diff --git a/src/app/case-studies/_ui/index.tsx b/src/app/case-studies/_ui/index.tsx new file mode 100644 index 0000000..62922bf --- /dev/null +++ b/src/app/case-studies/_ui/index.tsx @@ -0,0 +1,75 @@ +import Link from 'next/link'; +import { Link as ExternalLink } from '@trussworks/react-uswds'; + +interface ContainerProps { + children: React.ReactNode; +} + +const PageContainer = ({ children }: ContainerProps) => { + return ( +
+ {children} +
+ ); +}; + +const ContentContainer = ({ children }: ContainerProps) => { + return ( +
+ {children} +
+ ); +}; + +const SectionContentContainer = ({ children }: ContainerProps) => { + return
{children}
; +}; + +const ReturnToCaseStudiesLink = () => { + return ( + + Return to all case studies + + ); +}; + +const Text = ({ children }: ContainerProps) => { + return

{children}

; +}; + +const UnorderedList = ({ children }: ContainerProps) => { + return ( + + ); +}; + +const ReadMore = ({ href, linkText }: { href: string; linkText: string }) => { + return ( + <> +

Read more about our work

+ + {linkText} + + + ); +}; + +export { + ContentContainer, + PageContainer, + SectionContentContainer, + ReturnToCaseStudiesLink, + Text, + UnorderedList, + ReadMore, +}; diff --git a/src/app/case-studies/la-county/styles.scss b/src/app/case-studies/_ui/styles.scss similarity index 100% rename from src/app/case-studies/la-county/styles.scss rename to src/app/case-studies/_ui/styles.scss diff --git a/src/app/case-studies/la-county/page.tsx b/src/app/case-studies/dibbs-pipeline/page.tsx similarity index 60% rename from src/app/case-studies/la-county/page.tsx rename to src/app/case-studies/dibbs-pipeline/page.tsx index 9ee7927..3963839 100644 --- a/src/app/case-studies/la-county/page.tsx +++ b/src/app/case-studies/dibbs-pipeline/page.tsx @@ -1,27 +1,29 @@ -import Link from 'next/link'; -import { Link as ExternalLink } from '@trussworks/react-uswds'; -import './styles.scss'; +import { + PageContainer, + ContentContainer, + ReturnToCaseStudiesLink, + SectionContentContainer, + UnorderedList, + Text, + ReadMore, +} from '../_ui'; +import '../_ui/styles.scss'; -export default function LaCountyCaseStudy() { +const DibbsPipeline = () => { return ( -
-
+ +
- - Return to all case studies - +

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

-
+

The challenge

-

+ Timely access to electronic case reporting (eCR) data is critical for public health departments to respond swiftly to @@ -47,14 +49,14 @@ export default function LaCountyCaseStudy() { investigation less onerous for epidemiologists and other public health staff. -

-
+ +
-
+

The solution

-

+ 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 @@ -64,8 +66,8 @@ export default function LaCountyCaseStudy() { 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: -

-
    + +
  • Conducted discovery research to understand eCR workflows, identify product support needs, and assess the value of @@ -95,49 +97,63 @@ export default function LaCountyCaseStudy() { LAC staff to use post-pilot that enables them to independently operate and customize the pipeline
    • -
-

- 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. -

+ + + 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. +
-
+
-
+

The results

-

- 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. -

-
+ +
  • + LAC staff can create program-specific data marts in a few hours + rather than months, giving disease teams access to processed eCR + data for case investigation and analysis +
    • +
  • + Case investigators on the Hepatitis team can receive eCR data + 95% faster (from 20 hours to 1 hour) +
    • +
  • + Case investigators can quickly and easily identify positive + Hepatitis A cases in an aggregated tabular format (versus + sifting through individual HTML files) resulting in ~12 minutes + of time savings per Hepatitis A case +
    • +
  • + Pipeline can save LAC technical staff 6 hours a week by + eliminating the need to manually run data processing scripts +
    • +
  • + LAC staff can operate the pipeline independently and + continuously expand use of eCR data to new program areas +
    • +     +
    -
    -

