Remote physiologic monitoring (RPM) is a set of codes that describes non-face-to-face monitoring and analysis of physiologic factors used to understand a patient's health status. For example, the RPM codes allow remote monitoring of oxygen saturation levels in patients with COVID-19.
A solution for you and your family that won't make you worry about your family members anywhere in the world. Within you finger tips you get frequent updates about your mother glucose level or your son body temperature. And we designed it to be free. Donate now and get a copy from Life Without Diabetes eBook and Free Tee from smart health.
You might have seen that a single medical case that cost thousands of dollars in treatment although that costly treatment in most cases will never bring back the patient's health. With your support, we can improve the efficiency of preventive care.
The project was built to help patients in developing countries to have access advanced healthcare solutions with little or no cost at all.
We targeted to save 5000 lives in the next 5 years and we aim to do that by building Artificial intelligence (AI) software ( for example Cardiac Arrest prediction test it now for FREE ) that will tell the monitoring team about chronic diseases complications probability for each patient. In addition to the ability to know pre-diseases probabilities for patient family members like in the case of people in the pre-diabetes stage.
The key benefit from our solutions is the ability to know when a patient's health condition is going to deteriorate before it really happens using artificial intelligence technology. There is a big difference when you enter a hospital walking on your leg or being carried.
The app in its current state aims to reduce the possibility of chronic complications for both diabetes and heart disease patients by keeping their vital health signs under hourly monitor either from their guardians or clinical team. Patients can use an affordable all-in-one FDA approved medical device that is integrated with our app through bluetooth to measure their vital signs. Those measurements are automatically sent to clinicians or guardians registered on the patient app.
The accumulation of patient measurement will allow the use of Artificial Intelligence technology for predicting chronic health conditions (specifically for diabetes and heart diseases) and prevent possible chronic complications. Patients will use affordable medical devices where both his guardians & a clinical team can monitor the patient's health in real-time through a mobile App
Prediabetes stage: It is start of the road having diabetes disease and can be recognized by fasting glucose level of 100 to 125 mg/dL (5.6 to 6.9 mmol/L )
In addition to the ability to know pre-diseases probabilities for patient family members like in the case of people in the pre-diabetes stage.
And the probability of being pre-diabetic for the patient family members (using BMI, fasting glucose measurement and family history).
Main beneficiaries :
Diabetes & pre diabetes patients
Cardiovascular diseases patients and their family members.
Elderly people and their family members
Sick people who need continuous monitoring (ex. Fever, heart problems, oxygen level problems).
People with Sleeping apnea
1) Bring the latest AI technology to the hands of patients in developing countries.
2) The app implemented an AI model to predict card vascular diseases for patients and is currently working on AI models to predict insulin doses based on the glucose level, meals and patient history with the target to Predict glucose levels from the eye images.
3) The App will help Endocrinologist (Diabetic clinicians) to prescribe accurate insulin doses based on the stored glucose measurement history during the day.
4) The app will keep patients under the monitor of the clinical team to reduce the chances of chronic complications. Answer Patient’s inquiries which will reduce the cost of health insurance and even government facilities.
5) Having a diabetic patient to use our app will help us reach patient family members to check their diabetic risk using AI. Clinicians can then recommend diet changes and weight loss programs.
BMI & fasting glucose level can predict pre-diabetes stage (published research).
6) Ability to estimate HbA1c and visualize blood glucose level.
7) Reduce government's spending on diabetes care as there are 39 million diabetic people in MENA alone and this figure will increase to 108 million by 2045.
8) Ability to link our app with weight loss applications as our app can capture moving steps, burned calories & burned fats, distance, speed along with sleeping quality.
9) Store patient vital health signs to centralized health care systems for predicting health care costs in the short and long terms.
key challenges we faced :
1) The main challenge was finding an affordable, accurate and FDA cleared medical device that is available with an SDK to integrate into our App.
We tested over 9 devices and to do that we spent a couple of thousand dollars and more than 36,000 work hours.
2) The medical device SDK came with no documentation so we spent weeks creating one.
3) The app development backlog was long and due to the limited resources, we decided to use well reputed open source modules & modify it to fit our app whenever possible to avoid building everything from scratch.
4) Sending patient health data to the clinicians App in real time was a challenging task at the start until we refactored the code.
5) Building a reliable AI framework that can exchange data with mobile applications was tricky so we relied on material from Stanford University for guidance.