Executive Summary

A leading healthcare organization was facing operational inefficiencies and revenue loss due to high patient appointment no-shows. To address this, Quantiphi developed a custom AI-powered machine learning solution that predicts the probability of a patient missing an appointment with 83% accuracy. The solution provided near real-time predictions, automated notifications, and enhanced scheduling efficiency—resulting in millions in potential cost savings through better resource utilization.

About the Client

Our client is a healthcare organization managing large volumes of patient appointments across facilities.

Industry: Healthcare
Size: Mid-to-large scale

Problem Statement

The organization was facing a growing challenge of missed patient appointments, which created inefficiencies in scheduling and led to substantial revenue loss. Without a proactive mechanism to anticipate no-shows, valuable resources often went underutilized, disrupting both operations and patient care continuity. To address this, the client aimed to develop a machine learning model capable of predicting the probability of a patient not showing up, thereby enabling smarter appointment management and more effective operational planning.

Challenges

High rate of patient no-shows affecting operational efficiency
Revenue loss due to unutilized appointment slots
Lack of proactive mechanisms to predict and prevent missed visits
Need for improved scheduling accuracy and patient communication

The Solution

Quantiphi designed a custom machine learning solution to predict the likelihood of a patient no-show, while simultaneously enabling actionable automation and identifying the key factors influencing both “Show” and “No-Show” scenarios.

Dual Predictive Models:
- Model 1 : Leveraged structured appointment data.
- Model 2 : Incorporated external factors like weather and patient distance.
Inference Pipeline:
- Designed for both batch predictions and near real-time updates.
- Automated ingestion, transformation, and refresh of data streams.
Explainability Features:
- Clear insights into influential factors contributing to a patient’s no-show probability.
Actionable Automation:
- Predictions integrated into downstream patient communication systems, enabling automated reminders and notifications to reduce missed visits.

Impacts

83% model accuracy in predicting appointment no-shows
Near real-time predictions enabling better operational planning
Automated notifications to reduce no-shows and improve attendance
Millions in potential cost savings through optimized resource utilization
Enhanced scheduling efficiency with predictive insights

Technologies Used

CatBoost

XGBoost

Google Compute Engine

Google Composer

BigQuery

Results

Achieved 83% accuracy in predicting patient appointment no-shows, improving scheduling reliability
Enabled near real-time predictions, allowing proactive operational planning and reduced disruptions
Integrated automated notifications, lowering missed visits and improving patient attendance
Delivered millions in potential cost savings through optimized staff and facility utilization
Provided enhanced scheduling efficiency with actionable, AI-driven predictive insights