diff --git a/src/app/case-studies/la-county/page.tsx b/src/app/case-studies/la-county/page.tsx new file mode 100644 index 0000000..9ee7927 --- /dev/null +++ b/src/app/case-studies/la-county/page.tsx @@ -0,0 +1,143 @@ +import Link from 'next/link'; +import { Link as ExternalLink } from '@trussworks/react-uswds'; +import './styles.scss'; + +export default function LaCountyCaseStudy() { + return ( +
+
+
+ + Return to all case studies + +

+ Creating a modular, cloud-based data processing pipeline for LA + County +

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+

The challenge

+

+ + Timely access to electronic case reporting (eCR) data is + critical for public health departments to respond swiftly to + disease outbreaks, especially during a public health emergency. + Unfortunately, not all public health jurisdictions can + effectively manage the flow of incoming eCR data. Due to + technical limitations with their existing disease surveillance + system, the Los Angeles County (LAC) Department of Public Health + faced challenges with processing eCR files, leaving this rich + source of data largely inaccessible to their disease + surveillance teams. + + + Because LAC's disease surveillance system couldn't process eCR + data fields, they set up a separate, patchwork data workflow to + collect eCR data. As a result, epidemiologists at LAC also had + to spend a considerable amount of time manually cleaning data + after it was processed. To efficiently monitor and respond to + disease outbreaks, LAC also needed to improve the overall + quality of the data processed through its disease surveillance + infrastructure. Better, more reliable data reduces the need for + manual cleaning and makes downstream analysis and case + investigation less onerous for epidemiologists and other public + health staff. + +

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+

The solution

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+ The DIBBs team worked with LAC to develop and deploy a + cutting-edge, modular data pipeline to automatically process and + enrich COVID-19 eCR files. This open-source, cloud-based + pipeline — composed of modular software components called Data + Integration Building Blocks (DIBBs) — helps significantly reduce + the time it takes for LAC's disease surveillance teams to + receive and act upon public health data, while also improving + the quality of that data. Over the course of the year-long + pilot, the DIBBs team: +

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  • + Conducted discovery research to understand eCR workflows, + identify product support needs, and assess the value of + processing eCR data for LAC disease surveillance teams +
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  • + Engaged LAC staff in an iterative software development process + with weekly agile ceremonies and regular product + demonstrations to continuously refine the pipeline +
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  • + Performed user acceptance testing with LAC staff to identify + and mitigate barriers to adoption for the DIBBs pipeline +
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  • + Compared the performance of LAC's pre-pilot data processing to + data processing after the DIBBs pipeline was deployed to test + record linkage performance and measure data quality +
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  • + Evaluated how the DIBBs pipeline affected the experience of + case investigators that monitor and report on Hepatitis A to + assess the pipeline's public health impact +
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  • + Developed a compendium of resources (i.e., Handoff Hub) for + LAC staff to use post-pilot that enables them to independently + operate and customize the pipeline +
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+ We are currently commencing pilots with jurisdictions to test + the eCR Viewer in a production data environment and further + validate the tool's downstream public health impact. Our aim is + to scale the eCR Viewer with a wide range of jurisdictions to + turn eCR into the go-to data source for case ascertainment and + investigation. +

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+

The results

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+ Following the pilot, LAC now has access to an automated feed of + analysis-ready eCR data with fields relevant to downstream disease + teams. LAC plans to continue to leverage the DIBBs pipeline + infrastructure to give additional disease teams access to + processed eCR data, including the HIV and STD prevention team and + the Community Outbreak Team (focused on viral respiratory + pathogens). Through the LAC pilot, the DIBBs team gained insights + on how to use and adapt our modular, open-source solutions to + solve data challenges for multiple disease surveillance systems + across public health jurisdictions. +

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+

Read more about our work

+ + Findings from a Los Angeles County Pilot - Executive Brief + +
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+
+
+ ); +} diff --git a/src/app/case-studies/la-county/styles.scss b/src/app/case-studies/la-county/styles.scss new file mode 100644 index 0000000..5f6c798 --- /dev/null +++ b/src/app/case-studies/la-county/styles.scss @@ -0,0 +1,5 @@ +.list__full-width { + li { + min-width: 100%; + } +} diff --git a/src/app/case-studies/page.tsx b/src/app/case-studies/page.tsx index 84f9ec2..556e1bb 100644 --- a/src/app/case-studies/page.tsx +++ b/src/app/case-studies/page.tsx @@ -29,7 +29,7 @@ export default function CaseStudies() { data pipeline that automatically processes and enriches eCR data to improve downstream data analysis and case investigation. - + View case study diff --git a/src/app/custom-styles.css b/src/app/custom-styles.css index f1f4775..585e16b 100644 --- a/src/app/custom-styles.css +++ b/src/app/custom-styles.css @@ -63,6 +63,7 @@ body { } h1 { + color: #224a58; font-size: 2.5rem; font-weight: 700; line-height: 3.125rem;