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Fixing a Billion Dollar Problem in Healthcare with IoT

By Ken Briodagh October 11, 2016

Healthcare facilities have a huge problem, amounting to billions of dollars in losses every year: no-show patients. Patients who miss scheduled appointments cost money in time, labor costs, equipment and facility infrastructure costs.

Now, according to a recent announcement and case study, healthcare analytics and technology services company Intermedix has developed a predictive analytics application for hospitals and clinics that predicts patient no-shows using Dataiku DSS. It was prototyped and delivered for a client within one month, and is now being used in more than 50 private clinics across the U.S.

Long-term, patient no-shows result in 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.

It’s estimated that between 5 and 10 percent of patients miss scheduled appointments, and primary care physicians lose an average of $228 for every no-show. Specialists lose even more.

The Intermedix no-show predictor is designed to assist local office managers in reducing the number of patients who miss appointments by using the Dataiku DSS to ingest and crunch historical appointment and demographic patient data. From there, a predictive model scores individual patients based on the probability that they will miss a scheduled appointment and the system automatically sends this output to the office managers at regular intervals, customized to the practice’s needs.

“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.”




Edited by Alicia Young

Editorial Director

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