(Respiratory and Biosignals Program)
Team Leader - Igor Penyasov.
Team Coordinators - Gur Propper, Mali Marton (IAI).
Development - Rami Werhaftig (Elta), Noam Frankel (Elta), Liad Pasker (Elta), Rafael Zanzouri, Carmen Loew, Sara Ben-Shabbat, Amir Yaron, Assaf Avnon, Moshe Zvili, Natalie Levin (IAI), Arie Helman (IAI), Lior Hagigi.
Architecture consulting - Annabel Levin, Inbar Hechter, Idan Yona.
Medical consulting within the group - Dr. Oren Tavor, Dr. Tal Petlon, Dr. Amnon Ariel, Sarit Hadar Shihrur, Yair Bier
COVID-19 patients with impaired respiratory function require frequent monitoring so that prompt action can be taken where emergencies arise. This poses a significant threat to medical staff who are repeatedly exposed to infected patients while carrying out checks. The challenge was to find a way to effectively monitor and analyze a large number of patients while reducing exposure of medical staff to infection.
The team developed Medic-All, an integrated system for the remote measurement and monitoring of patient’s breathing and biosignals. Medic-All continuously collects and analyzes the respiratory function and biosignal metrics that have been identified as the most relevant indicators of deterioration in Covod-19 patients. Medic-All automatically analyzes the data sending alerts to medical staff on deterioration or improvement in patient's condition. This allows medical staff to attend patients on a focused, per-need, basis saving time and effort. Medic-All offers the added benefit of allowing multiple patients to be remotely monitored simultaneously by a relatively small medical staff.
As an added benefit, Medic-All is designed to be integrated into a cost-effective 3rd party platform that could be readily manufactured and rolled out in the hundreds, in case standard monitors run out in hospitals.
How it works:
Establish a baseline - On admission of the patient to the hospital, the system will collect baseline data. These results ware compared to norms, and the system is programmed to generate notifications of deviation from norms.
Continuous remote measurement of patient metrics - Sensors (mostly in the form of wearables) collects patient data on a continuous basis. It also records and evaluates breathing sounds.
Alert medical staff about abnormalities - A cloud-based AI algorithm analyzes the audio file and identifies any abnormalities. The system cross-references to stored breathing and vital signs data and sends notifications to doctors regarding changes in the patient’s status
Central on-site server - The base of the system will be a central server (HUB) that will be located in the hospital/medical facility and will connect to sensors that take measurements from the patients (on one side) and to an AWS cloud service (on the other side).
Sensors - Sensors (mostly in the form of wearables) will be used to monitor patients. Advanced remote measurement systems will also be deployed to collect patient data.
A Beta version will shortly be deployed at Assuta Ashdod hospital following which adjustments and improvements will be made
The team anticipates further adjustments and improvements following wider deployment in hospital settings. Stage 2 enhancements will focus on improving integration with existing medical equipment as well as testing and refining AI/ML analysis of anonymous data to further improve early detection, provide care insights and support.
The current product has been developed by volunteers. Additional manpower will be required for stage 2 development, improvements and adjustments. Funding may be required for in-depth consulting, project management and additional research efforts.