Infrastructure for Admission Appropriateness Prediction

Admission appropriateness prediction requires formally documented, queryable clinical decision logic: payer-specific admission criteria (InterQual, Milliman levels of care), diagnosis-severity thresholds for inpatient vs. observation, and documentation requirements for justification. This must be structured as machine-readable rules—not a PDF of InterQual criteria but a queryable decision framework that the model can interrogate with patient severity indicators. Without formal logic at L4, the AI cannot reliably distinguish inpatient-qualifying from observation-qualifying presentations across payer plans.

Admission appropriateness prediction requires systematic capture of clinical indicators at presentation: vital signs, lab values, imaging results, presenting diagnosis, and social determinants. The UM function systematically captures admission reviews, continued stay reviews, and high-risk screening results through required documentation fields. This structured clinical capture, combined with payer authorization outcomes (approved, denied, appealed), provides the training dataset the model needs to learn which clinical-payer combinations predict admission appropriateness and denial risk.

The model requires formal ontology mapping clinical entities to admission criteria: Diagnosis → Severity.Score → AdmissionCriteria.Level → Payer.Plan → LevelOfCare.Recommendation. Social determinants must be structured fields (home support absent/present, transportation available) not free-text narrative. Discharge disposition categories (inpatient, observation, outpatient, SNF) must be formally typed. Without this ontological structure, the model cannot compute appropriateness scores that integrate clinical severity with payer-specific criteria and social needs—the core multi-variable prediction task.

Admission appropriateness prediction requires the model to access the EHR for real-time clinical data (vitals, labs, imaging), the UM software for current review status, and payer-specific admission criteria databases. API-based access to the EHR and UM system enables the model to evaluate admission appropriateness at the point of decision—when the ED physician is determining level of care. Without this API access, the model operates on batch data pulled hours later, too late to influence the admission decision.

Admission criteria evolve when UM vendors release new InterQual/Milliman versions, when CMS updates inpatient admission guidelines, and when payer contracts change. Event-triggered updates ensure the model's decision logic reflects current criteria rather than superseded versions. The model also recalibrates when denial pattern shifts signal that payer application of criteria has changed—a common occurrence with Medicare Advantage plans that apply criteria more aggressively than documented.

Admission appropriateness prediction integrates the EHR (clinical data source), UM software (review workflow), payer authorization systems (approval/denial tracking), and case management worklist (recommended level of care delivery). API-based connections enable the model to pull clinical indicators at admission, apply appropriateness logic, and push recommendations to the case manager's workflow—creating a closed decision support loop. Payer authorization outcomes feed back to recalibrate the model's denial risk scores.