PulsePathAI

Transforming Healthcare Through Intelligent Patient Flow

Strategic Case Study

Prepared for Product Manager Role Application

Market opportunity, solution design, business case, and strategic recommendations

Agenda

01

Executive Summary

Bottom-line recommendation

02

Market Opportunity

$2.3B TAM analysis

03

Problem Quantification

$4.2M annual loss analysis

04

Solution Overview

AI-powered optimization

05

Pilot Results

40% wait time reduction

06

Business Model

Three-tier SaaS pricing

07

Go-to-Market

$18M Year 1 target

08

Financial Projections

Path to $47M by Year 3

09

Risk Analysis

Mitigation strategies

10

Recommendations

Strategic next steps

PulsePathAI can capture 15% of the $2.3B patient flow optimization market

Executive Summary

                    Bottom Line: $47M revenue by 2027 through AI patient flow optimization, reducing wait times 40%
                

Situation & Complication

12-hour ED wait times lead to patient walkouts and low satisfaction
$4.2M annual loss per facility from inefficient resource allocation
Legacy systems fail to predict demand surges or optimize deployment

Key Insights & Solution

$2.3B TAM growing 23% CAGR with minimal AI competition
Pilots show 40% wait reduction and 28% resource improvement
89% prediction accuracy vs. 65% industry average
Profitability in 18 months with 15:1 LTV/CAC ratio

$2.3B market opportunity growing at 23% annually with fragmented competition

Market Sizing & Opportunity Analysis

Total Addressable

$2.3B

Serviceable Market

$580M

Target (Y1)

$87M

Insight: Market projected to $6.5B by 2030 driven by value-based care and 23% RN shortage

Market Drivers

Value-based care mandates
23% labor shortage in RNs
CMS efficiency requirements

Competitive Gap

Fragmented, no clear leader
Legacy scheduling tools only
No AI real-time optimization

EDs lose $4.2M annually per facility from inefficient patient flow

Problem Quantification

Insight: Patient walkouts (42%) most addressable; $830 revenue lost per walkout

Pain Points

6-12hr waits in ED
18% walkout LWBS
45% turnover nurses

Root Causes

300% variance demand
Static scheduling
40% manual coordination

ML-powered platform predicts flow and optimizes resources in real-time

Product Solution Architecture

🔮 Predictive Analytics

ML forecasts arrivals 4-6hrs ahead at 89% accuracy

⚡ Real-Time Optimization

Dynamic allocation adjusts every 15 minutes

📊 Analytics Dashboard

Executive KPIs with bottleneck identification

🔗 EHR Integration

Seamless with Epic, Cerner, 15+ via HL7/FHIR

Competitive Differentiation

89% accuracy vs. 65% industry avg
15-min cycles vs. hourly updates
30-day deploy vs. 6-12 months
Pre-built connectors reduce integration 80%

Pilots demonstrate 40% wait time reduction and 8-month ROI payback

Pilot Program Results (3 Systems, 6 Months)

Wait Time Reduction

-40%

8.5h → 5.1h

Resource Utilization

+28%

Staff deployment

Patient Satisfaction

+35pts

HCAHPS score

Insight: Wait times declined 12% monthly; walkout rates dropped 44% by M6

ROI: 8.2-month payback with $3.2M annual savings per facility

Three-tier SaaS pricing enables market penetration and value capture

Revenue Model & Pricing

Starter

$15K

per month

1 ED/dept
Basic predictive
Std dashboard

Professional

$45K

per month

3-5 depts
Advanced ML
Dedicated CSM

Enterprise

$120K

per month

System-wide
Custom AI
24/7 support

Insight: Q4 shows strong upsell momentum with Professional tier 3x growth

Unit Economics: CAC $28K (8-mo payback) | LTV $420K | LTV/CAC 15:1 | Gross 78%

Land-and-expand targeting 100 hospitals in Year 1 generates $18M ARR

Go-to-Market Strategy

Phase 1: Market Entry (M1-6)

Target: 300-500 bed hospitals, top 25 metros, >200K ED visits
Channels: Direct sales (6-person) + EHR partnerships
Goal: 25 Starter customers ($4.5M ARR)

Phase 2: Expansion (M7-12)

Upsell: 40% Starter→Professional (+$10.8M ARR)
New: 50 additional Starter ($9M ARR)
Enterprise: 3 large systems 500+ beds ($4.3M ARR)

