Tackle an Outbreak

Learning Objectives

After completing this unit, you’ll be able to:

• Describe the step-by-step process of disease surveillance.
  
• Identify how key features streamline each stage of disease surveillance.
  
• Summarize how real-time data and automation improve response times and outcomes.
  

Disease Surveillance in Action

In Midtown City, there’s a Salmonella outbreak. The Midtown Public Health agency receives multiple lab reports positive for the same Salmonella subspecies. Because the bacterial infection can spread rapidly when contaminated foods go unaddressed, public health officials must move fast. Let’s follow the Midtown Public Health team and see how they use Disease Surveillance to manage cases, conduct investigations, and coordinate a rapid, effective response.

Define the Disease

Before public health teams can investigate new cases, they need accurate disease definitions in place. Bethany, the Salesforce admin at the agency, downloads an updated disease definition PDF from a national health agency.

She uses Disease Surveillance to:

• Upload the PDF containing the latest diagnostic and epidemiological criteria.
  
• Convert it into a disease definition record, making it available to all disease surveillance teams.
  
• Ensure alignment with lab reports and case data, so new reports can be classified correctly.
  

Here’s the disease definition for Salmonella.

With standardized case definitions in place, case intake and classification becomes seamless. Every incoming lab report is now evaluated against the same criteria, ensuring consistent classification and reducing manual decision-making.

Speed Up Intake

Electronic lab reports (ELRs) flood in from local clinics and laboratories, each indicating Salmonella enterica Enteriditis. Smaller providers also fax in forms. Previously, the intake specialist spent hours manually re-entering data, hunting for duplicates, and classifying cases. Now, with Disease Surveillance, the specialist:

• Uploads and transforms lab reports: Whether faxed, PDF, or a direct data feed, the specialist brings them into Salesforce.
  
• Extracts key data with Agentforce: Agentforce parses each report and compares them to Bethany’s Salmonellosis case definition criteria.
  
• Identifies duplicates: The system flags any duplicate cases, such as a hospital-reported case that matches a separate lab-confirmed case for the same patient, or the same person misidentified as two individuals due to a transposed birthdate data entry error.
  
• Classifies the case: The system applies disease classification—marking each case as confirmed, suspected, or probable based on lab values and symptoms. Severe or high-risk patients are automatically prioritized for urgent follow-up.
  
• Assigns for investigation: The specialist routes verified cases to the Public Health Enteric Investigation Team.
  

Here, Agentforce interprets lab reports, disease definitions, and other case data to make an informed disease classification. Based on the data so far, this case is classified as Suspected based on the presence of specific predictors.

By the time the last fax is processed, every new Salmonella case is correctly classified and ready for the next stage.

Do a Thorough Investigation

As cases are classified, the chief communicable disease investigator determines how Salmonella is spreading in the community.

In the past, the team relied on phone calls, paper records, and scattered spreadsheets.

Now, Disease Surveillance and Agentforce help them:

1. Access unified case details: The investigator opens each patient’s single record—symptoms, lab dates, food history, cafe visit, and other exposure details—and avoids duplicate or missing info.
   
2. Conduct automated interviews: Agentforce prompts a standard assessment for every Salmonella case, ensuring no vital question goes unasked.
   
3. Assess exposures: Agentforce, informed by epidemiological insights, such as communicability, incubation periods, and modes of transmission of Salmonella, assesses unstructured case interview notes and suggests potential common sources of infection for further investigation. Several cases have mentioned eating at the same cafe.
   
4. Trigger field inspections: The investigator triggers an inspection workflow to investigate the cafe. An Environmental Health Officer is the field inspector who assesses food safety practices at the restaurant and collects food specimens and onsite data, which syncs back in real-time. The team identifies several food safety infractions and issues an order to close the cafe to address these issues.
   
5. Expand contact tracing: Additional symptoms or leads flow into contact chains, helping the team prioritize next steps. The investigator reinterviews some cases and identifies several more who ate at the cafe.
   
6. Declare an outbreak: Given that multiple cases are associated with the same cafe, the team declares an outbreak. Following the same steps that Bethany used to create the disease definition for Salmonellosis, the investigator creates a new outbreak case definition, which includes the requirements for a documented epidemiological link to the cafe.
   

