Health Data Management & Analysis.pdf

Health Data Management &
Analysis
By : Mohammed Jaber
Quality Health Management & Informatics specialist
CPHQ, CPHIMS, QHIMS, HQMS
Professional Diploma In Healthcare Quality
0
20
40
60
80
A B C D E F

Types of Data
1 2 1 2
How Many Eyes?
2 eyes
(Counted)
Discrete Data Continuous Data
How Long the ears?
5 cm
(Measured)
Nominal Data
What is a cat colour?
Gray
(Category,
Descriptive)
Ordinal Data
How a cat look?
Normal
Good
Amazing
(Rank)
Numerical / Quantitative Categorical / Qualitative
Prepared by: Mohammed Jaber
mohmohmohamad2017@gmail.com Whatsapp.00201271388254

Types of Data
1 2 1 2
Discrete Data Continuous Data Nominal Data Ordinal Data

Types of Data
1 2
Nominal Data Ordinal Data
Categorical / Qualitative
• Data categorized & counted.
• Categorical data not measured.
• Based on counts of members of discrete categories.
• Categorical data exist only as whole numbers ( number
of procedures, members, patients, deaths, events)
• Can be expressed as percentages, e.g. : CHF 20% of
patients.
• Qualitative data, describe qualities of categories as blood
type, intensity of burn, physician speciality.
• Qualitative data includes observations as data from case
studies, focus groups, interviews.
• Nominal: counting things of different names.
• Ordinal data: scores on ordered scale.

Types of Data
1 2
Nominal Data Ordinal Data
Categorical / Qualitative

Types of Data
1 2 1 2
You can simply
impress your
audience and add
a unique zing.
Discrete Data Continuous Data Nominal Data Ordinal Data

Types of Data
1 2
You can simply
impress your
audience and add
a unique zing.
Numerical / Quantitative • Measured on continuous scale
• Expressed in specific measurement
units (whole or fraction)
• Indicating amount or quantity of what
being measured.
• Quantitative data, because measure
interval between any two Points as
quantity.

Types of Data
1 2
You can simply
impress your
audience and add
a unique zing.
Numerical / Quantitative

Types of Data
1 2
You can simply
impress your
audience and add
a unique zing.
Numerical / Quantitative T-Shirt Color
Frequency
Distribution

Types of Data
1 2
You can simply
impress your
audience and add
a unique zing.
Numerical / Quantitative
No. of blue T-Shirt per classes
1 2 3 4 5 6 7
Class
Frequency
Density

Measurement of Central tendency
Modern PowerPoint Presentation
• Central tendency describes set of measures that indicates what
is the middle value of data.
• Statistical measures that describe central tendency:
Mean
Median
Mode.

Mean (Average)
50%
50%

Mean (Average)
• The mean is the average of the numbers.
• It is easy to calculate: add up all the numbers, then divide by
how many numbers there are.
In other words it is the sum divided by the count.

Mean (Average)
• Example 1 : What is the Mean of these numbers?
6, 11, 7
• Add the numbers: 6 + 11 + 7 = 24
• Divide by how many numbers (there are 3 numbers): 24 / 3 = 8
The Mean is 8

Mean (Average)
1,2,3,4,5,6,7,8,9
• Add the numbers: 1,2,3,4,5,6,7,8,9= 45
• Divide by how many numbers (there are 9 numbers): 45 / 9 = 5
The Mean is 5

Mean (Average)

Mean (Average)
1 , 8 , 3 , 2 , 6 Mean is 4

Mean (Average)
• Example 2: (2, 4, 6, 8, 100)
• After adding : 120 / 5
Mean = 24
The Mean
should not
used in this
case
It is not logical
average

Mean (Average)
• Example 2: (2, 4, 6, 8, 100)
Mean = 24
• 100 in this example considered an astronomical numbers –
outlier data:
- Numbers that are very different from remaining numbers, so
mean is pulled toward this astronomical value, so mean NOT
really indicate middle of data in this case.
Median is the choice

Median
• The middle number with equal number of values above and
below median.
• Median used with ordinal & interval data types.
• Arrange numbers from lowest to highest.
• The fiftieth percentile. That value at or above which 50% of cases
fall and at or below which 50% of cases fall.

