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For CEOs, CFOs, COOs & their teams

What your team can ask.

Real questions. Real answers. The kind of work that used to take an analyst, a deck designer, and a long Tuesday afternoon. Now done in the time it takes to read this paragraph.

Why did labor spike yesterday? Which buildings are below census targets? Show agency usage by region. Where are falls trending this quarter? Where is Medicare A trending? What does our relationship look like with Aetna? How are admissions from St. Jude vs. this time last year?
CEO CFO Board Strategy Operations
For the CEO

"What should I be focused on this month?"

Monday morning, before you've opened a single tab. Ask once. Get a 5-bullet executive briefing that pulls financial, operational, and clinical signals across every building, calls out what's drifting, and tells you what's worth your time today.

  • Surfaces concentration risk before it shows up in earnings.
  • Flags the buildings, regions, or measures that need attention this month.
  • Recommends the follow-up questions you'd ask if you had three more hours.
Was: 3–4 hours of digging. Now: faster than your first coffee.
Claude Claude · DashboardHC
What should I be focused on this month?
get_data_modeldata_review
Executive briefing · April 2026
  • Mountain region: occupancy down 0.6 pts vs. last month. Three buildings flagged.
  • AR > 90 days trending up at Riverview and Oakdale. Revenue at risk.
  • Falls down 4 vs. last month, portfolio-wide. Quality team's intervention is working.
  • Labor PPD over budget at 3 buildings. Agency utilization is the driver.
  • Bright spot: Northeast census up 21 net. Strong admissions month.
For the CFO

"Walk me through last month's financials."

Sit down with a coffee. Have your AI walk you through the month: revenue by region, AR concentration, labor variance, contribution margin by building. Drill into anything that doesn't add up.

  • Conversational drill-down on any metric, building, or payer.
  • Comparisons against budget, prior month, and same month last year, on demand.
  • The AI shows the math, so your team can verify the answer.
Was: half a day with your analyst. Now: a conversation while you eat lunch.
ChatGPT ChatGPT · DashboardHC
Walk me through last month financials by region. What's surprising?
get_data_modelfinancial_reviewdata_review
Revenue
$24.8M
▲ 2.3% vs. prior
AR > 90
12.8%
▼ 0.6 pts
Margin
14.2%
▼ 0.4 pts

Surprise: Mountain margin down 0.9 pts, entirely from labor PPD overrun concentrated in the region. Revenue's actually up there. Want me to dig into agency utilization?

For the executive team

"Prepare the board pack for next month."

Tell the AI which month and which sections you want. It pulls financials, operations, clinical, and forward-look data, drafts the narrative, and produces a deck-ready outline you can drop into PowerPoint or Slides. You polish, you don't author from scratch.

  • Structured outline with slide-by-slide guidance.
  • Real numbers, real comparisons, ready for board-room scrutiny.
  • Customizable narrative tone, investor-friendly or operationally direct.
Was: 2 weeks of analyst time. Now: 10-minute conversation + 30 minutes of polish.
Claude Last-month Board Pack
1
Executive Summary
Three big themes · bottom-line narrative
2
Financial Performance
Revenue, margin, cash · vs. budget & prior
3
Operations
Census, occupancy, and admission trends by region
4
Clinical & Quality
Falls, wounds, rehospitalizations
5
Payer Mix & Risk
Concentration, growth, opportunities
6
Forward Look & Asks
Q1 priorities · board approvals
For Strategy & Finance

"Run a payer-partnership analysis."

Going into a payer renegotiation? Ask the AI to break down your relationship: census trend by payer, length-of-stay, outcomes, denial patterns, payer concentration risk. Get the data story you need before you walk into the room.

  • Pinpoints which payers are actually growing your business.
  • Surfaces concentration and renegotiation risk early.
  • Builds the data foundation for the partnership conversation.
Was: a strategy consultant. Now: an afternoon.
Claude Payer-partnership analysis
Run a payer analysis on Plan A. Trends, concentration, renewal risk.
get_data_modelget_dimension · payerask_data · payer_mixask_data · los
Plan A · YTD vs. prior year
  • Census share: 22% → 28% (+6 pts). Your fastest-growing payer.
  • LOS: 24 days, 3 days above portfolio average.
  • Denials: 4.1%, below your overall 5.6%.
  • Concentration risk: Plan A now 28% of revenue at 4 buildings. Renewal in Q3.
Beyond chat: agents your team builds

The next layer: agents your team builds.

The use cases above are conversations. The next one is a blueprint. We provide the customizable warehouse and the MCP server. Your team builds the agents, in the AI you already use, with the workflow that fits your business. We think the next decade of operators belongs to the ones who lean into building this themselves.

An agent your team can build

Build an exception-watch agent.

Want a daily exception report? Your team can build an agent against DashboardHC's MCP that watches occupancy, AR, labor PPD, falls, and quality outliers across every building, then pings the right operator at 8am with the metric that's drifting, the buildings affected, and a recommended next step. We provide the data and the MCP; your team builds the workflow.

  • Per-operator digests: only the buildings each person manages.
  • Threshold-based or AI-judged anomaly detection: your call.
  • Posts to email, Teams, Slack, or wherever your team lives.
Was: caught after billing dropped or the survey came in. Now: when your team builds this, it pings at 8am every morning.
Claude Sample exception report · built by your team
Run nightly · delivered to Operations
  • ⚠ Riverview. Census fell to 84.2% (target 89%). 2nd day in a row. Suggest: review admissions pipeline.
  • ⚠ Oakdale. AR > 90 jumped 1.8 pts to 16.3%. Suggest: ping billing lead.
  • ⚠ Hillcrest. 3 falls in 48 hours. Suggest: pull rounds and assess.
  • ✓ All other buildings within thresholds.
The point

This is what AI changes.

None of these used to be impossible. They were just expensive in time, in headcount, in frustration. AI didn't invent the work. It collapsed the cost of doing it.

The operators who lean in get every advantage of the work, at none of the old cost. The ones who don't are paying yesterday's price for yesterday's answer.

Want to see one of these live?

30 minutes. We'll pick your favorite from this list, run it live against a real DashboardHC warehouse, and walk through what your version would look like.

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