AI/ML Boosts Patient Care and Saves $1.1M for a Medical Services Company
A physician-led medical services company used AI and Machine Learning to predict ER patient volumes, improving scheduling and staffing accuracy. By integrating historical data within their framework platform, they improved forecasting accuracy, leading to significant annual savings while enhancing overall patient satisfaction by reducing wait times.
By The Numbers
- $1.1MAnnual savings
- 85%Accuracy
Challenge
A large, physician-led, outsourced medical service company has contracts with over 800 ERs and treats more than 18M patients per year. Predicting patient arrival behaviors from past data and seasonality trends would deliver significant cost benefits along with providing an enhanced patient experience.
Solutions & Impact
A predictive AI/ML algorithm-based approach was used to analyze historical data, enabling ERs to predict patient patterns and perform time series forecasting. This allowed clinicians to plan the amount of time needed for each patient, along with predicting the appropriate staffing resources needed by utilizing their existing framework and integrating with external systems.
- Patient patterns generated based on historical data and seasonality with algorithm-based approach
- Allowed for integration with existing frameworks and external systems
- Increased scheduling accuracy
This approach not long assisted clinicians in identifying schedule gaps and effectively managing schedule volume but also increased operational efficiency by minimizing the issues of under or overstaffing leading to increased patient satisfaction and reduced wait times.
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