Are You Missing Critical Insights in Your Community Health Needs Assessment?

Effective CHNAs go beyond internal health records. To truly understand and address population-level health challenges, providers must integrate external data—like community demographics, social sentiment, and public health research.

Without these insights, many assessments simply identify issues without inspiring action. But with the right data, CHNAs can directly inform new partnerships, smarter resource allocation, and meaningful health interventions.

Why External Data Makes CHNAs More Impactful

✅ Reveal emerging health concerns through public sentiment and search behavior
✅ Identify stigmatized topics—like HIV or mental health—for targeted outreach
✅ Improve efficiency and resource alignment based on local trends
✅ Strengthen decision-making with research-backed, community-specific insights

How It Works

🔹 Use clustering analysis to segment community health patterns
🔹 Apply NLP and topic modeling to analyze academic research and social media trends
🔹 Run sentiment analysis to gauge public perceptions of conditions or services
🔹 Identify hidden risk factors with association rule mining

Real-World Impact: Identifying Stigma Through Search Trends

Researchers from Cornell and Microsoft analyzed 18 months of Bing search queries across 54 African nations to uncover health stigma patterns. They found women and users aged 18–24 searched more about HIV-related stigma, while those 35–49 searched most about natural cures—insights that inform smarter, more empathetic health messaging.

📩 Want to level up your CHNA with richer, more actionable data? Connect with Blue Street Data to get the third-party insights you need to design smarter, community-driven health strategies.

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Is “Quality” Killing Your AI? Defining Data Fit for Strategic Success

February 18th, 2026 / 1:00 PM EST

Every data investment carries risk unless you know how to measure its “fit” for the mission. Many organizations assume that “high-quality” data is sufficient for AI and analytics, only to discover too late that data fit is the real determinant of success. In this live session from Blue Street Data’s Building with Better Data series, Andy Hannah and Malcolm Hawker unpack why data that works for BI can be dangerous for AI, leading to model failure, wasted spend, and lost trust. You’ll learn how to define, measure, and validate data fit so your models deliver reliable, business-aligned outcomes. Reserve your spot today!