Reflection Paper

Baba0914

AC21620-Chapter1.pptx

Home >Business & Finance homework help >Management homework help >Reflection Paper

A Practical Approach to Analyzing Healthcare Data, Fourth Edition Chapter 1, Introduction to Data Analysis

Susan White, PhD, RHIA, CHDA

ahima.org

Learning Objectives

Understand types of data analysis

Review types of data

Explore the skills required for a career in healthcare data analytics

ahima.org

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

ahima.org

Data

Information

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-10 diagnosis codes are assigned to a patient to record diseases present or discovered during an encounter

Using a profile of the most common ICD-10 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

ahima.org

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

ahima.org

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

ahima.org

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

ahima.org

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

ahima.org

Variable Scales/Data Type

ahima.org

Overview of Data Type and Statistics

ahima.org

Inferential Statistics - CMS

ahima.org

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

ahima.org

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

ahima.org

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

ahima.org

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

ahima.org

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

ahima.org

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

ahima.org

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

ahima.org

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

Reflection Paper

image3.jpg

image4.png

image5.png

image6.png

image7.png

image1.jpg

image2.jpg