Develop AI-Powered Growth Strategies

AI-powered forecasts to inform market selection, service line growth and capital allocation

“Directionally correct” strategies are improbable.

In the absence of precise data, many health economy stakeholders settle for “directionally correct” insights, assumptions or general trends that appear reasonable but are fundamentally based on insufficient evidence. In contrast, probabilistic predictions powered by AI and drawn from comprehensive, longitudinal datasets can inform true “evidence-based” strategies.

FLAWED ASSUMPTIONS

Despite abundant evidence that demand for healthcare services is flat, legacy demand forecasts assume perennial growth

MISSING VARIABLES

Burden of disease is not 1:1 with demand for healthcare services, with is impacted by cost, access, convenience and therapeutic advancements

ONE-SIZE-FITS-ALL MODELS

National models ignore local market dynamics, masking geographic variation in demand

Develop evidence-based strategies for growth

Leverage forecasts built from more than 90,000 machine-learning models to predict where and how care will be delivered – generating market-specific projections with confidence intervals to quantify uncertainty.

Identify growing – and shrinking – demand across markets

• Identify high-growth service lines across U.S. markets
• Detect variation by ZIP code to identify opportunities within markets
• Quantify uncertainty with confidence bands

Develop service lines to meet projected patient needs

• Prioritize markets and service lines with growing demand
• Target the patient populations that are likely to drive future utilization

Allocate capital effectively based on probabilistic demand forecasts

• Identify high-growth markets for de novo sites or M&A
• Forecast care gaps to prioritize strategic capital allocation