Streamline Health Solutions, Inc (STRM) Fair Value & Analysis
Healthcare · US · Market cap $23.2M
Fair value as of: Jun 25, 2026
Analysis
Streamline Health Solutions, Inc (STRM) currently trades at $0.4250, while our model-based Fair Value estimate is $0.5600 — implying the stock looks roughly 31.8% undervalued today. We read business quality at 95/100 (high quality), in the Healthcare sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.
About the company
Streamline Health Solutions, Inc. provides health information technology solutions and associated services for hospitals and health systems in North America. The company offers RevID Automated Revenue Reconciliation, a cloud-based SaaS solution that delivers automated, daily charge reconciliation that identifies discrepancies between a provider's clinical and billing departments and ensures that every medical service is tracked, accounted for, and billed; eValuator Coding Analysis Platform, a cloud-based SaaS solution that delivers the capability of fully automated analysis on billing codes entered by a healthcare provider's coding team; Data Comparison Engine, a cloud-based SaaS system synchronization module that reconciles data in different software used by hospitals within their operations; Coding & CDI Solutions, provides an on-premise or cloud-based software suite that enhances the productivity of CDI and Coding staff and enables sharing of patient data; and Financial Managemen…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.