Every dashboard the nine pages name, classified by the data it needs.
So here is the whole list, every one of the 89, with what each needs sitting next to it. Sorted so the ones you can build come first. No page in the sample does this, which is the only reason this article exists.
Two things before the table. Rank means rank on DuckDuckGo and Bing, which agree on eight of ten hosts; Google served a CAPTCHA to automated requests, so this is a picture of one index rather than of the search market. And none of what follows makes the nine pages wrong. Hospitals are healthcare. The word on the tin simply covers four different businesses. Most of the nine do say who they are writing for, and say it well: Upsolve prints a Who Uses It line under all ten of its examples, Quantize prints a Beneficiaries block, Vidi names its client types in the opening paragraph. What none of them does is sort the examples by it, so the reader still has to.
The scale test, before you copy anything
Every example you are about to see is buildable by somebody. The question is whether that somebody is you. Four questions sort them.
Does it need a bed, an emergency department, an operating room or an imaging suite? Length of stay, bed occupancy, ED throughput, door to doctor time, readmission within thirty days, radiology repeat and reject rates. These are the most common dashboards in the genre and they are inpatient instruments.
Does it need data about people who did not visit you? Risk scores, total cost of care, social determinants, network leakage, polypharmacy across a member panel. These come from claims feeds a payer or an accountable care organisation receives.
Does it need a protocol or a country? Clinical trial enrolment, pharma sales territories, national admissions statistics, transplant registries.
Does it need a system you have not connected? Patient satisfaction needs a survey tool. Staffing and overtime need payroll. Recruitment needs an applicant tracking system. Marketing needs ad platforms or call tracking. Denials and net collection need the remittance file from the clearinghouse. Every one of those is a practice-scale question, and none of them is answerable from the appointment book and the ledger.
Nine of the 89 clear all four. Another twenty-one clear the first three and fail only the fourth, and which of those you can reach depends on what you have already wired up. So rather than one percentage, here is where you sit:
Those eleven need a modality flag on every visit, which the two telehealth dashboards are built entirely around; an applicant tracking system; IT audit logs; EHR clinical values such as HbA1c; cost accounting; an EHR-to-CRM link; a consumer fitness tracker; a medical device maker's service fleet; and the PDGM period rule that home-health agencies bill under. Some of those are a purchase away. Others belong to a different kind of company than yours.
I am deliberately not giving you a single headline percentage. Where you draw the line changes it a lot: count only the three exports and it is 9 of 89; count everything an organisation could build from its own operational data, hospitals and payroll and cost accounting included, and it is 64 of 89. Both are defensible and neither is a fact about the world. The table is the finding. The percentage would just be my rule, wearing a number.
Every one of the 89
Grouped by what it needs, then by publisher. Argue with any row: the classification is a judgement about what each page says its dashboard contains, and where a page's heading and its description disagree, the description wins. That is why Vidi's Hospital Referrals Dashboard sits in the first group, and LeadSquared's Center management dashboard for multi-location hospitals sits in the third.
Yours to build · 9 of 89
Nothing but the appointment book, the ledger and the provider roster. Where a page also lists a metric that needs something else, the row says so.
Needs one more system · 21 of 89
Some are a bolt-on you may already have. Others belong to a different kind of company altogether, and the row says which.
