Scheduling Wizard

Roadmap & Position in Healthcare Operations

Automates physician scheduling for academic medical centers with proprietary optimization.

Company Overview

Builds logistics infrastructure to automate physician scheduling and care coordination for academic medical centers using a proprietary Optimization Programming Language.

What They're Building

The company's public product roadmap & what they're committed to building.

Scheduling Wizard has publicly confirmed expansion from automated block/call/clinic scheduling into broader care coordination tools, including patient triaging and provider management, working with partner departments at UCSF and other leading institutions. They have 19 departments across 14 hospitals including contracts with Mass General, Johns Hopkins, UT Southwestern, LA General, and UCF. Their proprietary Optimization Programming Language is central to their public product narrative, with a stated goal of eliminating hundreds of hours of manual scheduling per program.

Latest Intelligence

Zeitgeist tracks private signals to determine where the company is heading strategically.

No Signals Yet

Competitors

Legacy Scheduling

QGenda (physician scheduling market leader), Amion, ShiftAdmin, MedHub (residency management), Lightning Bolt.

EHR-Embedded Scheduling

Epic Cadence, Cerner/Oracle Health scheduling modules.

AI-Native Healthcare Ops

Qventus (AI-driven hospital operations), LeanTaaS (OR and infusion center optimization), Olive AI (healthcare automation, pivoted).

General Workforce Scheduling

Deputy, When I Work (not healthcare-specific).

Scheduling Wizard

's Moat:

Proprietary Optimization Programming Language for physician scheduling encodes constraint logic that legacy tools like QGenda handle with generic rules engines. 19 departments across 14 hospitals (Mass General, Johns Hopkins, UT Southwestern) validates the approach at the most complex end of the market. Academic medical center scheduling is a narrow, high-pain niche.

How They're Leveraging AI

AI Use Overview:

Using constraint optimization for complex scheduling, ML-driven care routing, and predictive staffing analytics.

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