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eonum has developed a tool for the development of patient classification systems. This enables the editing of complex medical decision trees and decision logics and data-driven evaluation.

In the following blog article, Joël Bessire describes his start at eonum, which he experienced during a data science internship in 2016. Today, Joël Bessire is a Data Scientist and Software Engineer and a permanent team member at eonum AG.

Would you like to launch a new drug in Switzerland, but don't know how big the market potential is? In which hospitals occurs the indication of your product and in which quantity? Are you working in health policy and would like to evaluate different health relevant indicators? Do you have questions about regional or cantonal characteristics of the healthcare system? With our combination of know-how, data and tools, we are able to answer a wide range of questions about the Swiss healthcare landscape.

In a joint project with the University of Bern, eonum has developed a new online search interface for medical catalogs (ICD - International Classification of Diseases, CHOP - The Swiss procedure classification based on ICD9-CM and SwissDRG, the Swiss system for Diagnosis Related Groups and many other catalogues as well). The new search service can be found at medcodesearch.ch. The focus was on a simple, user-friendly operation to find the relevant codes directly.

qm1.ch is a hospital comparison platform by Qualitätsmedizin Schweiz that was implemented and developed by eonum. On the qm1.ch platform doctors and interested people have the possibility to draw information about case numbers, quality and infrastructure features of Swiss hospitals in different fields. The figures are all extracted from public sources (Federal Office of Public Health, Federal Statistical Office, ANQ, H+, hospital websites etc.). You can find the hospital comparison platform at qm1.ch.

With effect from 2017 and the implementation of SwissDRG 6.0 and retroactive for the versions 4.0 and 5.0, many innovations will occur for our coding control software Casematch. The emphasis of the development is the complete replacement of the statistical backend from a Java based solution to a Python based. With the used frameworks Keras and Tensorflow, that are developed and used by Google, we rely consequently on Deep Learning. Deep Learning is a new approach of machine learning that set new standards in multiple fields over the last five years. This includes among others object recognition in images or videos, bioinformatics, speech recognition or text analysis.

Together with a group of students from the INF from the University of Bern, eonum has developed a tool for the visualization of the Swiss hospital statistics issued by the Federal Statistical Office (FSO).

For the analysis of new problems, we use our in-house development. Using modular design principles, a data pipeline suited for the problem at hand can be built and evaluated. A data pipeline consists of readers, tranformers, classifiers, regressors, normalizer, parameter optimizer, output generators and other modules.

The Heritage Health Prize is a competition to find the best algorithm for the identification of patients which will be admitted to hospital in the next year using historical claims data.