Median
In odd numbers
• In set of data (3, 3, 5, 6, 8)
• median is the middle
• (median = 5)
In even numbers
• In set of data (3, 3, 5, 6, 8, 9)
• Median (5 + 6)= 11 / 2 = 5.5
• Median 5.5

Median
Modern Median is 6resentation
• Example 2: (2, 4, 6, 8, 100)
Mean = 24
The Mean
should not
used in this
case
It is not logical
average

• Age distribution of diaper users
1,1,1,1,1,2,2,3,4,50,87,88,88,88,88,88,89,89,90
Mean is 45.3
ModerMedian is 50resentation
?

• Age distribution of diaper users
1,1,1,1,1,2,2,3,4,50,87,88,88,88,88,88,89,89,90
Mode is the choice
?

Mode

Mode
• Data set may have one or two modes.
• This measure of central tendency best used with nominal data.
• Example: (2, 4, 4, 6, 8) ...mode = 4.
• Example : ( 2,4,4,6,6,8) …. mode = 4,6
• Example : ( 2,4,4,6,6,6,8) ….. mode = 6
• If there is frequent number Mode is Zero.
• If there are many it is a multimodal:
If 2 it is bimodal
If 3 it is trimodal

Normal and skewed distribution

Range
• Shows lowest and highest numbers in set of numbers.
• It is not telling anything about numbers between two values.
• Calculated by subtracting lowest number from highest number.
Examples:
2, 4 , 6 , 8 , 10
so range will be 2 , 10
Or (10 – 2 ) = 8

Frequency Distribution
• Table or graphic displaying all unique values in a data set along
with the number of times each value appears within the set.
Frequency
Distribution

Frequency Distribution
Simple Frequency
Distribution
Grouped Frequency
Distribution
Cumulative Frequency
Distribution

Relative frequency/percentage:
• Calculation of proportion, or part – to –whole relationship.
• Percentage is calculated by dividing the part (single object) by the
whole (total number of cases in group) and multiplying by 100
Example:
• Number of ortho clinic visitors 250
• Number of OPD visitors 1000
250
Percentage = ‫ـــــــــــ‬ = .25 x 100 = 25%
1000

Ratio:
Is a comparison, relation between two groups.
Example:
The ratio of male staff to female staff is:
Word Form
5 to 6
Colon Form
5 : 6
Fraction Form
5
‫ــــــــــ‬ or 5/6
6

Ratio:
• Is a comparison, relation between two things.
Considerations:
1. The ratio should be in the most simple formula
2. Same unit should be used for the same groups
60
‫ــــــ‬
72
5
‫ــــــ‬
6
For example : the ration is 40 cm to 2 m .It cannot be displayed as:
40:2 we should use either CM or M for both 40 : 200

Ratio:
• Is a comparison, relation between two things.
Considerations:
3. The ratio is not real fraction, it indicate relation only:

Rate:
• It is a ratio that involves a period of time

Standard deviation
• Measure spread of distribution, computed value describe amount
of variability in particular distribution.
• The more values cluster around mean, the smaller amount of
variability or dispersion.

Standard Deviation
• In normal distribution curve:
• 68.2 % of population between (mean +/- 1 SD) (X +/- 1 σ)
• 95.4% of population between (mean +/- 2 SD) (X +/- 2 σ)
• 99.7% of population between (mean +/- 3 SD) (X +/- 3 σ)

Standard Deviation & 6 Sigma
LSL LSL
USL USL
6 Sigma reduces the variation

Standard Deviation & 6 Sigma

Data Collection
IMPORTANCE OF DATA COLLECTION:
 Integrity of The Research :A key reason for collecting data, be it through quantitative or
qualitative methods is to ensure that the integrity of the research question is indeed maintained.
 Reduce the likelihood of errors: The correct use of appropriate data collection of
methods reduces the likelihood of errors consistent with the results.
 Decision Making: To minimize the risk of errors in decision making, it is important that accurate
data is collected so that the researcher doesn't make uninformed decisions.
 Save Cost and Time: Data collection saves the researcher time and funds that would
otherwise be misspent without a deeper understanding of the topic or subject matter.
 To support a need for a new idea, change and/or innovation or
improvement project :To prove the need for a change in the norm or the introduction of new
information that will be widely accepted, it is important to collect data as evidence to support these claims.