- Healthcare Recruitment Dashboard · boldbi.com — an applicant tracking system
- Patient Experience Analysis · boldbi.com — a patient survey tool
- Case study: Cleveland Clinic patient experience · data.folio3.com — a patient survey tool
- Telehealth Dashboard · data.folio3.com — a modality flag on each visit, plus connection quality and satisfaction scores
- Chronic disease management dashboard · gooddata.ai — EHR clinical values such as HbA1c
- Cost of care dashboard · gooddata.ai — cost accounting: overheads, supplies, cost per procedure
- Patient billing dashboard · gooddata.ai — an AR ledger with payment dates, plus insurance reimbursements
- Staff performance dashboard · gooddata.ai — payroll and HR records
- Compliance: HIPAA Access Monitoring · knowi.com — IT audit logs
- Patient Experience Dashboard · knowi.com — CAHPS or NPS, call logs and portal activation
- Revenue Cycle: Denials and AR Performance · knowi.com — 835 remits and 837 claims
- Patient dashboard · leadsquared.com — an EHR-to-CRM link, plus a survey: its first KPI is satisfaction
- Fitbit Fitness Dashboard · quantizeanalytics.co.uk — a consumer fitness tracker
- Patient Satisfaction Dashboard · upsolve.ai — a patient survey tool
- Staffing and Resource Utilization Dashboard · upsolve.ai — payroll and time-and-attendance
- Telehealth Performance Dashboard · upsolve.ai — a modality flag on each visit, plus call quality telemetry and patient feedback
- Doctor Overtime Dashboard · vidi-corp.com — payroll
- Healthcare Marketing Dashboard · vidi-corp.com — call tracking; the page names CallRail
- Hospital LUPA Dashboard · vidi-corp.com — visit and billing records, plus the PDGM 30-day period rule
- Medical Scanner Dashboard · vidi-corp.com — a device maker's service fleet
- Patient Health Distribution Dashboard · vidi-corp.com — a mobile app collecting clinical data
Needs a hospital · 34 of 89
Beds, an emergency department, length of stay, admissions, operating rooms or imaging. I read each page's description to place these; I have not written a per-row source line for them.
- Cancer Diagnosis Dashboard · boldbi.com
- Healthcare Executive Dashboard · boldbi.com
- Hospital Management Dashboard · boldbi.com
- Orthopaedic Clinical Variation Dashboard · boldbi.com
- Patient Health Monitoring · boldbi.com
- Radiology Rejection Analysis Dashboard · boldbi.com
- Case study: Cleveland Clinic, hospital-acquired infections · data.folio3.com
- Case study: Henry Mayo Newhall, ED analytics · data.folio3.com
- Case study: Mayo Clinic YES Board, ED flow · data.folio3.com
- Emergency Department Dashboard · data.folio3.com
- Hospital Bed Management Dashboard · data.folio3.com
- Patient Care Dashboard · data.folio3.com
- Emergency department dashboard · gooddata.ai
- Hospital operations dashboard · gooddata.ai
- Infection control dashboard · gooddata.ai
- Patient dashboard · gooddata.ai
- Resource utilization dashboard · gooddata.ai
- Supply chain management dashboard · gooddata.ai
- Clinical Operations: Patient Flow and Throughput · knowi.com
- Quality and Safety: Readmissions · knowi.com
- Center management dashboard, multi-location hospitals · leadsquared.com
- Hospital performance / operational dashboard · leadsquared.com
- Physician performance dashboard · leadsquared.com
- Healthcare - Length of Stay · quantizeanalytics.co.uk
- Patient360 Healthcare Dashboard · quantizeanalytics.co.uk
- Emergency Room (ER) Dashboard · upsolve.ai
- Hospital Operations Dashboard · upsolve.ai
- Patient Monitoring Dashboard · upsolve.ai
- Quality and Compliance Dashboard · upsolve.ai
- Hospital Referrals Dashboard · vidi-corp.com
- Hospital Service Profitability Dashboard · vidi-corp.com
- Patient Health Indicator Dashboard · vidi-corp.com
- Patient Summary Dashboard · vidi-corp.com
- Pocket Dashboard For Hospitals & Care Homes · vidi-corp.com
Needs a payer's claims · 11 of 89
Claims across a member population, risk scores or payer contracts.
- Ambulatory Surgery Centers (ASC) · arcadia.io
- Benchmarking · arcadia.io
- High-Cost Members · arcadia.io
- Network Integrity · arcadia.io
- Polypharmacy · arcadia.io
- Quality Performance · arcadia.io
- Social Determinants of Health (SDoH) · arcadia.io
- Transitional Care Management (TCM) · arcadia.io
- Population Health Dashboard · data.folio3.com
- Claims Analytics: Payer Performance · knowi.com
- Population Health Dashboard · upsolve.ai
Needs a country · 10 of 89
National or regional statistics, not one organisation's own data.
