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%
Illustrative scenario — not a client engagement.
ValleyCare Urgent Clinics is a composite, not a client. The bilingual patient population and the Winter Texan seasonal swing are real features of urgent care in the Rio Grande Valley. The firm profile, every figure, and every chart on this page are modeled to show how the approach would work. Nothing here reports a measured client outcome.
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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