HonorHealth sees $62M in savings by reducing length of stay

The perioperative solution helped HonorHealth surgeons and schedulers easily find and book OR time in seconds via a digital booking interface. This helped reduce unutilized OR time, enabling HonorHealth to schedule more cases overall.

In addition, intelligent block release nudges helped free up unused time weeks in advance, so schedulers could fill the time with best-fit cases. Machine learning algorithms predict which blocks are likely to go unused and send nudges to the block owner that provides context about the low likelihood of the time being used and the positive impact that releasing it would have on their block utilization rate. Then, once the block is released Qventus automatically offers the available time to the best-fit surgeon based on case type, surgeon preferences/history, and more.

Qventus has helped HonorHealth’s frontline teams improve discharge planning and reduce length of stay. One core feature that enabled this success is Disposition Intelligence, where localized machine learning algorithms auto-populate estimated discharge dates (EDD) throughout a patient’s stay, and identifies opportunities for earlier discharge or lower level of care discharges. This is done using existing EHR data, so care teams don’t need to add additional inputs.

In addition, Flow Prioritization uses machine learning to analyze patient and census data to determine the optimal sequence in which to complete orders that optimize patient flow. Thanks to this, HonorHealth has achieved a 7% increase in the completion of high-priority orders – which correlates to 0.4 fewer excess days.