- Coronavirus Disease (COVID-19) Analysis · boldbi.com
- Drug and Substance Abuse Dashboard · boldbi.com
- Poison Control KPI Dashboard · boldbi.com
- Coronavirus (COVID-19) Cases · quantizeanalytics.co.uk
- Disease-Related Death · quantizeanalytics.co.uk
- GP Healthcare in England · quantizeanalytics.co.uk
- Kidney Transplants Dashboard · quantizeanalytics.co.uk
- NHS Hospital Admitted Patient Care Activity · quantizeanalytics.co.uk
- Rural Hospital Closures · quantizeanalytics.co.uk
- The Shape of HealthCare Spending · quantizeanalytics.co.uk
Needs a protocol · 4 of 89
A trial, a registry or pharma sales territories.
- Clinical Trials Dashboard · boldbi.com
- Clinical Trial Dashboard · upsolve.ai
- Power BI Pharma OpEx Dashboard · vidi-corp.com
- Power BI Pharma Sales Dashboard · vidi-corp.com
Three of the nine pages, Arcadia, Bold BI and Quantize, put nothing in the first group. One qualifier on Quantize: inside its GP Healthcare in England example sits a sub-dashboard called Appointment Scheduling and Attendance, with bookings, cancellations and no-show rates. It is first-group material and it is not one of the ten the page counts, so neither do I. Say the sentence as none of their counted examples rather than nothing at all. Arcadia's eight are all population health: Quality Performance, Transitional Care Management, Ambulatory Surgery Centers, High-Cost Members, Social Determinants of Health, Benchmarking, Network Integrity and Polypharmacy. Even the surgery centre one is a payer view, modelling savings by contract type for an audience the page names as ACO leaders. Quantize's ten are mostly national: GP Healthcare in England, NHS Admitted Patient Care Activity, Rural Hospital Closures, OECD spending, a global COVID tracker, disease-related deaths and kidney transplants.
The page that serves a practice best is LeadSquared, with four of eight: financial, no-show, referral and appointment. LeadSquared sells a CRM, which is the one product in the sample that lives at the front desk. With one such publisher among nine I would not call that a pattern, but it is the obvious thing to check next time.
The nine you can actually build
Grouped by what they run on. The publisher is in brackets so you can go and look at the layout.
Money, five of them. Revenue Cycle (Folio3), Financial Performance (Upsolve), Healthcare Service Profitability (Vidi), Revenue cycle management (GoodData), Financial or revenue-based (LeadSquared). All five are built on charges and payments. Vidi's is the plainest: it compares billed against paid across time and discipline, which is the gross collection rate under another name. Four of the five also list a metric that needs something else, usually denial rate, and the table above says which. Take the core, leave the extras until you have the remittance file. GoodData's patient billing dashboard looked like a sixth until I read its KPIs: outstanding balances and average patient payment time need an A/R ledger with payment dates, which is not one of the three exports.
The schedule, three. No-show, Referral and Appointment, all from LeadSquared. They run on the appointment table: booked, arrived, cancelled, did not attend, plus a referral source if you record one. One table, which makes them the cheapest group here to build. I briefly moved Vidi's Hospital Referrals into this group and then moved it back: its page says it was built for a hospital in the US, and wanting a row in my own column is not a reason to overrule that.
More than one site, one. Knowi's Multi-Clinic Operations Dashboard, which lists visit volume by clinic and provider, provider utilization, panel size per PCP, same-day access rate, revenue per visit and referral completion. It is the closest thing in the sample to a dashboard for an outpatient group, and it is one section on the seventh result.
Notice what is thin. Only one of the nine is about providers, and it is Knowi's multi-clinic section rather than a dashboard in its own right. The sample has plenty of provider dashboards, but they land elsewhere: LeadSquared's physician performance page opens with time of discharge and Johns Hopkins, and Vidi's overtime dashboard is written for hospitals and runs on payroll. Patient dashboards are plentiful too, and almost all of them are clinical monitoring rather than anything a front desk would recognise.
The six numbers, counted across nine pages
I have written a separate article about each of six numbers, so the list was fixed before the counting started.
One page names four of the six outright: Knowi, with no-show rate, provider utilization, revenue per visit and AR days. Every other page names three or fewer.
