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Force for Health Academy

Population Health Program Builder

Design standards-aligned curricula that teach math, health, epidemiology, and actuarial science using real population health data — powered by PHIT and the FFH Academy.
Learn It Program Design Foundations — Core Concepts ▼

Before building your program, understand the foundations. A strong population health program connects health literacy, community resources, and measurable outcomes into a structured learning or intervention pathway.

Core Concepts

PHIT Framework
The Personal Health Intelligence Tracker measures 10 wellness domains. Every program week should map to at least one PHIT domain so participants can track their growth with real data.
Learn It / Live It / Share It
FFH's 3-phase learning model. Learn It: Acquire knowledge. Live It: Apply through simulation and real-world practice. Share It: Teach others and earn badges.
Standards Alignment
Programs must align with recognized standards (ISTE, NHES, NGSS, PHIT) to qualify for institutional adoption, grant funding, and CEU credits.
Backwards Design
Start with the outcome you want (reduced diabetes, health literacy improvement) and design backwards: assessment → activities → objectives → content.

How to Use This Module

1. Set your audience and duration. 2. Select PHIT domains your program will address. 3. Align with education or industry standards. 4. Configure each week using the suggested FFH resources — games, bingo cards, simulations, and community events. 5. Review the auto-generated curriculum summary.

📐 Program Designer & Curriculum Map
Weekly Modules

Select a week from the left to configure its content.

📋 Curriculum Summary
Configure your program above to generate a summary.
Learn It Epidemiology Foundations — How Disease Moves Through Populations ▶

Epidemiology is the study of how diseases are distributed across populations and what factors drive those patterns. Before using the simulator, understand these core concepts:

Core Concepts

Prevalence vs. Incidence
Prevalence: Total existing cases at a point in time (how many people HAVE the disease). Incidence: New cases in a time period (how many people GET the disease). Prevention reduces incidence; treatment affects prevalence.
Risk Factors & SDOH
Disease doesn't happen randomly. Social Determinants of Health — income, education, food access, housing, environment — predict health outcomes more strongly than genetics or healthcare access.
Dose-Response Relationship
More intervention → more impact, but not linearly. Early gains are largest (reaching the most at-risk first). This is why the simulator uses diminishing returns — each additional percentage of reach yields less per-person impact.
Comorbidity & Cost Multipliers
People rarely have one condition. Diabetes + hypertension + obesity creates exponential cost — not additive. A person with all three costs 4-6x more than someone with one. This is why prevention of any one condition reduces total cost disproportionately.
Health Literacy as an Intervention
People who understand their health make better decisions. Health literacy improvement of 20-30% correlates with 15-25% reduction in ER visits, medication errors, and missed appointments. This is what FFH programs deliver.
Data Sources
The simulator defaults come from CDC PLACES, AHRQ HCUP, CMS claims data, and County Health Rankings. Real community data should replace defaults when planning actual interventions.

How to Use This Simulator

1. Load a real-world scenario preset OR set your own population parameters. 2. Adjust disease prevalence sliders to match your target community (use CDC PLACES for real data). 3. Set intervention parameters — how many people FFH will reach and what improvements are expected. 4. Read the results: before vs. after metrics, cost savings, and the interpretation narrative. 5. Try extreme values to understand which variables matter most (sensitivity analysis).

🔬 Epidemiology Simulator

Model how disease prevalence, risk factors, and interventions interact in a population. Change any input and watch outcomes update in real time.

📍 Scenario Presets — Real-World Crisis Templates

Load a real-world community health scenario. Parameters are pre-set from published data — adjust any slider after loading.

👥 Population Parameters
100,000
12%
35%
33%
18%
8%
💊 FFH Intervention Parameters
10%
25%
15%
20%
📊 Simulation Results — Before vs After FFH Intervention
📝 Interpretation Narrative
Learn It Healthcare Finance Foundations — The Math Behind Health Plans ▶

Every health plan, government program, and grant application runs on the same math. Understanding these concepts lets you speak the language of healthcare finance — and prove that prevention programs are worth funding.

