Explore
Browse the system map, inspect pathways, and get oriented before making changes.
Best first stop when you want to understand how the system is connected.
Upstream Investments Save Lives. Proving It Shouldn't Take Years.
HealthSystems connects housing, income, and environmental interventions to health outcomes — with the evidence to back every link. A decision support platform that makes causal evidence computable, comparable, and actionable.
Evidence-backed causal pathways|From policy to health outcomes|Geographic stratification|Peer-reviewed sources only
Start Here
Choose the workspace that matches your next step.
HealthSystems uses one shared evidence base across three entry points. Explore when you need orientation, Analyze when you need the broader analysis workbench, and Flow when you want the 4-step MVP from question framing through comparison.
Platform status
Checking shared workspace readiness
Confirming platform readiness across Explore, Analyze, Build, and Ops.
Workspace
Analyze
Turn evidence into scenarios, ranked interventions, budgets, and decision-ready outputs.
Start here when you need the broader analysis workbench rather than the 4-step MVP flow.
Flow
Use the 4-step guided flow to move from question framing through pathway review, results, and comparison.
Best when you want the MVP path from question to comparison without opening the build tools.
QuestionFrame the intervention question and pick the outcome you want to improve.NetworkInspect the connected pathways before you commit to a full run.ResultsReview the ranked outcome shifts and evidence-backed pathway summary.CompareHold multiple runs side by side and compare tradeoffs before export.
The Challenge
The Evidence-to-Action Gap
State health departments allocate billions to housing interventions, community health workers, and food security programs. Hospitals invest in population health. Foundations fund structural change. None can answer the question that matters: what do these investments actually produce?
The evidence exists — thousands of studies document how housing instability affects health, how income support reduces emergency utilization, how built environment shapes chronic disease. But that evidence is scattered across journals, locked in disciplinary silos, inaccessible to the decision-makers who need it. Upstream investments lose to crisis response because crisis response has visible metrics.
What's needed is a system that reads the literature, extracts the causal chains, and makes the evidence computable. That's what we built.
Use Cases
Who Uses This
State Health Departments
Trace how a $50M housing stability investment cascades to reduced ED visits through specific mechanism chains — each backed by meta-analytic evidence. Compare intervention scenarios and identify which upstream investments produce the broadest downstream effects across your jurisdiction.
Hospital Systems & ACOs
Map which community-level structural conditions drive your highest-cost patient populations. Identify the social determinants most connected to your crisis outcomes. Quantify the return on community health investments that reduce avoidable utilization.
Foundations & Research Organizations
Evaluate cross-portfolio impact by seeing how your housing, food security, and workforce investments connect to shared health endpoints. Identify leverage points where a single investment generates disproportionate returns across multiple grantee outcomes.
Process
From Literature to Decision Support
How published research becomes decision-ready evidence
1
Search & Collect
The platform searches major research databases, gathers full-text papers where available, and filters out weak or irrelevant sources before review starts.
2
Extract & Validate
A paired AI review pulls out who affects what, through which pathway, and how large the reported effect is. Findings that do not clear both checks go to human review.
3
Synthesize & Quantify
When studies can be combined, the platform pools their findings, estimates likely ranges, and rates evidence strength. Effects are converted into a shared format so results can be compared fairly.
4
Map & Simulate
The evidence is turned into an interactive system map. Users follow likely routes from intervention to outcome, compare options, run scenarios by place or population, and spot leverage points with broad downstream effects.
Platform
What the Platform Does
Meta-Analysis & Quantification
When studies line up, the platform combines their results, estimates likely ranges, and rates evidence strength. Incompatible measures stay separate so the summary stays honest.
Geographic & Population Stratification
Estimates can be adjusted with local data and broken out by place, race, income, and other population differences.
Systems Dynamics & Simulation
Test how an intervention changes outcomes over time, including feedback loops, delays, and tipping points when small shifts lead to larger system changes.
Evidence Confidence Scoring
Every projection includes a confidence score that reflects evidence strength, time coverage, validation status, and sample size so users can see where the evidence is solid and where caution is needed.
Housing to Health
Trace how housing policy connects to emergency utilization through documented causal chains.
Income to Wellbeing
See how income support programs reduce chronic disease burden across demographics.
Environment to Equity
Map how built environment interventions produce different effects by race, income, and geography.
Roadmap
What's Next
Building Now
- Equity-stratified outcome projections across race, income, and geography
- Actor network mapping — which organizations intervene at which nodes
- Real-time data integration for dynamic prevalence updating
- Public API access for third-party integration
On the Horizon
- Multi-geography deployment with jurisdiction-specific calibration
- Policy change detection and automated re-scoring
- Collaborative mechanism review with domain expert annotation
- Integration with state-level SDOH data warehouses
Team
Who We Are
Built at Harvard by a team combining urban planning, health sciences, and quantitative policy analysis — advised by leaders in social epidemiology, health systems leadership, and complexity science.
Maurice El Helou
Co-Founder
Harvard GSD (Urban Planning + Design Studies) | McMaster Health Sciences
Background in eco-social theory and built environment health impacts. Previously worked with Hamilton's public health department on data strategy.
NJ
Noah Johnson
Co-Founder
Harvard GSD (Urban Planning) | Urban Institute (4 years)
Quantitative policy analysis experience building government decision-support tools for housing and food access.
Advisors
IK
Ichiro Kawachi
Professor of Social Epidemiology, Harvard T.H. Chan School of Public Health
RM
Robert MacIsaac
Former CEO, Hamilton Health Sciences
JC
Justin Cook
Complexity Science, Harvard MDE
Currently co-developing with Public Health Ontario.
Partner With Us
Deploy Evidence-Based Decision Support
We're working with state health departments, hospital systems, and foundations to deploy the platform in real decision-making contexts. Early partners shape product direction.
Currently co-developing with Public Health Ontario.