Bicovery

Bicovery is a Machine Learning based med-tech solution that helps to monitor the mental and physical state of people diagnosed for Bipolar Disorder. Forecasting the upcoming mental crises it alerts the user, his caregivers, and health consultants.

We have launched 2 months ago. No live users traction yet.
Milestones reached: Tech prototype, medical data model developed together with Regional Center of Psychosomatic Disorders. Medical trials start planned for November 2019.

People suffering from BD - most interested in recovering faster and cheaper
Family/Caregivers who manage patient treatment and help him/her live through it.

Hospitals and caregiving facilities that organize and manage treatment. They need to control and observe the patient state and behavior, detect associated side effects, adjust the treatment accordingly and in time.

Non-profit social support organizations - communities founded to help patients tackle the disorder in cooperation with scientists, institutes, etc. They are interested in patient recovery, easier integration into society, and support between the patients

We aim the
- UA as a test market (first trials planned for Q4 2019 in 5 cities)
- DACH region and satellites for first market penetration
- Israel and the US for further expansion

DACH estimated market volume: $1.2-1.6B
Global estimated market volume: $100-120B

Bipolar disorder (BD) is a mental illness that causes dramatic shifts in a person’s mood, energy, and ability to think clearly. People with BD experience high and low moods — known as mania and depression — which differ from the typical ups and downs most people feel. Impact of bipolar disorder: manic episodes that can be very disruptive and lead to conflicts with family, friends, and society. During a manic phase, people can become aggressive or impulsive. It can lead to risky behavior, relationship breakdowns, financial problems, and even legal issues if someone behaves erratically as part of their condition. People with bipolar disorder have a 15(!) times greater risk of suicide than the general population. It accounts for up to 25% of all suicides. Bipolar disorder has other health consequences and can often occur alongside other conditions such as diabetes, anxiety, cardiovascular disease, and drug or alcohol abuse.

Current conventional approaches include long drug therapy and hospital observation. They have a lot of associated side effects - some medications can increase the patient's appetite or cause changes in metabolism leading to a weight gain.

Our approach is based on a “self-learning system” that “educates itself” and built on each patient regular behavioral patterns. It indicates any further abnormalities in patient life rhythms and state more efficiently. This approach will allow generating specific patient-adjusted daily monitoring. It will also show patient behavior changes. Timely alerts and reactions, including healthcare specialist consultations, may prevent serious treatment disruptions, decrease of overall therapy efficiency, following more drugs prescriptions, treatment timeline, and cost increase.
The fact that system will be built on AI-based analytics using the data markers of each specific patient for “education” allows suggesting usage of the basic set of the hardware sensors as the source of initial data input. These sensors like a heart rate monitor, active movements sensor, pedometer, environmental light sensor, GPS location, daylight time, weekday related activity, and others combined with pattern oriented analysis will give rich data stream to make risk assessment with a precision needed for timely and targeted alerts to the patient, caregivers, and healthcare providers. The benefit of this model lies in decreasing the hardware related cost for end-user and production, extending the range of potential users, and improving the day-by-day usability for the patient.

We develop a solution for indication and filtering of patient behavior changes for suffered person with diagnosis of bipolar disorder. Our product considers integration with an alarm hospitals for correcting the methods of treatment and patient state observation. Solution allow to prevent becoming the disease to dangerous state for patient health and dangerous to the social surround like a family and a work.
The approach includes the following functionality:
collect data from one of the suitable health trackers via mobile application
store data on a server so it’s available through the secured channels all over the world
use machine learning models to recognize behavior patterns and improve the models as amount of data grow
send smart notifications via email and mobile apps when recognize negative changes
provide statistical data to physicians and research institute for further research

Our main competitors:

Mindpax - digital technology company that helps people who suffer from bipolar affective disorder and schizophrenia; offers solutions such as mobile application and wearable devices.

Movisens - uses electrodermal activity (EDA) sensor and activity sensor enabling and supporting innovative work in the fields of sport psychology, health psychology.

Competitive Advantages :

Bicovery collects health data automatically and uses ML-models to predict crises rather than rely on the user’s answers. This allows to get data and monitor patient’s state continuously to predict crises in time. Other features such as notifying caregivers and a doctor about abnormal state and giving a doctor 24/7 bring the system to the new level compared with the competitors.

$50 (first year) and $100 (further years) monthly subscription

Growth for the upcoming 18 months in terms of the KPIs:
Sales start Q1 2020
MRR $10K reached Q3 2020
MRR $20K reached Q4 2020. Break even
MRR $75K reached Q3 2021. ROI

Bicovery will be sold in the subscription model to the end-users and their relatives/caregivers who are interested in controlling a patient’s state and preventing crises.
We will use physicians as partners so they recommend software to their patients. This allows physicians to control their patients closely with less face to face visits and leads to an increasing number of patients that one physician can handle.
We will create awareness by writing a blog and talking to existing communities.

growing the team and further product development

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We see potential challenges related to data security, data management, and interoperability.
Another significant area to think about is a formalization of trial results and solution scientific basis. While issues such as data security and privacy are challenging the market growth, hospitals and clinics generate a pool of patient’s vital statistics by using wearable medical devices with Bluetooth and cloud connectivity. Rising cybercrimes and hacking incidents pose a significant constraint to the wearable medical devices market providing secure and confidential platforms to patients. Thus, created solution needs to be built with HIPPA security compliance, GDPR compliance, and according to the EU Medical Device Regulation (MDR).
By implementing AI-based software solution that uses the conventional non-invasive and non-medical devices that already passed the certifications, we are minimizing the regulatory implications on our project to none or minimal class certifications (may be required for certain types of trials).

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