Case Study: Large Hospital Group cuts costs and boosts bed utilization by predicting patient length-of-stay with Cloudera

Intel and Cloudera Help a Large Hospital Group Allocate Resources by Predicting Patient Length-of-Stay

A large Hospital Group partnered with Intel and Cloudera to address unpredictable patient lengths-of-stay that drive up operating costs and reduce bed and staff utilization—an issue made more urgent by Medicare’s prospective payment system. Hospital administrators needed reliable, admission‑time discharge predictions to reduce occupancy variance, allocate resources more efficiently, and avoid policies that could raise readmission rates.

By combining EMR and socioeconomic data in a Cloudera enterprise data hub and applying Intel‑built Random Forests models, the group improved length‑of‑stay prediction accuracy by 25–40% (bringing two‑day window accuracy to about 80%). The solution improved scheduling for ~30,000 patients, increased facility utilization by ~5% (potentially serving ~10,000 more patients annually), and delivered significant savings—around $120M in annual cost reductions and roughly $15M in medical service cost savings.