R-LABSHealthcare + Housing Innovation Cluster
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Mission 1 · Intelligence Layer

Population health embedded with built environment intelligence

Stage: Coalition formingR-LABS Solution Accelerator

Build a regional intelligence layer connecting population health, public health, housing supply, demographics, and the built environment.

Population health embedded with built environment intelligence illustrative image

The challenge

Health, housing, and planning decisions are made with separate data that rarely meets.

Who experiences it

Planners, health system leaders, and housing providers making long-horizon investment decisions.

Why current systems fall short

Datasets are held in separate institutions, with different geographies, formats, and permissions.

Why it crosses sectors

Population health needs are shaped by where and how housing is built, yet planning and health rarely share a model.

If it stays unresolved

Capital continues to flow to places and building types that do not match emerging health and aging needs.

The opportunity

An integrated regional data model that supports practical planning decisions.

What is already known

Public datasets on demographics, housing supply, and aggregate health indicators already exist.

Demonstrated elsewhere

Integrated geospatial planning tools have supported regional planning in other jurisdictions.

What R-LABS proposes to test

Combine aggregate, non-identifiable data into one regional model and test it on a real planning question.

What still requires validation

Data-sharing permissions, decision usefulness, and maintenance model (To be validated).

The coalition

Who needs to be at the table

Roles are proposed. No organization is implied to have agreed to participate. Select “This could be us” on any role to carry it into the participation form.

Municipalities and public agencies

Proposed role
Why it matters
Municipalities shape land use, approvals, housing programs, and community services.
Could contribute
Planning data, policy alignment, potential land or program levers, and approval navigation.
Could gain
Evidence for planning decisions and a coordinated route to housing and health targets.
Next practical step
Identify a planning or housing lead to review the mission brief.

Healthcare systems

Proposed role
Why it matters
Health systems see the downstream cost of unstable housing and fragmented care first.
Could contribute
Clinical leadership, a defined patient cohort definition, operational insight, and a pilot host setting.
Could gain
A tested pathway that may improve patient flow and outcomes, with shared evaluation.
Next practical step
Nominate a sponsor to join a 60-minute problem-definition session.

Technology and data partners

Proposed role
Why it matters
Shared data and practical tools make coordination between sectors possible.
Could contribute
Data platforms, integration capability, analytics, and privacy-by-design expertise.
Could gain
A real-world proving ground and a path to replicable deployment.
Next practical step
Describe a relevant capability in a non-confidential summary.

Researchers and evaluators

Proposed role
Why it matters
Independent evaluation separates what works from what simply sounds good.
Could contribute
Evaluation design, baseline methods, literature review, and outcome measurement.
Could gain
Applied research partnerships and access to real-world implementation settings.
Next practical step
Propose an evaluation approach or relevant prior work.

Developers, builders, and architects

Proposed role
Why it matters
Physical delivery needs design, construction, and cost discipline from the start.
Could contribute
Site analysis, design concepts, cost estimates, and delivery methods.
Could gain
Early positioning on mission-aligned projects with clearer demand and partners.
Next practical step
Propose a candidate site or delivery approach for preliminary review.

Select one or more roles above

Continue to participation →

First proof of value

A defined region, integrated data model, and practical planning use case.

Proposed plan, not a guaranteed schedule
  1. 1

    Define the problem and target population

  2. 2

    Identify the host organization and local context

  3. 3

    Assemble the minimum viable coalition

  4. 4

    Establish a baseline and evidence plan

  5. 5

    Design and cost a bounded pilot

  6. 6

    Agree on a go / no-go decision

Proposed scope
One region, aggregate data only, one planning use case.
Required participants
  • Host organization
  • Lead sponsor
  • Evaluation partner
  • R-LABS Solution Accelerator
Key assumptions
  • A host is willing to share non-confidential context
  • A bounded use case can be defined
  • Baseline data is accessible through approved channels
Data or evidence needed
  • Inventory of public and aggregate datasets
  • Data-sharing permissions
  • Planner interviews
Deliverables
  • Integrated data model
  • Prototype map
  • Planning use-case report
Indicative timeframe
Proposed: 90 days of discovery and design
Success measures
  • Agreed problem statement
  • Committed minimum viable coalition
  • Costed and fundable pilot design
Decision gate
Coalition decides go / no-go on a bounded pilot.

Economics explorer

Test the assumptions

Every input is an editable, illustrative assumption, not verified project data. Replace with local information as it becomes available.
Illustrative assumptions only
Editable assumptions

Baseline utilization

2,000 bed days / yr

Modelled change

0 bed days / yr

Gross avoided-cost estimate

$0

Pilot + first-year cost

$0

Expected change defaults to 0% because no validated effect exists yet. Gross avoided cost is not equivalent to cash savings realized by any organization.

Moonshot

A national intelligence platform

Embedded in municipal, healthcare, and housing planning so that where we build, and what we build, reflects the health needs of the people who will live there.

Target requires coalition agreement.