Case Study: Intermedix achieves rapid patient no-show prediction and cost savings with Dataiku

Predicting Patient No-Shows to Optimize Scheduling Systems & Reduce Costs

Intermedix, a technology-enabled services and SaaS provider for healthcare founded in 2002, faced the common industry challenge that 5–10% of scheduled patients miss appointments, causing significant lost revenue and operational inefficiency. To tackle this, Intermedix partnered with Dataiku and used Dataiku DSS to build an operational no-show predictor to help office managers optimize scheduling.

Using Dataiku DSS, Intermedix automated ingestion of historical, appointment, and demographic data and built predictive models that score patients and time slots, with reports fed to clinics 2–3 times per week. Dataiku enabled the three-person data science team to prototype and deploy the solution in one month (about one-third the typical time), and the system is now helping more than 50 clinics reduce costs and improve scheduling accuracy.

Intermedix

John Enderle

Data Scientist