DTS Optimization identifies missing diagnoses that directly affect DTS grouping and reimbursement — making invisible revenue loss visible and correctable.
DTS classification depends on documented diagnoses. When diagnoses are missing or incomplete, classification is incorrect — even when the care was fully provided. The loss is structural, not incidental.
Across departments, a meaningful share of cases is undercoded. The financial impact is structural and accumulates quietly — invisible until you measure it.
"Up to 10% of hospital revenue is lost to undercoding."
Doctors focus on care. Documentation becomes secondary — pushed to the end of the shift, fragmented across systems, squeezed by time. The result is structural undercoding.
High workload
Time pressure
Complex documentation requirements
Fragmented information across systems
DTS Optimization analyzes patient context and existing documentation, identifies diagnoses that were never recorded but materially affect DTS classification, and presents them with rationale — for the doctor to accept.
Hospitals capture revenue that already corresponds to care delivered. Coding becomes more consistent across teams. Care does not change — but the classification finally matches it.
"Revenue loss is invisible. We make it visible — and correctable."
The system starts at diagnosis capture. The horizon adds per-case financial impact and department-level pattern detection.
Identify missing diagnoses that affect DTS classification.
Estimate the revenue at stake for every case the system flags.
Surface department-level undercoding patterns and outliers.
Revenue loss due to undercoding is often invisible. Discens Machina makes it visible — and correctable.
A demo walks through how DTS Optimization identifies missing diagnoses in your own historical cases — under your control, on your infrastructure.