Core Concepts

Cost of Disease
The total financial burden a disease places on a population. Includes direct costs (treatment, hospitalization, medication) and indirect costs (lost productivity, disability, premature death). Diabetes alone costs the US $412 billion annually.
PMPM (Per Member Per Month)
The universal unit of healthcare cost measurement. Total cost ÷ member-months. Health plans price premiums, measure performance, and benchmark against PMPM. A typical commercial plan runs $450-600 PMPM; Medicare Advantage runs $900-1,200 PMPM.
ROI (Return on Investment)
ROI = (Savings - Cost) ÷ Cost × 100%. A 200% ROI means every $1 invested returns $3 ($1 original + $2 profit). Health plan decision-makers typically require >150% projected ROI over 2-3 years for new programs.
Excess Cost / Attributable Cost
The difference between what a sick person costs and what a healthy person costs. This is the "preventable" portion — the money that could be saved if the disease were prevented. This is the target for FFH interventions.
Breakeven Analysis
How long until the program pays for itself. Breakeven = Program Cost ÷ Annual Savings × 12 (in months). Programs that break even under 18 months are highly fundable. Under 12 months is exceptional.
Time Horizon & Discounting
Prevention savings grow over time (compound effect), but decision-makers discount future dollars. A program that saves $1M in Year 3 is worth less than one saving $1M in Year 1. This is why demonstrating early wins matters.

How to Use This Lab

Work through Exercises 1 → 2 → 3 in order. Each builds on the previous. Exercise 1 calculates the total disease burden and auto-feeds into Exercise 2 (PMPM). Exercise 3 models an FFH intervention ROI. Change the inputs to model your own community or health plan population.

💰 Actuarial Cost Lab

Learn to calculate the true cost of disease, Per Member Per Month (PMPM) rates, and return on investment using real healthcare data. Follow the guided exercises or build your own cost models.

📘 Exercise 1: Cost of Disease in a Population
1 Set your population and disease prevalence below.
2 Formula: Total Disease Burden = (Population × Prevalence × Cost/Patient) + (Population × (1-Prevalence) × Cost/Healthy)
Total Population Health Cost
—
📗 Exercise 2: Per Member Per Month (PMPM)
1 PMPM is how health plans price and measure costs. Convert annual cost to monthly, then to per-member.
2 Formula: PMPM = Total Annual Cost ÷ Member Months
Per Member Per Month Cost
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📙 Exercise 3: Intervention ROI Calculator
1 Model an FFH intervention: How much does it cost, how many people does it reach, and how much does it reduce costs?
2 Formula: ROI = (Total Savings over Time Horizon - Intervention Cost) ÷ Intervention Cost
Learn It IDEAS Framework & Social Determinants of Health ▶

The IDEAS framework is FFH's structured approach to community health improvement. It turns complex public health problems into actionable, measurable projects that students, health workers, and community leaders can execute.

The IDEAS Cycle

I — Investigate
Define the problem using data. What health crisis exists? Who is affected? What are the root causes? Use CDC PLACES, County Health Rankings, EPA data, and community surveys to build your evidence base.
D — Design
Create an intervention plan with SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound). Map your logic model: inputs → activities → outputs → outcomes → impact.
E — Execute
Implement in phases. Phase 1 (30 days): Quick wins and baseline measurement. Phase 2 (60-90 days): Full program deployment. Phase 3 (ongoing): Monitoring, adjustment, and scaling.
A — Assess
Measure outcomes against baseline. Did disease rates drop? Did cost decrease? Did health literacy improve? Use the Epi Simulator and Actuarial Lab to model projected vs. actual results.
S — Share
Publish findings, create replicable playbooks, and present to stakeholders. Every IDEAS project should produce a case study that other communities can adapt. This is how one project becomes a movement.

