Case Study: OhioHealth achieves $2.2M in savings and optimizes nurse staffing with SAS Analytics

Reducing costs by forecasting demand for nursing resources

OhioHealth, a not‑for‑profit health system with 29,000 staff across 11 hospitals and 200+ facilities, faced costly staffing shortfalls when nurses and other clinicians left or moved internally—forcing reliance on overtime and agency labor that runs 1.5–2x the cost of a full‑time employee. To maintain care quality while lowering costs, the organization needed to forecast demand for nursing resources and plan hiring well ahead (about a six‑month lead time) to avoid reactive, expensive staffing responses.

Using SAS Analytics, OhioHealth built an ensemble, machine‑learning forecasting model that mapped patient demand against current and posted positions and guided proactive hiring at Riverside Methodist Hospital. The data‑driven approach saved about $2.2 million in the first fiscal year, stabilized staffing and morale, helped achieve top‑quartile patient satisfaction, and shifted the organization from reactive staffing to proactive, analytics‑based workforce planning.

OhioHealth

Chris Clinton

System Vice President of Value Transformation