What Does Data Modernization Look Like?

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Click on the cards below to connect to related stories in the 2022 DMI Snapshot. You can also download a printable version of this page [PDF – 486 KB] or the full visual booklet [6 MB, 40 Pages].

Data modernization looks like…

An epidemiologist spending the day investigating COVID cases instead of entering them into a spreadsheet
two people discussing data posted on a digital wall
A community health worker knowing precisely which neighborhood needs a pop-up vaccine clinic
community health worker using a device with a COVID-19 contact tracing app
A public health analyst producing a report that used to take two days in just two clicks of a button
woman wearing a mask with a tracking app on device
A research team analyzing more than 12 million health records to understand latent tuberculosis infection
team working together to access data
A responder catching an uptick in overdose deaths and sending fentanyl test strips to the frontline
person using laptop with data on screen
A data scientist visualizing social media feeds to help monitor suicide trends
woman looking at data on screen
A multi-state healthcare network saving 160,000 staff hours that would have been spent on paperwork
network
A policymaker using a real-time wastewater surveillance dashboard to send resources where they're needed most
person using ipad

Made possible through technologies like…

Automated, electronic reporting that replaces paper and reduces the burden on health department staff
A suite of cloud-based tools that saves time and costs while analyzing more kinds of data faster than ever before
A data catalog where CDC’s experts can see which data exist in any part of the agency on any disease or condition
A North Star Architecture for data that can be used by every level of public health to collect, transform, and share information
One front door for state and local partners to send data to all of CDC instead of sending to multiple programs in multiple ways
Common standards that help different data systems across public health and healthcare speak the same language
Dashboards that visualize data and make it available to the public, researchers, and policymakers in real time
Novel approaches like artificial intelligence and machine learning that can discover relationships in the data that are hard for traditional methods to find

And by finding new ways to…

Bring people from across public health, government, academia, and industry together to find common solutions to common data problems
people reviewing data at a meeting
Offer public health staff opportunities to learn by doing and to access the latest data science training anytime, anywhere
hands on work by doing
Open up the data so that more people have access to the information they need for decisions, both in emergencies and every day
research team
Modernize the policies and agreements that inform how data are exchanged so that we can get information where it needs to go to save lives
women participating in meeting
Make sure that health equity is "baked in" from the start on everything we do
kids doing yoga
Use resources wisely by making sure everything we invest in is connected, flexible, and sustainable for the future
man examining digital resources

Resulting in…

Data increased

Data increased

20,000+

healthcare facilities delivering automated, real-time case reports — up from just 187 before the pandemic

20,000+

healthcare facilities delivering automated, real-time case reports — up from just 187 before the pandemic

91%

complete race information in syndromic surveillance — up from 80% in 2019 — which is critical important for understanding health disparities

91%

complete race information in syndromic surveillance — up from 80% in 2019 — which is critical important for understanding health disparities

~90%

of death records coded automatically using natural language processing — up from 75% with the previous system – with data to jurisdictions in minutes

~90%

of death records coded automatically using natural language processing — up from 75% with the previous system – with data to jurisdictions in minutes

Time saved

Time saved

~80%

decrease in development time for CDC’s response and surveillance programs using new Cloud components

~80%

decrease in development time for CDC’s response and surveillance programs using new Cloud components

1+

year reduction in the time it takes to understand suicide death trends thanks to new “nowcasting” capabilities

1+

year reduction in the time it takes to understand suicide death trends thanks to new “nowcasting” capabilities

~5.5

days from sample collection to data available in CDC’s wastewater surveillance system — down from ~4 weeks

~5.5

days from sample collection to data available in CDC’s wastewater surveillance system — down from ~4 weeks

People connected

People connected

3,700+

training hours completed by CDC staff in the Data Academy — an 83% increase over the previous year

3,700+

training hours completed by CDC staff in the Data Academy — an 83% increase over the previous year

18

multi-sector meetings of the Consortium for Data Modernization, covering 24 high-priority topics

18

multi-sector meetings of the Consortium for Data Modernization, covering 24 high-priority topics

200+

CDC staff actively engaged in defining priorities and solving challenges through DMI Implementation Teams

200+

CDC staff actively engaged in defining priorities and solving challenges through DMI Implementation Teams