A fifth is arguable and I will not claim it. Knowi's claims dashboard lists paid-to-allowed ratio by payer, which is the same arithmetic as net collection rate. But it sits among time to adjudication and network leakage, where allowed is being read as the plan's share rather than the practice's collections, and those are different numbers with the same shape. I mention it because my first two passes missed it entirely, and because a reader checking my work will find it.
Three pages name none of the six: Arcadia, Vidi and Bold BI, across 33 dashboards between them. Two of those zeroes are closer than they look, and one of them is closer than I said above. Vidi's service profitability dashboard compares billed against paid, which I called the gross collection rate under another name two sections ago; it is not the phrase, but it is the metric, and leaving that out of this paragraph would be picking the flattering near-miss. Bold BI's is thinner: it lists revenue per case, and its page never says what a case is. Vidi uses the British spelling of utilisation three times, twice about medical device fleets and once in a list of things its dashboards consolidate. Neither is the metric, and I would rather say that out loud than let a finding rest on a spelling.
Arcadia's zero needs its own note, because I counted it as a one until the last pass. Its page does say patient retention twice in its body text, both times inside its network integrity dashboard, about a health system retaining patients inside its own provider network and about in-network and out-of-network spending. That is a claims metric that happens to share a name. I rejected Quantize's retention for being a workforce metric in a staffing sentence; the same standard sends this one out too.
Two honest notes about that table, both of which cut against me.
The collection rate row says collection rate, not gross collection rate, because neither page that names it says which one it means. Folio3 writes collection rates and collection percentages; GoodData writes collection rates. The distinction between gross and net is the whole subject of one of my own articles, and it would be dishonest to award these pages a precision they did not use.
And the six are not the only candidates. Running the same search for terms I did not choose: denial rate appears on four of the nine pages, which beats five of my six. Clean claim rate on two, payer mix on one. Denial rate is not in my list because a practice cannot compute it from an appointment book and a ledger. It needs the remittance file. That is a real limit on the list, and it is the same limit that keeps net collection rate off a practice dashboard: the ledger records what you billed and what you collected, and it does not record what you were owed.
Why the gap exists, and why it is not a conspiracy
The obvious explanation is that vendors write hospital content because hospitals have budgets. That is probably true and it is not the whole story.
The other half is that hospital metrics are standardised and practice metrics are not. Length of stay, readmission rate and bed occupancy have regulatory definitions, national benchmarks and a reporting apparatus behind them. A vendor writing about length of stay knows what it means and knows the reader will recognise it.
Collection rate has no such apparatus, which is why two of the nine pages name it without saying which of the two rates they mean. Days in A/R is worse, because the number changes with the denominator and the averaging window and almost nobody states either.
So the gap in the content sits downstream of a real gap in the field. The definitions are genuinely unsettled. That is not a reason to skip the numbers. It is a reason to write the definition on the dashboard, next to the number. Exactly one page in the sample says the same thing, and says it better than I am about to: Knowi's rule is that every KPI carries a numerator, a denominator, exclusions, a refresh cadence and a named owner. It then does it inline, which is how you get sentences like bed occupancy rate is occupied beds divided by staffed beds. That is a hospital metric and the discipline is the transferable part.
What a practice dashboard actually has on it
What you were owed, against what arrived. Net collection rate is collected divided by allowed, and it is the version that finds the leak. Its awkward truth is that most practice management exports do not carry the allowed amount, so what you can usually compute is the gross version, collected divided by billed. Compute the gross one if that is what you have, label it gross, and know that it cannot see a contractual write-off.
How long the money takes. Days in A/R tells you whether a healthy collection rate is arriving this month or in four. State the denominator and the window on the tile, because the widely quoted targets assume a denominator many practices are not using.
What a visit is worth. Revenue per visit moves for reasons you control: coding, service mix, which providers see which patients.
Whether the schedule is full. Provider utilization against your own target. The 80 to 89 per cent range in circulation comes from a vendor-authored MGMA article about exam room management, sourced to that vendor's own whitepaper. It is not a provider benchmark and there is no published one.
Who does not turn up. No-show rate is the only one of the six the genre reliably covers, and the definition still needs stating: same-day cancellations, late arrivals turned away, and backfilled slots all move it.
Who comes back. Patient retention over a window you choose and then write down.
