A single number for where the opportunity is.
The ProxiScore rolls everything ProxiSuite knows about an account into one number from 0 to 100. Higher means a better place to spend your next hour. It is a prioritization signal built to sort your book, so the noise drops and the real targets stand out. It is not a grade on the account, and it is not a promise. It is a starting point for your judgment, not a replacement for it.
0 is skip, 100 is drop everything.
The scale runs red to blue. Red is low opportunity, blue is high. Every score also carries a letter grade so you can sort at a glance.
A grade is a national ranking, not a fixed number. Every week we score every facility in the country under your exact setup, the same lenses, the same weights, the same directions, the same blend mode, and cut that national distribution into the bands above. An A means the account sits in the top 10% of the country for the way you are hunting.
This matters more than it sounds. A fixed cutoff cannot work across setups, because a score built from two lenses and a score built from twenty five are not the same measurement. Averaging more lenses pulls every account toward the middle, so a fixed bar that looks fair on two lenses becomes unreachable on twenty five. Ranking against the country under your own setup is the only version that means the same thing whether you own one module or all of them.
It also means your territory tells you the truth. Grades are not curved to your account list, so a patch with nothing in it will show nothing in it, and a rich patch will show a lot. If you flip a lens to hunt distressed accounts and your grades drop, that is the real answer: those accounts are rarer. We would rather say that than hand you an A on a mediocre account because it was the best of a thin bunch.
Where we have not yet computed a national reference for a setup, the grade falls back to fixed cutoffs and is labelled as provisional in the app. A provisional letter is a rough convention, not a national claim, and we mark the difference rather than quietly blurring it.
Where the Financial lens sits.
Financial is anchored to an absolute fact: 50 means breakeven, a zero reported margin. Above 50 is a positive margin, below 50 is negative. That is deliberate, and it is not the same thing as "average".
The consequence is worth stating plainly rather than leaving you to find it. The typical US hospital does not run at breakeven. All-payer total margin was around 6.4% in 2023 (KFF and MedPAC, independently, from CMS cost report data), so the typical hospital lands near 70 on this lens, not near 50. That is the honest answer to "how far above water is this account", which is the question the lens asks. It is not a claim that the typical hospital is unusually strong.
Most other lenses rank against peers instead, so their middle really is 50. Because a lens with a narrow spread would otherwise quietly deliver less influence than the weight you set on it, we measure each lens's real national spread and correct the weights so your sliders mean what they say. That correction is measured from public data, never estimated, and a lens we cannot measure is left alone rather than guessed at.
The lenses.
The ProxiScore is a blend of twenty five lenses. Twenty are a public-data read on the account, the same for everybody. Five are your own history with it, which is why two reps working the same account can land on different numbers, and should. Every lens lands in one of four buckets: how close it is, how big the prize is, how reachable the relationship is, and why now. You only ever see one number, the blend. A single lens on its own is just the ProxiScore weighted fully to that lens.
Proximity
How close the account sits to your base. Computed from real drive geography, and the one lens that cannot be gamed.
Size (how big the prize is)
The demographic weight of the area around the account, its addressable population and fit.
How big the account is, from its certified bed count, log-scaled so a roughly 1000-bed system tops out.
How big the local market is and how contestable it is, together. Demand is the total certified beds in the account's area, ranked against peer areas. Openness is a Herfindahl index over each facility's share of those beds, so a market split between many players reads open and one owned by a single system reads closed. The two are combined as a geometric mean, because a large market held by one system is a worse door than a mid-sized one split five ways. Needs a bed count, so it sits out on providers and pharmacies, which have no size measure at all.
How much disease the surrounding population actually carries, from public CDC PLACES prevalence. Two areas with the same headcount are not the same prize if one of them is far sicker. Community counts people, this counts need.
How many clinicians are actually affiliated with the account, from the public CMS affiliation file. A bed count says how big the building is. This says how many people work in it, which is closer to how much gets bought.
Openness (how reachable the relationship is)
The account margin from public CMS cost reports, using total margin where it is reported and falling back to operating margin. Total margin is the fairer read: charity, children's and safety-net hospitals run negative operating margins by design and cover the gap with philanthropy and investment income, so operating margin alone would call them distressed when they are not. Read it as a ready buyer or a savings target, your call.
How open the relationship already is, from public Open Payments money flowing in. Open Payments only covers physicians, non-physician practitioners and teaching hospitals, so most hospitals have no filing at all and the lens sits out. An empty result means CMS does not require the disclosure, not that no money moved.
Budget headroom and Title I status from public school finance data. Where Title I status is simply unpublished the lens reads it as unknown, never as a no.
Your real contract position, ours, open, or competitor held. Objective, from your own Contracts tool.
Federal award dollars already flowing to the account, from public USAspending data. ProxiMatch resolves the account to its federal recipient on name and location before any dollars are attached, so an unmatched or ambiguous account shows no data rather than the wrong recipient. Money already moving reads as a warm, engaged relationship.
The rhythm of your contact rather than the raw count, measured as the typical gap between one visit and the next. Steady beats sporadic at the same total, because a relationship you turn up to every six weeks is a different asset from six visits in a fortnight and nothing since. Turning up twice a week does not score double: rewarding raw frequency would reward padding the log.
How often you actually get in, from the share of your logged attempts that reached someone. This is the lens that makes two reps diverge most on the same account, and that divergence is the point rather than a flaw: a door that opens for one rep and not another is a real difference in the opportunity. Set the direction in the score drawer, because a hard door reads as a closed account to most reps and as an unworked opening to a rep whose whole pitch is that they can get in.
