Community Health Analytics for Highly Vulnerable Populations

In December, we set out to determine if publicly available information could be assimilated to produce actionable intelligence for companies supporting people living within Section 202 and USDA Rural housing. We wanted to iterate quickly, use new “vibe-coding” tools and AI to support analysis and software development. Our results have been extraordinary!

For context, we targeted vulnerable populations near subsidized housing. We targeted two U.S. Government programs as our current assumption is that residents of this housing stock is particularly vulnerable.

Section 202 primarily refers to the HUD's Supportive Housing for the Elderly program, a federal initiative that provides funding to nonprofits to develop affordable rental housing with supportive services for low-income seniors (age 62+) and people with disabilities, allowing them to live independently. This program serves about 400,000 very low-income older adult households (over 62) in supportive housing communities. Still an Unmet Need - Millions of older adults (over 2.25 million) who qualify for assistance remain unassisted due to program limitations. 

USDA Rural Housing programs, authorized under the Housing Act of 1949, provide loans, grants, and guarantees for affordable housing and community facilities in rural areas, targeting low-to-moderate-income individuals and families who can't get conventional financing, with major sections including Section 502 (direct & guaranteed home loans for purchase/build/repair), Section 515 (multi-family rental housing), and Section 504 (home repair grants for elderly/low-income). These programs aim to increase access to decent, safe, and sanitary housing in eligible rural communities. USDA Rural Rental Housing (Section 515 & 514): Approximately 410,000 units exist across nearly 14,000 developments.

Our hypothesis / Objective:

Open data assets from various U.S. Government sources combined with other publicly available data can be assimilated in a manner that highlights critical healthcare gaps around subsidized housing stock (specifically Section 202 and USDA Rural Housing). The National Emergency Medical Service DB (NEMSIS) is the most comprehensive publically available data source to understand EMT calls and ED visits. This data, at the proper grain and coupled with other public sources, can be an extremely useful source of information to understand critical gaps in care. Vibe coding tools and AI can accelerate business processes such as data architecture, data exploration, business analysis, data validation and application development in order to produce a product within one week. 

Business Processes

• Business analysis

• Data exploration

• Data architecture

• Application development 

• Data validation

• UX testing

• Curiosity

Results

We began sourcing the