Patient facing conversational interface implemented as a skill for Alexa
We aim to extend the longevity and healthspan of women with heart disease through early prediction of adverse events and deteriorating health.
The goal is to drastically reduce the number of hospital readmissions and deaths for women that recently have been diagnosed with heart failure.
Heart Disease and Autoimmune Conditions in Women
Our initial focus is addressing women that not only have heart disease but also suffer from autoimmune conditions such as Lupus, Rheumatoid Arthritis, Multiple Sclerosis, Psoriasis and Inflammatory Bowel Disease. These are common co-morbidities with heart disease that can increase fifty-fold the risk for fatalities and affect younger than usual patients.
These women is a large under-served patient segment with different needs and characteristics than the rest of the heart disease population.
One in four women live with autoimmune related conditions with typical onset in child bearing age. Autoimmune diseases are a family of more than 100 conditions, all having in common that the immune system turns on its own body, resulting in periodic flare-ups of inflammation. Heart failure is the number one cause of death in people with Lupus. Younger people with severe psoriasis has a more than double risk of dying from heart disease, and may suffer their first cardio event by age 40.
Our solution uses our patent-pending AI technology to predict individual risk for autoimmune inflammation, which is associated with increased risk for heart disease incidents. Heart disease patients experience good days and bad days in their condition, similar to the recurring symptom flare-ups we monitor and forecast for autoimmune patients. We will use data collected through the system to extend our predictor to heart disease incidents.
We empower patients by delivering personalized chronic disease management through a voice-based intelligent agent. It encourages adherence to treatment when it matters the most, complemented by a telemedicine dashboard to aid remote medical monitoring and timely interventions by their healthcare team.
Patient Experience (Alexa)
We have a patient facing voice experience implemented as a skill for Alexa - Amazon’s cloud-based voice service available on tens of millions of devices from Amazon and third-party device manufacturers. Here is an example of patient conversation with our Alexa based service:
Patient: Alexa, tell LadyBeats I feel exhausted today.
Alexa: Are you also feeling out of breath?
Patient: Yes, I can’t do my regular activities.
Alexa: This fits with your biometric data. You may have a flare-up in progress.
Patient: Please schedule a telehealth visit with my health team.
Alexa: Your appointment has been scheduled in twenty minutes. Make sure you take your medication.
Patient: Will do. Thanks.
Twenty minutes later the telehealth team is on a voice conference with the patient on her Echo device.
Monitoring Wellness and Symptoms
We support unobtrusive passive collection of personal health data through a wide range of diffuse devices. This is achieved by integrating with health data aggregators. They provide important cardiovascular vitals and continuous access to health data from hundreds of sources including personal in-home medical devices and wearables from companies such as:
These sources provide biometric data that can be used to measure health status and gauge deteriorating health from change over time.
Our business model is based on reducing readmissions and contributing to development of new treatments, both lucrative opportunities. Most of what is known about heart failure is extrapolated from research on middle aged men. We hope that the data collected through our system can help remedy this situation.
We are an award-winning team with a substantial track record in patient driven health innovation. Predictably Well is a registered Public Benefit Corporation incorporated as a Delaware C-Corp. Our purpose is to improve the life of people diagnosed with chronic diseases.
The project was started during the hackathon period.