Healthcare Finance Intelligence — Cubot BI
Solution

Healthcare
Finance Intelligence

Healthcare organisations generate financial data across every department, specialty, and payer relationship. The challenge is bringing it together — connecting clinical operations to financial outcomes, and turning the combined picture into something a CFO, a finance director, or a department head can act on.

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Key financial indicators
82%
Bed Occupancy Rate
Across all wards and specialties
↑ 3.2pts MoM
4.1 days
Avg Length of Stay
Vs 4.4 days same period last year
↓ 0.3 days
94%
Claims Acceptance Rate
First-pass acceptance by payer
↑ 1.8pts
18 days
Days in AR
Average receivables collection cycle
↑ 2 days vs target
74%
Theatre Utilisation
Scheduled vs available theatre time
Stable
6.2%
Denial Rate
Claims denied by payer — by reason
↓ 0.8pts
EBITDA
Margin by Specialty
Profitability drilled to department level
↑ 1.4pts QoQ
18%
Agency Staff Spend
As % of total workforce cost
↑ Watch
Capabilities

Finance and operations in one view

Clinical decisions drive financial outcomes. The platform connects operational data — beds, theatres, procedures, staff — to the financial model, giving finance teams the full picture rather than just the accounting output.

Revenue Cycle Intelligence
Claims submission, denial rates, denial reasons by payer and procedure, resubmission tracking, days in AR, and collection rates — tracked automatically from billing system data.
Payer Mix Analytics
Revenue contribution by insurance payer, government scheme, and self-pay — with margin analysis per payer type. Know which relationships are profitable and how the mix is shifting.
Cost per Episode
Operational costs — staff, consumables, equipment, facility — allocated to the patient and episode level. Know which procedures and specialties are profitable and which are not.
Bed & Resource Utilisation
Occupancy rates, average length of stay, theatre utilisation, ICU efficiency — operational metrics linked directly to financial outcomes for each ward and specialty.
Workforce Cost Analytics
Headcount, grade mix, agency vs permanent ratio, overtime patterns, and productivity per staff grade — linked to cost and output per department.
Supply Chain & Pharmacy
Drug and consumable spend, stock wastage, procurement patterns by department. Pharmacy spend tracked against budget and benchmarked across periods.
Scenario Intelligence

Pre-built scenarios on live data

Financial planning in healthcare is typically done in spreadsheets disconnected from real operational data. This platform ingests your financial model templates, maps them to live data, and pre-calculates scenario outcomes — so the CFO sees the impact of key variables on EBITDA without manually rebuilding the model each time.

Scenarios are pre-calculated from your own operational and financial data each cycle. The starting point is always current — not a number someone typed in last quarter.

Variables modelled
Volume
Bed Occupancy Rate
Impact of occupancy change on revenue and fixed cost coverage
Revenue
Payer Mix Shift
Margin impact of private vs government vs self-pay ratio change
Revenue
Reimbursement Rate
P&L sensitivity to government tariff or payer contract change
Cost
Agency Staff Ratio
Cost saving from reducing agency dependency
Operations
Length of Stay
Throughput and revenue impact of 0.5 day LOS reduction
Growth
New Service Line
Projected revenue vs cost for a new specialty or procedure
Scenario comparison — EBITDA impact Pre-calculated
Scenario
Revenue
EBITDA
Rating
Occupancy 88% + Private 45%
High volume, favourable payer mix
+12.4%
+18.2%
Best
Occupancy 85% + LOS −0.5 days
Improved throughput, current payer mix
+7.1%
+9.4%
Upside
Current — Base Case
Actuals as of last reporting period
—
—
Base
Tariff cut −5% + Agency +20%
Adverse reimbursement and cost pressure
−6.3%
−14.8%
Worst
Key sensitivity finding

Payer mix has 3× the EBITDA sensitivity of occupancy rate in the current cost structure. A 5% shift toward private payers outperforms a 5% occupancy improvement on margin impact.

Revenue Cycle

From procedure to payment

Every step from clinical encounter to cash collection is tracked — giving finance teams visibility into where revenue is leaking, which payers are slow, and where denial rates can be reduced.

01
Procedure Completed
Volume tracked
02
Claim Submitted
94% first-pass
03
Denial Rate by Payer & Reason
6.2% denied
04
Resubmission Tracking
Recovery rate
05
Days in AR by Payer
18 days avg
06
Cash Collected
Collection rate
Payer Mix

Revenue and margin by payer

Not all revenue is equal. Payer mix determines margin as much as volume. The platform tracks revenue contribution, collection speed, and profitability per payer relationship — and flags when the mix is shifting.

Private Insurance
42%
High
Government Scheme
35%
Mid
Self-Pay
15%
High
Corporate / TPA
8%
Low
Data Sources

Connects to your systems

Financial and operational data from across the organisation — billing, clinical, HR, supply chain — unified into one governed data model without manual consolidation.

Billing
Hospital Billing System
Claims, invoices, payer codes, procedure billing, denial data
Clinical
Hospital Management System
Admissions, discharges, bed occupancy, procedure records, LOS
Finance
ERP & General Ledger
Cost centres, departmental accounts, budget vs actuals
Workforce
HR & Payroll System
Headcount, grade, agency vs permanent, overtime, productivity
Supply
Pharmacy & Procurement
Drug spend, consumable usage, stock wastage, purchase orders
Receivables
Accounts Receivable
Outstanding claims, ageing analysis, collection tracking by payer
Theatre
Theatre & Scheduling System
Utilisation, cancellation rates, procedure mix, turnaround times
Contracts
Payer Contract Data
Reimbursement rates, tariff schedules, contract terms by payer

Discuss your healthcare finance priorities

Get in touch to explore how the platform maps to your data sources, reporting requirements, and planning needs.

info@cubotbi.com
www.cubotbi.com
CUBOT BI for Healthcare