Development environment for patient classification systems

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.

We have developed a new software module for special needs in medical controlling, billing or quality measurement. This module allows the development of own classification systems for patients, so-called patient classification systems. With our software we want to enable different actors in the health care sector, public health research or and authorities to develop such systems according to their own conditions and objectives.

Construction kit for a grouper
The decision tree is transferred directly into a medical logic and on this basis a grouper is provided, which classifies and groups the corresponding input data respectively patient data according to the logic.

Possible applications:

  • Development of comparison groups for monitoring or benchmarking
    Example: Formation of case groups, which allows the analysis of case numbers by department/specialty over more than four years.
  • Development of flat-rate tariffs
    Example: Development and technical implementation of outpatient flat rates in surgery.
  • Development of quality indicators
    Example: Annotation of cases with frequent complications for quality measurement

Parameters and functionalities of the software:

  • Visual representation and editing of the decision tree
  • Data-based development: Evaluation of all development steps using relevant performance and cost data
  • Medical logic based on ICD, procedures, age, sex, length of stay, and other variables, can be randomly combined.
  • Automated ICD and procedures mapping facilitates the transition to new catalogs.

The software can be used either independently by the customer or as part of a joint project by us. If you have questions or are interested in the software, please do not hesitate to contact us at info@eonum.ch.

Display of the decision tree. Classification by chapters. Edit the individual decision nodes with medical logic language.