What signals belong in a healthcare Fit Score?

The signals that belong in a healthcare Fit Score are the ones that both predict a good customer and exist for most accounts: firmographic size, structural ownership and setting, specialty or taxonomy, technographic signals, and the consumer and digital signals, reviews, social, and web, that carry demand where registries are thin. A signal that predicts well but is missing for half your accounts cannot carry much weight.

This guide is the signal library. For how to weight these signals into a working score, see how do I build a Fit Score, its companion.

Which signal categories matter most?

Good signals span the full source palette. Registries size the opportunity and describe how an account is structured, the company website, reviews, and social carry the consumer demand that registries never see, LinkedIn and job postings reveal who buys and where they are investing, and your own internal data records how similar accounts have actually behaved. No single source is enough, and registries in particular are strong for identity and structure but thin for consumer signal. The ranking power comes from triangulating across all of them.

Which sources lead for which verticals?

The mix shifts by vertical. For clinical and billing-driven verticals, hospitals, cardiology, imaging, revenue-cycle, registries such as NPPES, CMS, and PECOS carry most of the signal because identity, size, and setting are well documented. For aesthetics, behavioral health, dental, and other consumer or cash-pay verticals, registries are thin, so the web, social, and reviews lead: an active Instagram, a RealSelf presence, Google review volume, and a live financing page tell you more about demand than any registry does. Most consumer and cash-pay healthcare sits in that second group, which is why the full palette matters.

How do I avoid signals that mislead?

Drop signals that correlate with something you do not want, or that are stale or sparse. A signal available for only a handful of accounts adds noise, not ranking power. Prefer signals verifiable across sources so they exist for the whole market. SignalRx pulls the full palette in alongside your CRM, registries for identity and structure, the company website, Google reviews, Instagram and other social, LinkedIn, and job postings for the signals registries miss, resolves ownership so a physician group or MSO reads as one account, and where a record arrives sparse fills the gaps by triangulating across those sources so it still becomes scoreable, then writes the fields onto every account and contact in Salesforce, your system of record, and feeds them into the Fit Score. What makes the scores reliable is that they combine that external palette with your own internal signals, your revenue, support, and product-usage data tracked over time, with inference deciding which source to trust when they disagree, so the resulting scores and Tags become Revenue Plays and Salesforce lists the team acts on.

What makes the signal set yours?

External sources describe the market the same way for everyone. Your own internal data, revenue, product usage, support history, and win and loss patterns, is the layer no competitor can see, and it is what turns a generic fit model into yours. It tells you which external signals actually predicted the customers who stayed and grew, so it both adds signal and reweights the rest. Triangulated against the external palette, internal data is the differentiating layer of the score.

Do negative signals belong too?

Yes. Attributes that predict a bad fit, the wrong setting, too small, a competitor's captive account, should pull the score down rather than simply be absent. That keeps low-fit accounts out of the top of the list.

What does this look like on one account?

Illustrative, based on how SignalRx customers work. Summit Cardiology Associates is a hospital-affiliated group, 12 locations, specialty cardiology, running a target platform. Its size and specialty signals score strongly, but the hospital-affiliation structural signal tempers the score, landing it at Moderate Fit with a health tier of Maintain. For a clinical group like this, registries carry most of the signal; an aesthetics or dental group would lean instead on reviews, social, and its website. The ownership type and NPPES specialty are written as Tags so it groups with similar accounts.

Key takeaways

• Use signals that both predict fit and exist for most accounts.
• Cover the full palette, registries for identity and structure, web, reviews, and social for consumer demand, LinkedIn and job postings for people and intent.
• Let sources lead by vertical, registries for clinical and billing, web, social, and reviews for aesthetics, behavioral, and dental.
• Your own internal data is the differentiating layer; include negative signals so bad-fit accounts score low, not blank.

See it in action: Close Better Leads with SignalRx.

Related: How do I build a Fit Score? and How do I score with incomplete data?