Infrastructure for Length of Stay Prediction

LOS prediction requires documented definitions of discharge criteria and planning procedures—what constitutes 'discharge ready,' which diagnosis groups have evidence-based LOS benchmarks, and how payer coverage limitations map to expected stay durations. Discharge planning procedures are defined and high-risk identification protocols established in the baseline. However, the LOS prediction logic—how social determinants modify clinical benchmarks, which comorbidity combinations extend expected stays—is not formally documented. The model applies statistical patterns against documented clinical benchmarks.

LOS prediction accuracy requires systematic capture of admission diagnosis, comorbidities, social determinant assessments, payer type, and daily clinical trajectory updates. The UM function captures admission reviews and continued stay reviews through required documentation fields in the UM software. Daily clinical status—improving, stable, worsening—captured through structured nursing assessments and physician notes. This systematic multi-day capture provides both the training dataset and the real-time inputs for daily LOS updates as the clinical course evolves.

LOS prediction models require consistent schema: diagnosis codes (ICD-10) standardized, comorbidity indices calculated from structured fields, discharge disposition categories uniformly typed, and social determinant screening results coded categorically. The baseline confirms discharge disposition categories are defined and UM denial reason codes are standardized. This schema enables the model to compute LOS predictions across diagnosis groups and compare actual vs. predicted variance systematically across the patient population.

Daily LOS prediction updates require API access to current clinical data: latest vitals trends, outstanding orders, pending consult results, and social work placement status. UM software integrates with the EHR for chart review, and case management worklists receive risk scores. API-based access enables the model to refresh LOS predictions each morning as new clinical information is documented overnight, populating daily case management prioritization lists with current discharge delay risk scores.

LOS benchmarks and model weights require periodic recalibration as patient population acuity changes, payer mix shifts, and post-acute resource availability evolves. The UM function updates criteria when InterQual/Milliman releases new versions and recalibrates readmission risk models periodically. However, LOS model recalibration is not event-triggered—it occurs when someone notices systematic prediction error rather than when input conditions change. This periodic, complaint-driven maintenance is sufficient for planning purposes but limits prediction currency.

LOS prediction integrates the EHR (clinical data), UM software (review workflow and risk scores), case management worklist (daily prioritization), and capacity planning tools (census management). API-based connections enable predicted LOS to flow to bed management dashboards and case management prioritization in near real-time. LOS variance data—actual vs. predicted—feeds back to the model for continuous calibration. This integration makes LOS prediction actionable rather than informational, embedding it in the daily discharge planning workflow.