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INTRODUCTION:
Treatment recommendations (guidelines) are commonly represented in text form, this form of written recommendations causes several negative effects; 1. Complexity; for a specific treatment to be defined various parameters need to be obtained. Treatment options within a single disease entity are also manifold; several parameters need to be defined before treatment is initiated. In such a setup, many different combinations can arise and lead to different compositions and timing, resulting in large therapeutic variations. This sheer complexity makes it even more difficult to overlook recommendations when written in plain text. 2. Updates: Evidence based medicine is not static and new results, treatment recommendations based on new knowledge, have to be incorporated into existing guidelines. To maintain and update plain written text documents in a concise and consistent way needs a lot of effort and is quite inefficient. Comparison: In the current state, it is very difficult to compare different treatment strategies applied in different departments. With guidelines written in plain text it is very difficult to detect relevant and important differences in treatment strategies applied in different departments.
METHOD:
To improve the handling of guidelines the concept of Dodes (diagnostic nodes) has been developed. Dodes are based on linked nodes and additionally contain descriptive information and recommendations. Dodes are organized hierarchically into Dode trees. Dode categories must be clearly defined. We have performed a test in representing treatment recommendations in Dode tree format, the direct comparison of different guidelines implemented by Dodes and a transfer of guidelines to a new staging system successfully.
RESULTS:
Dode representations provided a valuable tool for guideline manipulation as well as interdisciplinary discussions. A centralized and neutral Dode database can provide standardization, which is a requirement for the comparison of recommendations. Centralized administration of Dode categories can provide information about diagnostic criteria (Dode categories) underutilized in existing recommendations (Dode trees).
CONCLUSION:
Radiation Oncology provides the ideal environment for the implementation of Dode tree recommendations due to the treatment phase being initiated only when sufficient diagnostic parameters have been collected. Representing clinical recommendations in Dode trees improves their manageability, handling and updateability.