South Texas Urgent Care Network
Patient Volume Forecasting & No-Show Prediction for a Multi-Clinic Network
31%
Wait Time Reduction
avg across clinics
$215K
Labor Savings
annual
19%
No-Show Rate
from 30%
01The Challenge
A five-clinic urgent care network across McAllen, Edinburg, Mission, Pharr, and Weslaco was struggling with unpredictable patient volumes, chronic overstaffing during slow hours, understaffing during surges, and a 30% no-show rate that wasted provider time and disrupted scheduling.
Three problems drove the engagement:
- Staffing mismatch: All five clinics used the same flat staffing model — 6 providers from 9am to 5pm — regardless of actual demand. Monday mornings routinely hit 30+ patients per hour while Saturday afternoons saw 8. Providers were either idle or overwhelmed.
- No-show waste: Nearly 1 in 3 scheduled appointments resulted in a no-show. The network had no way to predict which patients would miss, no overbooking strategy, and no outreach system to reduce no-shows.
- Winter Texan surge: Between October and March, patient volumes increase 40% as seasonal residents ("Winter Texans") arrive in the Rio Grande Valley. The clinics didn't adjust staffing for this predictable annual pattern.
Data Landscape
02Our Approach
We built three interlocking systems: a patient volume forecast that predicts demand 14 days ahead, a no-show classifier that flags high-risk appointments for proactive outreach, and a staffing optimizer that matches provider hours to predicted patient flow.
- XGBoost Classifier — no-show prediction model identifying high-risk appointments for proactive outreach and overbooking
- Prophet Forecasting — daily patient volume forecast with Winter Texan seasonality and flu-season regressors
- Staffing Optimizer — demand-matched staffing recommendations by hour, shifting providers to match actual patient flow
- Clinic Benchmarking — multi-metric comparison across 5 clinics for wait times, satisfaction, and operational efficiency
- Pandas + Recharts — data pipeline and interactive operations dashboard for clinic managers
Visit Records
120K+ visits
Volume Forecast
14-day ahead
No-Show Model
AUC = 0.82
Staff Optimizer
Hourly scheduling
Ops Dashboard
Real-time metrics
03Key Findings
Patient Volume by Day & Hour
Average patient volume across all clinics by day-of-week and hour. Monday mornings (8–11am) are consistently the highest volume window. Weekends drop significantly. This pattern drives the staffing optimizer — matching provider count to these demand curves eliminates both wait times and idle hours.
No-Show Prediction — Feature Importance
SHAP values showing which features most influence no-show predictions. Prior no-show history is the strongest predictor — patients with 2+ prior no-shows are 3.4x more likely to miss again. Appointment lead time is second: appointments booked 14+ days out have double the no-show rate.
No-Show Classifier — ROC Curve
Receiver Operating Characteristic curve for the XGBoost no-show classifier. AUC = 0.82 indicates strong discrimination between show and no-show patients. At the operating threshold (FPR = 0.15), the model catches 65% of no-shows — enough to enable targeted reminder calls and strategic overbooking.
Clinic Performance Comparison
Five-axis radar comparing clinics on patient volume, average wait time (inverted — higher is better), satisfaction score, no-show rate (inverted), and Winter Texan patient percentage. Mission and Weslaco handle Winter Texan surges best; McAllen Central has the highest volume but longest waits.
Staffing Optimization — Current vs Recommended
Provider count by hour: current flat staffing (dashed) vs demand-matched recommendation (solid). The optimizer shifts 2 providers to the 8–11am surge window and reduces afternoon/evening coverage. Net result: same total provider-hours, 31% lower wait times.
04Business Impact
Projected Annual Value
$215K labor savings + 31% wait time reduction
The staffing optimizer redistributed the same total provider-hours across the day to match actual demand. Monday morning coverage increased from 6 to 8 providers; Saturday afternoon coverage dropped from 6 to 3. Average wait times fell from 47 to 32 minutes network-wide.
The no-show model enabled a two-pronged intervention: automated SMS reminders 48 hours before flagged appointments, plus strategic overbooking of 2 slots per high-risk session. The combined effect reduced no-shows from 30% to 19%, recovering ~4,200 appointment slots per year.
Winter Texan volume forecasting allowed the network to hire 3 temporary providers each October instead of scrambling in November. Advance planning cut temporary staffing costs by 22% while maintaining satisfaction scores above 4.2/5.0 during peak season.
05Technical Details
Volume Forecast (Prophet)
- Granularity: daily, per-clinic
- Regressors: flu_index, temperature, day_of_week, is_holiday
- Seasonality: Winter Texan (Oct–Mar) + flu season (Nov–Feb)
- Accuracy: MAPE = 11.2% on 3-month holdout
No-Show Classifier (XGBoost)
- Features: prior_noshows, lead_time, insurance, day, distance, age, time_slot, provider
- Target: no_show (binary classification)
- Performance: AUC = 0.82, precision = 0.71 at recall = 0.65
- Threshold: optimized for actionable outreach capacity (~15% FPR)
Staffing Optimizer
- Method: constrained optimization (minimize wait time subject to budget)
- Constraint: total provider-hours per week ≤ current budget
- Granularity: hourly staffing levels by day-of-week
- Validation: 4-week A/B test at McAllen Central before network rollout
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