What Are Social Determinants of Health (SDOH)?

SDOH are the conditions where people are born, grow, live, work, and age. They account for 80-90% of health outcomes — far more than clinical care. The major categories include economic stability, education access, healthcare access, neighborhood and built environment, and social and community context. Every IDEAS project should identify which SDOH factors are driving the health crisis and target interventions at those root causes, not just the symptoms.

How to Use This Module

1. Select a crisis scenario template or start custom. 2. Work through each IDEAS phase — Investigate is the most important; spend time defining the problem with real data. 3. Use the Epi Simulator (Module 2) to model your baseline population and projected intervention impact. 4. Use the Actuarial Cost Lab (Module 3) to build your financial case. 5. Generate the full project report to share with stakeholders.

🏘️ IDEAS Community SDOH Project Planner

Plan a real-world community health intervention using the IDEAS framework — Investigate the problem, Design a solution, Execute the plan, Assess outcomes, and Share results. Built for SDOH remediation, municipal health crises, and community activation projects.

📍 Project Scenario
🔄 IDEAS Framework Phases
I
Investigate
Define the problem, gather data, identify root causes and affected populations
▼
D
Design
Create the intervention plan, set goals, define success metrics
▶
E
Execute
Implement the plan, mobilize resources, track milestones
▶
A
Assess
Measure outcomes, compare to baseline, calculate ROI
▶
S
Share
Publish findings, create replicable model, inspire other communities
▶
📊 Project Impact Summary
Complete the phases above to generate your project impact summary.
Learn It Hypothesis Testing — Building the Evidence Case for Health Interventions ▶

Every funded health program starts with a hypothesis: "If we do X, then Y will improve by Z." This lab teaches you to formulate, test, and present that hypothesis using real cost and outcome data — the same process used by health plans, government agencies, and grant review committees.

Core Concepts

Null vs. Alternative Hypothesis
Null: "The intervention has no effect." Alternative: "The intervention reduces disease burden and costs." Your job is to provide enough evidence to reject the null. The simulator helps you model whether the numbers support your alternative hypothesis.
Counterfactual Analysis
Compare what happens WITH your intervention (Scenario B) to what happens WITHOUT it (Scenario A). The difference is the attributable impact — the value your program creates. This is exactly what funders and health plan CFOs need to see.
Sensitivity Analysis
Change one variable at a time to see which inputs matter most. If your hypothesis fails when reach drops from 15% to 10%, that tells you reach is critical. If it still holds when cost reduction drops from 20% to 12%, the hypothesis is robust.
Compounding Effect
Health interventions compound over time — Year 2 builds on Year 1 gains. The "Year-over-Year Improvement" slider models this. Programs with strong engagement typically show 8-15% annual compounding as behaviors become habits and peer effects multiply.

The Hypothesis Workflow

1. State your hypothesis in plain language — include the intervention, target population, expected outcome, and timeframe. 2. Set Scenario A (baseline) — what happens with no intervention. Use real data from CDC PLACES or your health plan's claims. 3. Set Scenario B (with FFH) — what happens with your proposed program. Be conservative on reductions; funders distrust optimistic projections. 4. Read the verdict — the model tells you if the hypothesis is supported, partially supported, or not supported. 5. Run sensitivity analysis — adjust variables to find the minimum viable intervention that still produces positive ROI.

🧪 Hypothesis Testing Lab

Test a health intervention hypothesis by defining a scenario, running before/after simulations, and comparing projected outcomes. This is how you build the evidence case before investing in a program.

💡 Define Your Hypothesis
🅰️ Scenario A — Baseline (No Intervention)
🅱️ Scenario B — With FFH Intervention
📊 Hypothesis Results — Scenario A vs B
⚖️ Hypothesis Verdict
Force for Health Academy — Population Health Program Builder — Powered by PHIT Framework & FFH Academy
Data sources: CDC PLACES, AHRQ HCUP, CMS, BLS OES — Not for clinical use without validation