Our Solution
Overview
Our minimum viable product, Atrial Insights, is an AFib Classification Dashboard which centers on two core elements: timestamped AFib data and an explainable, data-driven classification approach.
The research on explainable modeling shows that when users understand how decisions are made, their trust and engagement increase significantly. Studies in the field have demonstrated that these insights not only improve clinical decision-making but also encourage patients to adhere more closely to their treatment plans and lifestyle modifications.
By combining precise, timestamped data with an explainable classification method, we enable timely interventions that can improve patient outcomes and reduce complications. This personalized approach not only benefits individuals but also supports clinicians in optimizing care. Moreover, as users experience the clear value and transparency of the system, we anticipate higher adoption rates among both patients and healthcare providers. And with every interaction, we gather further insights that can help refine our approach, paving the way for even more effective AFib management in the future.
Key Dashboard Features
- Easy to use: Simply upload your file, enter some contextual information, and your dashboard will be ready to go!
- Tabs of information: Different types of insights, like summary statistics of time-of-day trends, are contained in separate tabs for users to focus on what is important to them
- Interactive: Allows users to tag episodes with an activity to better identify trends in what they were doing during onset
How It Works
The MVP is a web-app where users can upload Electrocardiogram (ECG) data and view their AFib episodes over the course of the recording.
Benefits
By recording the exact times of AFib episodes, our dashboard provides users with a clear picture of when events occur. This information is invaluable for personalizing lifestyle adjustments. When individuals can correlate an AFib episode with specific moments in their day—be it after exercise, during periods of high stress, or following a poor night’s sleep—they gain the insight needed to modify their behavior. This kind of targeted feedback can drive meaningful changes, reducing the frequency or severity of future episodes.
From a clinical perspective, these precise timestamps offer significant advantages. Healthcare providers can use this information to tailor treatment plans more accurately. Rather than relying solely on retrospective or generalized data, clinicians can now see the exact patterns and triggers behind each patient’s AFib episodes. This enables a more personalized approach, whether that means fine-tuning medication regimens or offering specific lifestyle recommendations. Research supports this methodology; studies have demonstrated that understanding the timing of AFib episodes can uncover hidden triggers and guide more effective treatment decisions.
Rather than operating as a “black box,” our model highlights which parts of the ECG data most influence the classification of an episode. This transparency is crucial. For clinicians, it means a clearer understanding of the decision-making process behind each diagnosis, which enhances trust in the tool and fosters better integration into clinical practice. For patients, it demystifies the process. They’re not just told that an episode occurred—they can see the specific factors that contributed to that classification. This level of insight empowers users to take proactive steps in managing their heart health.
For patients
- Improved transparency
- More control over personal health
For doctors
- Low-cost diagnostic tool
- Enables more timely intervention