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Intermedix has developed a predictive analytics application for hospitals and clinics that predicts patient no-shows, a problem that costs the healthcare industry billions of dollars each year.  The application was built by a small team of data scientists at Intermedix using Dataiku DSS and was prototyped and delivered within one month.

New York, NY – A leading provider of healthcare analytics and technology-enabled services, Intermedix, has developed a solution to address no-show patients using Dataiku Data Science Studio (DSS), the all-in-one predictive analytics and data science platform.  The problem of patients missing scheduled appointments costs the healthcare industry billions of dollars of lost revenue each year.  The predictive analytics software solution developed by Intermedix was built, tested and deployed by a small team of data scientists in just one month using Dataiku DSS and is now being used in more than 50 private clinics across the US.

Patient No Shows: A Billion-Dollar Problem

The unfortunate reality is that patient no-shows in the healthcare industry are extremely common, and industry-wide, this adds up to billions of dollars of losses each year. The long-term effect of this phenomenon is lowered reimbursement for providers and negative impacts on adherence, quality, and clinical outcome measures for patients. More and more organizations are turning towards advanced analytics to reduce the probability of no-shows and their associated costs using heterogeneous data to optimize scheduling systems.

The inability of healthcare organizations—big or small, public or private—to deal with the no-show issue has had a profound effect on patients’ medical health and on providers’ financial health.

Studies have shown that 5 – 10% of patients miss scheduled appointments. Primary care physicians lose an average revenue of $228 for every no-show, and lost revenue for specialists is even higher. In addition, overhead costs including staffing, insurance and utilities remain on the books. 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 associated with patient no-shows.

Developing a Predictive Solution

Intermedix decided to develop and operationalize a no-show predictor that would assist local office managers in reducing the number of patients who miss appointments. The data science team set up Dataiku DSS to ingest and crunch historical appointment and demographic patient data. From there, they built a predictive model that scores individual patients based on the probability that they will miss a scheduled appointment. Dataiku DSS automatically sends this output to the office managers at regular intervals customized to their practice’s needs.

Thanks to the predictive report, local office managers and schedulers can make informed decisions on scheduling and proactively target reminders to the patients most likely to miss their appointments.

From Prototype to Deployment in One Month

Typically, developing and deploying such an application to cover site-specific patterns would take more than three months. Equipped with Dataiku DSS, Intermedix’s data science team was able to prototype and deliver the solution to more than 50 clinics in just one month.

“DSS slashed the amount of time it took to analyze our data, produce a working model and deploy a solution, all while improving the accuracy of our predictions,” said John Enderele, data scientist at Intermedix. “The platform will enable us to more rapidly identify our clients’ needs and respond with innovative, data-driven solutions to make them successful.”

Intermedix’s solution was made possible by the technology behind Dataiku, maker of the predictive analytics software platform Dataiku Data Science Studio (DSS). Dataiku DSS makes it possible for organizations to reap the benefits of data science thanks to a collaborative interface for both expert and beginner analysts and data scientists. Dataiku offers a complete and accessible advanced analytics software platform that allows teams made of different skill sets to streamline the process from raw data to predicted output, all in one tool.

Dataiku DSS can be used to quickly build predictive services and data products that transform raw data into business impacting products including:

  • Churn Analytics
  • Fraud Detection
  • Logistic Optimization
  • Data Management
  • Demand Forecasting
  • Spatial Analytics
  • Lifetime Value Optimization
  • Predictive Maintenance
  • and much more

To learn more about solutions provided by Dataiku DSS visit:

About Intermedix

Intermedix delivers technology-enabled services and SaaS solutions to health care providers, government agencies and corporations. The company supports more than 15,000 health care providers with practice management, revenue cycle management and data analytic tools. Intermedix connects more than 95 percent of the U.S. population with crisis management and emergency preparedness technologies.

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About Dataiku

Dataiku develops Dataiku Data Science Studio, the unique advanced analytics software solution that enables companies to build and deliver their own data products more efficiently. Thanks to a collaborative and team-based user interface for data scientists and beginner analysts, to a unified framework for both development and deployment of data projects, and to immediate access to all the features and tools required to design data products from scratch, users can easily apply machine learning and data science techniques to all types, sizes, and formats of raw data to build and deploy predictive data flows.

More than 80 customers in industries ranging from e-commerce, to industrial factories, to finance, to insurance, to healthcare, and pharmaceuticals use DSS on a daily basis to collaboratively build predictive dataflows to detect fraud, reduce churn, optimize internal logistics, predict future maintenance issues, and more. Dataiku has offices in Paris and New York.

Dataiku raised $3.7 million last year from two investors to grow its sales and tech team and international development initiatives.  



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