Turning Data into Action
Having trusted data is one thing—knowing how to use it is another. This is part 2 in a blog series from the expert webinar hosted by Champ Software, featuring Erin Lord-Kunz from RHIhub. During the webinar, Erin shared practical examples of using RHIhub for public health work in real scenarios, including Minnesota Maternal & Child Health data and determining whether a community is considered rural.
Read part 1 in the blog series: Why Trusted Data Matters in Public Health.
Pulse Check Points
Using a Reliable Data Source to Locate Maternal & Child Health Data for Minnesota
In the webinar, a practical walkthrough demonstrated how to locate Minnesota Maternal and Child Health (MCH) data using RHIhub.
For example, users can explore curated topic pages which aggregate research, data sources, and program models in one place.
The value of that example was not just the topic itself—it was the workflow. Rather than starting with a chatbot and hoping the output is current and traceable to its source, the process begins with a curated data resource and works outward to the original sources.
Instead of pulling fragmented or inconsistent figures from multiple places, this approach helps teams access data that is easier to verify, cite, and defend in planning, grant writing, and reporting.
Am I a Rural Health Agency by the Federal Definition – Why it Matters
Another example addressed a key eligibility question:
How do you determine if your population is considered rural?
RHIhub provides a dedicated tool to help answer this question using federal definitions and geographic criteria:
https://www.ruralhealthinfo.org/am-i-rural
This matters because rural status can impact:
- Grant eligibility
- Program design
- Funding opportunities
These tools are designed to remove ambiguity and ensure that public health professionals are working from definitions and data that align with federal expectations.
You Need More than Just Data in Public Health
The examples listed above for Maternal Child Health and what defines rural, highlight a broader point: public health professionals don’t just need data—they need:
- Context
- Validation
- Applicability
AI tools may summarize concepts quickly, but they do not reliably provide authoritative sourcing or guarantee alignment with funding requirements.