And a seventh, if you get your remittances. Denial rate appears on four of the nine pages, which beats five of my six. Only no-show rate is named more often. It is not in my list because it needs the remittance file rather than the ledger. If you do get your remits, put denial rate and clean claim rate next to the six and treat this section as eight numbers, not six.
Six numbers, one screen. There is a longer version in the five numbers to check every Monday, and the tool-specific versions in Power BI dashboard examples and Excel dashboard examples.
How to read any list of examples, including this one
Find the denominator before you admire the chart
Not every number is a ratio, but the ones that get compared are. If the bottom half of the fraction is a bed-day, an enrolled member or a national population, the dashboard is not about your practice however good it looks.
Ask which system the data comes out of
Not which department cares about it. A staffing dashboard and a no-show dashboard both look like operations; one comes from payroll and the other from the appointment book, and only one of those is a file you already have.
Count the examples, not the adjectives
A page that says comprehensive four times and names three dashboards has told you less than a page that names twelve and describes each in two lines.
Check who wrote it and what they sell
Not to dismiss it. A payer analytics company writes excellent payer dashboards. Read the examples as evidence of what the vendor is good at, then ask whether that is what you are.
Ask which numbers on the screenshot are real
A published example is built to be photographed, and template files ship the same way: some tiles are hard-coded because the data behind them is not in the export. Mine does this too, and the list is on my own data dictionary page. Ask any seller for theirs.
Ask what happens in month two
A screenshot shows a layout. It does not show what you do to refresh it, and refresh is the part that decides whether a dashboard survives.
What my own template does with this
I sell a template, so the obvious objection is that I counted 89 dashboards and concluded the market has a gap shaped like my product. The objection is not that the arithmetic is wrong. It is that the classification rule might be my import format wearing a lab coat.
It partly is, and that is why the fourth question exists and why the range is 9 of 89 at one end and 64 of 89 at the other rather than a single number. My rule is the three sources my file reads. A looser rule, one that asks only whether an organisation can build a dashboard from its own operational data, puts hospital and practice on the same side and pulls in payroll and cost accounting with them: 64 of 89, and on that reading there is no gap at all, only a genre serving a bigger reader than you. The ladder in the second section is the version of this that is about you rather than about my rule.
Concretely, what Clinic Vitals is. Two separate files, not one: a web edition at $39, which is an HTML file you open in a browser and point at an Excel export, and a Power BI edition at $99. Both read the same three sheets and produce the same five pages.
Of the six numbers, the web edition computes four from your data: revenue per visit, provider utilization, no-show rate and patient retention, the last as a cohort table built from first-visit months in your own export. Collection rate it computes in the gross form only, collected divided by billed, and labels it that way, because the export carries no allowed amount. Days in A/R it does not compute at all, and cannot, because the export has one date per visit and no payment date. If days in A/R is the number you came for, my file is not the answer and I would rather say so here than let you find out afterwards.
Three more things about it, following the rules I just gave you. Not every tile is computed. The provider rating is a number you type into the roster, not something the file works out, and my data dictionary page lists which widgets need data the three exports do not carry. That page is where I would look before buying anybody's template, mine included, and if a seller has no equivalent page, ask why. The charge column is optional, and if you leave it out the instruction is to set it equal to revenue, which makes the gross collection rate read 100 per cent. That is a footgun, and it should be labelled on the tile rather than in a data dictionary. And the date basis is the visit date: revenue is cash in the door, stamped on the day the service happened, so recent months read low and revise upward as payments land. That is the same schema limit that stops the file computing days in A/R, seen from the other side.
It also does not do the things in the fourth question. No patient satisfaction, because that needs a survey tool. No telehealth split, because there is no modality column. No referral dashboard, though there is an acquisition source chart that is a smaller thing. No denials, no AR ageing. And nothing on the hospital or payer side of the test: no length of stay, no readmissions, no risk adjustment, no claims analytics.
Month two is the same three exports by hand, about a quarter of an hour. Nothing refreshes itself. That is the honest answer to my own fifth reading rule, and it is the rule my product answers worst.
