Презентация на тему 6 Essential Data Analyst Skills for Your Healthcare Organization

Презентация на тему 6 Essential Data Analyst Skills for Your Healthcare Organization, предмет презентации: Аналитика. Этот материал содержит 17 слайдов. Красочные слайды и илюстрации помогут Вам заинтересовать свою аудиторию. Для просмотра воспользуйтесь проигрывателем, если материал оказался полезным для Вас - поделитесь им с друзьями с помощью социальных кнопок и добавьте наш сайт презентаций ThePresentation.ru в закладки!

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6 Essential Data Analyst Skills for Your Healthcare Organization

― John Wadsworth


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Data Analyst Skillsets Drive Success

Smart healthcare groups are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy to improve their care delivery and cost.
But you need more than a solid software foundation.

To fully leverage the EDW investment, you need to have the right people with the right skills—strong healthcare data analyst skills.


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Producers and Consumers of Data and Information

Most healthcare workers will never directly interact with an EDW. They’ll never look for trends by querying a database, write reports or analyze data.
Someone else will actually pull data from the EDW, analyze it, and then produce a report. These people are producers.
And the people that receive these reports can be classified as consumers.


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Producers and Consumers of Data and Information

It’s common for health system to invest millions of dollars and multiple years into a data warehouse and still have dissatisfied consumers.
If the team lacks the skills to manage and leverage an EDW, it can create real problems for a health system’s enterprise-wide analytics strategy.


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6 Skills Healthcare Data Analysts Need

To support sustained outcomes improvement, there are six skills needed among staff members (either analysts or architects) tasked with analytics.
Structured query language
Export, transform, and load (ETL)
Data modeling
Data analysis
Business intelligence (BI) reporting
Telling the story of the visualizations


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6 Skills Healthcare Data Analysts Need

1: Structured query language

An analytics team member needs to be able to talk directly to and manipulate databases through structured query language (SQL).
Highly experienced data analysts should also bring skills with these kinds of toolsets:

Microsoft Access GUI

Crystal Reports

A business intelligence (BI) tool


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6 Skills Healthcare Data Analysts Need

2: Export, transform, and load (ETL)

The data expert needs to be able to perform export, transform, and load (ETL) processes. Simply put, you need to take data from one system and put it into another.
In an EDW, a user pulls data from disparate systems (e.g., EHRS, finance, human resources) that don’t talk to one another.
This movement of data is done through the ETL process.


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6 Skills Healthcare Data Analysts Need

3: Data modeling

Data modeling is a fancy way to say that you write code that models real-world processes and workflows.
Consider the needs for a hospital admission process:
Patient demographic data
Insurance information
Patient clinical history
Admitting diagnosis
Attending physician
Other as-needed data



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6 Skills Healthcare Data Analysts Need

4: Data analysis

An analytics team member needs to be able to make sense of the data once it is in the EDW.
A good analyst using complex thinking around set theory and analysis through SQL, can extract pertinent insights and drive cost and quality improvements.


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6 Skills Healthcare Data Analysts Need

5: Business intelligence (BI) reporting

An analytics team member needs to be able to present data in a way that is intuitive to nontechnical users.
The visual representation must be simple to interpret by a lay audience. This skill separates an average analyst from a stellar one.


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6 Skills Healthcare Data Analysts Need

6: Telling the story of the visualizations

The analytics team member must be able to effectively interpret and transmit the stories embedded in the data.
BI reporting gives contextual meaning in bits and pieces, or a micro view. Telling the story means providing a logical flow that pulls together a coherent story―a big picture view.


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Staffing Models for Skills Management

Staffing in healthcare is primarily based on two models:
Generalist model
Specialist model
Specialists tend to focus on their particular skillset where data architect and analysts within the generalist staffing model may support a wider range of functionality on process improvement teams.


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Staffing Models for Skills Management

The important thing is expertise across all of these skills within the team.
Both generalist and specialist staffing models can work as long as:
The skill is covered with a high degree of expertise
Coordination and handoffs between technical team members are seamless.


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Unlocking the Value of a Healthcare EDW

Ensuring coverage of all six skills skills will help any organization get the most value out of their EDW.
Turning data into actionable information is what good analytics is all about: unlocking data to drive meaningful, sustainable change.


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More about this topic

4 Ways to Enable Healthcare Data Analysts to Provide Their Full Value Russ Staheli, Technical Director
The Best Organizational Structure for Healthcare Analytics John Wadsworth, VP of Client Technical Operations
Health Data Stewardship and Its Importance in Healthcare Analytics John Wadsworth, VP of Client Technical Operations
3 Frequent Mistakes in Healthcare Data Analytics John Wadsworth, VP of Client Technical Operations
Finding $5.7 Million with a Healthcare Data Warehouse John Wadsworth, VP of Client Technical Operations

Link to original article for a more in-depth discussion.
6 Essential Data Analyst Skills for Your Healthcare Organization


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For more information:




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Other Clinical Quality Improvement Resources

Click to read additional information at www.healthcatalyst.com

John Wadsworth joined Health Catalyst in September 2011 as a senior data architect. Prior to Catalyst, he worked for Intermountain Healthcare and for ARUP Laboratories as a data architect. John has a Master of Science degree in biomedical informatics from the University of Utah, School of Medicine.


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