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ruglon.com

Ruglon

The no-show proof schedule for telehealth

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Summary

An AI-powered smart scheduling tool that predicts no-show probability for each appointment, dynamically adjusts scheduling rules, and automates interventions to maximize billable utilization and stabilize cash flow for telehealth clinics.

Target Audience

Cash-flow-sensitive telehealth clinic operators (owners, practice managers, CFOs) running single or multi-location virtual care practices who personally feel the revenue loss from no-shows.

Economic Engine

Usage-based pricing ($0.50 per scheduled appointment, capped at a monthly max per provider) with a free tier for up to 100 appointments/month to prove ROI.

Point of Difference

Unlike generic scheduling software, Ruglon specifically optimizes for cash flow by treating no-show risk as a probabilistic variable and dynamically adjusting scheduling policies per patient and time slot, turning scheduling into a revenue optimization engine.

Problem Statement

Cash-flow-sensitive telehealth clinic operators lose 10-20% of revenue to patient no-shows and last-minute cancellations, creating cash flow gaps and wasted clinician capacity that directly threatens their thin margins.

Solution

Combines a smart scheduling engine with a no-show risk prediction model (using public data enrichment and clinic-specific history), a rules engine for dynamic overbooking and reminder escalation, and a reporting layer that benchmarks no-show rates against anonymized peers.

Core Value Proposition

Increases billable appointment volume by 12-18% without adding clinicians, directly improving monthly cash flow by $8K-$20K per clinic depending on size.

Killer Features

  • Risk score per slot: Shows predicted no-show probability for each time slot before scheduling, helping staff make informed decisions
  • Smart overbooking engine: Automatically double-books low-risk slots and adjusts as predictions update in real-time
  • Benchmark dashboard: Compares clinic's no-show rate and cash flow impact to anonymized peers, motivating improvement
  • Automated intervention cascade: Triggers different reminders based on risk level (e.g., SMS for low risk, phone call for high risk) to preempt cancellations

Pros

  • Direct ROI from day one with immediate reduction in no-shows
  • Integrates with existing telehealth platforms via API, minimizing switching cost
  • Generates proprietary benchmark data that becomes more valuable with scale
  • Low risk due to usage-based pricing tied to actual appointments

Cons

  • Requires clinic to share sensitive patient appointment data
  • No-show prediction models may have bias if not carefully tuned across diverse patient populations
  • Clinics with already very low no-show rates may see less value
  • Initial integration effort with EMR/Telehealth platform can be 2-4 weeks

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