MPC (Medical Priority Care) Sync

MPC Sync: A Forge-powered clinical dashboard. It uses Jira V3 APIs to aggregate high-priority MPC project data into a real-time medical handoff interface, ensuring secure, split-second care shifts.

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Inspiration

Clinical handoffs—the moments when patient care transitions between medical shifts—are high-risk periods where communication failures can lead to life-threatening errors. We noticed that while healthcare teams often use Jira for administrative tracking, critical clinical alerts frequently get buried in general task noise. We were inspired to build MPC Sync to provide "pit-crew" precision to healthcare providers, ensuring that every vital alert is seen and acted upon immediately.

What it does

MPC Sync is a specialized clinical dashboard built on the Atlassian Forge platform. It acts as a real-time "Command Center" for medical staff by:

  • Filtering Noise: Dynamically scans the MPC project to isolate only High and Highest priority medical tickets.
  • Instant Visualization: Presents Patient IDs, Clinical Summaries, and Assigned Medical Staff in a high-contrast, scan-ready interface.
  • Ensuring Accountability: Displays exactly which staff member is responsible for each critical case, eliminating "diffusion of responsibility" during shift changes.

How we built it

The app is built using a modern, serverless architecture that "Runs on Atlassian" to ensure enterprise-grade security and data residency.

  • Backend: Developed with Forge Resolvers using the Jira Cloud V3 API. We utilized optimized JQL queries to fetch real-time clinical data.
  • Frontend: Built with Forge Reconciler and the latest UI Kit components to ensure a native look and feel within Jira.
  • Mathematical Prioritization: We implemented a logic layer to rank tickets based on urgency:

$$\text{Priority Score} = \sum (\text{Urgency} + \text{Risk Factor})$$

Challenges we ran into

The primary challenge was the recent retirement of legacy Jira search endpoints, which resulted in 410 Gone errors during early development. We also faced hurdles with "shallow" API responses that only returned ticket IDs without the necessary clinical summaries.

Atlassian Rovo Dev was instrumental in solving these issues. We used Rovo Dev to:

  1. Identify the correct migration path to the /rest/api/3/search/jql endpoint.
  2. Debug and implement explicit field mapping (&fields=summary,priority,assignee) to ensure all patient data rendered correctly.

Accomplishments that we're proud of

  • Native Integration: Successfully building a tool that feels like a natural extension of Jira while serving a highly specialized medical purpose.
  • Security First: Achieving a "Zero-Trust" architecture where sensitive medical data never leaves the Atlassian cloud, making it inherently compliant with data residency needs.
  • Rapid Iteration: Leveraging AI-assisted development to move from a blank screen to a functional clinical dashboard in record time.

What we learned

We learned that the Atlassian ecosystem is a powerful platform for Specialized Industries like Healthcare. We discovered that Forge provides a unique advantage for medical apps because it solves the "Security vs. Utility" dilemma by keeping data local to the tenant. We also learned that Rovo Dev is a massive force multiplier, turning complex API troubleshooting into a streamlined conversation.

What's next for MPC (Medical Priority Care) Sync

The future of MPC Sync involves deeper clinical integrations:

  • Biometric Triggers: Using Forge Webhooks to automatically create "Highest Priority" tickets when external vitals monitors detect patient distress.
  • Rovo Agent Integration: Developing a custom Rovo Agent that can automatically summarize clinical histories for the incoming shift.
  • Mobile Optimization: Enhancing the dashboard for the Jira Mobile app to support doctors on the move during rounds.

🛠️ Technical Implementation Snippets

Backend: Optimized Clinical Data Resolver

This snippet demonstrates how we used the Jira Cloud V3 API with explicit field mapping to ensure data accuracy in a medical context.

// Optimized JQL fetch for high-priority clinical tickets
resolver.define('fetchCriticalTickets', async () => {
  const jql = 'project = "MPC" AND priority in (High, Highest) ORDER BY created DESC';
  try {
    const response = await asUser().requestJira(
      route`/rest/api/3/search/jql?jql=${jql}&fields=summary,priority,assignee`
    );
    const data = await response.json();

    // Explicitly mapping data to the clinical dashboard schema
    return data.issues.map(issue => ({
      key: issue.key,
      summary: issue.fields?.summary || "No Summary Provided",
      priority: issue.fields?.priority?.name || "High",
      assignee: issue.fields?.assignee?.displayName || "Unassigned"
    }));
  } catch (error) {
    console.error('Clinical Data Fetch Error:', error);
    return [];
  }
});

Frontend: High-Precision Clinical Dashboard

This snippet showcases the use of Forge UI Kit to create a scan-ready interface for doctors and nurses.

// React-based dashboard for medical shift transitions
const App = () => {
  const [tickets, setTickets] = useState(null);

  useEffect(() => {
    invoke('fetchCriticalTickets').then(data => setTickets(data));
  }, []);

  return (
    <Box padding="space.400">
      <Heading size="xlarge">🏥 MPC Sync: Clinical Handoff</Heading>
      <Table>
        <Head>
          <Cell><Text><Strong>Patient ID</Strong></Text></Cell>
          <Cell><Text><Strong>Summary</Strong></Text></Cell>
          <Cell><Text><Strong>Priority</Strong></Text></Cell>
        </Head>
        {tickets?.map(t => (
          <Row key={t.key}>
            <Cell><Badge appearance="primary">{t.key}</Badge></Cell>
            <Cell><Text>{t.summary}</Text></Cell>
            <Cell><Badge appearance="important">{t.priority}</Badge></Cell>
          </Row>
        ))}
      </Table>
    </Box>
  );
};

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