Hm306 week 1 ppt 1

1. © 2016© 2016
A Practical Approach to Analyzing
Healthcare Data
Chapter 1 – Introduction to Data
Analysis

2. © 2016
Data Analysis
• Healthcare is a data
driven business
• Data collected
– Diagnostic tests
– Services provided
– Costs and payment
– Diagnosis and procedure
codes
• What is data analysis?
– Task of transforming,
summarizing or modeling
data to allow the end user
to make meaningful
conclusions Information
Data
Data
Data

3. © 2016
Primary vs Secondary Analysis
• Primary data analysis is the use of data for its primary
purpose
– Example: Billing and claims data’s primary use it to
determine services rendered and payment to from a
patient or third party payer
– Performing an analysis of typical payment received from a
payer for emergency visits is a primary use
• Secondary data analysis is the use of data beyond its
primary purpose
– Example: ICD-9 diagnosis codes are assigned to a patient
to record diseases present or discovered during an
encounter
– Using a profile of the most common ICD-9 diagnosis
categories for the purposes of determining the patient load
by service line is a secondary use.
– Be aware of primary use and evaluate the secondary use
is valid and reliable

4. © 2016
Types of Statistical Analysis
• Descriptive statistics
– Characterizes the distribution of the data
– Estimates the center or ‘typical’ value
– Measures the spread or variation in the
data
• Inferential statistics
– Using sample data to make conclusions or
decisions regarding a population
– Not practical to observe the entire
population
– Often accompanied with a probability of
making an incorrect decision based on the
sample

5. © 2016
Structured vs Unstructured Data
• Structured data
– AKA Discrete data
– Data stored in fields that may be delineated
– Values can be listed and validated
– Examples
• Patient age
• CPT code
• Laboratory test values
• Unstructured data
– Free form text captured in narrative form
– May be stored in a database field, but the content in not
limited to values of a variable
– Examples
• Progress notes in an EHR
• Comments in a patient satisfaction survey
• Radiologist’s report of an x-ray result

6. © 2016
Qualitative Data
• Qualitative data
– Describes observations about a subject
– Typically free text or comments
– May be recoded or placed into categories for analysis
– Example:
• A nurse describes a patient as having pale skin tone.
• Survey question: What do you like most about this course?
• Data scales typically used for recoding qualitative data:
– Nominal - categories without a natural order
• Diagnosis codes
• Clinical units
• Colors
– Ordinal – categories with a natural order
• Patient satisfaction surveys
• Patient severity scores
• Evaluation and management code levels

7. © 2016
Quantitative Data
• Quantitative data
– Naturally numeric
– May be categorical (ordinal or nominal)
• Data scales found in quantitative analysis
– Interval – numeric values where the distance between two
values has meaning, but there is no true zero and the
interpretation is not preserved when multiplying/dividing
• Temperature
• Dates
– Ratio – numeric values where zero has meaning and
multiplying/dividing values has meaning
• Length of stay
• Age
• Weight

8. © 2016
Variable Scales/Data Type

9. © 2016
Overview of Data Type and
Statistics

10. © 2016
Inferential Statistics - CMS

11. © 2016
Exploratory Data Analysis and Data Mining
• Exploratory Data Analysis (EDA)
– Used to uncover patterns in data
– Typically a secondary use of data
– Primarily graphical analysis (plots,
trends, etc.)
• Data Mining
– Also looking for patterns in data
– Adds in descriptive statistics and more
formal statistical techniques
– May be used for benchmarking and
determining high/lower performers

12. © 2016
Predictive Modeling
• Historical data is used to build models to
determine most likely outcome in future
• Data mining is used to identify the potentially best
predictors
• Maybe a simple function (linear regression) or
more involved models (neural networks)
• Examples
– Used by CMS for pre-payment reviews to fight fraud
– Used by credit card companies to prevent fraud
– Used by providers to identify missed charges

13. © 2016
Data Analyst Skills
• Must be able to combine:
– Content knowledge (coded data, healthcare business
process, etc.)
– Understanding of the strengths and weaknesses of various
data elements
– Data acquisition skills through querying databases or
effectively writing specifications for queries
– Ability to identify the appropriate statistical technique to
apply
– Familiarity with analytic software to produce the required
output
– Present the analysis to the end user so that it may be the
basis for business decisions

14. © 2016
Opportunities for HIM Professionals
• HIM Professionals are uniquely positioned to:
– Understand data structures and coding systems
– Understand available data and methods for
integration
– Can communicate with both finance and IT staff
– Act as a business analyst—far more valuable
than a pure data analyst

15. © 2016
Entry Level Health Data Analyst
Responsibilities
• Working with data
– Identify, analyze, and interpret trends or patterns in
complex data sets
– In collaboration with others, interpret data and develop
recommendations on the basis of findings
– Perform basic statistical analyses for projects and reports
• Reporting Results
– Develop graphs, reports, and presentations of project
results, trends, data mining
– Create and present quality dashboards
– Generate routine and ad hoc reports

16. © 2016
Mid-level Health Data Analyst
Responsibilities
• Work collaboratively
– with data and reporting
– the database administrator to help produce effective production management
– utilization management reports in support of performance management related to
utilization, cost, and risk with the various health plan data
– monitor data integrity and quality of reports on a monthly basis
– in monitoring financial performance in each health plan
• Develop and maintain
– claims audit reporting and processes
– contract models in support of contract negotiations with health plans
• Develop, implement, and enhance evaluation and measurement models for
the quality, data and reporting, and data warehouse department programs,
projects, and initiatives for maximum effectiveness
• Act as a business analyst
– Recommend improvements to processes, programs, and initiatives by using
analytical skills and a variety of reporting tools
– Determine the most appropriate approach for internal and external report design,
production, and distribution, specific to the relevant audience

17. © 2016
Senior-level Health Data Analyst
Responsibilities
• Understand and address the information needs of
governance, leadership, and staff to support continuous
improvement of patient care processes and outcomes
• Lead and manage efforts to enhance the strategic use of
data and analytic tools to improve clinical care processes
and outcomes continuously
• Work to ensure the dissemination of accurate, reliable,
timely, accessible, actionable information (data analysis)
to help leaders and staff actively identify and address
opportunities to improve patient care and related
processes
• Work actively with information technology to select and
develop tools to enable facility governance and leadership
to monitor the progress of quality, patient safety, service,
and related metrics continuously throughout the system

18. © 2016
Senior-level Health Data Analyst
Responsibilities
• Engage and collaborate with information technology and senior leadership to
create and maintain:
– a succinct report (e.g., dashboard),
– a balanced set of system assessment measures, that conveys status and direction
of key system-wide quality and patient safety initiatives for the trustee quality and
safety committee and senior management;
– present this information regularly to the quality and safety committee of the board to
ensure understanding of information contained therein
• Actively support the efforts of divisions, departments, programs, and clinical
units to identify, obtain, and actively use quantitative information needed to
support clinical quality monitoring and improvement activities
• Function as an advisor and technical resource regarding the use of data in
clinical quality improvement activities
• Lead analysis of outcomes and resource utilization for specific patient
populations as necessary
• Lead efforts to implement state-of-the-art quality improvement analytical tools
(i.e., statistical process control)
• Play an active role, including leadership, where appropriate, on teams
addressing system-wide clinical quality improvement opportunities