Success Metrics

35% win rate vs. 22% industry
75-day sales cycle
130% net revenue retention

Marketing Mix

Case studies & ROI calculator
HIMSS, ACEP conferences
20% peer referral program

Path to $47M revenue and profitability by Year 3 with strong economics

3-Year Financial Projections

Insight: Break-even Month 18 with improving margins - Year 3 shows 23% operating margin at $47M

Year 1 ARR

$18M

Year 2 ARR

$32M

Year 3 ARR

$47M

Key Assumptions

Growth: 78% Y1→Y2, 47% Y2→Y3 (top SaaS) | Churn: 15% (vs. 20%)
Margin: 78% gross Y2 | Break-even: M18 with $12M raised

Key risks include EHR integration and regulatory; mitigation strategies in place

Risk Assessment & Mitigation

1 EHR Integration

High | Complex IT environments
Mitigation: Pre-built connectors; 30-day guarantee

2 Regulatory (HIPAA, FDA)

High | Clinical workflow changes
Mitigation: CDS design; HITRUST certified; BAA-compliant

3 Model Accuracy & Adoption

Medium | Clinicians may distrust AI
Mitigation: Continuous retraining; explainable AI; phased rollout

4 Competitive Pressure

Medium | EHR vendors may compete
Mitigation: Speed advantage; superior ML; Epic/Cerner partnerships

Immediate actions: Raise Series A, expand pilots, finalize Epic partnership

Strategic Recommendations

1

Raise Series A ($12M)

Fund GTM expansion, product development, and regulatory compliance - Target close Q1 2026

2

Scale Pilots to 10 Hospitals

Build customer references and refine product-market fit across diverse hospital types

3

Finalize Epic App Orchard Integration

Critical partnership for market access and credibility - Accelerates enterprise sales

4

Hire VP Sales + 5 AEs

Build enterprise sales capability for direct selling motion and complex sales cycles

5

Achieve HITRUST Certification

De-risk security concerns for enterprise buyers - Required for health system procurement

PulsePathAI positioned to become category leader in AI-powered patient flow

Conclusion & Next Steps

                    Investment Thesis: $2.3B market with proven ROI, strong economics, clear path to leadership
                

Key Takeaways

Large, growing market: Patient flow critical for value-based care
Proven solution: 40% wait time reduction, 8-month payback
Attractive economics: 78% gross margins, 15:1 LTV/CAC, M18 profitability
Defensible moat: Proprietary ML, EHR partnerships, network effects
Strong execution: Experienced team, clear GTM, manageable risks

Recommended Decision

Proceed with Series A fundraising to capture first-mover advantage. Target close by Q1 2026.

Questions & Discussion

Anticipated questions and prepared responses

Q&A Preparation

Q: How do you differentiate from Epic's native patient flow modules?

A: Epic's modules are scheduling-focused with static rules. We're prediction-focused with dynamic ML that adapts in real-time. We're also EHR-agnostic and partner with Epic through App Orchard rather than competing directly.

Q: What's your customer acquisition strategy for the first 25 hospitals?

A: Warm outbound through our clinical advisory board connections (7 CMOs across different systems), conferences (HIMSS, ACEP), and leveraging pilot success stories. We're targeting hospitals already familiar with our advisors.

Q: How do you handle hospitals with multiple EHR systems?

A: Our HL7/FHIR connectors support multi-EHR environments. We aggregate data at the interface layer and normalize it before feeding into our ML models. This is actually a competitive advantage - legacy vendors struggle with heterogeneous systems.

Q: What if hospitals don't trust your AI recommendations?

A: Three-part approach: (1) Explainable AI dashboard showing prediction rationale, (2) Shadow mode deployment where clinicians see recommendations without obligation, (3) Progressive trust-building starting with low-stakes suggestions before high-impact ones.

Q: How do you plan to achieve 89% prediction accuracy across different hospitals?

A: Transfer learning from our base models plus hospital-specific retraining. Each hospital has unique patterns, but our models learn general principles (flu season, shift changes) that transfer. We require minimum 3 months of historical data before going live.

Q: What's your defensibility if a large tech company enters this space?

A: Network effects - our models improve with more hospital data. Plus deep EHR integrations create switching costs. We're building partnerships with Epic/Cerner now rather than competing, which gives us distribution they won't have.

Thank You

Questions?

Ready to discuss next steps