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Merge pull request #63 from CDCgov/jw/cs-prototype
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Create DIBBs Prototype case study page
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@jakewheeler
jakewheeler authored Dec 4, 2024
2 parents a0a4901 + c5f956d commit c22d98f
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147 changes: 147 additions & 0 deletions src/app/case-studies/dibbs-prototype/page.tsx
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import {
PageContainer,
ContentContainer,
ReturnToCaseStudiesLink,
SectionContentContainer,
Text,
UnorderedList,
ReadMore,
} from '../_ui';

const DibbsPrototype = () => {
return (
<PageContainer>
<ContentContainer>
<section id="heading">
<ReturnToCaseStudiesLink />
<h1>
Building a prototype for modernized public health infrastructure in
Virginia
</h1>
</section>
<section id="challenge">
<SectionContentContainer>
<h2>The challenge</h2>
<Text>
<span>
Data coming into public health departments is often messy,
unstandardized, and incomplete. At the same time,
epidemiologists lack the tools and methods to efficiently turn
incoming data into meaningful intelligence that can drive timely
public health action. During a public health crisis, this
combination can slow down the entire data pipeline, from
ingestion to processing to analysis. The Virginia Department of
Health (VDH) experienced exactly this problem during the
COVID-19 pandemic. Along with the high volume and spikes in
COVID-19 data throughout the pandemic, VDH's public health data
surveillance system relied on time- and resource-intensive
manual processes. Data processing was slow, systems timed out,
and there was no single source of truth for incoming data.
</span>
<span>
VDH wanted to improve their processes for making incoming data
from healthcare partners analysis-ready to more efficiently
inform public health action. Specifically, they wanted to
combine different data streams (ELR, eCR, and VXU) into a single
database, where they could then quickly and easily perform
analyses with minimal manual effort. Additionally, local
jurisdictions within Virginia wanted to geocode the data to
identify gaps in vaccination and then perform targeted outreach,
such as holding vaccination drives within schools or apartment
complexes.
</span>
<span>
However, as it stood, VDH's existing system introduced
inefficiencies and uncertainty into data processing, compromised
their ability to share data analysis and findings to inform
public health action, and ultimately reduced confidence in the
integrity of the data itself.
</span>
</Text>
</SectionContentContainer>
</section>
<section id="solution">
<SectionContentContainer>
<h2>The solution</h2>
<Text>
The DIBBs team partnered with VDH to experiment with new
approaches for storing, processing, and linking different incoming
data streams. In an effort to improve VDH's disease surveillance
infrastructure, our team:
</Text>
<UnorderedList>
<li>
Engaged in discovery efforts to establish an understanding of
the data workflow at VDH, from the receipt of public health data
through processing to analysis
</li>
<li>
Built a cloud-based prototype data processing pipeline for VDH
that brought disparate data streams together into a single
database using the Fast Healthcare Interoperability Resources
(FHIR) standard to standardize data elements
</li>
<li>
Developed a white paper as a central reference point for
learnings from the pilot project to apply to other public health
jurisdictions
</li>
</UnorderedList>
<Text>
Our team constructed this prototype pipeline using a set of
open-source, modular tools known as Data Integration Building
Blocks (DIBBs). When combined together, DIBBs can increase data
processing speed for incoming data across a wide range of data
formats (e.g., eCR, ELR, VXU). In the VDH test environment, the
pipeline processed incoming data faster than the existing manual
methods, created a source of truth, and removed the need for
duplicative processes. Data that moved through this prototype
pipeline was standardized, deduplicated, geocoded, and linked, and
patient-level records were created to use for analysis.
Additionally, the pipeline converted raw data into a tabular,
human-readable format (e.g,. spreadsheet), enabling
epidemiologists to quickly find data they needed. From this
prototype, our team has continued to test and iterate on DIBBs
products with a wide range of public health departments to solve
their toughest data challenges.
</Text>
</SectionContentContainer>
</section>
<section id="results">
<SectionContentContainer>
<h2>The results</h2>
<UnorderedList>
<li>
Built a prototype DIBBs pipeline that significantly improved
data processing speed and broke down silos between different
streams
</li>
<li>
Reduced patient record duplication by 19% across data streams
</li>
<li>
Using the prototype pipeline, VDH went from being able to handle
5,800 incoming HL7 messages per hour, at peak, to 20,000
messages per hour
</li>
<li>
The DIBBs pipeline can generate a tabular, analysis-ready data
mart for ~380,000 patient resources in under 15 minutes
</li>
</UnorderedList>
</SectionContentContainer>
</section>
<section id="read-more">
<SectionContentContainer>
<ReadMore
href="https://github.com/CDCgov/phdi/blob/main/publications/DMI_VAWhitePaper_V3.pdf"
linkText="Findings From a Virginia Pilot - White Paper"
/>
</SectionContentContainer>
</section>
</ContentContainer>
</PageContainer>
);
};

export default DibbsPrototype;
5 changes: 4 additions & 1 deletion src/app/case-studies/page.tsx
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Expand Up @@ -120,7 +120,10 @@ export default function CaseStudies() {
ingestion pipeline that improves data processing and analysis to
more efficiently inform public health action.
</Text>
<LinkButton variant="primary" href="/">
<LinkButton
variant="primary"
href="/case-studies/dibbs-prototype"
>
View case study
</LinkButton>
</div>
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