BridgeHealth AI

Eradicating health disparities by bridging gaps in access for underserved families.

Idea in Health Care


We are bridging gaps in access to advance health equity for underserved families. In the US, disparities persist because the health & social safety net is too complex & overwhelming for vulnerable patients to navigate – but these disparities will cost the US $1T by 2040. Our vision is a generative AI “co-pilot” for Care Teams – to help vulnerable families access the benefits they deserve and claim better health for their communities.


BridgeHealth AI has been funded and supported by MITdesignX, Harvard HealthLab Accelerators (H2A), and Harvard Innovation Labs (i-Lab). We pitched as finalists in the 2023 MedTech Collaborative Pitch Competition.


In the US, health disparities persist because: 

1) The health/social services landscape is too complex for vulnerable patients to navigate (56% patients don’t understand available benefits). 

2) Processes are opaque, fragmented, and burdensome (<50% of health providers coordinate with social/community providers).

3) Marginalized groups distrust these systems (70% of minorities, LGBTQ+, and people with disabilities distrust healthcare). 


Though resources exist across health, food/nutrition, housing, heating, etc. – $230B in federal benefits go unclaimed every year. These missed opportunities lead to poor health outcomes and rising costs, projected to reach $1T by 2040.


Care professionals – esp. nurses and social workers (SWs) – try to help, but they’re also overwhelmed and frustrated. They still rely heavily on Google searches and word of mouth, and these manual administrative tasks eat up ~25% of their time, instead of 100% of time focusing on care, leaving patients in need.


Our vision is TurboTax meets Expedia for health & social services: An AI-driven E2E benefits navigation tool serving as the one-stop-shop for health equity. The AI-enabled “co-pilot” cuts through complexity to guide benefits navigation end to end – from awareness to enrollment to education to renewal. Most importantly, it’s designed for community-based use to meet marginalized patients where they are. 


AI is a problem-solution fit to the fragmentation/information problems. LLMs can process high volumes of technical information (ex: long documents from insurance companies on complex insurance) & present easily digestible summaries for overwhelmed patients (solving for health literacy barriers). The “co-pilot” can connect the dots for a significantly more continuous journey throughout benefits navigation:

– Awareness: Screens needs and identifies the most relevant benefits for a patient’s specific circumstances

– Resource Matching: Provides address/directions, website, contact info near a zip code

– Eligibility: Real-time benefits verification

– Enrollment: Chatbot can support form-fill, helping users apply for benefits directly (rather than cumbersome, repetitive forms)

– Usage/Education: Summarizes complex information across dozens of benefits in easily digestible ways (e.g., “what is my deductible”)

– Streamline Administrative Tasks: SWs can leverage to draft emails, ask questions, and find resources to reduce the manual burden of tasks.