Data Collection
Determine What Information You Want to Collect:
 Set a Timeframe for Data Collection
 Determine Your Data Collection Method
 Collect the Data
 Analyze the Data and Implement Your Findings

Data Collection
Primary vs Secondary Data:
 Primary data is data that is collected by a researcher from first-
hand sources, using methods like surveys, interviews, or
experiments. It is collected with the research project in mind,
directly from primary sources.
 Secondary data is data gathered from studies, surveys, or
experiments that have been run by other people or for other
research.

Data Collection

Types of Sampling Technique

Sample size
JCI guideline for sampling:
• These sample size are considered as statistically significant and
can be applied to measure activities per specific period:
Population size
Sample Size
100% of all available cases
< 30 cases
30 cases
30 – 100 cases
50 cases
101 – 500 cases
70 cases
> 500 cases

Once data are collected , it is the time to analyze it

Data Analysis:
• Data analysis “is a process of inspecting, cleaning, transformin
g, and modeling data with the goal of discovering useful
information, suggesting conclusions, and supporting decision-
making”
AHIMA

Data Analysis:
Data Analysis = Detective Work
• Finding patterns
• Seeing trends
• Identifying anomalies
• Answering question

Types of Analysis:
Descriptive statistics:
Used to summarize the center and shape of the distribution
of a variable of interest.
Inferential statistics:
Is the set of techniques that are used to make conclusion
about the population of interest based on the analysis of a
sample of data.

Structured data:
Is data elements that stored in field specifically designed for
it.
It maybe used as a parameter in the a query or summarized
by counting the frequency distribution of value or average.
Examples:
Age, admission date and gender
Structured Vs unstructured Data:

Unstructured data:
Is free form text data that is captured in a narrative form.
It is stored in database, but the field not allow for querying
based on its value.
Examples:
Progress note , Radiology report
Structured Vs unstructured Data:

Data, Information, Knowledge,
and Wisdom (DIKW)

From Context to Content

The Process of Data Analysis

Data Governance
A system of decision rights and accountabilities for information-
related processes, executed according to agreed-upon models
which describe who can take what actions with what information,
and when, under what circumstances, using what methods.
Data Governance Institute
Data governance helps health care organizations better manage
and realize value from their data.

Data Governance

Goal of Data Governance

Display & statistical tools:
• Used to measure performance.
• Collect, display data different variables.
• Tools used for problem identification, data analysis, solution
planning, outcome evaluation.
• Appropriate display is responsibility of quality professional.
• Appropriate data presentation determines whether appropriate.
• Decision is made and appropriate action is taken.
• How data is aggregated and displayed critical to outcome &
ultimate effectiveness of process.

• Organized and summarized data for sample or population.
• There are several issues with using tables displaying data over
time:
The information displayed without any guidance as to what is
important within the table, then it will be difficult for the reader
to tell easily what is important within the table.
Sometimes colors are used as traffic light colors (red, yellow,
green) to attract focus toward some data, not demonstrate
common cause variation of data over time.
Tables:

Tables:

• A Pie Chart is a type of graph that displays data in a circular
graph. The pieces of the graph are proportional to the fraction of
the whole in each category. In other words, each slice of the pie is
relative to the size of that category in the group as a whole. The
entire “pie” represents 100 percent of a whole, while the pie
“slices” represent portions of the whole.
Pie Chart:

• Pie charts give you a snapshot of how a group is broken down
into smaller pieces.
Pie Chart:

• Display of comparisons between different groups of discrete
objects that cannot be ordered.
• Used for categorical variables, each set of bars represent
category.
Bar chart – Bar graph:

Bar chart – Bar graph:

7 Quality Tools

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• Bar graph of one continuous variable .
• Because continuous bars are blended by connecting them at
frequency midpoints so bars are no longer discrete.
Histogram:

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Histogram:
No. of blue T-Shirt per Classes
1 2 3 4 5 6 7
Class

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Histogram:

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Check sheet:
The check sheet is a form (document) used to collect data in real
time at the location where the data is generated. The data it captures
can be quantitative or qualitative. When the information is quantitative
, the check sheet is sometimes called a tally sheet.