Sources and method
The sample is the top ten results for healthcare dashboard examples, US English, captured on 20 August 2026. Google served a CAPTCHA to automated requests and was not used, so this is a picture of one index rather than of the search market. DuckDuckGo and Bing returned eight of the same ten hosts, which is weaker corroboration than it sounds, because DuckDuckGo's results are substantially Bing-derived. The two hosts the engines disagree on are Vidi and LeadSquared, and those two carry five of the nine practice examples. Bing's other two hosts were never fetched, so I will not tell you what Bing's ten would give, only that the sample leans on two pages a single engine returned. One of the ten, a Microsoft Fabric community thread, sits behind a bot challenge and could not be fetched, so the analysed set is nine pages.
Counting rule. Seven of the nine pages declare their own list, in their title or their numbering: Arcadia 8, Upsolve 10, Quantize 10, Bold BI 12, Vidi 13, Knowi 7, LeadSquared 8. For those, the page's list is the list. Folio3 numbers six examples and names four more case-study dashboards under their own headings, so it counts as ten. GoodData declares no list, so the eleven dashboards it names in its body text are counted. An earlier version of this article counted only h2 and h3 headings, said 75, and was wrong three ways: it dropped Folio3's four case studies, it took GoodData's three section headings for its whole offering when the page names eleven dashboards in body text, and it missed two Quantize entries because that page writes its headings as h1.
Classification was applied to each example's own title and the page's own description of it. Where a page's description contradicts its heading, the description wins: LeadSquared's centre management dashboard is filed as hospital because the heading says multi-location hospitals and the KPIs include bed occupancy.
The metric count searched each page's plain text for a term list fixed before counting, with every match read in context. Two corrections came out of that reading. Quantize's retention rates is workforce retention, in a sentence about staffing levels and full-time equivalent positions, so it is not counted. And AR days on Knowi was missed by the first pass, which searched only for days in A/R and its longer forms; adding it moves days in A/R from two pages to three and makes Knowi the one page naming four of the six.
One limit worth stating. Counting what a page names is not the same as counting what its screenshots show, and a page could illustrate a practice-scale dashboard under a hospital-scale heading. I read the surrounding description for each of the 89, which reduces that risk rather than removing it.
Questions people actually ask
A single screen reporting an organisation’s own operating numbers on a fixed schedule. The word covers four different businesses, which is why published examples vary so much: a hospital dashboard reports beds, length of stay and emergency department throughput; a payer or accountable care dashboard reports risk and total cost of care across a member population; a public health dashboard reports regional statistics; and a practice dashboard reports the appointment book and the ledger. Only the last runs on data a small outpatient practice owns.
Of the 89 examples named across the nine pages analysed here, nine sit closest to an outpatient practice: five about money, three about the schedule and one for a multi-site group. Four of those five money dashboards also list a metric that needs the remittance file, so take the core and leave the extras. A further 21 are one system away — a survey tool, payroll, remittances, call tracking — and the article names the system for each, because some you could buy tomorrow and some belong to a different kind of company entirely.
Six: net collection rate, days in A/R, revenue per visit, provider utilization, no-show rate and patient retention. Counting across the nine ranking pages, no page names more than four of them, and no-show rate is the only one that appears on a majority. Denial rate, which is not on the list because it needs the remittance file rather than the ledger, appears on four of the nine and so beats five of the six.
There is no published benchmark for outpatient provider utilization. The 80 to 89 per cent range in circulation comes from a vendor-authored MGMA article about exam room management, sourced to that vendor’s own whitepaper, not to a study of providers. Set the target from your own baseline and write it on the dashboard next to the number.
WRITTEN BY
Olha · clinic data analyst
I build the reporting our managers open every morning at a multi-branch medical clinic — and package it so other practices don't have to start from scratch.
Published on 20 August 2026. Two limits worth stating outside the body text. This counts what each page names, in headings or in body text, so a page could illustrate a practice-scale dashboard under a hospital-scale heading and the method would not see it; I read the surrounding description for each of the 89, which reduces that risk rather than removing it. And a search result is a snapshot: these nine pages are the ones ranking on 20 August 2026, not a fixed canon. I sell a template built on the practice-scale numbers, which is exactly why every figure above is a count you can redo from the sources listed.