The depth of what has actually happened, weighted rather than counted, so an in-service or a demo outranks a drive-by and an email counts least. Ten emails and one in-service are not the same relationship even though they are eleven events.
Timing (why now)
Provider shortage in the account county from HRSA HPSA data. Underserved reads as unmet demand or a constrained buyer.
The public CMS quality gap. A weak record is your clinical opening.
The public HCAHPS patient-experience gap. Reads alongside Clinical rather than on top of it: both come off the same CMS Compare release and a hospital that trails on one usually trails on the other, so the score treats them as related instead of counting one problem twice.
Nurse hours per resident day and turnover, against a state baseline rather than a national one, because staffing norms are a state story. Post-acute only, from the CMS payroll-based journal.
Health survey score and fines, against the same state baseline. Post-acute only. Understaffed homes draw more deficiencies, so this and Staffing are treated as related rather than as two independent openings.
The public performance gap from test scores, graduation, and College Scorecard.
An approaching contract expiration is a real opening no matter who holds the account now.
Which way the account is actually moving, from year over year change in the public figures already on file. Level tells you how big it is today. This tells you whether today is the high point or the low one.
How hard the account is running against what it can hold, from public utilization figures. A full building and an empty one buy differently, and neither reads off a bed count alone.
How long since you last had real contact, from your own log. Not how long since anything happened at the account: an imported note is not you walking in. An account you have never contacted sits this lens out rather than scoring it zero, because never been and been a long time ago are different facts and only one of them is a warning.
Direction rather than level: whether your contact is picking up or falling away, comparing the recent half of the window against the earlier half. A warm account going quiet and a cold account waking up are both worth knowing, and neither shows up in a count. Needs history in both halves, so it sits out on an account you have only just started working.
You tune it. Then it is yours.
Weights you set
Slide any lens up or down. The weights normalize themselves, so you never have to make them add to 100. Turn everything off but one and the ProxiScore becomes that single lens.
Two ways to blend
Balanced treats a weak lens and a strong one as a trade-off. Focused punishes a weak spot on any lens you care about, so an account has to be good everywhere to score high.
Direction is yours
Several lenses read two ways. A thin margin can mean a savings target or a risky buyer. A shortage can mean unmet demand or a constrained account. You pick which reading fits how you sell.
It learns from your wins
Mark the accounts you actually closed. If your wins score low under your current weights, ProxiSuite says so and suggests a tilt that fits your real book.
Confidence, in the open.
A score built on one lens is not as trustworthy as one built on five. Every ProxiScore carries a confidence read, High, Medium, or Low, based on how much of what you own actually had data for that account. Thin coverage drags confidence down and the score tells you so, rather than pretending it saw everything.
When a score is leaning on less than a third of the lenses you own, it says thin out loud and tells you how many lenses it could actually see. Two important things it never does: it never invents a value for a lens with no data, and it never marks an account down for data it did not report. A lens with nothing to say sits out, and its weight is shared across the lenses that did report. An account with less data is not a worse account, and ProxiScore will not pretend otherwise.
Separation: which lenses actually tell your accounts apart.
A lens that scores every account on your screen the same cannot help you choose between them. If all forty hospitals on your list sit in the same county, Proximity is not sorting anything, no matter how heavily it is weighted. A lens where accounts scatter is where the decision actually lives.
Open the score drawer and ProxiScore measures that, live, off the accounts loaded right now. Every lens reports on the same 0 to 100 scale, so we compare their spreads directly using standard deviation. Before we measure, values are trimmed to the 5th and 95th percentile of your list, so one absurd account cannot decide what your whole territory gets weighted on. Your account keeps its real value in its own score; only the spread math sees the trimmed copy.
Then it offers. It does not act. Press Tune to this list and the weights fit to what separates your accounts, and the badge changes to say Tuned to your list along with how many accounts it fitted against. Touch any slider or preset afterward and that claim clears itself, because it stopped being true.
Three things worth being straight about. This is a read on your list, not a fact about the country: load a different territory and the answer moves. It optimizes for telling accounts apart, not for what the score means in absolute terms. And it needs at least eight accounts carrying a lens before it will say anything, because below that a spread is noise. No lens is ever removed or zeroed out by it, only eased back.
What the ProxiScore is not.
- It is not a rating of quality. It does not judge whether a hospital gives good care or a school teaches well. It ranks sales opportunity for a rep, nothing more.
- It is not professional advice. Nothing here is clinical, financial, legal, or investment advice, and it is not a diagnosis or a compliance determination. It is for informational use in sales planning only.
- It is not a guarantee. Dollar figures and opportunity reads are modeled estimates, not billed, audited, or realized amounts. Results are neither typical nor promised.
- It is built from public data we do not independently verify. Sources like CMS, Census, HRSA, IPEDS, and FDA can contain errors, gaps, or lag. Do your own research and verify anything you act on.
- It is your configuration once you tune it. Default weights are ProxiSuite neutral baseline. Adjusted weights and lens directions reflect your choices, not ours.
- It is not affiliated with or endorsed by any agency. ProxiSuite uses public federal data but is not connected to CMS or any government body.
The ProxiScore and this explanation are provided as is, for informational purposes only, without warranty of accuracy, completeness, or fitness for a particular purpose. Questions: support@proxisuite.com.