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Check sheet:
Classification check sheet
Defect location check sheet
Frequency check sheet Check List

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Scatter Diagram:
Scatter diagram. Also called: scatter plot, X-Y graph. The scatter
diagram graphs pairs of numerical data, with one variable on each
axis, to look for a relationship between variables. If the variables are
correlated, the points will fall along a line or curve.

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Bivariate Vs Univariate data:
Univariate data : only look at one variable.
Example :
height of some thing.
Bivariate data: relation of two variables.
Example:
relation of height and weight of something

Regression Analysis:
statistical technique that allows one to compare entire distribution of
one variable with entire distribution of another variable to determine
how strongly two sets of variable are correlated (interrelated).

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Scatter Diagram:

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Scatter Diagram:
NO relationship" ( r = 0 )

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Control Chart:
• line graph compares actual performance or change over time to the
mean. change over time to the mean.
• Includes both upper and lower control limits.
• Display normal variations & special cause variations over time.
• Best used with continuous data.
Control Chart Run Chart

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Control Chart:
• Assist problem identification, data analysis, assist problem
identification, outcome evaluation.
• Control charts provide basis to:
Determine capability of process (degree of control).
Identify special causes. special causes.
• Control charts are more precise than run charts in identifying
special cause variations in process.
• Data that fall between UCL & LCL represent predictable variation,
common cause controlled system.
• This variation in data will form normal distribution, normal bell
shaped curve.

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Control Chart:
• UCL & LCL represent "tails" of curve, set at +3 / -3 SD ,representing
that 99.7 % of data should fall within those limits.

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Control Chart:
• Special cause variation is indicated in one of the following cases:
Any value outside UCL or LCL
Shift.
Trend.

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Control Chart: (Shift)
• Consists of eight or more consecutive points in consecutive points
in a row above or below the mean.
• Any point exactly found on the mean NOT counted because it is ne
ither above nor below mean.

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Control Chart: (Trend)
• consists of six consecutive points going up or coming down.
• If two or more consecutive data points are the same value one of
them not counted.

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Control Chart: (Random or common cause vs assignable or special cause)
Assignable or special cause
Random or common cause
Extrinsic - outlier
Intrinsic - inliers
Variation from what normally expected, tails
of bell shaped curve.
Occurring noise in process
Sentinel events , one time occurrence,
unique out of the ordinary
Chronic – persistent. Resolving more time
consuming, and difficult
More easily identified and resolved by QI
teams
Normal range of variation in process
Needs case specific focused review & RCA
Goal: not to eliminate, but to reduce
variation in process enough to produce &
sustain stability.
- If negative: quickly changed or eliminated.
- If positive:analyzed for possible replication
Refers to less desirable parts of process.

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Pareto Diagram:
• Special form of vertical bar graph with bars in rank order of
occurrence from highest to lowest.
• Shows comparison of causes of problems.
• Rank order prioritizes them, with most common cause or most
frequently occurring variable graphed first.
• Each bar represents a different problem or variable.
• Pareto used in problem identification, data analysis, outcome
evaluation.

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Pareto Diagram:
• Purpose to determine where to focus improvement efforts, look for
"vital few" from “trivial many" to determine where to start making
improvements.
• Pareto principle that 20% of the whole represent 80% of problem.
• Cumulative frequency distribution: at each value in distribution
column, Cumulative frequency is calculated as sum of frequency of
that values plus frequencies of all points of smaller values.
• Pareto provides tool to drill down to fine details of problem.

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Pareto Diagram:

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Flow Chart
• Pictorial representation display all process steps and their
• interrelationships.
• Display ideal sequence of steps once actual process is known.
• Identify and describe current process (RCA) and FMEA.
• Also known as process map.
It is process tool too.

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Flow Chart

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Cause and Effect Diagram:
• Display of relationship between "effect" and possible "causes"
impacting it.
• Used to gather all possible causes, ultimate goal to reach root
causes of problem.
• This diagram is visualization of relationships between outcome of
particular system or process, major categories of system and sub-
causes.
• Used to:
Analyze dispersion: why this cause happen.
Classify process: identify all steps.
Enumerate causes.
Used in root cause analysis.

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• After brainstorming possible causes may be:
• M for manufacturing, methods, materials, manpower, machines,
measurement, and Mother Nature(environment).
• P for people, prices, promotion, places, policies, procedures,
products
• S for service, surroundings, suppliers, systems, and skills.

