WRIE — Workforce Retention Intelligence Engine
WRIE is the operating record for labor in senior housing & care. It evaluates 40+ behavioral signals against the same governed operating record clinical, scheduling, and payroll write to — so retention interventions reach the right employee before turnover is locked in.
Predict turnover 90 days out
WRIE surfaces predicted 90-day attrition risk at the employee level with documented pilot accuracy. Signals include schedule volatility, shift acuity load, peer cohort movement, overtime patterns, missed shifts, and clock anomalies — read directly from the canonical shift record, not estimated from a separate HRIS export.
Route interventions inside the same record
High-risk employees flow into targeted retention workflows — manager 1:1s, schedule adjustments, float-pool reassignments, and compensation reviews — and every action is logged against the same shift and employee record. HR, the DON, and the scheduler see the same workforce picture in real time.
Pair with HPRD targeting and agency controls
WRIE’s retention signal feeds the same operating record that powers acuity-weighted scheduling, HPRD targeting, and agency-spend controls. When retention risk and coverage exposure intersect, leadership intervenes before the gap requires agency fill.
Frequently asked questions
- How does WRIE ensure predictions are used ethically?
- WRIE is designed with strict ethical guardrails. Predictions inform supportive interventions only — schedule flexibility, mentorship, development opportunities — never punitive actions. Individual risk scores are visible only to direct supervisors and HR, with aggregate-only reporting for executives. The system includes bias detection auditing and transparent methodology documentation.
- What data does WRIE analyze?
- WRIE analyzes 40+ signals already flowing through SeniorCRE: scheduling patterns, time & attendance, PTO usage, shift swap frequency, overtime trends, certification status, and peer interaction patterns. No additional data entry is required beyond normal operational workflows.
- How accurate are the turnover predictions?
- We do not publish a model-accuracy figure; it is withheld pending paid-pilot validation. The model is continuously retrained on intervention outcomes — tracking whether recommended actions actually reduced flight risk — improving accuracy over time as it learns facility-specific patterns.
- Can WRIE differentiate between voluntary and involuntary turnover?
- Yes. WRIE models are trained separately for voluntary departure risk (the primary use case) and involuntary termination patterns. The system focuses retention interventions on employees at risk of voluntary departure, while flagging systemic issues that may indicate performance management gaps.
https://seniorcre.com/ai-systems/wrie