    Read more about our work

    - + - Findings from a Los Angeles County Pilot - Executive Brief - -
    + linkText="Findings from a Los Angeles County Pilot - Executive Brief" + /> +
    -
    -
    + + ); -} +}; + +export default DibbsPipeline; diff --git a/src/app/case-studies/ecr-viewer/page.tsx b/src/app/case-studies/ecr-viewer/page.tsx new file mode 100644 index 0000000..2283a8b --- /dev/null +++ b/src/app/case-studies/ecr-viewer/page.tsx @@ -0,0 +1,148 @@ +import { + ContentContainer, + PageContainer, + ReturnToCaseStudiesLink, + SectionContentContainer, + Text, + UnorderedList, +} from '../_ui'; + +const EcrViewer = () => { + return ( + + +
    + +

    + Surfacing actionable insights from electronic case reporting data +

    +
    +
    + +

    The challenge

    + + + Electronic case reporting (eCR) is intended to make disease + reporting faster and easier by automating the process of + exchanging case report information between electronic health + records (EHRs) and public health departments. However, public + health departments often face challenges making use of + electronic case reporting (eCR) data in case ascertainment and + case investigation. + + + When an eCR file arrives at a public health department, it + contains more than just information on the reportable condition + — it also includes data from a patient's entire health record, + such as demographics, diagnoses, comorbidities, occupation, + immunizations, medications, and more. The volume of information + contained within an eCR makes it difficult for public health + staff to figure out why the eCR was sent to them and where it + should go next. To add to this challenge, each eCR file contains + pages and pages of data that don't appear in a consistent order + or with consistent formatting. + + + As a result, it can take significant time and effort for public + health staff to review incoming eCRs. Many public health + departments still choose to manually contact healthcare + providers for clinical information, which remains a + time-consuming and onerous process for both healthcare providers + and public health departments. To fulfill the promise of eCR, + public health staff need to be able to quickly find key + information from incoming eCR data so they can take timely + public health action. + + +     +
    +
    + +

    The solution

    +
    + + To make eCR data more usable for public health staff, the DIBBs + team has developed the eCR Viewer, an intuitive interface that + surfaces a summary of condition-specific information in a more + readable format at the top of the eCR file. Using the eCR + Viewer, public health staff can easily find data relevant to the + reportable condition. The eCR Viewer also orders and organizes + data consistently regardless of which electronic medical record + system generated the eCR. Because the eCR Viewer makes it easier + for public health staff to find clinical information for case + investigation, eCR becomes a more useful data source, thereby + reducing the need to manually contact healthcare providers. + + + To date, the DIBBs team has undertaken the following work + related to the eCR Viewer: + + +
  • + Conducted generative research with staff at public health + departments to understand how eCR fits into case ascertainment + and case investigation workflows +
    • +
  • + Developed concept designs of the eCR Viewer and gathered + feedback from users to develop a lightweight MVP +
    • +
  • + Built out the product vision and measurement plan to ensure + the eCR Viewer will meet the intended impact for time savings +
    • +
  • + Partnered with the General Dynamics Information Technology + (GDIT) team to integrate the eCR Viewer into the CDC's + National Electronic Disease Surveillance System (NEDSS) Base + System (NBS) with pilot jurisdictions +
    • +
  • + Identified pilot jurisdictions to test and validate the eCR + Viewer in their public health data workflows; NBS pilot + partners include the states of Maine and Tennessee +
    • +
  • + Established a pilot partnership with the city of Philadelphia + to evaluate using the eCR Viewer outside of a surveillance + system as a web-based tool hosted by CDC +
    • +     + + 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. + +
    +     +
    +
    + +

    The results

    + +
  • + Completed design and development of an eCR Viewer MVP and + validated its potential time savings with public health staff +
    • +
  • + Based on user testing with an MVP, the eCR Viewer allows staff + to process an eCR file for case ascertainment in five clicks + rather than twenty-five clicks +
    • +
  • + Based on user journey mapping in Maine, the eCR Viewer enables + staff to process all eCR files in a queue (over 5,000) in just + one week rather than 4.5 months +
    • +     +     +
    +     +     + ); +}; + +export default EcrViewer; diff --git a/src/app/case-studies/page.tsx b/src/app/case-studies/page.tsx index 556e1bb..13c804f 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 @@ -64,7 +64,7 @@ export default function CaseStudies() { key information from eCR files to make them more useful for monitoring the spread of reportable conditions. - + View case study