Background: Postoperative care of cardiac surgery patients is complex and often fraught with
an increased morbidity and mortality due to potential complications. Even with intensive
and continuous monitoring of patients it is often difficult to interpret the early
signs of life-threatening complications from the vast amount of monitoring data before
actual symptoms occur. AI-based monitoring systems can assist medical personal by
predicting incipient complications in an early stage, which gains valuable time for
treatment. Although the trend in medical AI systems is going toward the use of explainable
AI models, the results and type of explanations vary widely and are hard to understand.
We introduce a novel dashboard system that interprets the AI decisions and visualizes
them in a uniform and comprehensible way, tailored toward medical personal.
Method: Following a user-centered approach, we captured the system requirements by holding
a survey with 25 heart surgeons, as well as conducting interviews with medical personal
from different wards. The answers allowed us to identify the necessary data that is
needed in critical decision making, when facing possible complications and capture
preferred display options of the users. A first prototype dashboard, based on vue.js
was then developed and fed with data of existing AI models. The different types of
results and explanations of the AI models were analyzed and matching visualizations
styles were chosen.
Results: The resulting system uses the data and explanations of different, existing AI prediction
models and matches them with suitable visualization styles. The user can then choose
the level of detail he prefers, ranging from a simple scoring system to a highly detailed
view with precise information about alarming values.
Conclusion: We developed an interactive dashboard system that is capable of visualizing complex
AI decisions in a comprehensive way for medical personal. The system can interpret
the results of different AI models, matches them with suitable visualization options
and displays them to the user uniformly. The user can individualize the dashboard
and choose a level of detail that suits his needs. First user tests are currently
performed to ensure the comprehensibility of our dashboard; further studies to evaluate
and improve the usability and user experience in a clinical setting are planned.
