Arbeitsort dieser Stelle Basel und Zurich

Plausibility of DRG Cases (Master Thesis / Internship)


For almost ten years now, inpatient care provided by hospitals are paid on basis of diagnosis related grouping, short DRG. In a DRG system, each inpatient case is assigned to a specific DRG based on medical criteria and patient information. The hospitals will then get reimbursed according to the cost weight of the resulting DRG.

The insurers (health insurance, accident insurance, etc.) have a natural interest, but are also required by law, to check each inpatient invoice carefully to see if the assigned DRG and the resulting amount is correct. The important question is if the necessary medical information like diagnoses and procedures are correctly provided by the hospitals.

The goal of this project is to develop and implement an algorithm that helps checking inpatient invoices by finding similar past cases or comparable examples. The examined case should then be enriched with meaningful hints how cases alike were handled to support decisions about plausibility of the case.

Challenges: Understanding the complex business between insurance companies and hospitals, as well as between the private and public sector, will be your first challenge. The main topic is to propose a meaningful algorithm that can simplify the work of insurance companies when it comes to investigating DRG cases. If time allows it, also a visualization of the algorithms result can be developed. And of course, the algorithm with its hints and visualizations should be implemented and work in practice – because at ELCA, we make it work!

What you will learn: You will develop your skills in data analysis, machine learning and statistics, as well as programming and implementing a proof of concept

In this role

In this project, the goal is to develop and implement a proof of concept of an algorithm that is able to classify or enrich a given DRG case with helpful information such that the responsible person gains some insight on how to proceed with the case based on past experience.

What we offer

Join our team as intern and you will find a young, dynamic, and culturally diverse working environment.

About your profile

  • Interest in data science and machine learning
  • Prior knowledge and experience with classification problems and nearest neighbor algorithms a plus but not required.
  • Software engineering: Java, Python, ML libraries
  • A plus: understanding of docker

If you are INTERESTED in applying for this position, please send us your complete application (CV, cover letter, letter of reference, diplomas and certificates).

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