Editorial & Advertiser Disclosure Global Banking And Finance Review is an independent publisher which offers News, information, Analysis, Opinion, Press Releases, Reviews, Research reports covering various economies, industries, products, services and companies. The content available on globalbankingandfinance.com is sourced by a mixture of different methods which is not limited to content produced and supplied by various staff writers, journalists, freelancers, individuals, organizations, companies, PR agencies Sponsored Posts etc. The information available on this website is purely for educational and informational purposes only. We cannot guarantee the accuracy or applicability of any of the information provided at globalbankingandfinance.com with respect to your individual or personal circumstances. Please seek professional advice from a qualified professional before making any financial decisions. Globalbankingandfinance.com also links to various third party websites and we cannot guarantee the accuracy or applicability of the information provided by third party websites. Links from various articles on our site to third party websites are a mixture of non-sponsored links and sponsored links. Only a very small fraction of the links which point to external websites are affiliate links. Some of the links which you may click on our website may link to various products and services from our partners who may compensate us if you buy a service or product or fill a form or install an app. This will not incur additional cost to you. A very few articles on our website are sponsored posts or paid advertorials. These are marked as sponsored posts at the bottom of each post. For avoidance of any doubts and to make it easier for you to differentiate sponsored or non-sponsored articles or links, you may consider all articles on our site or all links to external websites as sponsored . Please note that some of the services or products which we talk about carry a high level of risk and may not be suitable for everyone. These may be complex services or products and we request the readers to consider this purely from an educational standpoint. The information provided on this website is general in nature. Global Banking & Finance Review expressly disclaims any liability without any limitation which may arise directly or indirectly from the use of such information.


Florian Douetteau, CEO of Dataiku.

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.

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: http://www.dataiku.com/solutions/