Abstract
Knowledge about breast cancer (treatment) is evolving rapidly. This makes it difficult to keep guidelines up to date, even though they are essential for the quality of care. The current textual guidelines are extensive and not easily applicable in practice. They do not follow the care path of the patient. Therefore, a new methodology is needed to present guidelines in a compact manner and to perform targeted updates. More must also be learned from real-world healthcare outcomes from daily practice.
The first part of this thesis discusses which (guideline-based) systems have been described to support multidisciplinary team decision-making. A scoping review identified twenty different systems, of which only three have been further investigated. We have developed a model to support the implementation of clinical decision support systems.
Part two describes clinical decision trees as a new method for displaying guidelines. The textual breast cancer guideline was converted into data-driven clinical decision trees that follow the care path, and made accessible via an app. Research into the applicability of these decision trees showed that the availability of relevant data during multidisciplinary decision-making was often insufficient. Further points of attention were the limited mention of reasons for deviating from the guideline and the often failure to mention multiple treatment options where the guideline does recommend this.
The third part describes the value of clinical decision trees to analyze real-world data. For this purpose, data from the Dutch Cancer Registry were projected onto the clinical decision trees. This provided valuable insights into actually provided care and guideline compliance, whereby hospitals could be characterized as early innovators or slow adopters.
In the final part, clinical decision trees are positioned as a platform for a self-learning healthcare system. Decision trees can support multidisciplinary decision-making for individual patients, by involving both knowledge from randomized clinical trials and knowledge obtained through analysis of real-world data. This can generate new hypotheses and help guideline committees in adapting guidelines.
This thesis emphasizes the need to report clinical data in a standardized and structured manner for optimal data-driven decision support to improve the quality of care.
The first part of this thesis discusses which (guideline-based) systems have been described to support multidisciplinary team decision-making. A scoping review identified twenty different systems, of which only three have been further investigated. We have developed a model to support the implementation of clinical decision support systems.
Part two describes clinical decision trees as a new method for displaying guidelines. The textual breast cancer guideline was converted into data-driven clinical decision trees that follow the care path, and made accessible via an app. Research into the applicability of these decision trees showed that the availability of relevant data during multidisciplinary decision-making was often insufficient. Further points of attention were the limited mention of reasons for deviating from the guideline and the often failure to mention multiple treatment options where the guideline does recommend this.
The third part describes the value of clinical decision trees to analyze real-world data. For this purpose, data from the Dutch Cancer Registry were projected onto the clinical decision trees. This provided valuable insights into actually provided care and guideline compliance, whereby hospitals could be characterized as early innovators or slow adopters.
In the final part, clinical decision trees are positioned as a platform for a self-learning healthcare system. Decision trees can support multidisciplinary decision-making for individual patients, by involving both knowledge from randomized clinical trials and knowledge obtained through analysis of real-world data. This can generate new hypotheses and help guideline committees in adapting guidelines.
This thesis emphasizes the need to report clinical data in a standardized and structured manner for optimal data-driven decision support to improve the quality of care.
| Original language | English |
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| Qualification | Doctor of Philosophy |
| Awarding Institution |
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| Supervisors/Advisors |
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| Award date | 3 Dec 2024 |
| Place of Publication | Enschede |
| Publisher | |
| Print ISBNs | 978-90-365-6338-3 |
| Electronic ISBNs | 978-90-365-6339-0 |
| DOIs | |
| Publication status | Published - Dec 2024 |
