From a44e3cf3ae2d4b236167765f73137b9e00007b12 Mon Sep 17 00:00:00 2001 From: Jake Wheeler Date: Tue, 3 Dec 2024 10:17:17 -0500 Subject: [PATCH] add dibbs prototype page --- src/app/case-studies/dibbs-prototype/page.tsx | 147 ++++++++++++++++++ src/app/case-studies/page.tsx | 5 +- 2 files changed, 151 insertions(+), 1 deletion(-) create mode 100644 src/app/case-studies/dibbs-prototype/page.tsx diff --git a/src/app/case-studies/dibbs-prototype/page.tsx b/src/app/case-studies/dibbs-prototype/page.tsx new file mode 100644 index 00000000..1c436b7d --- /dev/null +++ b/src/app/case-studies/dibbs-prototype/page.tsx @@ -0,0 +1,147 @@ +import { + PageContainer, + ContentContainer, + ReturnToCaseStudiesLink, + SectionContentContainer, + Text, + UnorderedList, + ReadMore, +} from '../_ui'; + +const DibbsPrototype = () => { + return ( + + +
+ +

+ Building a prototype for modernized public health infrastructure in + Virginia +

+
+
+ +

The challenge

+ + + 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. + + + 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. + + + 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. + + + +
+
+ +

The solution

+ + 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: + + +
  • + 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 +
    • +
  • + 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 +
    • +
  • + Developed a white paper as a central reference point for + learnings from the pilot project to apply to other public health + jurisdictions +
    • +     + + 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. + +     +
    +
    + +

    The results

    + +
  • + Built a prototype DIBBs pipeline that significantly improved + data processing speed and broke down silos between different + streams +
    • +
  • + Reduced patient record duplication by 19% across data streams +
    • +
  • + 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 +
    • +
  • + The DIBBs pipeline can generate a tabular, analysis-ready data + mart for ~380,000 patient resources in under 15 minutes +
    • +     +     +
    +
    + + + +
    +     +     + ); +}; + +export default DibbsPrototype; diff --git a/src/app/case-studies/page.tsx b/src/app/case-studies/page.tsx index 13c804fb..eed7b1b7 100644 --- a/src/app/case-studies/page.tsx +++ b/src/app/case-studies/page.tsx @@ -120,7 +120,10 @@ export default function CaseStudies() { ingestion pipeline that improves data processing and analysis to more efficiently inform public health action. - + View case study