Enhancing Physician Satisfaction and Patient Safety Through an Artificial Intelligence–Driven Scheduling System in Anesthesiology

The bottom line

Replacing manual scheduling with an algorithmic system was followed by higher physician satisfaction, more approved vacations, and over a thousand fewer intraoperative care transitions in six months — a rare case where a scheduling change registered on a patient-safety metric. This supports treating scheduling infrastructure as clinical infrastructure. As a single-site before-after study, concurrent trends cannot be fully excluded.

What the source found

Before-after implementation study at Ochsner Health replacing manual spreadsheet scheduling with algorithm-driven system (400+ rules); six months post-implementation: physician satisfaction scores rose, vacation approvals increased, and >1,000 fewer intraoperative transitions of care compared to prior approach — linking scheduling change to a measurable patient-safety metric.

Limitations and applicability

Single institution; before-after design without randomized control; secular trends cannot be fully ruled out; generalizing algorithm approach requires context.

Population and setting

Anesthesiology department physicians at Ochsner Health (New Orleans)

Used in FairShare guides

• Can AI Scheduling Actually Reduce Burnout and Improve Safety?

About this evidence

Research question / practical issue

How does this source inform Break Relief, Fatigue, and Safe Handoffs and Leadership, Culture, and Fairness for the population it studied?

Study design, evidence tier, and source class

Peer Reviewed Before After Study · primary source · evidence tier 2

Source type: Peer Reviewed Before After Study · primary · Evidence tier 2

Topics: Break Relief, Fatigue, and Safe Handoffs, Leadership, Culture, and Fairness

Audience: Anesthesiologists, Department Leaders