HCAT vs LFMD

Health Catalyst, Inc vs LifeMD, Inc. — Valuation Comparison 2026

HCAT

Health Information Services
Health Catalyst, Inc
Quality
5.9
out of 10
Value Trap
43
WARN
Price
$1.40
Last close
Models
11/13
Active
VS

LFMD

Health Information Services
LifeMD, Inc.
Quality
7.7
out of 10
Value Trap
6
SAFE
Price
$4.59
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType HCAT Fair ValueHCAT Upside LFMD Fair ValueLFMD Upside
Bayesian DCF Intrinsic $4.65 +274.7% $1.77 -61.4%
Earnings Power Value Intrinsic $3.89 -22.3%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $0.29 -79.9%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for HCAT vs LFMD — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

HCAT vs LFMD — Which Stock Is More Undervalued?

LFMD scores higher with a 7.7/10 quality rating vs HCAT's 5.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Health Catalyst, Inc (HCAT) and LifeMD, Inc. (LFMD) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

HCAT currently trades at $1.40 with a QOC of 5.9/10, while LFMD trades at $4.59 with a QOC of 7.7/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).