
Ration
Rate
Fraction

8. Statistics Overview STATISTICAL BASICS Measures of central tendency
Common measures: mean median, mode
Other concepts
Standard deviation
Statistically significant
t-test
Null hypothesis

9. Statistics Overview DATA COLLECTION Aggregate data collection
Specific data combined into larger groupings
Can be used to describe a bigger concept
Retrospective data collection
Converting paper records to computerized abstract
Concurrent data collection
Data gathered at the time they are entered
Uses modern interface and transmission standards

10. Statistics Overview DATA COLLECTION
Seven broad categories of patient data
Dates
Counts
Test results
Diagnoses
Procedures
Treatment outcomes
Assessments

11. Statistics Overview STATISTICAL FORMULAE Daily inpatient census
Number of patients present at the official census-taking time daily
Inpatient service day
Services received by one patient during one 24-hour period

12. Statistics Overview
STATISTICAL FORMULAE
Inpatient service day (cont.)

13. Statistics Overview STATISTICAL FORMULAE Length of stay
Number of calendar days between admission and discharge

14. Statistics Overview RATE FORMULAE
Rate formulae in hospitals and health care settings
Number of times something happens
Divided by the number of time it could have happened
General pattern applied to the context of everyday life
Provides a basic understanding of how the formula works
Helps its application in the health care setting make sense

15. Statistics Overview RATE FORMULAE Morbidity rates Prevalence rates
Incidence rates
Case fatality rates

16. Statistics Overview
RATE FORMULAE
Mortality rates

17. Statistics Overview RATE FORMULAE
Rates commonly referred to using percentage form

18. Data Presentation CLASSIFICATIONS OF DATA Discrete data
Specific values are distinct and separate
Points between values are not considered valid
Continuous data
Values that lie within a certain range
Categorical data
Values belonging to the set can be sorted into categories
Categories do not overlap

19. Data Presentation CLASSIFICATIONS OF DATA Nominal data Ordinal data
Values are classified
Logical ordering of values is not required
Ordinal data
Ordered data set
Differences between values are not important
Interval data
Both ordered and constant
Contains no natural zero as a point of reference

20. Data Presentation PRESENTATION METHODS Bar graph Line graph Pie chart
Used for frequency of categorical data
Line graph
Similar to bar graph
Frequently used to display time trends
Pie chart
Represents frequency of data
Is a circle divided into sections

21. Data Presentation PRESENTATION METHODS Histogram Frequency polygon
Vertical axis contains continuous intervals for categories
Conveys how a single variable is used continuously
Frequency polygon
Resembles a histogram
Well-suited to superimposing data within one graph
Should only be used to display numerical values

22. Regression Analysis INVESTIGATING RELATIONSHIPS Regression analysis
Investigates relationships between variables
Models relationships and determines their magnitude
Determines correlation between at least one variable and another
Correlation
Mutual relation or interdependence

23. Regression Analysis INVESTIGATING RELATIONSHIPS Strength Weakness
Can establish correlations
Weakness
Cannot always prove causation
Predictive power
Can be used to solve a problem and predict the future outcome

24. Regression Analysis VARIABLES
Simple regression or univariate regression
Only one variable will provide the answer
Bivariate regression
Two variables will provide the answer
Multivariate regression
Numerous variables are used to provide the answer

25. Regression Analysis REGRESSION ANALYSIS MODELS
Pearson product-moment correlation
Determines whether a relationship exists between variables
Both variables must be numbers (interval or ratio)
ANOVA test (F-test)
Compare more than two groups
Early warning system to test a hypothesis

26. Regression Analysis REGRESSION ANALYSIS MODELS Chi-square
Measure relationships between two variables
Variables are categorical
Used in qualitative studies
Determines degree of association among qualitative variables

27. Regression Analysis GROUPER PROGRAMS Grouper program examples Groupers
Diagnosis related groups (DRGs)
All patient refined-diagnosis related groups (APR-DRGs)
Ambulatory payment classification groups (APCs)
Groupers
Place patients into similar categories
Provide predictive values for patient stays

28. Health Information Management Statistics
Most commonly used statistical formulas
Inpatient service days
Occupancy ratios
Mortality rates
Length of stay
Discharge days
Frequency statistics
Traditionally captured in acute care environments

29. Health Information Management Statistics
PRODUCTIVITY
Labor analytics
Determining staff for a work area
Enough staff
Too many staff
Working with a staffing shortage
Labor ratio
Shows how much earned revenue is spent on labor expenses

30. Health Information Management Statistics
PRODUCTIVITY
Productivity tracking of medical transcription
How quickly recorded physician reports are typed
Productivity tracking of coding
How quickly health records are coded

31. Health Information Management Statistics
STATISTICAL TOOLS
Control charts
A graph with statistically generated upper and lower control limits
Used to measure key processes over time
Software for control chart management
Statistical Process Control (SPC)
Used in the Six Sigma process
Aid in creation and use of control charts

32. Health Information Management Statistics
STATISTICAL TOOLS

33. Health Information Management Statistics
STATISTICAL TOOLS
Trend charts
Graphical presentation of data
Shows patterns or shifts according to time
Differ from control charts because they focus on time patterns
Can be created with software applications

34. Health Information Management Statistics
STATISTICAL TOOLS

35. Health Information Management Statistics
STATISTICAL TOOLS
Process capability analysis
A process control technique
Ensures continued improvement to the process
Should not be used if the control chart shows a wide range of variability

36. Health Information Management Statistics
STATISTICAL TOOLS

37. Summary
Statistics are categorized by type, with descriptive statistics playing the largest role in health care
Relationships among data variables are the focus of regression analysis
Pearson product-moment correlation, ANOVA test, and chi-square are all used in regression analysis
Statistical tools solve process problems, and improve productivity