The AI Operating System Helping ERG Make Scheduling Resilient
Capture personal preferences, holiday rules, APC tiers, and productivity data in one system — making schedules that are more profitable and provider-friendly.
Describe
ERG Has Outgrown Manual, "Oral-History" Scheduling
ERG’s scheduling works because experienced schedulers know the nuances of provider preferences and site-specific rules. But that “oral history” is impossible to scale. Describe documents those preferences and judgement, and makes scheduling resilient.
Provider-First Culture
ERG prioritizes provider happiness, preferences, fairness, and retention — and this should be protected as ERG modernizes scheduling.
Clinical Judgment
Clinician-schedulers understand ED rhythm, provider dynamics, and what a realistic schedule feels like.
Where Generic Scheduling Tools Break
There are too many variables involved for humans to optimize schedules manually.
- —Preferences live in scheduler's heads and are not properly documented
- —Consumes several tedious weeks of scheduler time every month
- —Low visibility into productivity metrics when pairing providers together
- —Contract obligations, payroll cost, and preferences are impossible to optimize by hand
- —Shift templates aren't optimized using volume/arrivals forecasting
- —Humans naturally over- and understaff because they can't weigh every variable at once
- —Fairness reporting for nights, holidays, and weekends becomes endless spreadsheet work
Schedule
Human schedulers cannot manually hold every variable at once.
Data-driven Scheduling for ERG
Describe's AI generates optimized schedules by analyzing ERG's operational data and provider preferences — turning what schedulers hold in their heads into a transparent system that scales.
Site-specific models trained on ERG's historic demand that deliver hourly volume forecasts.
Staff to Patient Demand — Not Historical Assumptions
Instead of fixed templates, Describe minimizes cost by using custom-trained AI to predict patient arrivals and staff to volume.
Key takeaway
Scheduling more efficiently means matching provider coverage to actual demand — so ERG can reduce waste, protect patient care, and give providers more time off.
AI-optimized scheduling keeps coverage closer to actual arrivals.
ERG-Specific AI Use Cases
Turn Sensitive Knowledge Into a Resilient, Scalable System
ERG's scheduling knowledge is valuable, but too much of it lives informally today. Describe captures provider preferences, site rules, and scheduler judgment into structured workflows that can be reviewed, updated, and taught to new schedulers.
The goal is not to replace the scheduler, but rather to make ERG's scheduling knowledge durable.
Reduce Overstaffing & Understaffing With Volume-Aware Templates
Use patient volume forecasts to optimize templates by site, day, zone, and hour — reducing unnecessary coverage while adding support where demand actually requires it.
Productivity-Aware Provider Pairing
Using productivity metrics, Describe avoids risky pairings — never two historically slower providers during peak — and pairs newer physicians with faster, experienced ones to support onboarding. Describe analyzes provider performance patterns and helps pair clinicians who work well together.
Never Use Spreadsheets Again
ERG's schedulers should not need spreadsheets to answer whether nights, holidays, weekends, high-utilization days, and preferences are distributed fairly. This exact fairness logic is built into Describe's algorithm.
Manual Scheduling Software, or an AI Operating System for ERG
The question is whether Qgenda can truly capture the complexity of ERG's scheduling — or whether the team will end up rebuilding the same non-resilient workarounds in a new system.
ERG is early enough in the Qgenda implementation (~1-2 months) that now is the right time to evaluate whether Describe fits ERG's needs — before months of workarounds become embedded.
Describe for ERGA Low-Risk, In-Person Implementation Approach
Describe's 2-month scheduling implementation pairs ERG with a dedicated, in-person Implementation Engineer who will lead the pilot, starting with one site (e.g. Georgia site or Lakeland).
Knowledge Capture & Data Setup
Work with Dr. Gutierrez, Shauna, Erin, and Dylan to gather roster, templates, contracts, rules, preferences, and holiday logic. Capture what lives in schedulers' heads.
AI Scheduler Configuration
Customize Describe around ERG's goals: provider happiness, fairness, transparent scheduling, and shift-template efficiency.
Month 1 Schedule Generation
MONTH 1 VALUE, LOW-RISKDescribe generates real schedules in Month 1 for the pilot region so ERG can compare it against the manual process.
Weekly Feedback
Meet weekly with ERG's core team to review progress, gather feedback, adjust rules, and customize Describe.
Provider Onboarding
After finishing admin onboarding and core customization, we onboard providers in Month 2.
Expand Across ERG
Once schedule quality is proven, expand from the pilot region to additional ERG sites.
Resilient Schedules That Improve Margins
Includes AI scheduling, volume forecasting, shift optimization, analytics, implementation, and 24/7 ongoing support.
Describe pays for itself by conservatively removing just ~14 shifts (or 14 shifts-worth of hours) from the monthly template — less than 0.5% of ERG's total monthly scheduled hours. Even a 0.5% efficiency increase from AI (vs. the current manual process) means ERG profits from Describe.
Proof from Baptist Health
5-site ED group using Describe scheduling.
Scheduling Is Where We Start
Once Describe is integrated into scheduling, ERG can expand the same operating system into credentialing, payroll, provider performance scorecards, and financial intelligence.
- Provider onboarding
- Document tracking
- Shift-based pay
- RVU / productivity calculations
- Site-level performance
- Provider trends & action plans
- Revenue leakage alerts
- Billing vendor scorecards
from now
Scheduling is resilient, volume-based staffing is routine, and providers have one place to see their pay breakdown, performance metrics, and credentialing. An intelligence layer lives on top of financial data to help boost revenue.