This cohesive approach helps Midtown Public Health trace the outbreak before it spreads further.

Detect and Contain the Outbreak

As cases increase, an epidemiologist on the team confirms how far Salmonella has spread and identifies emerging patterns.

Instead of juggling CSV exports and spreadsheets, the epidemiologist uses Outbreak Dashboard. Here’s what the dashboard offers.

1. Real-time views: You see updates roll in in real time as investigators finish interviews or lab results post. This dynamic dashboard tracks case locations, ages, and onset dates, making it easier to see the bigger picture.
   
2. Cluster detection: Agentforce flags unusual spikes or outlier patients, helping you quickly notice any secondary clusters beyond the main cafe outbreak. Plotting the cases on a map helps you ‌visualize geographic clusters of cases and exposures. You form additional hypotheses about sources of infection and extent of spread.
   
3. Predictive insights: Using integrated analytics, you identify potential risk factors—such as a particular food item or time window of exposure—and recommend additional testing or interviews to rule out further spread.
   

To support deeper analysis and team coordination, the epidemiologist turns to the Disease Surveillance Console App. This central workspace pulls together all existing cases, investigation statuses, and trend data, making it easier to manage the full picture.

Armed with these insights, the epidemiologist confirms the cafe as the heart of the outbreak and tracks peripheral cases to make sure they don’t indicate a larger or ongoing spread.

Keep Stakeholders Informed

Once the outbreak source is identified, officials quickly report details to local and state authorities. Historically, staff compiled data by hand, risking errors or delays.

Now, Disease Surveillance accelerates the reporting with:

1. Automated reports: As soon as the team updates a case, that data flows into Disease Surveillance’s built-in reporting engine. When it’s time to file official updates, staff simply click a button to generate a situation report—complete with total cases, recent lab results, and exposure timelines.
   
2. FHIR-compliant data sharing: Instead of juggling PDF attachments or unstructured CSV files, the agency sends data in standardized FHIR formats, speeding up acceptance by state and federal systems.
   
3. Audit trails for accountability: Every case edit, classification change, and lab result import is automatically logged. If higher-level authorities have questions or need detailed records, the agency can show exactly when and why updates were made.
   

By smoothing out reporting and compliance, Midtown Public Health stays focused on outbreak response instead of struggling with last-minute spreadsheets.

Engage the Community

With the cafe closed after inspection, anxious residents want trustworthy information: How to spot symptoms, whether to quarantine, how to prevent spread, and when it’s safe to visit local eateries again. In the past, Midtown Public Health struggled with inconsistent press releases or delayed social media posts.

With Disease Surveillance, communication and engagement become much more proactive.

1. Omnichannel alerts: Health Promotion communications managers push text and email notifications to those in affected zip codes, explaining next steps if symptoms appear.
   
2. Targeted campaigns: Healthcare providers and cafe visitors get tailored messages, while the broader public sees general advisories.
   
3. Community portals: Citizens can log in to find FAQs, self-report symptoms, and follow official outbreak bulletins, reducing call volume and misinformation.
   

Transparent outreach not only helps contain the outbreak but also builds public trust in the agency’s response.

A Swift, Successful Containment

By pairing Salesforce Disease Surveillance with their own expertise, the Midtown Public Health team traced the Salmonella outbreak to its source, acted decisively, and kept residents informed. Here’s a summary of how real-time data and automation improved response times and outcomes.

• Faster intake: Automated data capture shaved hours off the reporting window.
  
• Centralized case details: Investigators accessed one source of truth, eliminating time-consuming hunts for information.
  
• AI-driven insights: Real-time dashboards and predictive analytics revealed the outbreak and its source early, preventing wider spread.
  
• Streamlined reporting: Automated submissions freed staff from manual data wrangling and supported regulatory compliance.
  
• Informed community: Targeted campaigns and intuitive portals provided timely, transparent updates for residents.
  

The team’s swift response didn’t happen by accident—it was built on a structured foundation of connected data. Every insight, alert, and automated task is powered by a flexible, rules-driven data model.

In the next unit, let’s go under the hood to explore how this model works. Learn how records like disease definitions, cases, investigations, and outbreaks all fit together to support a fast, accurate, and scalable surveillance system.

Resources

• Salesforce Help: Set Up Assessment for Disease Surveillance