Provider & Clinical Operations
Capacity, denials, staffing, and contract performance for health systems
Operational and financial decision science for health systems, with mandated staffing ratios entered as hard constraints and every model scoped to stay clear of clinical recommendation. All models run under an executed BAA.
Challenges We Solve
Common pain points that structured decision models eliminate.
Capacity Planned On An Average
Census forecasts presented as a single number collapse exactly when surge arrives. Project the surge and trough alongside the expected case so staffing is sized against the range units will actually see.
Denials Worked One Appeal At A Time
A high denial rate paired with a high overturn rate is a payer behavior problem, not a documentation problem. Separate the two so contracting fixes what appeals cannot.
Leakage Invisible In Aggregate
Revenue never invoiced does not appear in a revenue report. Compare realized yield, charge lag, and underpayment per cohort against contracted terms to find where earned revenue is not landing.
Ratios Traded Against Cost
Staffing models that optimize toward a mandated minimum become patient-safety exhibits. Ratios belong in the constraint set, and an infeasible result is a budget conversation, not a waiver.
Use Cases
How teams use DecisionLedger to make better decisions.
Forecasts census across surge and trough scenarios and sizes flex staffing against the surge case rather than the expected case, smoothing electives into the projected trough.
Premium labor spend reduced without adding fixed capacity
Separates payer-behavior denials from documentation denials by pairing denial rate with appeal overturn rate, then takes the high-overturn payers to contract renegotiation.
Denials removed at source instead of appealed one at a time
Runs unit-level staffing optimization with mandated ratios as hard floors, and escalates the budget when the problem returns infeasible rather than relaxing a ratio.
Coverage decisions that keep the safety constraint binding
Measurable Impact
Based on platform benchmarks across early adopters.
Capacity Planning
Single-point census forecast
Surge, expected, and trough range
Denials
Appealed individually
Root-caused by payer and service
Revenue Integrity
Leakage invisible in aggregate
Detected per cohort
Staffing
Ratios traded against budget
Ratios as hard constraints
Built for Health Systems
Operational and financial decisions, with the clinical boundary drawn in code rather than in policy.
Capacity Intelligence
Census projection across surge, expected, and trough scenarios, sized for flex staffing rather than for an average day.
Revenue Integrity
Denial root cause and leakage detection scoped to underpayment against contract. It never recommends a code, which keeps recovery work clear of False Claims Act exposure.
Constrained Optimization
Staffing and OR block allocation with mandated ratios and service commitments as hard constraints, and an infeasible result reported as the finding.
Connects With
Part of 150+ native integrations across CRM, marketing, finance, HR, ecommerce, and analytics
Salesforce
HubSpot
Stripe
Shopify
Google Analytics 4
Workday
QuickBooks
Snowflake
Slack
Zendesk
GitHub
Meta Ads
Mailchimp
NetSuite
Jira
Power BISalesforceHubSpotStripeShopifyGoogle Analytics 4WorkdayQuickBooksSnowflakeSlackZendeskGitHubMeta AdsMailchimpNetSuiteJiraPower BIFeatured Models
Pre-built decision models ready to run with your data.
Claim Denial Root Cause
Finds payer, department and code combinations whose denial behavior departs from the population, ranked by recoverable dollars.
Clinical Staffing Ratio Optimizer
Allocates nursing and technician coverage against acuity-adjusted demand, with mandated ratios entered as hard constraints.
Hospital Census & Capacity Forecast
Projects inpatient census across demand scenarios so staffing, diversion and elective scheduling decisions are made against a range rather than a point estimate.
OR Block Utilization Optimizer
Allocates operating-room block time across services by realised utilization and contribution margin, under turnover and staffing constraints.
Patient No-Show Prediction
Evaluates overbooking strategies against attendance probability, trading idle clinic capacity against patient wait and overtime.
Prior Authorization Burden
Compares approaches to prior-authorisation handling on administrative cost and care-delay impact by payer and service line.
Revenue Cycle Leakage Detector
Detects charge-capture gaps, contractual underpayment and write-off drift by comparing realised yield against expected yield per cohort.
Value-Based Contract Performance
Projects position against quality and total-cost benchmarks in a risk contract, with shared-savings and downside-risk scenarios.
How It Works
Three steps to structured, auditable decisions.
Connect Operational & Revenue Data
Pull census and scheduling from the EHR, claims and remittance from the clearinghouse, and contracted rates from managed care, under an executed BAA with PHI handling in place.
Model Capacity and Yield
Forecast census across demand scenarios, detect denial and leakage outliers by payer and service line, and optimize staffing and block allocation inside mandated constraints.
Act Contractually, Not Just Operationally
Take high-overturn payers to renegotiation, route low-overturn denials to documentation, and model risk-contract position before the benchmark is agreed.
Your census forecast is an average, your denials are worked one appeal at a time, and your staffing model quietly treats a mandated ratio as a target. The data to fix all three already exists.
Spreadsheet census forecasts
A single planned number that offers no range to staff against
Appeal-by-appeal denial work
Treating a payer behavior pattern as thousands of individual documentation problems
Calendar-driven block schedules
Block time allocated by history and negotiation rather than realized utilization
Generic staffing tools
Optimizers that treat a mandated ratio as an objective to trade against cost