Healthcare Operations Intelligence
Your operation should know what’s happening before you ask.
Claritus.One connects the signals across every location, determines what actually deserves attention, and coordinates the people and AI agents responsible for acting on it. It keeps working whether or not anyone is looking at a dashboard.
Good morning.
64 centers are scheduled to open today. Three need attention.
- Coverage
- 0.82
- below 1.00
- Proj. wait
- 41 min
- over target
- Overtime
- $0
- unchanged
- Reading overnight signals across 64 centers…
01 · The raw material
Your organization is already producing the answers.
Every one of these is a real operational signal. The problem is that they arrive on nine different clocks, in nine different shapes, to nine different people.
So the question stops being “do we have the data.” You have it. The question is whether anything in your stack is capable of holding all of it at once, and noticing when two of them disagree.
A coverage gap is almost never invisible. It is just never in the same place as the forecast that predicted it.
Yesterday’s visit report
EMR export · Emailed at 6:00 AM
Provider schedule
Scheduling system · Published weekly
Labor hours
Payroll · Available after the pay period
Overtime tracker
Regional spreadsheet · Updated when someone remembers
Wait times
Front desk observation · Anecdotal
Call abandonment
Telephony platform · Weekly summary
Denials
Revenue cycle platform · Monthly close
Open positions
HRIS · Requisition status only
Site escalations
Group text thread · Whenever it is urgent
One operational model
02 · The shift
Most software stops at the first question.
Reporting tells you what happened. It does not tell you why, and it certainly does not do anything about it. Those are three different jobs, and an operator running 60 locations needs all three.
See what matters.
Understand why.
Act on it.
Door-to-door time at Westlake is 47 minutes.
This is what a dashboard gives you, and it is genuinely useful. It is also where most of the stack stops. Somebody still has to notice the cell, decide it matters, and go find out why.
Five-day trend · WLK · no explanation attached
03 · Intelligence
Dashboards tell you where to look. Claritus tells you what deserves attention.
Every morning the platform has already read the night. It compares actuals to forecast, this week to the same week last year, each site to its market, and every published schedule to the demand it will actually meet. What comes out is not a refreshed report. It is a short list, in priority order, with the reasoning attached.
Good morning.
Yesterday finished 0.0% above forecast across the network, on 0 visits. North Atlanta accounted for most of the variance, and it was volume rather than acuity. Two items need attention before today’s opening, and one from Tuesday has closed.
Under the brief
Anomaly detection that knows the pattern
A Saturday at 60% of Tuesday is normal. A Tuesday at 60% of Tuesday is not. The baseline is per site, per day-part, per season.
Forecasting that reaches the schedule
Predicted demand is only useful if something compares it to the hours you actually published. That comparison runs nightly.
Reasoning across systems, not inside one
Labor variance rarely explains itself from payroll alone. The answer usually sits between the schedule, the forecast, and the visit log.
Explanations grounded in the numbers
Every sentence in a brief traces back to a metric you can open. Nothing is asserted that cannot be inspected.
04 · Intelligence becomes work
An insight nobody acts on is just a well-written complaint.
This is the part most operations software leaves to you. It surfaces the finding and then hands you a coordinator’s afternoon: pull the schedule, check who is credentialed, check who is near their hours, call two people, update the site.
In Claritus.One that sequence is the product. The observation carries everything needed to execute it, and the work happens under the same permissions and approval rules your organization already runs on.
Westlake and Mountain Brook are trending below minimum provider coverage tomorrow from 4–7 PM.
One click turns this observation into a task queue
Staffing Agent
Investigate coverage · 2 sites · 4–7 PM tomorrow
- Read the published schedules for both sites
- Compare against the latest demand forecast
- Find providers who are eligible and available
- Check overtime and weekly hour limits
- Draft coverage options and rank them
- Approval gate · regional operator
05 · AI Workforce
Software that doesn’t just report the work. It does the work.
Not assistants, and not a chat window. These are members of a digital operations team. Each one has a role, an objective it is measured against, a queue you can look at, permissions it cannot exceed, and an escalation policy that decides when a human has to weigh in.
- Agents deployed
- 5
- Tasks today
- 287
- Awaiting a human
- 1
- Coordinator time returned
- 21h 55m
Open a row to see how each agent is bounded
The governing idea
Autonomy is granted, not assumed. An agent that can correct a member ID does not get to publish a schedule. An agent that can draft a schedule does not get to publish it either. You decide where each line sits, and every action an agent takes stays on the record with the reasoning that produced it.
06 · Data & integrations
Don’t replace the systems your organization runs on. Make them work together.
Nobody is migrating an EMR because an analytics vendor asked them to. Claritus.One reads from the systems you already have and writes back into them, which means the schedule of record stays the schedule of record.
Intelligence
Briefs, anomalies, forecasts, explanations
Operations
Workflows across scheduling, flow, revenue cycle
AI Workforce
Agents executing inside their permissions
Operational context layer
One model of locations, providers, shifts, visits, hours, and money
Every system names things differently. This is where a provider, a site, a shift and a visit become the same entity regardless of which platform reported them.
Systems of record — they stay exactly where they are
Systems of record
- EMR
- Scheduling
- HRIS
- Payroll & Time
- Revenue Cycle
- CRM & Engagement
- Contact Center
- BI & Warehouse
- Spreadsheets
Operational context layer
A provider, a site, a shift and a visit become the same entity no matter which platform reported them.
What Claritus does with it
Intelligence
Briefs, anomalies, forecasts, explanations
Operations
Workflows across scheduling, flow, revenue cycle
AI Workforce
Agents executing inside their permissions
Why the middle layer matters
A point-to-point integration gets data from one place to another. It does not decide that the “Dr. Okafor” in the schedule and the “OKAFOR, J” in payroll are one person, or that a site called Westlake in one system and WLK in another is one location. Until something resolves that, cross-system reasoning is guesswork. That resolution is what the context layer is for, and it is why the rest of the platform can say anything useful at all.
07 · Built for urgent care
This is the day. Not a quarterly review of the day.
Ten things that happen across a 60-site network between opening and close. None of them wait for a meeting, and no human is going to catch all of them by watching.
Opening readiness
Who is actually going to unlock the door at 62 sites, and which two are one call-off away from not opening.
Call-offs
A text to a site lead is not a staffing decision. Somebody has to know the coverage impact within minutes, not at end of day.
Intraday volume
Demand moves in hours, not quarters. A morning running 11% hot changes what the afternoon needs.
Provider coverage risk
One provider with three declined shifts and a pending PTO request is a pattern, not an incident.
Patient queues
Nine waiting at one site while a clinic five miles away sits at a 22-minute wait is a routing problem, not a capacity problem.
Regional performance
Whether a market is genuinely underperforming or simply has a harder mix, and which of the two the numbers actually support.
Overtime, before it happens
Overtime discovered in payroll is a report. Overtime caught on Tuesday is a decision.
Missed breaks
A compliance exposure that shows up as a timekeeping edit weeks later, if it shows up at all.
Closing decisions
Whether the last hour is worth staffing at this site, tonight, given what walked through the door at 5.
Tomorrow’s schedules
Checked against the latest forecast while the operation is closed, so the morning starts with decisions instead of discovery.
Request a demo
You already have the data. Claritus.One turns it into an operation that can think and act.
Bring the two or three operating questions that cost you the most right now. We will walk through how Claritus.One answers them against a network shaped like yours, and where the AI Workforce would take over the work behind them.