ATLAS Tutorial: Data Sources - Person

Summary

This video introduces the data sources capability in the Atlas platform, demonstrating how to select and analyze various reports, such as the person report. It showcases the ability to visualize data through graphs, like the year of birth distribution and gender, race, and ethnicity composition. The script highlights the platform's functionality to identify anomalies and characterize data, providing insights into data source quality and composition.

Takeaways

• 📊 The Atlas platform offers a 'person reporting' feature that provides insights into data sources through various graphs and reports.
• 🔍 Users can select specific data sources to study and generate reports, such as the 'person report', which includes multiple graphs.
• 📈 The first graph in the person report shows the distribution of the year of birth, with the x-axis representing the year of birth and the y-axis showing the number of persons born in that year.
• 👀 Hovering over a specific year on the graph reveals the number of persons born in that year, providing detailed insights into the data distribution.
• 📊 An example from the script shows a data source with 1.3 million persons born in 1990 and an unusual spike in 1929 with 1 million patients, indicating potential anomalies.
• 👤 The person report also includes donut plots for gender distribution, revealing the proportion of male and female patients, such as 50.5% female and 49.5% male in the example provided.
• 🌐 The script mentions that the race and ethnicity data may vary depending on the data source, with some sources lacking matched concepts for these attributes.
• 🔄 Changing the data source can result in different year of birth distributions and compositions by gender, race, and ethnicity, as illustrated in the script.
• 📊 The script provides an example of a different data source where 59% of the patients are white, 9.2% are black or African-American, and 2% are Asian, with 29% of patients' race unidentified.
• 🌐 Similarly, for ethnicity, 93% of patients in the example do not have a match concept, while 6.3% are identified as Hispanic or Latino.
• 🔗 For more information about the Odyssey platform, including details on Atlas and additional data sources and reports, the script directs viewers to visit odyssey.org.

Q & A

What is the primary focus of the video?

The primary focus of the video is to provide an introduction to the person reporting capability within the data sources capability in the Atlas platform.

How can users select a data source in the Atlas platform?

Users can select a data source by choosing the data source they are interested in studying from the available options in the Atlas platform.

What information does the person report provide?

The person report provides a series of graphs showing the year of birth distribution, gender, race, and ethnicity of the persons in the selected data source.

What does the first graph in the person report display?

The first graph displays the year of birth distribution, with the x-axis showing the year of birth and the y-axis showing the number of persons born in that year.

How can anomalies in the year of birth distribution be identified?

Anomalies in the year of birth distribution can be identified by looking for unusual spikes or drops in the graph. For example, a spike in 1929 indicates an unusual number of persons born that year.

What does the gender donut plot show?

The gender donut plot shows the distribution of gender within the data source, classifying persons as either male or female and displaying their number and proportion.

What information is missing in the race distribution of the first data source?

The first data source does not have any matched concepts for race, meaning that race is unknown for this population.

How does the ethnicity information appear in the first data source?

The ethnicity information in the first data source is unknown.

What changes can be observed when switching data sources in the Atlas platform?

When switching data sources, the person report will update to show different year of birth distributions and compositions by gender, race, and ethnicity based on the new data source.

How does the second data source differ in terms of race and ethnicity information?

In the second data source, race information is available with 59% identified as white, 9.2% as black or African American, 2% as Asian, and 29% unknown. Ethnicity information shows 93% of patients without a matched concept, and 6.3% identified as Hispanic or Latino.