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Key Performance Indicators (KPIs)
• Indicators are metrics that have context assigned to them so that
both the baseline data and the target or goal are depicted. These
metrics indicate whether the current performance is good or bad,
convey how close or far the current performance is from the target
goal, and show whether performance is trending toward meeting
the target or trending away from the target. Indicators are much
more useful as they provide a trigger for action. Indicators are often
placed on performance dashboards.

Seven Characteristics of KPIs
• Expressed in nonfinancial terms
• Timely
• Senior management focused
• Simple
• Team-based
• Significant organizational
impact
• Evidence based

SMART KPIs
Percentage of successful wristband scans reported on a monthly basi
s for all inpatient units:
Target: 93% or greater

Type of KPIs
Structure 0 Example: Staff Ratio in
ICU

The Data Tells a Story
“Show me the shape of things, not the
quantities.”
–Stephen Few
Visualization is powerful, but even more
powerful is the ability to connect visuals to tell a
story with data

The Data Tells a Story

Choose the Right Format

Dashboards

Infographic

Nawaf Bader Ali
10045678
Saudi
62
CT Brain, Chest X-Ray, CBC
CVA
Dr. Ali Ahmed
Issue :
Low Physician compliance in filling
referral form
- Set the time frame of data collection,
(determine is that retrospective or
prospective?
- Determine data collection method
and sample size
- Build your measure (Indicator)
- Collect the data
- Analyze it
- Feed the measure

Nawaf Bader Ali
10045678
Saudi
62
CVA
Dr. Ali Ahmed
Issue :
referral form
Set the time frame of data collection,
(determine is that retrospective or
prospective)?
As the forms are available, we will
work retrospectively

Nawaf Bader Ali
10045678
Saudi
62
CVA
Dr. Ali Ahmed
Issue :
referral form
Determine data collection method
and sample size
Stratified random sampling as we
divided the sample as strata (Groups)
per their specialty, we will take a
sample o every physician forms
randomly.
Sample size 30 forms/physician forms

Nawaf Bader Ali
10045678
Saudi
62
CVA
Dr. Ali Ahmed
Issue :
referral form
Build your measure (Indicator)
Convert qualitative data (Forms) to
numeric quantitative data

1
1 Hospital Information
2 Patient Information
2
3 Referrer Details
4 Physical Examination
5 Investigation
7 Procedures
8 Treatment
9 Patient Condition
10 Reason for Referral
11 Dr. Information
3
4
5
7
8
9
10
6 Investigation 6
12 Recieve Information
11
12
Build your measure (Indicator)

1 2
3
4
5
7
8
9
10
6
11
12

Nawaf Bader Ali
10045678
Saudi
62
CVA
Dr. Ali Ahmed
Compliance Average :
For this form according to our
measure it is
27%

A Picture Is Worth a Thousand Words
The hospital Compliance Average in filling referral form is
55 %
Medical
Ortho
Surgical
Compliance Average 44%
53%
68%
Hospital Compliance Average 55%
55%
55%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Compliance
Average
Departments
Compliance Average Per Departments

0%
10%
20%
30%
40%
50%
60%
70%
80%
Morning
Evening
Night
Compliance Average Per Duties
Compliance Average Hospital Compliance Rate

Physician Compliance Average
Dr. S 87%
Dr. R 73%
Dr. Q 64%
Dr. P 63%
Dr. O 62%
Dr. N 61%
Dr. M 61%
Dr. L 61%
Dr. B 57%
Dr. K 53%
Dr. J 53%
Dr. I 52%
Dr. H 52%
Dr. G 52%
Dr. F 51%
Dr. D 43%
Dr. E 42%
Dr. A 42%
Dr. C 51%
Dr. Ahmed Ali 37%
Hospital Average 55%

48%
50%
52%
54%
56%
58%
60%
62%
64%
66%
68%
Jan Feb March April May Jun Jul Aug Sep Oct Non Dec
Compliance
Average
Months
Hospital Referral Form Compliance Average during 2021
Monthly Complince Average Mean

Medical
30%
Ortho
35%
Surgical
35%
Proportion per specialty
Medical Ortho Surgical

Registration and Survey
Discussion

Health Data Management & Analysis.pdf

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