Infrastructure for Readmission Risk Prediction

Readmission risk prediction requires explicit, current documentation of CMS HRRP (Hospital Readmissions Reduction Program) definitions, condition-specific inclusion/exclusion criteria, and post-discharge intervention protocols. Case managers need findable, standardized guidance on which risk scores trigger home health referral vs. follow-up call vs. SNF placement. CMS measure specifications are formally published, providing the scoring anchor, but organizations must additionally document their intervention escalation protocols and social determinants screening workflows.

Systematic capture at discharge is mandatory: diagnosis codes, prior utilization history, discharge disposition, follow-up appointment scheduling, and social determinants screening results must all be captured via structured discharge workflow templates. EHR-mandated discharge documentation fields enforce baseline capture. The ML model trains on this discharge-time data to predict 30-day outcomes, requiring consistent field completion across all discharges—not ad-hoc documentation.

Readmission prediction requires consistent schema across all discharge records: ICD-10 diagnosis codes, prior admission dates, discharge disposition codes, and follow-up plan fields must share uniform structure across encounters and facilities. HL7 HQMF-based measure specifications provide the definitional framework. The model needs to compute features like 'number of ED visits in prior 6 months' and 'days since last admission'—calculations requiring temporally structured encounter records with consistent field definitions.

The readmission prediction model must access EHR discharge summaries, prior admission histories, ED utilization records, pharmacy medication lists, and care management workflow tools via API. Risk scores must be surfaced in the case manager's workflow at the point of discharge planning—not in a separate analytics portal requiring manual log-in. API access to CRM and population health tools enables the recommended intervention worklists to reach care coordinators in their existing tools.

Readmission risk model maintenance follows a periodic review cadence—typically triggered by CMS rule updates or annual HRRP penalty calculations—rather than event-triggered updates. The model's risk factors (diagnosis weights, utilization patterns) evolve slowly enough that scheduled recalibration aligned with CMS annual rulemaking is operationally acceptable. This differs from sepsis detection where real-time currency is clinically critical. However, model drift from population changes (e.g., post-COVID utilization shifts) may go undetected between scheduled reviews.

Readmission prediction requires API-based connections between EHR (clinical and utilization data), care management platform (intervention assignment), HIE or external admission feeds (community readmission events), and post-acute network systems (SNF, home health). The model must identify readmissions that occur outside the system—patients who were discharged and readmitted to a competing hospital—requiring HIE integration. This multi-system context assembly is necessary for accurate 30-day readmission attribution.