Patient Scheduling Optimization (Patient No Show Predictive Analytics)
Company Size
1,000+
Region
- America
Country
- United States
Product
- Intermedix technology-enabled services
- Dataiku DSS
Tech Stack
- Dataiku DSS
- Python
- R
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Business Operation
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
Intermedix, founded in 2002, delivers technology-enabled services and SaaS solutions to healthcare providers, government agencies, and corporations. As a leading provider of technology-enabled solutions for the global health and safety net, Intermedix supports more than 15,000 healthcare providers with practice management, revenue cycle management, and data analytic tools. The company connects more than 95 percent of the U.S. population with crisis management and emergency preparedness technologies. Intermedix is now helping more than 50 clinics reduce costs related to patient no-shows.
The Challenge
The healthcare industry is grappling with a high rate of patient no-shows, with studies indicating that 5-10% of scheduled patients miss their appointments. This has a significant impact on the financial health of healthcare organizations and their ability to care for other patients. Primary care physicians lose an average revenue of $228 for every no-show, and the lost revenue for specialists is even higher. When a patient misses an appointment, overhead costs including staffing, insurance, and utilities are not reimbursed. Cancellations with primary care physicians also impact the number of necessary specialist referrals those physicians can make. Combined, these factors contribute to significant revenue loss for physicians. To help minimize the occurrence of no-shows and thus reduce associated costs, Intermedix decided to develop and operationalize a no-show predictor that would assist office managers in scheduling appointments.
The Solution
Intermedix introduced Dataiku DSS to their data science team to develop and operationalize a no-show predictor. The team set up the automatic ingestion and crunching of historical, appointment, and demographic patient data. The data scientists built a predictive model that scores individual patients based on the probability of them missing an appointment for an allocated time slot. This output is automatically fed directly to each individual clinic several times per week depending on demand. Office managers and schedulers can use this predictive report to optimize physician schedules and patient appointments. The development and deployment of such an application to cover site-specific patterns would typically take 3+ months. However, with Dataiku DSS, Intermedix’s data science team was able to prototype and deliver the solution in just one month.
Operational Impact
Quantitative Benefit
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